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# Dataset Card for Never Ending Language Learning (NELL) ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://rtw.ml.cmu.edu/rtw/ - **Repository:** http://rtw.ml.cmu.edu/rtw/ - **Paper:** Never-Ending Learning. T. Mitchell, W. Cohen, E. Hruschka, P. Talukdar, J. Betteridge, A. Carlson, B. Dalvi, M. Gardner, B. Kisiel, J. Krishnamurthy, N. Lao, K. Mazaitis, T. Mohamed, N. Nakashole, E. Platanios, A. Ritter, M. Samadi, B. Settles, R. Wang, D. Wijaya, A. Gupta, X. Chen, A. Saparov, M. Greaves, J. Welling. In Proceedings of the Conference on Artificial Intelligence (AAAI), 2015 ### Dataset Summary This dataset provides version 1115 of the belief extracted by CMU's Never Ending Language Learner (NELL) and version 1110 of the candidate belief extracted by NELL. See http://rtw.ml.cmu.edu/rtw/overview. NELL is an open information extraction system that attempts to read the Clueweb09 of 500 million web pages (http://boston.lti.cs.cmu.edu/Data/clueweb09/) and general web searches. The dataset has 4 configurations: nell_belief, nell_candidate, nell_belief_sentences, and nell_candidate_sentences. nell_belief is certainties of belief are lower. The two sentences config extracts the CPL sentence patterns filled with the applicable 'best' literal string for the entities filled into the sentence patterns. And also provides sentences found using web searches containing the entities and relationships. There are roughly 21M entries for nell_belief_sentences, and 100M sentences for nell_candidate_sentences. From the NELL website: - **Research Goal** To build a never-ending machine learning system that acquires the ability to extract structured information from unstructured web pages. If successful, this will result in a knowledge base (i.e., a relational database) of structured information that mirrors the content of the Web. We call this system NELL (Never-Ending Language Learner). - **Approach** The inputs to NELL include (1) an initial ontology defining hundreds of categories (e.g., person, sportsTeam, fruit, emotion) and relations (e.g., playsOnTeam(athlete,sportsTeam), playsInstrument(musician,instrument)) that NELL is expected to read about, and (2) 10 to 15 seed examples of each category and relation. Given these inputs, plus a collection of 500 million web pages and access to the remainder of the web through search engine APIs, NELL runs 24 hours per day, continuously, to perform two ongoing tasks: Extract new instances of categories and relations. In other words, find noun phrases that represent new examples of the input categories (e.g., "Barack Obama" is a person and politician), and find pairs of noun phrases that correspond to instances of the input relations (e.g., the pair "Jason Giambi" and "Yankees" is an instance of the playsOnTeam relation). These new instances are added to the growing knowledge base of structured beliefs. Learn to read better than yesterday. NELL uses a variety of methods to extract beliefs from the web. These are retrained, using the growing knowledge base as a self-supervised collection of training examples. The result is a semi-supervised learning method that couples the training of hundreds of different extraction methods for a wide range of categories and relations. Much of NELL’s current success is due to its algorithm for coupling the simultaneous training of many extraction methods. For more information, see: http://rtw.ml.cmu.edu/rtw/resources ### Supported Tasks and Leaderboards [More Information Needed] ### Languages en, and perhaps some others ## Dataset Structure ### Data Instances There are four configurations for the dataset: nell_belief, nell_candidate, nell_belief_sentences, nell_candidate_sentences. nell_belief and nell_candidate defines: `` {'best_entity_literal_string': 'Aspect Medical Systems', 'best_value_literal_string': '', 'candidate_source': '%5BSEAL-Iter%3A215-2011%2F02%2F26-04%3A27%3A09-%3Ctoken%3Daspect_medical_systems%2Cbiotechcompany%3E-From%3ACategory%3Abiotechcompany-using-KB+http%3A%2F%2Fwww.unionegroup.com%2Fhealthcare%2Fmfg_info.htm+http%3A%2F%2Fwww.conventionspc.com%2Fcompanies.html%2C+CPL-Iter%3A1103-2018%2F03%2F08-15%3A32%3A34-%3Ctoken%3Daspect_medical_systems%2Cbiotechcompany%3E-grant+support+from+_%092%09research+support+from+_%094%09unrestricted+educational+grant+from+_%092%09educational+grant+from+_%092%09research+grant+support+from+_%091%09various+financial+management+positions+at+_%091%5D', 'categories_for_entity': 'concept:biotechcompany', 'categories_for_value': 'concept:company', 'entity': 'concept:biotechcompany:aspect_medical_systems', 'entity_literal_strings': '"Aspect Medical Systems" "aspect medical systems"', 'iteration_of_promotion': '1103', 'relation': 'generalizations', 'score': '0.9244426550775064', 'source': 'MBL-Iter%3A1103-2018%2F03%2F18-01%3A35%3A42-From+ErrorBasedIntegrator+%28SEAL%28aspect_medical_systems%2Cbiotechcompany%29%2C+CPL%28aspect_medical_systems%2Cbiotechcompany%29%29', 'value': 'concept:biotechcompany', 'value_literal_strings': ''} `` nell_belief_sentences, nell_candidate_sentences defines: `` {'count': 4, 'entity': 'biotechcompany:aspect_medical_systems', 'relation': 'generalizations', 'score': '0.9244426550775064', 'sentence': 'research support from [[ Aspect Medical Systems ]]', 'sentence_type': 'CPL', 'url': '', 'value': 'biotechcompany'} `` ### Data Fields For nell_belief and nell_canddiate configurations. From http://rtw.ml.cmu.edu/rtw/faq: * entity: The Entity part of the (Entity, Relation, Value) tripple. Note that this will be the name of a concept and is not the literal string of characters seen by NELL from some text source, nor does it indicate the category membership of that concept * relation: The Relation part of the (Entity, Relation, Value) tripple. In the case of a category instance, this will be "generalizations". In the case of a relation instance, this will be the name of the relation. * value: The Value part of the (Entity, Relation, Value) tripple. In the case of a category instance, this will be the name of the category. In the case of a relation instance, this will be another concept (like Entity). * iteration_of_promotion: The point in NELL's life at which this category or relation instance was promoted to one that NELL beleives to be true. This is a non-negative integer indicating the number of iterations of bootstrapping NELL had gone through. * score: A confidence score for the belief. Note that NELL's scores are not actually probabilistic at this time. * source: A summary of the provenance for the belief indicating the set of learning subcomponents (CPL, SEAL, etc.) that had submitted this belief as being potentially true. * entity_literal_strings: The set of actual textual strings that NELL has read that it believes can refer to the concept indicated in the Entity column. * value_literal_strings: For relations, the set of actual textual strings that NELL has read that it believes can refer to the concept indicated in the Value column. For categories, this should be empty but may contain something spurious. * best_entity_literal_string: Of the set of strings in the Entity literalStrings, column, which one string can best be used to describe the concept. * best_value_literal_string: Same thing, but for Value literalStrings. * categories_for_entity: The full set of categories (which may be empty) to which NELL belives the concept indicated in the Entity column to belong. * categories_for_value: For relations, the full set of categories (which may be empty) to which NELL believes the concept indicated in the Value column to belong. For categories, this should be empty but may contain something spurious. * candidate_source: A free-form amalgamation of more specific provenance information describing the justification(s) NELL has for possibly believing this category or relation instance. For the nell_belief_sentences and nell_candidate_sentences, we have extracted the underlying sentences, sentence count and URLs and provided a shortened version of the entity, relation and value field by removing the string "concept:" and "candidate:". There are two types of sentences, 'CPL' and 'OE', which are generated by two of the modules of NELL, pattern matching and open web searching, respectively. There may be duplicates. The configuration is as follows: * entity: The Entity part of the (Entity, Relation, Value) tripple. Note that this will be the name of a concept and is not the literal string of characters seen by NELL from some text source, nor does it indicate the category membership of that concept * relation: The Relation part of the (Entity, Relation, Value) tripple. In the case of a category instance, this will be "generalizations". In the case of a relation instance, this will be the name of the relation. * value: The Value part of the (Entity, Relation, Value) tripple. In the case of a category instance, this will be the name of the category. In the case of a relation instance, this will be another concept (like Entity). * score: A confidence score for the belief. Note that NELL's scores are not actually probabilistic at this time. * sentence: the raw sentence. For 'CPL' type sentences, there are "[[" "]]" arounds the entity and value. For 'OE' type sentences, there are no "[[" and "]]". * url: the url if there is one from which this sentence was extracted * count: the count for this sentence * sentence_type: either 'CPL' or 'OE' ### Data Splits There are no splits. ## Dataset Creation ### Curation Rationale This dataset was gathered and created over many years of running the NELL system on web data. ### Source Data #### Initial Data Collection and Normalization See the research paper on NELL. NELL searches a subset of the web (Clueweb09) and the open web using various open information extraction algorithms, including pattern matching. #### Who are the source language producers? The NELL authors at Carnegie Mellon Univiersty and data from Cluebweb09 and the open web. ### Annotations #### Annotation process The various open information extraction modules of NELL. #### Who are the annotators? Machine annotated. ### Personal and Sensitive Information Unkown, but likely there are names of famous individuals. ## Considerations for Using the Data ### Social Impact of Dataset The goal for the work is to help machines learn to read and understand the web. ### Discussion of Biases Since the data is gathered from the web, there is likely to be biased text and relationships. [More Information Needed] ### Other Known Limitations The relationships and concepts gathered from NELL are not 100% accurate, and there could be errors (maybe as high as 30% error). See https://en.wikipedia.org/wiki/Never-Ending_Language_Learning We did not 'tag' the entity and value in the 'OE' sentences, and this might be an extension in the future. ## Additional Information ### Dataset Curators The authors of NELL at Carnegie Mellon Univeristy ### Licensing Information There does not appear to be a license on http://rtw.ml.cmu.edu/rtw/resources. The data is made available by CMU on the web. ### Citation Information @inproceedings{mitchell2015, added-at = {2015-01-27T15:35:24.000+0100}, author = {Mitchell, T. and Cohen, W. and Hruscha, E. and Talukdar, P. and Betteridge, J. and Carlson, A. and Dalvi, B. and Gardner, M. and Kisiel, B. and Krishnamurthy, J. and Lao, N. and Mazaitis, K. and Mohammad, T. and Nakashole, N. and Platanios, E. and Ritter, A. and Samadi, M. and Settles, B. and Wang, R. and Wijaya, D. and Gupta, A. and Chen, X. and Saparov, A. and Greaves, M. and Welling, J.}, biburl = {https://www.bibsonomy.org/bibtex/263070703e6bb812852cca56574aed093/hotho}, booktitle = {AAAI}, description = {Papers by William W. Cohen}, interhash = {52d0d71f6f5b332dabc1412f18e3a93d}, intrahash = {63070703e6bb812852cca56574aed093}, keywords = {learning nell ontology semantic toread}, note = {: Never-Ending Learning in AAAI-2015}, timestamp = {2015-01-27T15:35:24.000+0100}, title = {Never-Ending Learning}, url = {http://www.cs.cmu.edu/~wcohen/pubs.html}, year = 2015 } ### Contributions Thanks to [@ontocord](https://github.com/ontocord) for adding this dataset.

nell

2022-03-02T23:29:22+00:00

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2024-01-18T11:10:17+00:00

TAGS #task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #annotations_creators-machine-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-100M<n<1B #size_categories-10M<n<100M #size_categories-1M<n<10M #source_datasets-original #language-English #license-unknown #relation-extraction #text-to-structured #text-to-tabular #region-us

# Dataset Card for Never Ending Language Learning (NELL) ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: URL - Paper: Never-Ending Learning. T. Mitchell, W. Cohen, E. Hruschka, P. Talukdar, J. Betteridge, A. Carlson, B. Dalvi, M. Gardner, B. Kisiel, J. Krishnamurthy, N. Lao, K. Mazaitis, T. Mohamed, N. Nakashole, E. Platanios, A. Ritter, M. Samadi, B. Settles, R. Wang, D. Wijaya, A. Gupta, X. Chen, A. Saparov, M. Greaves, J. Welling. In Proceedings of the Conference on Artificial Intelligence (AAAI), 2015 ### Dataset Summary This dataset provides version 1115 of the belief extracted by CMU's Never Ending Language Learner (NELL) and version 1110 of the candidate belief extracted by NELL. See URL NELL is an open information extraction system that attempts to read the Clueweb09 of 500 million web pages (URL and general web searches. The dataset has 4 configurations: nell_belief, nell_candidate, nell_belief_sentences, and nell_candidate_sentences. nell_belief is certainties of belief are lower. The two sentences config extracts the CPL sentence patterns filled with the applicable 'best' literal string for the entities filled into the sentence patterns. And also provides sentences found using web searches containing the entities and relationships. There are roughly 21M entries for nell_belief_sentences, and 100M sentences for nell_candidate_sentences. From the NELL website: - Research Goal To build a never-ending machine learning system that acquires the ability to extract structured information from unstructured web pages. If successful, this will result in a knowledge base (i.e., a relational database) of structured information that mirrors the content of the Web. We call this system NELL (Never-Ending Language Learner). - Approach The inputs to NELL include (1) an initial ontology defining hundreds of categories (e.g., person, sportsTeam, fruit, emotion) and relations (e.g., playsOnTeam(athlete,sportsTeam), playsInstrument(musician,instrument)) that NELL is expected to read about, and (2) 10 to 15 seed examples of each category and relation. Given these inputs, plus a collection of 500 million web pages and access to the remainder of the web through search engine APIs, NELL runs 24 hours per day, continuously, to perform two ongoing tasks: Extract new instances of categories and relations. In other words, find noun phrases that represent new examples of the input categories (e.g., "Barack Obama" is a person and politician), and find pairs of noun phrases that correspond to instances of the input relations (e.g., the pair "Jason Giambi" and "Yankees" is an instance of the playsOnTeam relation). These new instances are added to the growing knowledge base of structured beliefs. Learn to read better than yesterday. NELL uses a variety of methods to extract beliefs from the web. These are retrained, using the growing knowledge base as a self-supervised collection of training examples. The result is a semi-supervised learning method that couples the training of hundreds of different extraction methods for a wide range of categories and relations. Much of NELL’s current success is due to its algorithm for coupling the simultaneous training of many extraction methods. For more information, see: URL ### Supported Tasks and Leaderboards ### Languages en, and perhaps some others ## Dataset Structure ### Data Instances There are four configurations for the dataset: nell_belief, nell_candidate, nell_belief_sentences, nell_candidate_sentences. nell_belief and nell_candidate defines: '' {'best_entity_literal_string': 'Aspect Medical Systems', 'best_value_literal_string': '', 'candidate_source': '%5BSEAL-Iter%3A215-2011%2F02%2F26-04%3A27%3A09-%3Ctoken%3Daspect_medical_systems%2Cbiotechcompany%3E-From%3ACategory%3Abiotechcompany-using-KB+http%3A%2F%URL%2Fhealthcare%2Fmfg_info.htm+http%3A%2F%URL%URL%2C+CPL-Iter%3A1103-2018%2F03%2F08-15%3A32%3A34-%3Ctoken%3Daspect_medical_systems%2Cbiotechcompany%3E-grant+support+from+_%092%09research+support+from+_%094%09unrestricted+educational+grant+from+_%092%09educational+grant+from+_%092%09research+grant+support+from+_%091%09various+financial+management+positions+at+_%091%5D', 'categories_for_entity': 'concept:biotechcompany', 'categories_for_value': 'concept:company', 'entity': 'concept:biotechcompany:aspect_medical_systems', 'entity_literal_strings': '"Aspect Medical Systems" "aspect medical systems"', 'iteration_of_promotion': '1103', 'relation': 'generalizations', 'score': '0.9244426550775064', 'source': 'MBL-Iter%3A1103-2018%2F03%2F18-01%3A35%3A42-From+ErrorBasedIntegrator+%28SEAL%28aspect_medical_systems%2Cbiotechcompany%29%2C+CPL%28aspect_medical_systems%2Cbiotechcompany%29%29', 'value': 'concept:biotechcompany', 'value_literal_strings': ''} '' nell_belief_sentences, nell_candidate_sentences defines: '' {'count': 4, 'entity': 'biotechcompany:aspect_medical_systems', 'relation': 'generalizations', 'score': '0.9244426550775064', 'sentence': 'research support from [[ Aspect Medical Systems ]]', 'sentence_type': 'CPL', 'url': '', 'value': 'biotechcompany'} '' ### Data Fields For nell_belief and nell_canddiate configurations. From URL * entity: The Entity part of the (Entity, Relation, Value) tripple. Note that this will be the name of a concept and is not the literal string of characters seen by NELL from some text source, nor does it indicate the category membership of that concept * relation: The Relation part of the (Entity, Relation, Value) tripple. In the case of a category instance, this will be "generalizations". In the case of a relation instance, this will be the name of the relation. * value: The Value part of the (Entity, Relation, Value) tripple. In the case of a category instance, this will be the name of the category. In the case of a relation instance, this will be another concept (like Entity). * iteration_of_promotion: The point in NELL's life at which this category or relation instance was promoted to one that NELL beleives to be true. This is a non-negative integer indicating the number of iterations of bootstrapping NELL had gone through. * score: A confidence score for the belief. Note that NELL's scores are not actually probabilistic at this time. * source: A summary of the provenance for the belief indicating the set of learning subcomponents (CPL, SEAL, etc.) that had submitted this belief as being potentially true. * entity_literal_strings: The set of actual textual strings that NELL has read that it believes can refer to the concept indicated in the Entity column. * value_literal_strings: For relations, the set of actual textual strings that NELL has read that it believes can refer to the concept indicated in the Value column. For categories, this should be empty but may contain something spurious. * best_entity_literal_string: Of the set of strings in the Entity literalStrings, column, which one string can best be used to describe the concept. * best_value_literal_string: Same thing, but for Value literalStrings. * categories_for_entity: The full set of categories (which may be empty) to which NELL belives the concept indicated in the Entity column to belong. * categories_for_value: For relations, the full set of categories (which may be empty) to which NELL believes the concept indicated in the Value column to belong. For categories, this should be empty but may contain something spurious. * candidate_source: A free-form amalgamation of more specific provenance information describing the justification(s) NELL has for possibly believing this category or relation instance. For the nell_belief_sentences and nell_candidate_sentences, we have extracted the underlying sentences, sentence count and URLs and provided a shortened version of the entity, relation and value field by removing the string "concept:" and "candidate:". There are two types of sentences, 'CPL' and 'OE', which are generated by two of the modules of NELL, pattern matching and open web searching, respectively. There may be duplicates. The configuration is as follows: * entity: The Entity part of the (Entity, Relation, Value) tripple. Note that this will be the name of a concept and is not the literal string of characters seen by NELL from some text source, nor does it indicate the category membership of that concept * relation: The Relation part of the (Entity, Relation, Value) tripple. In the case of a category instance, this will be "generalizations". In the case of a relation instance, this will be the name of the relation. * value: The Value part of the (Entity, Relation, Value) tripple. In the case of a category instance, this will be the name of the category. In the case of a relation instance, this will be another concept (like Entity). * score: A confidence score for the belief. Note that NELL's scores are not actually probabilistic at this time. * sentence: the raw sentence. For 'CPL' type sentences, there are "[[" "]]" arounds the entity and value. For 'OE' type sentences, there are no "[[" and "]]". * url: the url if there is one from which this sentence was extracted * count: the count for this sentence * sentence_type: either 'CPL' or 'OE' ### Data Splits There are no splits. ## Dataset Creation ### Curation Rationale This dataset was gathered and created over many years of running the NELL system on web data. ### Source Data #### Initial Data Collection and Normalization See the research paper on NELL. NELL searches a subset of the web (Clueweb09) and the open web using various open information extraction algorithms, including pattern matching. #### Who are the source language producers? The NELL authors at Carnegie Mellon Univiersty and data from Cluebweb09 and the open web. ### Annotations #### Annotation process The various open information extraction modules of NELL. #### Who are the annotators? Machine annotated. ### Personal and Sensitive Information Unkown, but likely there are names of famous individuals. ## Considerations for Using the Data ### Social Impact of Dataset The goal for the work is to help machines learn to read and understand the web. ### Discussion of Biases Since the data is gathered from the web, there is likely to be biased text and relationships. ### Other Known Limitations The relationships and concepts gathered from NELL are not 100% accurate, and there could be errors (maybe as high as 30% error). See URL We did not 'tag' the entity and value in the 'OE' sentences, and this might be an extension in the future. ## Additional Information ### Dataset Curators The authors of NELL at Carnegie Mellon Univeristy ### Licensing Information There does not appear to be a license on URL The data is made available by CMU on the web. @inproceedings{mitchell2015, added-at = {2015-01-27T15:35:24.000+0100}, author = {Mitchell, T. and Cohen, W. and Hruscha, E. and Talukdar, P. and Betteridge, J. and Carlson, A. and Dalvi, B. and Gardner, M. and Kisiel, B. and Krishnamurthy, J. and Lao, N. and Mazaitis, K. and Mohammad, T. and Nakashole, N. and Platanios, E. and Ritter, A. and Samadi, M. and Settles, B. and Wang, R. and Wijaya, D. and Gupta, A. and Chen, X. and Saparov, A. and Greaves, M. and Welling, J.}, biburl = {URL booktitle = {AAAI}, description = {Papers by William W. Cohen}, interhash = {52d0d71f6f5b332dabc1412f18e3a93d}, intrahash = {63070703e6bb812852cca56574aed093}, keywords = {learning nell ontology semantic toread}, note = {: Never-Ending Learning in AAAI-2015}, timestamp = {2015-01-27T15:35:24.000+0100}, title = {Never-Ending Learning}, url = {URL year = 2015 } ### Contributions Thanks to @ontocord for adding this dataset.

1ff0d6520a4bea6faa791781baf3cb2fc87ff563

# Dataset Card for Neural Code Search ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [facebookresearch / Neural-Code-Search-Evaluation-Dataset](https://github.com/facebookresearch/Neural-Code-Search-Evaluation-Dataset/tree/master/data) - **Repository:** [Github](https://github.com/facebookresearch/Neural-Code-Search-Evaluation-Dataset.git) - **Paper:** [arXiv](https://arxiv.org/pdf/1908.09804.pdf) ### Dataset Summary Neural-Code-Search-Evaluation-Dataset presents an evaluation dataset consisting of natural language query and code snippet pairs, with the hope that future work in this area can use this dataset as a common benchmark. We also provide the results of two code search models (NCS, UNIF) from recent work. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages EN - English ## Dataset Structure ### Data Instances #### Search Corpus The search corpus is indexed using all method bodies parsed from the 24,549 GitHub repositories. In total, there are 4,716,814 methods in this corpus. The code search model will find relevant code snippets (i.e. method bodies) from this corpus given a natural language query. In this data release, we will provide the following information for each method in the corpus: #### Evaluation Dataset The evaluation dataset is composed of 287 Stack Overflow question and answer pairs ### Data Fields #### Search Corpus - id: Each method in the corpus has a unique numeric identifier. This ID number will also be referenced in our evaluation dataset. - filepath: The file path is in the format of :owner/:repo/relative-file-path-to-the-repo method_name - start_line: Starting line number of the method in the file. - end_line: Ending line number of the method in the file. - url: GitHub link to the method body with commit ID and line numbers encoded. #### Evaluation Dataset - stackoverflow_id: Stack Overflow post ID. - question: Title fo the Stack Overflow post. - question_url: URL of the Stack Overflow post. - answer: Code snippet answer to the question. ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization The most popular Android repositories on GitHub (ranked by the number of stars) is used to create the search corpus. For each repository that we indexed, we provide the link, specific to the commit that was used.5 In total, there are 24,549 repositories. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators Hongyu Li, Seohyun Kim and Satish Chandra ### Licensing Information CC-BY-NC 4.0 (Attr Non-Commercial Inter.) ### Citation Information arXiv:1908.09804 [cs.SE] ### Contributions Thanks to [@vinaykudari](https://github.com/vinaykudari) for adding this dataset.

neural_code_search

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["cc-by-nc-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M", "n<1K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["extractive-qa"], "paperswithcode_id": "neural-code-search-evaluation-dataset", "pretty_name": "Neural Code Search", "config_names": ["evaluation_dataset", "search_corpus"], "dataset_info": [{"config_name": "evaluation_dataset", "features": [{"name": "stackoverflow_id", "dtype": "int32"}, {"name": "question", "dtype": "string"}, {"name": "question_url", "dtype": "string"}, {"name": "question_author", "dtype": "string"}, {"name": "question_author_url", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "answer_url", "dtype": "string"}, {"name": "answer_author", "dtype": "string"}, {"name": "answer_author_url", "dtype": "string"}, {"name": "examples", "sequence": "int32"}, {"name": "examples_url", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 296848, "num_examples": 287}], "download_size": 383625, "dataset_size": 296848}, {"config_name": "search_corpus", "features": [{"name": "id", "dtype": "int32"}, {"name": "filepath", "dtype": "string"}, {"name": "method_name", "dtype": "string"}, {"name": "start_line", "dtype": "int32"}, {"name": "end_line", "dtype": "int32"}, {"name": "url", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1452630278, "num_examples": 4716814}], "download_size": 121112543, "dataset_size": 1452630278}]}

2024-01-18T11:10:18+00:00

TAGS #task_categories-question-answering #task_ids-extractive-qa #annotations_creators-expert-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1M<n<10M #size_categories-n<1K #source_datasets-original #language-English #license-cc-by-nc-4.0 #arxiv-1908.09804 #region-us

# Dataset Card for Neural Code Search ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: facebookresearch / Neural-Code-Search-Evaluation-Dataset - Repository: Github - Paper: arXiv ### Dataset Summary Neural-Code-Search-Evaluation-Dataset presents an evaluation dataset consisting of natural language query and code snippet pairs, with the hope that future work in this area can use this dataset as a common benchmark. We also provide the results of two code search models (NCS, UNIF) from recent work. ### Supported Tasks and Leaderboards ### Languages EN - English ## Dataset Structure ### Data Instances #### Search Corpus The search corpus is indexed using all method bodies parsed from the 24,549 GitHub repositories. In total, there are 4,716,814 methods in this corpus. The code search model will find relevant code snippets (i.e. method bodies) from this corpus given a natural language query. In this data release, we will provide the following information for each method in the corpus: #### Evaluation Dataset The evaluation dataset is composed of 287 Stack Overflow question and answer pairs ### Data Fields #### Search Corpus - id: Each method in the corpus has a unique numeric identifier. This ID number will also be referenced in our evaluation dataset. - filepath: The file path is in the format of :owner/:repo/relative-file-path-to-the-repo method_name - start_line: Starting line number of the method in the file. - end_line: Ending line number of the method in the file. - url: GitHub link to the method body with commit ID and line numbers encoded. #### Evaluation Dataset - stackoverflow_id: Stack Overflow post ID. - question: Title fo the Stack Overflow post. - question_url: URL of the Stack Overflow post. - answer: Code snippet answer to the question. ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization The most popular Android repositories on GitHub (ranked by the number of stars) is used to create the search corpus. For each repository that we indexed, we provide the link, specific to the commit that was used.5 In total, there are 24,549 repositories. #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators Hongyu Li, Seohyun Kim and Satish Chandra ### Licensing Information CC-BY-NC 4.0 (Attr Non-Commercial Inter.) arXiv:1908.09804 [cs.SE] ### Contributions Thanks to @vinaykudari for adding this dataset.

6d7d51fc9f50abeba0ab018b672f604aad3a47dc

# Dataset Card for NewsCommentary ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://opus.nlpl.eu/News-Commentary.php - **Repository:** None - **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf - **Leaderboard:** [More Information Needed] - **Point of Contact:** [More Information Needed] ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.

news_commentary

2022-03-02T23:29:22+00:00

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2024-01-18T11:10:20+00:00

TAGS #task_categories-translation #annotations_creators-found #language_creators-found #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #language-Arabic #language-Czech #language-German #language-English #language-Spanish #language-French #language-Italian #language-Japanese #language-Dutch #language-Portuguese #language-Russian #language-Chinese #license-unknown #region-us

# Dataset Card for NewsCommentary ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: None - Paper: URL - Leaderboard: - Point of Contact: ### Dataset Summary ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @abhishekkrthakur for adding this dataset.

3533b4525d6e410e4916aef934e134c033dfcc2e

# Dataset Card for "newsgroup" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [http://qwone.com/~jason/20Newsgroups/](http://qwone.com/~jason/20Newsgroups/) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [NewsWeeder: Learning to Filter Netnews](https://doi.org/10.1016/B978-1-55860-377-6.50048-7) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 929.27 MB - **Size of the generated dataset:** 124.41 MB - **Total amount of disk used:** 1.05 GB ### Dataset Summary The 20 Newsgroups data set is a collection of approximately 20,000 newsgroup documents, partitioned (nearly) evenly across 20 different newsgroups. To the best of my knowledge, it was originally collected by Ken Lang, probably for his Newsweeder: Learning to filter netnews paper, though he does not explicitly mention this collection. The 20 newsgroups collection has become a popular data set for experiments in text applications of machine learning techniques, such as text classification and text clustering. does not include cross-posts and includes only the "From" and "Subject" headers. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### 18828_alt.atheism - **Size of downloaded dataset files:** 14.67 MB - **Size of the generated dataset:** 1.67 MB - **Total amount of disk used:** 16.34 MB An example of 'train' looks as follows. ``` ``` #### 18828_comp.graphics - **Size of downloaded dataset files:** 14.67 MB - **Size of the generated dataset:** 1.66 MB - **Total amount of disk used:** 16.33 MB An example of 'train' looks as follows. ``` ``` #### 18828_comp.os.ms-windows.misc - **Size of downloaded dataset files:** 14.67 MB - **Size of the generated dataset:** 2.38 MB - **Total amount of disk used:** 17.05 MB An example of 'train' looks as follows. ``` ``` #### 18828_comp.sys.ibm.pc.hardware - **Size of downloaded dataset files:** 14.67 MB - **Size of the generated dataset:** 1.18 MB - **Total amount of disk used:** 15.85 MB An example of 'train' looks as follows. ``` ``` #### 18828_comp.sys.mac.hardware - **Size of downloaded dataset files:** 14.67 MB - **Size of the generated dataset:** 1.06 MB - **Total amount of disk used:** 15.73 MB An example of 'train' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### 18828_alt.atheism - `text`: a `string` feature. #### 18828_comp.graphics - `text`: a `string` feature. #### 18828_comp.os.ms-windows.misc - `text`: a `string` feature. #### 18828_comp.sys.ibm.pc.hardware - `text`: a `string` feature. #### 18828_comp.sys.mac.hardware - `text`: a `string` feature. ### Data Splits | name |train| |------------------------------|----:| |18828_alt.atheism | 799| |18828_comp.graphics | 973| |18828_comp.os.ms-windows.misc | 985| |18828_comp.sys.ibm.pc.hardware| 982| |18828_comp.sys.mac.hardware | 961| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @incollection{LANG1995331, title = {NewsWeeder: Learning to Filter Netnews}, editor = {Armand Prieditis and Stuart Russell}, booktitle = {Machine Learning Proceedings 1995}, publisher = {Morgan Kaufmann}, address = {San Francisco (CA)}, pages = {331-339}, year = {1995}, isbn = {978-1-55860-377-6}, doi = {https://doi.org/10.1016/B978-1-55860-377-6.50048-7}, url = {https://www.sciencedirect.com/science/article/pii/B9781558603776500487}, author = {Ken Lang}, } ``` ### Contributions Thanks to [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

newsgroup

2022-03-02T23:29:22+00:00

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2024-01-18T11:10:22+00:00

TAGS #task_categories-text-classification #task_ids-multi-class-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-unknown #region-us

Dataset Card for "newsgroup" ============================ Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: NewsWeeder: Learning to Filter Netnews * Point of Contact: * Size of downloaded dataset files: 929.27 MB * Size of the generated dataset: 124.41 MB * Total amount of disk used: 1.05 GB ### Dataset Summary The 20 Newsgroups data set is a collection of approximately 20,000 newsgroup documents, partitioned (nearly) evenly across 20 different newsgroups. To the best of my knowledge, it was originally collected by Ken Lang, probably for his Newsweeder: Learning to filter netnews paper, though he does not explicitly mention this collection. The 20 newsgroups collection has become a popular data set for experiments in text applications of machine learning techniques, such as text classification and text clustering. does not include cross-posts and includes only the "From" and "Subject" headers. ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### 18828\_alt.atheism * Size of downloaded dataset files: 14.67 MB * Size of the generated dataset: 1.67 MB * Total amount of disk used: 16.34 MB An example of 'train' looks as follows. #### 18828\_comp.graphics * Size of downloaded dataset files: 14.67 MB * Size of the generated dataset: 1.66 MB * Total amount of disk used: 16.33 MB An example of 'train' looks as follows. #### 18828\_comp.URL * Size of downloaded dataset files: 14.67 MB * Size of the generated dataset: 2.38 MB * Total amount of disk used: 17.05 MB An example of 'train' looks as follows. #### 18828\_comp.URL.hardware * Size of downloaded dataset files: 14.67 MB * Size of the generated dataset: 1.18 MB * Total amount of disk used: 15.85 MB An example of 'train' looks as follows. #### 18828\_comp.URL.hardware * Size of downloaded dataset files: 14.67 MB * Size of the generated dataset: 1.06 MB * Total amount of disk used: 15.73 MB An example of 'train' looks as follows. ### Data Fields The data fields are the same among all splits. #### 18828\_alt.atheism * 'text': a 'string' feature. #### 18828\_comp.graphics * 'text': a 'string' feature. #### 18828\_comp.URL * 'text': a 'string' feature. #### 18828\_comp.URL.hardware * 'text': a 'string' feature. #### 18828\_comp.URL.hardware * 'text': a 'string' feature. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @mariamabarham, @thomwolf, @lhoestq for adding this dataset.

86fd2b61b3165f5ee2b2dbe2fd67d072e4d90ef3

# Dataset Card for NewsPH ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Filipino Text Benchmarks](https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks) - **Repository:** - **Paper:** [Investigating the True Performance of Transformers in Low-Resource Languages: A Case Study in Automatic Corpus Creation](https://arxiv.org/abs/2010.11574) - **Leaderboard:** - **Point of Contact:** [Jan Christian Blaise Cruz](jan_christian_cruz@dlsu.edu.ph) ### Dataset Summary Raw collection of news articles in Filipino. Used to produce the NewsPH-NLI dataset in Cruz et al. (2020) ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Tagalog/Filipino ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - `text` (`str`) The dataset is in plaintext and only has one field ("text"). It can be used for language modeling. ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@jcblaisecruz02](https://github.com/jcblaisecruz02) for adding this dataset.

newsph

2022-03-02T23:29:22+00:00

{"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["fil", "tl"], "license": ["gpl-3.0"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"], "paperswithcode_id": "newsph-nli", "pretty_name": "NewsPH-NLI", "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "config_name": "newsph", "splits": [{"name": "train", "num_bytes": 298833914, "num_examples": 2190465}], "download_size": 104086466, "dataset_size": 298833914}}

2024-01-18T11:10:26+00:00

TAGS #task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-Filipino #language-Tagalog #license-gpl-3.0 #arxiv-2010.11574 #region-us

# Dataset Card for NewsPH ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: Filipino Text Benchmarks - Repository: - Paper: Investigating the True Performance of Transformers in Low-Resource Languages: A Case Study in Automatic Corpus Creation - Leaderboard: - Point of Contact: Jan Christian Blaise Cruz ### Dataset Summary Raw collection of news articles in Filipino. Used to produce the NewsPH-NLI dataset in Cruz et al. (2020) ### Supported Tasks and Leaderboards ### Languages Tagalog/Filipino ## Dataset Structure ### Data Instances ### Data Fields - 'text' ('str') The dataset is in plaintext and only has one field ("text"). It can be used for language modeling. ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @jcblaisecruz02 for adding this dataset.

eaad1c46b5575d0bb8388ed334faaf23686ec47a

# Dataset Card for NewsPH NLI ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [NewsPH NLI homepage](https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks) - **Repository:** [NewsPH NLI repository](https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks) - **Paper:** [Arxiv paper](https://arxiv.org/pdf/2010.11574.pdf) - **Leaderboard:** - **Point of Contact:** [Jan Christian Cruz](mailto:jan_christian_cruz@dlsu.edu.ph) ### Dataset Summary First benchmark dataset for sentence entailment in the low-resource Filipino language. Constructed through exploting the structure of news articles. Contains 600,000 premise-hypothesis pairs, in 70-15-15 split for training, validation, and testing. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The dataset contains news articles in Filipino (Tagalog) scraped rom all major Philippine news sites online. ## Dataset Structure ### Data Instances Sample data: { "premise": "Alam ba ninyo ang ginawa ni Erap na noon ay lasing na lasing na rin?", "hypothesis": "Ininom niya ang alak na pinagpulbusan!", "label": "0" } ### Data Fields [More Information Needed] ### Data Splits Contains 600,000 premise-hypothesis pairs, in 70-15-15 split for training, validation, and testing. ## Dataset Creation ### Curation Rationale We propose the use of news articles for automatically creating benchmark datasets for NLI because of two reasons. First, news articles commonly use single-sentence paragraphing, meaning every paragraph in a news article is limited to a single sentence. Second, straight news articles follow the “inverted pyramid” structure, where every succeeding paragraph builds upon the premise of those that came before it, with the most important information on top and the least important towards the end. ### Source Data #### Initial Data Collection and Normalization To create the dataset, we scrape news articles from all major Philippine news sites online. We collect a total of 229,571 straight news articles, which we then lightly preprocess to remove extraneous unicode characters and correct minimal misspellings. No further preprocessing is done to preserve information in the data. #### Who are the source language producers? The dataset was created by Jan Christian, Blaise Cruz, Jose Kristian Resabal, James Lin, Dan John Velasco, and Charibeth Cheng from De La Salle University and the University of the Philippines ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? Jan Christian Blaise Cruz, Jose Kristian Resabal, James Lin, Dan John Velasco and Charibeth Cheng ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [Jan Christian Blaise Cruz] (mailto:jan_christian_cruz@dlsu.edu.ph) ### Licensing Information [More Information Needed] ### Citation Information @article{cruz2020investigating, title={Investigating the True Performance of Transformers in Low-Resource Languages: A Case Study in Automatic Corpus Creation}, author={Jan Christian Blaise Cruz and Jose Kristian Resabal and James Lin and Dan John Velasco and Charibeth Cheng}, journal={arXiv preprint arXiv:2010.11574}, year={2020} } ### Contributions Thanks to [@anaerobeth](https://github.com/anaerobeth) for adding this dataset.

newsph_nli

2022-03-02T23:29:22+00:00

{"annotations_creators": ["machine-generated"], "language_creators": ["found"], "language": ["tl"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["natural-language-inference"], "paperswithcode_id": "newsph-nli", "pretty_name": "NewsPH NLI", "dataset_info": {"features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}], "splits": [{"name": "train", "num_bytes": 154510599, "num_examples": 420000}, {"name": "test", "num_bytes": 3283665, "num_examples": 9000}, {"name": "validation", "num_bytes": 33015530, "num_examples": 90000}], "download_size": 76565287, "dataset_size": 190809794}}

2024-01-18T11:10:28+00:00

TAGS #task_categories-text-classification #task_ids-natural-language-inference #annotations_creators-machine-generated #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-Tagalog #license-unknown #arxiv-2010.11574 #region-us

# Dataset Card for NewsPH NLI ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: NewsPH NLI homepage - Repository: NewsPH NLI repository - Paper: Arxiv paper - Leaderboard: - Point of Contact: Jan Christian Cruz ### Dataset Summary First benchmark dataset for sentence entailment in the low-resource Filipino language. Constructed through exploting the structure of news articles. Contains 600,000 premise-hypothesis pairs, in 70-15-15 split for training, validation, and testing. ### Supported Tasks and Leaderboards ### Languages The dataset contains news articles in Filipino (Tagalog) scraped rom all major Philippine news sites online. ## Dataset Structure ### Data Instances Sample data: { "premise": "Alam ba ninyo ang ginawa ni Erap na noon ay lasing na lasing na rin?", "hypothesis": "Ininom niya ang alak na pinagpulbusan!", "label": "0" } ### Data Fields ### Data Splits Contains 600,000 premise-hypothesis pairs, in 70-15-15 split for training, validation, and testing. ## Dataset Creation ### Curation Rationale We propose the use of news articles for automatically creating benchmark datasets for NLI because of two reasons. First, news articles commonly use single-sentence paragraphing, meaning every paragraph in a news article is limited to a single sentence. Second, straight news articles follow the “inverted pyramid” structure, where every succeeding paragraph builds upon the premise of those that came before it, with the most important information on top and the least important towards the end. ### Source Data #### Initial Data Collection and Normalization To create the dataset, we scrape news articles from all major Philippine news sites online. We collect a total of 229,571 straight news articles, which we then lightly preprocess to remove extraneous unicode characters and correct minimal misspellings. No further preprocessing is done to preserve information in the data. #### Who are the source language producers? The dataset was created by Jan Christian, Blaise Cruz, Jose Kristian Resabal, James Lin, Dan John Velasco, and Charibeth Cheng from De La Salle University and the University of the Philippines ### Annotations #### Annotation process #### Who are the annotators? Jan Christian Blaise Cruz, Jose Kristian Resabal, James Lin, Dan John Velasco and Charibeth Cheng ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators [Jan Christian Blaise Cruz] (mailto:jan_christian_cruz@URL) ### Licensing Information @article{cruz2020investigating, title={Investigating the True Performance of Transformers in Low-Resource Languages: A Case Study in Automatic Corpus Creation}, author={Jan Christian Blaise Cruz and Jose Kristian Resabal and James Lin and Dan John Velasco and Charibeth Cheng}, journal={arXiv preprint arXiv:2010.11574}, year={2020} } ### Contributions Thanks to @anaerobeth for adding this dataset.

5add216cc913e0644f910210237dc2d85133f80a

# Dataset Card for News Popularity in Multiple Social Media Platforms ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [UCI](https://archive.ics.uci.edu/ml/datasets/News+Popularity+in+Multiple+Social+Media+Platforms) - **Repository:** - **Paper:** [Arxiv](https://arxiv.org/abs/1801.07055) - **Leaderboard:** [Kaggle](https://www.kaggle.com/nikhiljohnk/news-popularity-in-multiple-social-media-platforms/code) - **Point of Contact:** ### Dataset Summary Social sharing data across Facebook, Google+ and LinkedIn for 100k news items on the topics of: economy, microsoft, obama and palestine. ### Supported Tasks and Leaderboards Popularity prediction/shares prediction ### Languages English ## Dataset Structure ### Data Instances ``` { "id": 35873, "title": "Microsoft's 'teen girl' AI turns into a Hitler-loving sex robot within 24 ...", "headline": "Developers at Microsoft created 'Tay', an AI modelled to speak 'like a teen girl', in order to improve the customer service on their voice", "source": "Telegraph.co.uk", "topic": "microsoft", "publish_date": "2016-03-24 09:53:54", "facebook": 22346, "google_plus": 973, "linked_in": 1009 } ``` ### Data Fields - id: the sentence id in the source dataset - title: the title of the link as shared on social media - headline: the headline, or sometimes the lede of the story - source: the source news site - topic: the topic: one of "economy", "microsoft", "obama" and "palestine" - publish_date: the date the original article was published - facebook: the number of Facebook shares, or -1 if this data wasn't collected - google_plus: the number of Google+ likes, or -1 if this data wasn't collected - linked_in: the number of LinkedIn shares, or -1 if if this data wasn't collected ### Data Splits None ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? The source headlines were by journalists, while the titles were written by the people sharing it on social media. ### Annotations #### Annotation process The 'annotations' are simply the number of shares, or likes in the case of Google+ as collected from various API endpoints. #### Who are the annotators? Social media users. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information License: Creative Commons Attribution 4.0 International License (CC-BY) ### Citation Information ``` @article{Moniz2018MultiSourceSF, title={Multi-Source Social Feedback of Online News Feeds}, author={N. Moniz and L. Torgo}, journal={ArXiv}, year={2018}, volume={abs/1801.07055} } ``` ### Contributions Thanks to [@frankier](https://github.com/frankier) for adding this dataset.

newspop

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["text-scoring"], "pretty_name": "News Popularity in Multiple Social Media Platforms", "tags": ["social-media-shares-prediction"], "dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "title", "dtype": "string"}, {"name": "headline", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "topic", "dtype": "string"}, {"name": "publish_date", "dtype": "string"}, {"name": "facebook", "dtype": "int32"}, {"name": "google_plus", "dtype": "int32"}, {"name": "linked_in", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 27927641, "num_examples": 93239}], "download_size": 30338277, "dataset_size": 27927641}}

2024-01-18T11:10:29+00:00

TAGS #task_categories-text-classification #task_ids-text-scoring #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-4.0 #social-media-shares-prediction #arxiv-1801.07055 #region-us

# Dataset Card for News Popularity in Multiple Social Media Platforms ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: UCI - Repository: - Paper: Arxiv - Leaderboard: Kaggle - Point of Contact: ### Dataset Summary Social sharing data across Facebook, Google+ and LinkedIn for 100k news items on the topics of: economy, microsoft, obama and palestine. ### Supported Tasks and Leaderboards Popularity prediction/shares prediction ### Languages English ## Dataset Structure ### Data Instances ### Data Fields - id: the sentence id in the source dataset - title: the title of the link as shared on social media - headline: the headline, or sometimes the lede of the story - source: the source news site - topic: the topic: one of "economy", "microsoft", "obama" and "palestine" - publish_date: the date the original article was published - facebook: the number of Facebook shares, or -1 if this data wasn't collected - google_plus: the number of Google+ likes, or -1 if this data wasn't collected - linked_in: the number of LinkedIn shares, or -1 if if this data wasn't collected ### Data Splits None ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? The source headlines were by journalists, while the titles were written by the people sharing it on social media. ### Annotations #### Annotation process The 'annotations' are simply the number of shares, or likes in the case of Google+ as collected from various API endpoints. #### Who are the annotators? Social media users. ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information License: Creative Commons Attribution 4.0 International License (CC-BY) ### Contributions Thanks to @frankier for adding this dataset.

aadff43a8a8916477e5efad629600e0157651d24

# Dataset Card for NewsQA ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://www.microsoft.com/en-us/research/project/newsqa-dataset/ - **Repository:** https://github.com/Maluuba/newsqa - **Paper:** https://www.aclweb.org/anthology/W17-2623/ - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary NewsQA is a challenging machine comprehension dataset of over 100,000 human-generated question-answer pairs. Crowdworkers supply questions and answers based on a set of over 10,000 news articles from CNN, with answers consisting of spans of text from the corresponding articles. ### Supported Tasks and Leaderboards [Needs More Information] ### Languages English ## Dataset Structure ### Data Instances ``` {'storyId': './cnn/stories/42d01e187213e86f5fe617fe32e716ff7fa3afc4.story', 'text': 'NEW DELHI, India (CNN) -- A high court in northern India on Friday acquitted a wealthy businessman facing the death sentence for the killing of a teen in a case dubbed "the house of horrors."\n\n\n\nMoninder Singh Pandher was sentenced to death by a lower court in February.\n\n\n\nThe teen was one of 19 victims -- children and young women -- in one of the most gruesome serial killings in India in recent years.\n\n\n\nThe Allahabad high court has acquitted Moninder Singh Pandher, his lawyer Sikandar B. Kochar told CNN.\n\n\n\nPandher and his domestic employee Surinder Koli were sentenced to death in February by a lower court for the rape and murder of the 14-year-old.\n\n\n\nThe high court upheld Koli\'s death sentence, Kochar said.\n\n\n\nThe two were arrested two years ago after body parts packed in plastic bags were found near their home in Noida, a New Delhi suburb. Their home was later dubbed a "house of horrors" by the Indian media.\n\n\n\nPandher was not named a main suspect by investigators initially, but was summoned as co-accused during the trial, Kochar said.\n\n\n\nKochar said his client was in Australia when the teen was raped and killed.\n\n\n\nPandher faces trial in the remaining 18 killings and could remain in custody, the attorney said.', 'type': 'train', 'questions': {'q': ['What was the amount of children murdered?', 'When was Pandher sentenced to death?', 'The court aquitted Moninder Singh Pandher of what crime?', 'who was acquitted', 'who was sentenced', 'What was Moninder Singh Pandher acquitted for?', 'Who was sentenced to death in February?', 'how many people died', 'How many children and young women were murdered?'], 'isAnswerAbsent': [0, 0, 0, 0, 0, 0, 0, 0, 0], 'isQuestionBad': [0, 0, 0, 0, 0, 0, 0, 0, 0], 'consensus': [{'s': 294, 'e': 297, 'badQuestion': False, 'noAnswer': False}, {'s': 261, 'e': 271, 'badQuestion': False, 'noAnswer': False}, {'s': 624, 'e': 640, 'badQuestion': False, 'noAnswer': False}, {'s': 195, 'e': 218, 'badQuestion': False, 'noAnswer': False}, {'s': 195, 'e': 218, 'badQuestion': False, 'noAnswer': False}, {'s': 129, 'e': 151, 'badQuestion': False, 'noAnswer': False}, {'s': 195, 'e': 218, 'badQuestion': False, 'noAnswer': False}, {'s': 294, 'e': 297, 'badQuestion': False, 'noAnswer': False}, {'s': 294, 'e': 297, 'badQuestion': False, 'noAnswer': False}], 'answers': [{'sourcerAnswers': [{'s': [294], 'e': [297], 'badQuestion': [False], 'noAnswer': [False]}, {'s': [0], 'e': [0], 'badQuestion': [False], 'noAnswer': [True]}, {'s': [0], 'e': [0], 'badQuestion': [False], 'noAnswer': [True]}]}, {'sourcerAnswers': [{'s': [261], 'e': [271], 'badQuestion': [False], 'noAnswer': [False]}, {'s': [258], 'e': [271], 'badQuestion': [False], 'noAnswer': [False]}, {'s': [261], 'e': [271], 'badQuestion': [False], 'noAnswer': [False]}]}, {'sourcerAnswers': [{'s': [26], 'e': [33], 'badQuestion': [False], 'noAnswer': [False]}, {'s': [0], 'e': [0], 'badQuestion': [False], 'noAnswer': [True]}, {'s': [624], 'e': [640], 'badQuestion': [False], 'noAnswer': [False]}]}, {'sourcerAnswers': [{'s': [195], 'e': [218], 'badQuestion': [False], 'noAnswer': [False]}, {'s': [195], 'e': [218], 'badQuestion': [False], 'noAnswer': [False]}]}, {'sourcerAnswers': [{'s': [0], 'e': [0], 'badQuestion': [False], 'noAnswer': [True]}, {'s': [195, 232], 'e': [218, 271], 'badQuestion': [False, False], 'noAnswer': [False, False]}, {'s': [0], 'e': [0], 'badQuestion': [False], 'noAnswer': [True]}]}, {'sourcerAnswers': [{'s': [129], 'e': [192], 'badQuestion': [False], 'noAnswer': [False]}, {'s': [129], 'e': [151], 'badQuestion': [False], 'noAnswer': [False]}, {'s': [133], 'e': [151], 'badQuestion': [False], 'noAnswer': [False]}]}, {'sourcerAnswers': [{'s': [195], 'e': [218], 'badQuestion': [False], 'noAnswer': [False]}, {'s': [195], 'e': [218], 'badQuestion': [False], 'noAnswer': [False]}]}, {'sourcerAnswers': [{'s': [294], 'e': [297], 'badQuestion': [False], 'noAnswer': [False]}, {'s': [294], 'e': [297], 'badQuestion': [False], 'noAnswer': [False]}]}, {'sourcerAnswers': [{'s': [294], 'e': [297], 'badQuestion': [False], 'noAnswer': [False]}, {'s': [294], 'e': [297], 'badQuestion': [False], 'noAnswer': [False]}]}], 'validated_answers': [{'s': [0, 294], 'e': [0, 297], 'badQuestion': [False, False], 'noAnswer': [True, False], 'count': [1, 2]}, {'s': [], 'e': [], 'badQuestion': [], 'noAnswer': [], 'count': []}, {'s': [624], 'e': [640], 'badQuestion': [False], 'noAnswer': [False], 'count': [2]}, {'s': [], 'e': [], 'badQuestion': [], 'noAnswer': [], 'count': []}, {'s': [195], 'e': [218], 'badQuestion': [False], 'noAnswer': [False], 'count': [2]}, {'s': [129], 'e': [151], 'badQuestion': [False], 'noAnswer': [False], 'count': [2]}, {'s': [], 'e': [], 'badQuestion': [], 'noAnswer': [], 'count': []}, {'s': [], 'e': [], 'badQuestion': [], 'noAnswer': [], 'count': []}, {'s': [], 'e': [], 'badQuestion': [], 'noAnswer': [], 'count': []}]}} ``` ### Data Fields Configuration: combined-csv - 'story_id': An identifier of the story. - 'story_text': Text of the story. - 'question': A question about the story. - 'answer_char_ranges': The raw data collected for character based indices to answers in story_text. E.g. 196:228|196:202,217:228|None. Answers from different crowdsourcers are separated by `|`; within those, multiple selections from the same crowdsourcer are separated by `,`. `None` means the crowdsourcer thought there was no answer to the question in the story. The start is inclusive and the end is exclusive. The end may point to whitespace after a token. Configuration: combined-json - 'storyId': An identifier of the story. - 'text': Text of the story. - 'type': Split type. Will be "train", "validation" or "test". - 'questions': A list containing the following: - 'q': A question about the story. - 'isAnswerAbsent': Proportion of crowdsourcers that said there was no answer to the question in the story. - 'isQuestionBad': Proportion of crowdsourcers that said the question does not make sense. - 'consensus': The consensus answer. Use this field to pick the best continuous answer span from the text. If you want to know about a question having multiple answers in the text then you can use the more detailed "answers" and "validated_answers". The object can have start and end positions like in the example above or can be {"badQuestion": true} or {"noAnswer": true}. Note that there is only one consensus answer since it's based on the majority agreement of the crowdsourcers. - 's': Start of the answer. The first character of the answer in "text" (inclusive). - 'e': End of the answer. The last character of the answer in "text" (exclusive). - 'badQuestion': The validator said that the question did not make sense. - 'noAnswer': The crowdsourcer said that there was no answer to the question in the text. - 'answers': The answers from various crowdsourcers. - 'sourcerAnswers': The answer provided from one crowdsourcer. - 's': Start of the answer. The first character of the answer in "text" (inclusive). - 'e': End of the answer. The last character of the answer in "text" (exclusive). - 'badQuestion': The crowdsourcer said that the question did not make sense. - 'noAnswer': The crowdsourcer said that there was no answer to the question in the text. - 'validated_answers': The answers from the validators. - 's': Start of the answer. The first character of the answer in "text" (inclusive). - 'e': End of the answer. The last character of the answer in "text" (exclusive). - 'badQuestion': The validator said that the question did not make sense. - 'noAnswer': The validator said that there was no answer to the question in the text. - 'count': The number of validators that agreed with this answer. Configuration: split - 'story_id': An identifier of the story - 'story_text': text of the story - 'question': A question about the story. - 'answer_token_ranges': Word based indices to answers in story_text. E.g. 196:202,217:228. Multiple selections from the same answer are separated by `,`. The start is inclusive and the end is exclusive. The end may point to whitespace after a token. ### Data Splits | name | train | validation | test | |---------------|-----------:|-----------:|--------:| | combined-csv | 119633 | | | | combined-json | 12744 | | | | split | 92549 | 5166 | 5126 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information NewsQA Code Copyright (c) Microsoft Corporation All rights reserved. MIT License Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. © 2020 GitHub, Inc. ### Citation Information @inproceedings{trischler2017newsqa, title={NewsQA: A Machine Comprehension Dataset}, author={Trischler, Adam and Wang, Tong and Yuan, Xingdi and Harris, Justin and Sordoni, Alessandro and Bachman, Philip and Suleman, Kaheer}, booktitle={Proceedings of the 2nd Workshop on Representation Learning for NLP}, pages={191--200}, year={2017} ### Contributions Thanks to [@rsanjaykamath](https://github.com/rsanjaykamath) for adding this dataset.

newsqa

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M", "10K<n<100K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["extractive-qa"], "paperswithcode_id": "newsqa", "pretty_name": "NewsQA", "config_names": ["combined-csv", "combined-json", "split"], "dataset_info": [{"config_name": "combined-csv", "features": [{"name": "story_id", "dtype": "string"}, {"name": "story_text", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answer_char_ranges", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 465942194, "num_examples": 119633}], "download_size": 0, "dataset_size": 465942194}, {"config_name": "combined-json", "features": [{"name": "storyId", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "questions", "sequence": [{"name": "q", "dtype": "string"}, {"name": "isAnswerAbsent", "dtype": "int32"}, {"name": "isQuestionBad", "dtype": "int32"}, {"name": "consensus", "struct": [{"name": "s", "dtype": "int32"}, {"name": "e", "dtype": "int32"}, {"name": "badQuestion", "dtype": "bool"}, {"name": "noAnswer", "dtype": "bool"}]}, {"name": "answers", "sequence": [{"name": "sourcerAnswers", "sequence": [{"name": "s", "dtype": "int32"}, {"name": "e", "dtype": "int32"}, {"name": "badQuestion", "dtype": "bool"}, {"name": "noAnswer", "dtype": "bool"}]}]}, {"name": "validated_answers", "sequence": [{"name": "s", "dtype": "int32"}, {"name": "e", "dtype": "int32"}, {"name": "badQuestion", "dtype": "bool"}, {"name": "noAnswer", "dtype": "bool"}, {"name": "count", "dtype": "int32"}]}]}], "splits": [{"name": "train", "num_bytes": 68667276, "num_examples": 12744}], "download_size": 0, "dataset_size": 68667276}, {"config_name": "split", "features": [{"name": "story_id", "dtype": "string"}, {"name": "story_text", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answer_token_ranges", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 362031288, "num_examples": 92549}, {"name": "test", "num_bytes": 19763673, "num_examples": 5126}, {"name": "validation", "num_bytes": 19862778, "num_examples": 5166}], "download_size": 0, "dataset_size": 401657739}]}

2024-01-18T11:10:32+00:00

TAGS #task_categories-question-answering #task_ids-extractive-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-100K<n<1M #size_categories-10K<n<100K #source_datasets-original #language-English #license-mit #region-us

Dataset Card for NewsQA ======================= Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: URL * Paper: URL * Leaderboard: * Point of Contact: ### Dataset Summary NewsQA is a challenging machine comprehension dataset of over 100,000 human-generated question-answer pairs. Crowdworkers supply questions and answers based on a set of over 10,000 news articles from CNN, with answers consisting of spans of text from the corresponding articles. ### Supported Tasks and Leaderboards ### Languages English Dataset Structure ----------------- ### Data Instances ### Data Fields Configuration: combined-csv * 'story\_id': An identifier of the story. * 'story\_text': Text of the story. * 'question': A question about the story. * 'answer\_char\_ranges': The raw data collected for character based indices to answers in story\_text. E.g. 196:228|196:202,217:228|None. Answers from different crowdsourcers are separated by '|'; within those, multiple selections from the same crowdsourcer are separated by ','. 'None' means the crowdsourcer thought there was no answer to the question in the story. The start is inclusive and the end is exclusive. The end may point to whitespace after a token. Configuration: combined-json * 'storyId': An identifier of the story. * 'text': Text of the story. * 'type': Split type. Will be "train", "validation" or "test". * 'questions': A list containing the following: + 'q': A question about the story. + 'isAnswerAbsent': Proportion of crowdsourcers that said there was no answer to the question in the story. + 'isQuestionBad': Proportion of crowdsourcers that said the question does not make sense. + 'consensus': The consensus answer. Use this field to pick the best continuous answer span from the text. If you want to know about a question having multiple answers in the text then you can use the more detailed "answers" and "validated\_answers". The object can have start and end positions like in the example above or can be {"badQuestion": true} or {"noAnswer": true}. Note that there is only one consensus answer since it's based on the majority agreement of the crowdsourcers. + 's': Start of the answer. The first character of the answer in "text" (inclusive). + 'e': End of the answer. The last character of the answer in "text" (exclusive). + 'badQuestion': The validator said that the question did not make sense. + 'noAnswer': The crowdsourcer said that there was no answer to the question in the text. + 'answers': The answers from various crowdsourcers. - 'sourcerAnswers': The answer provided from one crowdsourcer. * 's': Start of the answer. The first character of the answer in "text" (inclusive). * 'e': End of the answer. The last character of the answer in "text" (exclusive). * 'badQuestion': The crowdsourcer said that the question did not make sense. * 'noAnswer': The crowdsourcer said that there was no answer to the question in the text. + 'validated\_answers': The answers from the validators. - 's': Start of the answer. The first character of the answer in "text" (inclusive). - 'e': End of the answer. The last character of the answer in "text" (exclusive). - 'badQuestion': The validator said that the question did not make sense. - 'noAnswer': The validator said that there was no answer to the question in the text. - 'count': The number of validators that agreed with this answer. Configuration: split * 'story\_id': An identifier of the story * 'story\_text': text of the story * 'question': A question about the story. * 'answer\_token\_ranges': Word based indices to answers in story\_text. E.g. 196:202,217:228. Multiple selections from the same answer are separated by ','. The start is inclusive and the end is exclusive. The end may point to whitespace after a token. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information NewsQA Code Copyright (c) Microsoft Corporation All rights reserved. MIT License Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. © 2020 GitHub, Inc. @inproceedings{trischler2017newsqa, title={NewsQA: A Machine Comprehension Dataset}, author={Trischler, Adam and Wang, Tong and Yuan, Xingdi and Harris, Justin and Sordoni, Alessandro and Bachman, Philip and Suleman, Kaheer}, booktitle={Proceedings of the 2nd Workshop on Representation Learning for NLP}, pages={191--200}, year={2017} ### Contributions Thanks to @rsanjaykamath for adding this dataset.

83590eafc3516f95aea022d152ab25272c2b0c69

# Dataset Card for "newsroom" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://lil.nlp.cornell.edu/newsroom/index.html](https://lil.nlp.cornell.edu/newsroom/index.html) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 5.30 GB - **Total amount of disk used:** 5.30 GB ### Dataset Summary NEWSROOM is a large dataset for training and evaluating summarization systems. It contains 1.3 million articles and summaries written by authors and editors in the newsrooms of 38 major publications. Dataset features includes: - text: Input news text. - summary: Summary for the news. And additional features: - title: news title. - url: url of the news. - date: date of the article. - density: extractive density. - coverage: extractive coverage. - compression: compression ratio. - density_bin: low, medium, high. - coverage_bin: extractive, abstractive. - compression_bin: low, medium, high. This dataset can be downloaded upon requests. Unzip all the contents "train.jsonl, dev.josnl, test.jsonl" to the `tfds` folder. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages English (`en`). ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 5.30 GB - **Total amount of disk used:** 5.30 GB An example of 'train' looks as follows. ``` { "compression": 33.880001068115234, "compression_bin": "medium", "coverage": 1.0, "coverage_bin": "high", "date": "200600000", "density": 11.720000267028809, "density_bin": "extractive", "summary": "some summary 1", "text": "some text 1", "title": "news title 1", "url": "url.html" } ``` ### Data Fields The data fields are the same among all splits. #### default - `text`: a `string` feature. - `summary`: a `string` feature. - `title`: a `string` feature. - `url`: a `string` feature. - `date`: a `string` feature. - `density_bin`: a `string` feature. - `coverage_bin`: a `string` feature. - `compression_bin`: a `string` feature. - `density`: a `float32` feature. - `coverage`: a `float32` feature. - `compression`: a `float32` feature. ### Data Splits | name |train |validation| test | |-------|-----:|---------:|-----:| |default|995041| 108837|108862| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information https://cornell.qualtrics.com/jfe/form/SV_6YA3HQ2p75XH4IR This Dataset Usage Agreement ("Agreement") is a legal agreement with the Cornell Newsroom Summaries Team ("Newsroom") for the Dataset made available to the individual or entity ("Researcher") exercising rights under this Agreement. "Dataset" includes all text, data, information, source code, and any related materials, documentation, files, media, updates or revisions. The Dataset is intended for non-commercial research and educational purposes only, and is made available free of charge without extending any license or other intellectual property rights. By downloading or using the Dataset, the Researcher acknowledges that they agree to the terms in this Agreement, and represent and warrant that they have authority to do so on behalf of any entity exercising rights under this Agreement. The Researcher accepts and agrees to be bound by the terms and conditions of this Agreement. If the Researcher does not agree to this Agreement, they may not download or use the Dataset. By sharing content with Newsroom, such as by submitting content to this site or by corresponding with Newsroom contributors, the Researcher grants Newsroom the right to use, reproduce, display, perform, adapt, modify, distribute, have distributed, and promote the content in any form, anywhere and for any purpose, such as for evaluating and comparing summarization systems. Nothing in this Agreement shall obligate Newsroom to provide any support for the Dataset. Any feedback, suggestions, ideas, comments, improvements given by the Researcher related to the Dataset is voluntarily given, and may be used by Newsroom without obligation or restriction of any kind. The Researcher accepts full responsibility for their use of the Dataset and shall defend indemnify, and hold harmless Newsroom, including their employees, trustees, officers, and agents, against any and all claims arising from the Researcher's use of the Dataset. The Researcher agrees to comply with all laws and regulations as they relate to access to and use of the Dataset and Service including U.S. export jurisdiction and other U.S. and international regulations. THE DATASET IS PROVIDED "AS IS." NEWSROOM DISCLAIMS ALL WARRANTIES, EXPRESS OR IMPLIED, INCLUDING THE IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND NON-INFRINGEMENT. WITHOUT LIMITATION OF THE ABOVE, NEWSROOM DISCLAIMS ANY WARRANTY THAT DATASET IS BUG OR ERROR-FREE, AND GRANTS NO WARRANTY REGARDING ITS USE OR THE RESULTS THEREFROM INCLUDING, WITHOUT LIMITATION, ITS CORRECTNESS, ACCURACY, OR RELIABILITY. THE DATASET IS NOT WARRANTIED TO FULFILL ANY PARTICULAR PURPOSES OR NEEDS. TO THE EXTENT NOT PROHIBITED BY LAW, IN NO EVENT SHALL NEWSROOM BE LIABLE FOR ANY LOSS, DAMAGE OR INJURY, DIRECT AND INDIRECT, INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES, HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER FOR BREACH OF CONTRACT, TORT (INCLUDING NEGLIGENCE) OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, INCLUDING BUT NOT LIMITED TO LOSS OF PROFITS, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. THESE LIMITATIONS SHALL APPLY NOTWITHSTANDING ANY FAILURE OF ESSENTIAL PURPOSE OF ANY LIMITED REMEDY. This Agreement is effective until terminated. Newsroom reserves the right to terminate the Researcher's access to the Dataset at any time. If the Researcher breaches this Agreement, the Researcher's rights to use the Dataset shall terminate automatically. The Researcher will immediately cease all use and distribution of the Dataset and destroy any copies or portions of the Dataset in their possession. This Agreement is governed by the laws of the State of New York, without regard to conflict of law principles. All terms and provisions of this Agreement shall, if possible, be construed in a manner which makes them valid, but in the event any term or provision of this Agreement is found by a court of competent jurisdiction to be illegal or unenforceable, the validity or enforceability of the remainder of this Agreement shall not be affected. This Agreement is the complete and exclusive agreement between the parties with respect to its subject matter and supersedes all prior or contemporaneous oral or written agreements or understandings relating to the subject matter. ### Citation Information ``` @inproceedings{N18-1065, author = {Grusky, Max and Naaman, Mor and Artzi, Yoav}, title = {NEWSROOM: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies}, booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies}, year = {2018}, } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@yoavartzi](https://github.com/yoavartzi), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

newsroom

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["unknown"], "source_datasets": ["original"], "task_categories": ["summarization"], "task_ids": ["news-articles-summarization"], "paperswithcode_id": "newsroom", "pretty_name": "CORNELL NEWSROOM", "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "density_bin", "dtype": "string"}, {"name": "coverage_bin", "dtype": "string"}, {"name": "compression_bin", "dtype": "string"}, {"name": "density", "dtype": "float32"}, {"name": "coverage", "dtype": "float32"}, {"name": "compression", "dtype": "float32"}], "splits": [{"name": "test", "num_bytes": 472446866, "num_examples": 108862}, {"name": "train", "num_bytes": 4357506078, "num_examples": 995041}, {"name": "validation", "num_bytes": 473206951, "num_examples": 108837}], "download_size": 0, "dataset_size": 5303159895}}

2024-01-18T11:10:34+00:00

TAGS #task_categories-summarization #task_ids-news-articles-summarization #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-unknown #source_datasets-original #language-English #license-other #region-us

Dataset Card for "newsroom" =========================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: * Point of Contact: * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 5.30 GB * Total amount of disk used: 5.30 GB ### Dataset Summary NEWSROOM is a large dataset for training and evaluating summarization systems. It contains 1.3 million articles and summaries written by authors and editors in the newsrooms of 38 major publications. Dataset features includes: * text: Input news text. * summary: Summary for the news. And additional features: * title: news title. * url: url of the news. * date: date of the article. * density: extractive density. * coverage: extractive coverage. * compression: compression ratio. * density\_bin: low, medium, high. * coverage\_bin: extractive, abstractive. * compression\_bin: low, medium, high. This dataset can be downloaded upon requests. Unzip all the contents "URL, URL, URL" to the 'tfds' folder. ### Supported Tasks and Leaderboards ### Languages English ('en'). Dataset Structure ----------------- ### Data Instances #### default * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 5.30 GB * Total amount of disk used: 5.30 GB An example of 'train' looks as follows. ### Data Fields The data fields are the same among all splits. #### default * 'text': a 'string' feature. * 'summary': a 'string' feature. * 'title': a 'string' feature. * 'url': a 'string' feature. * 'date': a 'string' feature. * 'density\_bin': a 'string' feature. * 'coverage\_bin': a 'string' feature. * 'compression\_bin': a 'string' feature. * 'density': a 'float32' feature. * 'coverage': a 'float32' feature. * 'compression': a 'float32' feature. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information URL This Dataset Usage Agreement ("Agreement") is a legal agreement with the Cornell Newsroom Summaries Team ("Newsroom") for the Dataset made available to the individual or entity ("Researcher") exercising rights under this Agreement. "Dataset" includes all text, data, information, source code, and any related materials, documentation, files, media, updates or revisions. The Dataset is intended for non-commercial research and educational purposes only, and is made available free of charge without extending any license or other intellectual property rights. By downloading or using the Dataset, the Researcher acknowledges that they agree to the terms in this Agreement, and represent and warrant that they have authority to do so on behalf of any entity exercising rights under this Agreement. The Researcher accepts and agrees to be bound by the terms and conditions of this Agreement. If the Researcher does not agree to this Agreement, they may not download or use the Dataset. By sharing content with Newsroom, such as by submitting content to this site or by corresponding with Newsroom contributors, the Researcher grants Newsroom the right to use, reproduce, display, perform, adapt, modify, distribute, have distributed, and promote the content in any form, anywhere and for any purpose, such as for evaluating and comparing summarization systems. Nothing in this Agreement shall obligate Newsroom to provide any support for the Dataset. Any feedback, suggestions, ideas, comments, improvements given by the Researcher related to the Dataset is voluntarily given, and may be used by Newsroom without obligation or restriction of any kind. The Researcher accepts full responsibility for their use of the Dataset and shall defend indemnify, and hold harmless Newsroom, including their employees, trustees, officers, and agents, against any and all claims arising from the Researcher's use of the Dataset. The Researcher agrees to comply with all laws and regulations as they relate to access to and use of the Dataset and Service including U.S. export jurisdiction and other U.S. and international regulations. THE DATASET IS PROVIDED "AS IS." NEWSROOM DISCLAIMS ALL WARRANTIES, EXPRESS OR IMPLIED, INCLUDING THE IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND NON-INFRINGEMENT. WITHOUT LIMITATION OF THE ABOVE, NEWSROOM DISCLAIMS ANY WARRANTY THAT DATASET IS BUG OR ERROR-FREE, AND GRANTS NO WARRANTY REGARDING ITS USE OR THE RESULTS THEREFROM INCLUDING, WITHOUT LIMITATION, ITS CORRECTNESS, ACCURACY, OR RELIABILITY. THE DATASET IS NOT WARRANTIED TO FULFILL ANY PARTICULAR PURPOSES OR NEEDS. TO THE EXTENT NOT PROHIBITED BY LAW, IN NO EVENT SHALL NEWSROOM BE LIABLE FOR ANY LOSS, DAMAGE OR INJURY, DIRECT AND INDIRECT, INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES, HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER FOR BREACH OF CONTRACT, TORT (INCLUDING NEGLIGENCE) OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, INCLUDING BUT NOT LIMITED TO LOSS OF PROFITS, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. THESE LIMITATIONS SHALL APPLY NOTWITHSTANDING ANY FAILURE OF ESSENTIAL PURPOSE OF ANY LIMITED REMEDY. This Agreement is effective until terminated. Newsroom reserves the right to terminate the Researcher's access to the Dataset at any time. If the Researcher breaches this Agreement, the Researcher's rights to use the Dataset shall terminate automatically. The Researcher will immediately cease all use and distribution of the Dataset and destroy any copies or portions of the Dataset in their possession. This Agreement is governed by the laws of the State of New York, without regard to conflict of law principles. All terms and provisions of this Agreement shall, if possible, be construed in a manner which makes them valid, but in the event any term or provision of this Agreement is found by a court of competent jurisdiction to be illegal or unenforceable, the validity or enforceability of the remainder of this Agreement shall not be affected. This Agreement is the complete and exclusive agreement between the parties with respect to its subject matter and supersedes all prior or contemporaneous oral or written agreements or understandings relating to the subject matter. ### Contributions Thanks to @lewtun, @patrickvonplaten, @yoavartzi, @thomwolf for adding this dataset.

ffbcf8ba469d60d8f0c1fc6b33348f62095d6901

# Dataset Card for NJKP NER ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://nkjp.pl/index.php?page=0&lang=1 - **Repository:** - **Paper:** @book{przepiorkowski2012narodowy, title={Narodowy korpus j{\k{e}}zyka polskiego}, author={Przepi{\'o}rkowski, Adam}, year={2012}, publisher={Naukowe PWN} - **Leaderboard:** - **Point of Contact:** adamp@ipipan.waw.pl ### Dataset Summary A linguistic corpus is a collection of texts where one can find the typical use of a single word or a phrase, as well as their meaning and grammatical function. Nowadays, without access to a language corpus, it has become impossible to do linguistic research, to write dictionaries, grammars and language teaching books, to create search engines sensitive to Polish inflection, machine translation engines and software of advanced language technology. Language corpora have become an essential tool for linguists, but they are also helpful for software engineers, scholars of literature and culture, historians, librarians and other specialists of art and computer sciences. The manually annotated 1-million word subcorpus of the NJKP, available on GNU GPL v.3 ### Supported Tasks and Leaderboards Named entity recognition [More Information Needed] ### Languages Polish ## Dataset Structure ### Data Instances Two tsv files (train, dev) with two columns (sentence, target) and one (test) with just one (sentence). ### Data Fields - sentence - target ### Data Splits Data is splitted in train/dev/test split. ## Dataset Creation ### Curation Rationale This dataset is one of nine evaluation tasks to improve polish language processing. ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information GNU GPL v.3 ### Citation Information @book{przepiorkowski2012narodowy, title={Narodowy korpus j{\k{e}}zyka polskiego}, author={Przepi{\'o}rkowski, Adam}, year={2012}, publisher={Naukowe PWN} } ### Contributions Thanks to [@abecadel](https://github.com/abecadel) for adding this dataset.

nkjp-ner

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["other"], "language": ["pl"], "license": ["gpl-3.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "NJKP NER", "dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "target", "dtype": {"class_label": {"names": {"0": "geogName", "1": "noEntity", "2": "orgName", "3": "persName", "4": "placeName", "5": "time"}}}}], "splits": [{"name": "train", "num_bytes": 1612125, "num_examples": 15794}, {"name": "test", "num_bytes": 221092, "num_examples": 2058}, {"name": "validation", "num_bytes": 196652, "num_examples": 1941}], "download_size": 821629, "dataset_size": 2029869}}

2024-01-18T11:10:38+00:00

TAGS #task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-expert-generated #language_creators-other #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Polish #license-gpl-3.0 #region-us

# Dataset Card for NJKP NER ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: - Paper: @book{przepiorkowski2012narodowy, title={Narodowy korpus j{\k{e}}zyka polskiego}, author={Przepi{\'o}rkowski, Adam}, year={2012}, publisher={Naukowe PWN} - Leaderboard: - Point of Contact: adamp@URL ### Dataset Summary A linguistic corpus is a collection of texts where one can find the typical use of a single word or a phrase, as well as their meaning and grammatical function. Nowadays, without access to a language corpus, it has become impossible to do linguistic research, to write dictionaries, grammars and language teaching books, to create search engines sensitive to Polish inflection, machine translation engines and software of advanced language technology. Language corpora have become an essential tool for linguists, but they are also helpful for software engineers, scholars of literature and culture, historians, librarians and other specialists of art and computer sciences. The manually annotated 1-million word subcorpus of the NJKP, available on GNU GPL v.3 ### Supported Tasks and Leaderboards Named entity recognition ### Languages Polish ## Dataset Structure ### Data Instances Two tsv files (train, dev) with two columns (sentence, target) and one (test) with just one (sentence). ### Data Fields - sentence - target ### Data Splits Data is splitted in train/dev/test split. ## Dataset Creation ### Curation Rationale This dataset is one of nine evaluation tasks to improve polish language processing. ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information GNU GPL v.3 @book{przepiorkowski2012narodowy, title={Narodowy korpus j{\k{e}}zyka polskiego}, author={Przepi{\'o}rkowski, Adam}, year={2012}, publisher={Naukowe PWN} } ### Contributions Thanks to @abecadel for adding this dataset.

15a8962926fc0c3c4dc357c9b49e4501dd723676

# Dataset Card for "nli_tr" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/boun-tabi/NLI-TR](https://github.com/boun-tabi/NLI-TR) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 115.85 MB - **Size of the generated dataset:** 153.36 MB - **Total amount of disk used:** 269.21 MB ### Dataset Summary The Natural Language Inference in Turkish (NLI-TR) is a set of two large scale datasets that were obtained by translating the foundational NLI corpora (SNLI and MNLI) using Amazon Translate. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### multinli_tr - **Size of downloaded dataset files:** 75.52 MB - **Size of the generated dataset:** 79.47 MB - **Total amount of disk used:** 154.99 MB An example of 'validation_matched' looks as follows. ``` This example was too long and was cropped: { "hypothesis": "Mrinal Sen'in çalışmalarının çoğu Avrupa koleksiyonlarında bulunabilir.", "idx": 7, "label": 1, "premise": "\"Kalküta, sanatsal yaratıcılığa dair herhangi bir iddiaya sahip olan tek diğer üretim merkezi gibi görünüyor, ama ironik bir şek..." } ``` #### snli_tr - **Size of downloaded dataset files:** 40.33 MB - **Size of the generated dataset:** 73.89 MB - **Total amount of disk used:** 114.22 MB An example of 'train' looks as follows. ``` { "hypothesis": "Yaşlı bir adam, kızının işten çıkmasını bekçiyken suyunu içer.", "idx": 9, "label": 1, "premise": "Parlak renkli gömlek çalışanları arka planda gülümseme iken yaşlı bir adam bir kahve dükkanında küçük bir masada onun portakal suyu ile oturur." } ``` ### Data Fields The data fields are the same among all splits. #### multinli_tr - `idx`: a `int32` feature. - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). #### snli_tr - `idx`: a `int32` feature. - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). ### Data Splits #### multinli_tr | |train |validation_matched|validation_mismatched| |-----------|-----:|-----------------:|--------------------:| |multinli_tr|392702| 10000| 10000| #### snli_tr | |train |validation|test | |-------|-----:|---------:|----:| |snli_tr|550152| 10000|10000| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{budur-etal-2020-data, title = "Data and Representation for Turkish Natural Language Inference", author = "Budur, Emrah and "{O}zçelik, Rıza and G"{u}ng"{o}r, Tunga", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", abstract = "Large annotated datasets in NLP are overwhelmingly in English. This is an obstacle to progress in other languages. Unfortunately, obtaining new annotated resources for each task in each language would be prohibitively expensive. At the same time, commercial machine translation systems are now robust. Can we leverage these systems to translate English-language datasets automatically? In this paper, we offer a positive response for natural language inference (NLI) in Turkish. We translated two large English NLI datasets into Turkish and had a team of experts validate their translation quality and fidelity to the original labels. Using these datasets, we address core issues of representation for Turkish NLI. We find that in-language embeddings are essential and that morphological parsing can be avoided where the training set is large. Finally, we show that models trained on our machine-translated datasets are successful on human-translated evaluation sets. We share all code, models, and data publicly.", } ``` ### Contributions Thanks to [@e-budur](https://github.com/e-budur) for adding this dataset.

nli_tr

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["machine-generated"], "language": ["tr"], "license": ["cc-by-3.0", "cc-by-4.0", "cc-by-sa-3.0", "mit", "other"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["extended|snli", "extended|multi_nli"], "task_categories": ["text-classification"], "task_ids": ["natural-language-inference", "semantic-similarity-scoring", "text-scoring"], "paperswithcode_id": "nli-tr", "pretty_name": "Natural Language Inference in Turkish", "config_names": ["multinli_tr", "snli_tr"], "license_details": "Open Portion of the American National Corpus", "dataset_info": [{"config_name": "snli_tr", "features": [{"name": "idx", "dtype": "int32"}, {"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 71175743, "num_examples": 550152}, {"name": "validation", "num_bytes": 1359639, "num_examples": 10000}, {"name": "test", "num_bytes": 1355409, "num_examples": 10000}], "download_size": 40328942, "dataset_size": 73890791}, {"config_name": "multinli_tr", "features": [{"name": "idx", "dtype": "int32"}, {"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 75524150, "num_examples": 392702}, {"name": "validation_matched", "num_bytes": 1908283, "num_examples": 10000}, {"name": "validation_mismatched", "num_bytes": 2039392, "num_examples": 10000}], "download_size": 75518512, "dataset_size": 79471825}]}

2024-01-26T14:05:28+00:00

TAGS #task_categories-text-classification #task_ids-natural-language-inference #task_ids-semantic-similarity-scoring #task_ids-text-scoring #annotations_creators-expert-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended|snli #source_datasets-extended|multi_nli #language-Turkish #license-cc-by-3.0 #license-cc-by-4.0 #license-cc-by-sa-3.0 #license-mit #license-other #region-us

Dataset Card for "nli\_tr" ========================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: * Point of Contact: * Size of downloaded dataset files: 115.85 MB * Size of the generated dataset: 153.36 MB * Total amount of disk used: 269.21 MB ### Dataset Summary The Natural Language Inference in Turkish (NLI-TR) is a set of two large scale datasets that were obtained by translating the foundational NLI corpora (SNLI and MNLI) using Amazon Translate. ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### multinli\_tr * Size of downloaded dataset files: 75.52 MB * Size of the generated dataset: 79.47 MB * Total amount of disk used: 154.99 MB An example of 'validation\_matched' looks as follows. #### snli\_tr * Size of downloaded dataset files: 40.33 MB * Size of the generated dataset: 73.89 MB * Total amount of disk used: 114.22 MB An example of 'train' looks as follows. ### Data Fields The data fields are the same among all splits. #### multinli\_tr * 'idx': a 'int32' feature. * 'premise': a 'string' feature. * 'hypothesis': a 'string' feature. * 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2). #### snli\_tr * 'idx': a 'int32' feature. * 'premise': a 'string' feature. * 'hypothesis': a 'string' feature. * 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2). ### Data Splits #### multinli\_tr #### snli\_tr Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @e-budur for adding this dataset.

0edc7007b37706b838a7facd3115617b26ce4e5e

# Dataset Card for NLU Evaluation Data ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Github](https://github.com/xliuhw/NLU-Evaluation-Data) - **Repository:** [Github](https://github.com/xliuhw/NLU-Evaluation-Data) - **Paper:** [ArXiv](https://arxiv.org/abs/1903.05566) - **Leaderboard:** - **Point of Contact:** [x.liu@hw.ac.uk](mailto:x.liu@hw.ac.uk) ### Dataset Summary Dataset with short utterances from conversational domain annotated with their corresponding intents and scenarios. It has 25 715 non-zero examples (original dataset has 25716 examples) belonging to 18 scenarios and 68 intents. Originally, the dataset was crowd-sourced and annotated with both intents and named entities in order to evaluate commercial NLU systems such as RASA, IBM's Watson, Microsoft's LUIS and Google's Dialogflow. **This version of the dataset only includes intent annotations!** In contrast to paper claims, released data contains 68 unique intents. This is due to the fact, that NLU systems were evaluated on more curated part of this dataset which only included 64 most important intents. Read more in [github issue](https://github.com/xliuhw/NLU-Evaluation-Data/issues/5). ### Supported Tasks and Leaderboards Intent classification, intent detection ### Languages English ## Dataset Structure ### Data Instances An example of 'train' looks as follows: ``` { 'label': 2, # integer label corresponding to "alarm_set" intent 'scenario': 'alarm', 'text': 'wake me up at five am this week' } ``` ### Data Fields - `text`: a string feature. - `label`: one of classification labels (0-67) corresponding to unique intents. - `scenario`: a string with one of unique scenarios (18). Intent names are mapped to `label` in the following way: | label | intent | |--------:|:-------------------------| | 0 | alarm_query | | 1 | alarm_remove | | 2 | alarm_set | | 3 | audio_volume_down | | 4 | audio_volume_mute | | 5 | audio_volume_other | | 6 | audio_volume_up | | 7 | calendar_query | | 8 | calendar_remove | | 9 | calendar_set | | 10 | cooking_query | | 11 | cooking_recipe | | 12 | datetime_convert | | 13 | datetime_query | | 14 | email_addcontact | | 15 | email_query | | 16 | email_querycontact | | 17 | email_sendemail | | 18 | general_affirm | | 19 | general_commandstop | | 20 | general_confirm | | 21 | general_dontcare | | 22 | general_explain | | 23 | general_greet | | 24 | general_joke | | 25 | general_negate | | 26 | general_praise | | 27 | general_quirky | | 28 | general_repeat | | 29 | iot_cleaning | | 30 | iot_coffee | | 31 | iot_hue_lightchange | | 32 | iot_hue_lightdim | | 33 | iot_hue_lightoff | | 34 | iot_hue_lighton | | 35 | iot_hue_lightup | | 36 | iot_wemo_off | | 37 | iot_wemo_on | | 38 | lists_createoradd | | 39 | lists_query | | 40 | lists_remove | | 41 | music_dislikeness | | 42 | music_likeness | | 43 | music_query | | 44 | music_settings | | 45 | news_query | | 46 | play_audiobook | | 47 | play_game | | 48 | play_music | | 49 | play_podcasts | | 50 | play_radio | | 51 | qa_currency | | 52 | qa_definition | | 53 | qa_factoid | | 54 | qa_maths | | 55 | qa_stock | | 56 | recommendation_events | | 57 | recommendation_locations | | 58 | recommendation_movies | | 59 | social_post | | 60 | social_query | | 61 | takeaway_order | | 62 | takeaway_query | | 63 | transport_query | | 64 | transport_taxi | | 65 | transport_ticket | | 66 | transport_traffic | | 67 | weather_query | ### Data Splits | Dataset statistics | Train | | --- | --- | | Number of examples | 25 715 | | Average character length | 34.32 | | Number of intents | 68 | | Number of scenarios | 18 | ## Dataset Creation ### Curation Rationale The dataset was prepared for a wide coverage evaluation and comparison of some of the most popular NLU services. At that time, previous benchmarks were done with few intents and spawning limited number of domains. Here, the dataset is much larger and contains 68 intents from 18 scenarios, which is much larger that any previous evaluation. For more discussion see the paper. ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process > To build the NLU component we collected real user data via Amazon Mechanical Turk (AMT). We designed tasks where the Turker’s goal was to answer questions about how people would interact with the home robot, in a wide range of scenarios designed in advance, namely: alarm, audio, audiobook, calendar, cooking, datetime, email, game, general, IoT, lists, music, news, podcasts, general Q&A, radio, recommendations, social, food takeaway, transport, and weather. The questions put to Turkers were designed to capture the different requests within each given scenario. In the ‘calendar’ scenario, for example, these pre-designed intents were included: ‘set event’, ‘delete event’ and ‘query event’. An example question for intent ‘set event’ is: “How would you ask your PDA to schedule a meeting with someone?” for which a user’s answer example was “Schedule a chat with Adam on Thursday afternoon”. The Turkers would then type in their answers to these questions and select possible entities from the pre-designed suggested entities list for each of their answers.The Turkers didn’t always follow the instructions fully, e.g. for the specified ‘delete event’ Intent, an answer was: “PDA what is my next event?”; which clearly belongs to ‘query event’ Intent. We have manually corrected all such errors either during post-processing or the subsequent annotations. #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset The purpose of this dataset it to help develop better intent detection systems. ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Creative Commons Attribution 4.0 International License (CC BY 4.0) ### Citation Information ``` @InProceedings{XLiu.etal:IWSDS2019, author = {Xingkun Liu, Arash Eshghi, Pawel Swietojanski and Verena Rieser}, title = {Benchmarking Natural Language Understanding Services for building Conversational Agents}, booktitle = {Proceedings of the Tenth International Workshop on Spoken Dialogue Systems Technology (IWSDS)}, month = {April}, year = {2019}, address = {Ortigia, Siracusa (SR), Italy}, publisher = {Springer}, pages = {xxx--xxx}, url = {http://www.xx.xx/xx/} } ``` ### Contributions Thanks to [@dkajtoch](https://github.com/dkajtoch) for adding this dataset.

nlu_evaluation_data

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["intent-classification", "multi-class-classification"], "pretty_name": "NLU Evaluation Data", "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "scenario", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "alarm_query", "1": "alarm_remove", "2": "alarm_set", "3": "audio_volume_down", "4": "audio_volume_mute", "5": "audio_volume_other", "6": "audio_volume_up", "7": "calendar_query", "8": "calendar_remove", "9": "calendar_set", "10": "cooking_query", "11": "cooking_recipe", "12": "datetime_convert", "13": "datetime_query", "14": "email_addcontact", "15": "email_query", "16": "email_querycontact", "17": "email_sendemail", "18": "general_affirm", "19": "general_commandstop", "20": "general_confirm", "21": "general_dontcare", "22": "general_explain", "23": "general_greet", "24": "general_joke", "25": "general_negate", "26": "general_praise", "27": "general_quirky", "28": "general_repeat", "29": "iot_cleaning", "30": "iot_coffee", "31": "iot_hue_lightchange", "32": "iot_hue_lightdim", "33": "iot_hue_lightoff", "34": "iot_hue_lighton", "35": "iot_hue_lightup", "36": "iot_wemo_off", "37": "iot_wemo_on", "38": "lists_createoradd", "39": "lists_query", "40": "lists_remove", "41": "music_dislikeness", "42": "music_likeness", "43": "music_query", "44": "music_settings", "45": "news_query", "46": "play_audiobook", "47": "play_game", "48": "play_music", "49": "play_podcasts", "50": "play_radio", "51": "qa_currency", "52": "qa_definition", "53": "qa_factoid", "54": "qa_maths", "55": "qa_stock", "56": "recommendation_events", "57": "recommendation_locations", "58": "recommendation_movies", "59": "social_post", "60": "social_query", "61": "takeaway_order", "62": "takeaway_query", "63": "transport_query", "64": "transport_taxi", "65": "transport_ticket", "66": "transport_traffic", "67": "weather_query"}}}}], "splits": [{"name": "train", "num_bytes": 1447941, "num_examples": 25715}], "download_size": 5867439, "dataset_size": 1447941}}

2024-01-18T11:10:41+00:00

TAGS #task_categories-text-classification #task_ids-intent-classification #task_ids-multi-class-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-4.0 #arxiv-1903.05566 #region-us

Dataset Card for NLU Evaluation Data ==================================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: Github * Repository: Github * Paper: ArXiv * Leaderboard: * Point of Contact: x.liu@URL ### Dataset Summary Dataset with short utterances from conversational domain annotated with their corresponding intents and scenarios. It has 25 715 non-zero examples (original dataset has 25716 examples) belonging to 18 scenarios and 68 intents. Originally, the dataset was crowd-sourced and annotated with both intents and named entities in order to evaluate commercial NLU systems such as RASA, IBM's Watson, Microsoft's LUIS and Google's Dialogflow. This version of the dataset only includes intent annotations! In contrast to paper claims, released data contains 68 unique intents. This is due to the fact, that NLU systems were evaluated on more curated part of this dataset which only included 64 most important intents. Read more in github issue. ### Supported Tasks and Leaderboards Intent classification, intent detection ### Languages English Dataset Structure ----------------- ### Data Instances An example of 'train' looks as follows: ### Data Fields * 'text': a string feature. * 'label': one of classification labels (0-67) corresponding to unique intents. * 'scenario': a string with one of unique scenarios (18). Intent names are mapped to 'label' in the following way: ### Data Splits Dataset Creation ---------------- ### Curation Rationale The dataset was prepared for a wide coverage evaluation and comparison of some of the most popular NLU services. At that time, previous benchmarks were done with few intents and spawning limited number of domains. Here, the dataset is much larger and contains 68 intents from 18 scenarios, which is much larger that any previous evaluation. For more discussion see the paper. ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process > > To build the NLU component we collected real user data via Amazon Mechanical Turk (AMT). We designed tasks where the Turker’s goal was to answer questions about how people would interact with the home robot, in a wide range of scenarios designed in advance, namely: alarm, audio, audiobook, calendar, cooking, datetime, email, game, general, IoT, lists, music, news, podcasts, general Q&A, radio, recommendations, social, food takeaway, transport, and weather. > The questions put to Turkers were designed to capture the different requests within each given scenario. > In the ‘calendar’ scenario, for example, these pre-designed intents were included: ‘set event’, ‘delete event’ and ‘query event’. > An example question for intent ‘set event’ is: “How would you ask your PDA to schedule a meeting with someone?” for which a user’s answer example was “Schedule a chat with Adam on Thursday afternoon”. > The Turkers would then type in their answers to these questions and select possible entities from the pre-designed suggested entities list for each of their answers.The Turkers didn’t always follow the instructions fully, e.g. for the specified ‘delete event’ Intent, an answer was: “PDA what is my next event?”; which clearly belongs to ‘query event’ Intent. > We have manually corrected all such errors either during post-processing or the subsequent annotations. > > > #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset The purpose of this dataset it to help develop better intent detection systems. ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information Creative Commons Attribution 4.0 International License (CC BY 4.0) ### Contributions Thanks to @dkajtoch for adding this dataset.

43e375e48f7d710be5ca427726dc7246981bea69

# Dataset Card for NoReC ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** https://github.com/ltgoslo/norec - **Paper:** http://www.lrec-conf.org/proceedings/lrec2018/pdf/851.pdf - **Leaderboard:** [More Information Needed] - **Point of Contact:** [More Information Needed] ### Dataset Summary This dataset contains Norwegian Review Corpus (NoReC), created for the purpose of training and evaluating models for document-level sentiment analysis. More than 43,000 full-text reviews have been collected from major Norwegian news sources and cover a range of different domains, including literature, movies, video games, restaurants, music and theater, in addition to product reviews across a range of categories. Each review is labeled with a manually assigned score of 1–6, as provided by the rating of the original author. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The sentences in the dataset are in Norwegian (nb, nn, no). ## Dataset Structure ### Data Instances A sample from training set is provided below: ``` {'deprel': ['det', 'amod', 'cc', 'conj', 'nsubj', 'case', 'nmod', 'cop', 'case', 'case', 'root', 'flat:name', 'flat:name', 'punct'], 'deps': ['None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None'], 'feats': ["{'Gender': 'Masc', 'Number': 'Sing', 'PronType': 'Dem'}", "{'Definite': 'Def', 'Degree': 'Pos', 'Number': 'Sing'}", 'None', "{'Definite': 'Def', 'Degree': 'Pos', 'Number': 'Sing'}", "{'Definite': 'Def', 'Gender': 'Masc', 'Number': 'Sing'}", 'None', 'None', "{'Mood': 'Ind', 'Tense': 'Pres', 'VerbForm': 'Fin'}", 'None', 'None', 'None', 'None', 'None', 'None'], 'head': ['5', '5', '4', '2', '11', '7', '5', '11', '11', '11', '0', '11', '11', '11'], 'idx': '000000-02-01', 'lemmas': ['den', 'andre', 'og', 'sist', 'sesong', 'av', 'Rome', 'være', 'ute', 'på', 'DVD', 'i', 'Norge', '$.'], 'misc': ['None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', "{'SpaceAfter': 'No'}", 'None'], 'pos_tags': [5, 0, 4, 0, 7, 1, 11, 3, 1, 1, 11, 1, 11, 12], 'text': 'Den andre og siste sesongen av Rome er ute på DVD i Norge.', 'tokens': ['Den', 'andre', 'og', 'siste', 'sesongen', 'av', 'Rome', 'er', 'ute', 'på', 'DVD', 'i', 'Norge', '.'], 'xpos_tags': ['None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None']} ``` ### Data Fields The data instances have the following fields: - deprel: [More Information Needed] - deps: [More Information Needed] - feats: [More Information Needed] - head: [More Information Needed] - idx: index - lemmas: lemmas of all tokens - misc: [More Information Needed] - pos_tags: part of speech tags - text: text string - tokens: tokens - xpos_tags: [More Information Needed] The part of speech taggs correspond to these labels: "ADJ" (0), "ADP" (1), "ADV" (2), "AUX" (3), "CCONJ" (4), "DET" (5), "INTJ" (6), "NOUN" (7), "NUM" (8), "PART" (9), "PRON" (10), "PROPN" (11), "PUNCT" (12), "SCONJ" (13), "SYM" (14), "VERB" (15), "X" (16), ### Data Splits The training, validation, and test set contain `680792`, `101106`, and `101594` sentences respectively. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @InProceedings{VelOvrBer18, author = {Erik Velldal and Lilja {\O}vrelid and Eivind Alexander Bergem and Cathrine Stadsnes and Samia Touileb and Fredrik J{\o}rgensen}, title = {{NoReC}: The {N}orwegian {R}eview {C}orpus}, booktitle = {Proceedings of the 11th edition of the Language Resources and Evaluation Conference}, year = {2018}, address = {Miyazaki, Japan}, pages = {4186--4191} } ``` ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.

norec

2022-03-02T23:29:22+00:00

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2024-01-18T11:10:42+00:00

TAGS #task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-Norwegian Bokmål #language-Norwegian Nynorsk #language-Norwegian #license-cc-by-nc-4.0 #region-us

# Dataset Card for NoReC ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Repository: URL - Paper: URL - Leaderboard: - Point of Contact: ### Dataset Summary This dataset contains Norwegian Review Corpus (NoReC), created for the purpose of training and evaluating models for document-level sentiment analysis. More than 43,000 full-text reviews have been collected from major Norwegian news sources and cover a range of different domains, including literature, movies, video games, restaurants, music and theater, in addition to product reviews across a range of categories. Each review is labeled with a manually assigned score of 1–6, as provided by the rating of the original author. ### Supported Tasks and Leaderboards ### Languages The sentences in the dataset are in Norwegian (nb, nn, no). ## Dataset Structure ### Data Instances A sample from training set is provided below: ### Data Fields The data instances have the following fields: - deprel: - deps: - feats: - head: - idx: index - lemmas: lemmas of all tokens - misc: - pos_tags: part of speech tags - text: text string - tokens: tokens - xpos_tags: The part of speech taggs correspond to these labels: "ADJ" (0), "ADP" (1), "ADV" (2), "AUX" (3), "CCONJ" (4), "DET" (5), "INTJ" (6), "NOUN" (7), "NUM" (8), "PART" (9), "PRON" (10), "PROPN" (11), "PUNCT" (12), "SCONJ" (13), "SYM" (14), "VERB" (15), "X" (16), ### Data Splits The training, validation, and test set contain '680792', '101106', and '101594' sentences respectively. ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @abhishekkrthakur for adding this dataset.

e977ad72686ce377f3bf11473a183041a77cf6ec

# Dataset Card for NorNE: Norwegian Named Entities ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [NorNE](https://github.com/ltgoslo/norne/) - **Repository:** [Github](https://github.com/ltgoslo/norne/) - **Paper:** https://arxiv.org/abs/1911.12146 - **Leaderboard:** - **Point of Contact:** ### Dataset Summary NorNE is a manually annotated corpus of named entities which extends the annotation of the existing Norwegian Dependency Treebank. Comprising both of the official standards of written Norwegian (Bokmål and Nynorsk), the corpus contains around 600,000 tokens and annotates a rich set of entity types including persons,organizations, locations, geo-political entities, products, and events, in addition to a class corresponding to nominals derived from names. There are 3 main configs in this dataset each with 3 versions of the NER tag set. When accessing the `bokmaal`, `nynorsk`, or `combined` configs the NER tag set will be comprised of 9 tags: `GPE_ORG`, `GPE_LOC`, `ORG`, `LOC`, `PER`, `PROD`, `EVT`, `DRV`, and `MISC`. The two special types `GPE_LOC` and `GPE_ORG` can easily be altered depending on the task, choosing either the more general `GPE` tag or the more specific `LOC`/`ORG` tags, conflating them with the other annotations of the same type. To access these reduced versions of the dataset, you can use the configs `bokmaal-7`, `nynorsk-7`, `combined-7` for the NER tag set with 7 tags ( **`ORG`**, **`LOC`**, `PER`, `PROD`, `EVT`, `DRV`, `MISC`), and `bokmaal-8`, `nynorsk-8`, `combined-8` for the NER tag set with 8 tags (`LOC_` and `ORG_`: **`ORG`**, **`LOC`**, **`GPE`**, `PER`, `PROD`, `EVT`, `DRV`, `MISC`). By default, the full set (9 tags) will be used. See Annotations for further details. ### Supported Tasks and Leaderboards NorNE ads named entity annotations on top of the Norwegian Dependency Treebank. ### Languages Both Norwegian Bokmål (`bokmaal`) and Nynorsk (`nynorsk`) are supported as different configs in this dataset. An extra config for the combined languages is also included (`combined`). See the Annotation section for details on accessing reduced tag sets for the NER feature. ## Dataset Structure Each entry contains text sentences, their language, identifiers, tokens, lemmas, and corresponding NER and POS tag lists. ### Data Instances An example of the `train` split of the `bokmaal` config. ```python {'idx': '000001', 'lang': 'bokmaal', 'lemmas': ['lam', 'og', 'piggvar', 'på', 'bryllupsmeny'], 'ner_tags': [0, 0, 0, 0, 0], 'pos_tags': [0, 9, 0, 5, 0], 'text': 'Lam og piggvar på bryllupsmenyen', 'tokens': ['Lam', 'og', 'piggvar', 'på', 'bryllupsmenyen']} ``` ### Data Fields Each entry is annotated with the next fields: - `idx` (`int`), text (sentence) identifier from the NorNE dataset - `lang` (`str`), language variety, either `bokmaal`, `nynorsk` or `combined` - `text` (`str`), plain text - `tokens` (`List[str]`), list of tokens extracted from `text` - `lemmas` (`List[str]`), list of lemmas extracted from `tokens` - `ner_tags` (`List[int]`), list of numeric NER tags for each token in `tokens` - `pos_tags` (`List[int]`), list of numeric PoS tags for each token in `tokens` An example DataFrame obtained from the dataset: <table class="dataframe" border="1"> <thead> <tr style="text-align: right;"> <th></th> <th>idx</th> <th>lang</th> <th>text</th> <th>tokens</th> <th>lemmas</th> <th>ner_tags</th> <th>pos_tags</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>000001</td> <td>bokmaal</td> <td>Lam og piggvar på bryllupsmenyen</td> <td>[Lam, og, piggvar, på, bryllupsmenyen]</td> <td>[lam, og, piggvar, på, bryllupsmeny]</td> <td>[0, 0, 0, 0, 0]</td> <td>[0, 9, 0, 5, 0]</td> </tr> <tr> <th>1</th> <td>000002</td> <td>bokmaal</td> <td>Kamskjell, piggvar og lammefilet sto på menyen...</td> <td>[Kamskjell, ,, piggvar, og, lammefilet, sto, p...</td> <td>[kamskjell, $,, piggvar, og, lammefilet, stå, ...</td> <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</td> <td>[0, 1, 0, 9, 0, 15, 2, 0, 2, 8, 6, 0, 1]</td> </tr> <tr> <th>2</th> <td>000003</td> <td>bokmaal</td> <td>Og til dessert: Parfait à la Mette-Marit.</td> <td>[Og, til, dessert, :, Parfait, à, la, Mette-Ma...</td> <td>[og, til, dessert, $:, Parfait, à, la, Mette-M...</td> <td>[0, 0, 0, 0, 7, 8, 8, 8, 0]</td> <td>[9, 2, 0, 1, 10, 12, 12, 10, 1]</td> </tr> </tbody> </table> ### Data Splits There are three splits: `train`, `validation` and `test`. | Config | Split | Total | | :---------|-------------:|-------:| | `bokmaal` | `train` | 15696 | | `bokmaal` | `validation` | 2410 | | `bokmaal` | `test` | 1939 | | `nynorsk` | `train` | 14174 | | `nynorsk` | `validation` | 1890 | | `nynorsk` | `test` | 1511 | | `combined`| `test` | 29870 | | `combined`| `validation` | 4300 | | `combined`| `test` | 3450 | ## Dataset Creation ### Curation Rationale 1. A _name_ in this context is close to [Saul Kripke's definition of a name](https://en.wikipedia.org/wiki/Saul_Kripke#Naming_and_Necessity), in that a name has a unique reference and its meaning is constant (there are exceptions in the annotations, e.g. "Regjeringen" (en. "Government")). 2. It is the usage of a name that determines the entity type, not the default/literal sense of the name, 3. If there is an ambiguity in the type/sense of a name, then the the default/literal sense of the name is chosen (following [Markert and Nissim, 2002](http://www.lrec-conf.org/proceedings/lrec2002/pdf/11.pdf)). For more details, see the "Annotation Guidelines.pdf" distributed with the corpus. ### Source Data Data was collected using blogs and newspapers in Norwegian, as well as parliament speeches and governamental reports. #### Initial Data Collection and Normalization The texts in the Norwegian Dependency Treebank (NDT) are manually annotated with morphological features, syntactic functions and hierarchical structure. The formalism used for the syntactic annotation is dependency grammar. The treebanks consists of two parts, one part in Norwegian Bokmål (`nob`) and one part in Norwegian Nynorsk (`nno`). Both parts contain around 300.000 tokens, and are a mix of different non-fictional genres. See the [NDT webpage](https://www.nb.no/sprakbanken/show?serial=sbr-10) for more details. ### Annotations The following types of entities are annotated: - **Person (`PER`):** Real or fictional characters and animals - **Organization (`ORG`):** Any collection of people, such as firms, institutions, organizations, music groups, sports teams, unions, political parties etc. - **Location (`LOC`):** Geographical places, buildings and facilities - **Geo-political entity (`GPE`):** Geographical regions defined by political and/or social groups. A GPE entity subsumes and does not distinguish between a nation, its region, its government, or its people - **Product (`PROD`):** Artificially produced entities are regarded products. This may include more abstract entities, such as speeches, radio shows, programming languages, contracts, laws and ideas. - **Event (`EVT`):** Festivals, cultural events, sports events, weather phenomena, wars, etc. Events are bounded in time and space. - **Derived (`DRV`):** Words (and phrases?) that are dervied from a name, but not a name in themselves. They typically contain a full name and are capitalized, but are not proper nouns. Examples (fictive) are "Brann-treneren" ("the Brann coach") or "Oslo-mannen" ("the man from Oslo"). - **Miscellaneous (`MISC`):** Names that do not belong in the other categories. Examples are animals species and names of medical conditions. Entities that are manufactured or produced are of type Products, whereas thing naturally or spontaneously occurring are of type Miscellaneous. Furthermore, all `GPE` entities are additionally sub-categorized as being either `ORG` or `LOC`, with the two annotation levels separated by an underscore: - `GPE_LOC`: Geo-political entity, with a locative sense (e.g. "John lives in _Spain_") - `GPE_ORG`: Geo-political entity, with an organisation sense (e.g. "_Spain_ declined to meet with Belgium") The two special types `GPE_LOC` and `GPE_ORG` can easily be altered depending on the task, choosing either the more general `GPE` tag or the more specific `LOC`/`ORG` tags, conflating them with the other annotations of the same type. This means that the following sets of entity types can be derived: - 7 types, deleting `_GPE`: **`ORG`**, **`LOC`**, `PER`, `PROD`, `EVT`, `DRV`, `MISC` - 8 types, deleting `LOC_` and `ORG_`: **`ORG`**, **`LOC`**, **`GPE`**, `PER`, `PROD`, `EVT`, `DRV`, `MISC` - 9 types, keeping all types: **`ORG`**, **`LOC`**, **`GPE_LOC`**, **`GPE_ORG`**, `PER`, `PROD`, `EVT`, `DRV`, `MISC` The class distribution is as follows, broken down across the data splits of the UD version of NDT, and sorted by total counts (i.e. the number of examples, not tokens within the spans of the annotatons): | Type | Train | Dev | Test | Total | | :--------|-------:|-------:|-------:|-------:| | `PER` | 4033 | 607 | 560 | 5200 | | `ORG` | 2828 | 400 | 283 | 3511 | | `GPE_LOC`| 2132 | 258 | 257 | 2647 | | `PROD` | 671 | 162 | 71 | 904 | | `LOC` | 613 | 109 | 103 | 825 | | `GPE_ORG`| 388 | 55 | 50 | 493 | | `DRV` | 519 | 77 | 48 | 644 | | `EVT` | 131 | 9 | 5 | 145 | | `MISC` | 8 | 0 | 0 | 0 | To access these reduced versions of the dataset, you can use the configs `bokmaal-7`, `nynorsk-7`, `combined-7` for the NER tag set with 7 tags ( **`ORG`**, **`LOC`**, `PER`, `PROD`, `EVT`, `DRV`, `MISC`), and `bokmaal-8`, `nynorsk-8`, `combined-8` for the NER tag set with 8 tags (`LOC_` and `ORG_`: **`ORG`**, **`LOC`**, **`GPE`**, `PER`, `PROD`, `EVT`, `DRV`, `MISC`). By default, the full set (9 tags) will be used. ## Additional Information ### Dataset Curators NorNE was created as a collaboration between [Schibsted Media Group](https://schibsted.com/), [Språkbanken](https://www.nb.no/forskning/sprakbanken/) at the [National Library of Norway](https://www.nb.no) and the [Language Technology Group](https://www.mn.uio.no/ifi/english/research/groups/ltg/) at the University of Oslo. NorNE was added to 🤗 Datasets by the AI-Lab at the National Library of Norway. ### Licensing Information The NorNE corpus is published under the same [license](https://github.com/ltgoslo/norne/blob/master/LICENSE_NDT.txt) as the Norwegian Dependency Treebank ### Citation Information This dataset is described in the paper _NorNE: Annotating Named Entities for Norwegian_ by Fredrik Jørgensen, Tobias Aasmoe, Anne-Stine Ruud Husevåg, Lilja Øvrelid, and Erik Velldal, accepted for LREC 2020 and available as pre-print here: https://arxiv.org/abs/1911.12146. ```bibtex @inproceedings{johansen2019ner, title={NorNE: Annotating Named Entities for Norwegian}, author={Fredrik Jørgensen, Tobias Aasmoe, Anne-Stine Ruud Husevåg, Lilja Øvrelid, and Erik Velldal}, booktitle={LREC 2020}, year={2020}, url={https://arxiv.org/abs/1911.12146} } ``` ### Contributions Thanks to [@versae](https://github.com/versae) for adding this dataset.

norne

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["crowdsourced"], "language": ["no"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "NorNE: Norwegian Named Entities", "dataset_info": [{"config_name": "bokmaal", "features": [{"name": "idx", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "lemmas", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "NOUN", "1": "PUNCT", "2": "ADP", "3": "NUM", "4": "SYM", "5": "SCONJ", "6": "ADJ", "7": "PART", "8": "DET", "9": "CCONJ", "10": "PROPN", "11": "PRON", "12": "X", "13": "ADV", "14": "INTJ", "15": "VERB", "16": "AUX"}}}}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": 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{"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "lemmas", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "NOUN", "1": "PUNCT", "2": "ADP", "3": "NUM", "4": "SYM", "5": "SCONJ", "6": "ADJ", "7": "PART", "8": "DET", "9": "CCONJ", "10": "PROPN", "11": "PRON", "12": "X", "13": "ADV", "14": "INTJ", "15": "VERB", "16": "AUX"}}}}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-PROD", "6": "I-PROD", "7": "B-LOC", "8": "I-LOC", "9": "B-DRV", "10": "I-DRV", "11": "B-EVT", "12": "I-EVT", "13": "B-MISC", "14": "I-MISC"}}}}], "splits": [{"name": "train", "num_bytes": 10072260, "num_examples": 14174}, {"name": "validation", "num_bytes": 1278029, "num_examples": 1890}, {"name": "test", "num_bytes": 1023358, "num_examples": 1511}], "download_size": 20209253, "dataset_size": 12373647}, {"config_name": "combined-7", "features": [{"name": "idx", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "lemmas", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "NOUN", "1": "PUNCT", "2": "ADP", "3": "NUM", "4": "SYM", "5": "SCONJ", "6": "ADJ", "7": "PART", "8": "DET", "9": "CCONJ", "10": "PROPN", "11": "PRON", "12": "X", "13": "ADV", "14": "INTJ", "15": "VERB", "16": "AUX"}}}}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-PROD", "6": "I-PROD", "7": "B-LOC", "8": "I-LOC", "9": "B-DRV", "10": "I-DRV", "11": "B-EVT", "12": "I-EVT", "13": "B-MISC", "14": "I-MISC"}}}}], "splits": [{"name": "train", "num_bytes": 20104393, "num_examples": 29870}, {"name": "validation", "num_bytes": 2779723, "num_examples": 4300}, {"name": "test", "num_bytes": 2257594, "num_examples": 3450}], "download_size": 41118494, "dataset_size": 25141710}, {"config_name": "bokmaal-8", "features": [{"name": "idx", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "lemmas", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "NOUN", "1": "PUNCT", "2": "ADP", "3": "NUM", "4": "SYM", "5": "SCONJ", "6": "ADJ", "7": "PART", "8": "DET", "9": "CCONJ", "10": "PROPN", "11": "PRON", "12": "X", "13": "ADV", "14": "INTJ", "15": "VERB", "16": "AUX"}}}}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-PROD", "6": "I-PROD", "7": "B-LOC", "8": "I-LOC", "9": "B-GPE", "10": "I-GPE", "11": "B-DRV", "12": "I-DRV", "13": "B-EVT", "14": "I-EVT", "15": "B-MISC", "16": "I-MISC"}}}}], "splits": [{"name": "train", "num_bytes": 10032169, "num_examples": 15696}, {"name": "validation", "num_bytes": 1501730, "num_examples": 2410}, {"name": "test", "num_bytes": 1234272, "num_examples": 1939}], "download_size": 20909241, "dataset_size": 12768171}, {"config_name": "nynorsk-8", "features": [{"name": "idx", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "lemmas", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "NOUN", "1": "PUNCT", "2": "ADP", "3": "NUM", "4": "SYM", "5": "SCONJ", "6": "ADJ", "7": "PART", "8": "DET", "9": "CCONJ", "10": "PROPN", "11": "PRON", "12": "X", "13": "ADV", "14": "INTJ", "15": "VERB", "16": "AUX"}}}}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-PROD", "6": "I-PROD", "7": "B-LOC", "8": "I-LOC", "9": "B-GPE", "10": "I-GPE", "11": "B-DRV", "12": "I-DRV", "13": "B-EVT", "14": "I-EVT", "15": "B-MISC", "16": "I-MISC"}}}}], "splits": [{"name": "train", "num_bytes": 10072260, "num_examples": 14174}, {"name": "validation", "num_bytes": 1278029, "num_examples": 1890}, {"name": "test", "num_bytes": 1023358, "num_examples": 1511}], "download_size": 20209253, "dataset_size": 12373647}, {"config_name": "combined-8", "features": [{"name": "idx", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "lemmas", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "NOUN", "1": "PUNCT", "2": "ADP", "3": "NUM", "4": "SYM", "5": "SCONJ", "6": "ADJ", "7": "PART", "8": "DET", "9": "CCONJ", "10": "PROPN", "11": "PRON", "12": "X", "13": "ADV", "14": "INTJ", "15": "VERB", "16": "AUX"}}}}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-PROD", "6": "I-PROD", "7": "B-LOC", "8": "I-LOC", "9": "B-GPE", "10": "I-GPE", "11": "B-DRV", "12": "I-DRV", "13": "B-EVT", "14": "I-EVT", "15": "B-MISC", "16": "I-MISC"}}}}], "splits": [{"name": "train", "num_bytes": 20104393, "num_examples": 29870}, {"name": "validation", "num_bytes": 2779723, "num_examples": 4300}, {"name": "test", "num_bytes": 2257594, "num_examples": 3450}], "download_size": 41118494, "dataset_size": 25141710}]}

2024-01-18T11:10:44+00:00

TAGS #task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-expert-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Norwegian #license-other #arxiv-1911.12146 #region-us

Dataset Card for NorNE: Norwegian Named Entities ================================================ Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: NorNE * Repository: Github * Paper: URL * Leaderboard: * Point of Contact: ### Dataset Summary NorNE is a manually annotated corpus of named entities which extends the annotation of the existing Norwegian Dependency Treebank. Comprising both of the official standards of written Norwegian (Bokmål and Nynorsk), the corpus contains around 600,000 tokens and annotates a rich set of entity types including persons,organizations, locations, geo-political entities, products, and events, in addition to a class corresponding to nominals derived from names. There are 3 main configs in this dataset each with 3 versions of the NER tag set. When accessing the 'bokmaal', 'nynorsk', or 'combined' configs the NER tag set will be comprised of 9 tags: 'GPE\_ORG', 'GPE\_LOC', 'ORG', 'LOC', 'PER', 'PROD', 'EVT', 'DRV', and 'MISC'. The two special types 'GPE\_LOC' and 'GPE\_ORG' can easily be altered depending on the task, choosing either the more general 'GPE' tag or the more specific 'LOC'/'ORG' tags, conflating them with the other annotations of the same type. To access these reduced versions of the dataset, you can use the configs 'bokmaal-7', 'nynorsk-7', 'combined-7' for the NER tag set with 7 tags ( 'ORG', 'LOC', 'PER', 'PROD', 'EVT', 'DRV', 'MISC'), and 'bokmaal-8', 'nynorsk-8', 'combined-8' for the NER tag set with 8 tags ('LOC\_' and 'ORG\_': 'ORG', 'LOC', 'GPE', 'PER', 'PROD', 'EVT', 'DRV', 'MISC'). By default, the full set (9 tags) will be used. See Annotations for further details. ### Supported Tasks and Leaderboards NorNE ads named entity annotations on top of the Norwegian Dependency Treebank. ### Languages Both Norwegian Bokmål ('bokmaal') and Nynorsk ('nynorsk') are supported as different configs in this dataset. An extra config for the combined languages is also included ('combined'). See the Annotation section for details on accessing reduced tag sets for the NER feature. Dataset Structure ----------------- Each entry contains text sentences, their language, identifiers, tokens, lemmas, and corresponding NER and POS tag lists. ### Data Instances An example of the 'train' split of the 'bokmaal' config. ### Data Fields Each entry is annotated with the next fields: * 'idx' ('int'), text (sentence) identifier from the NorNE dataset * 'lang' ('str'), language variety, either 'bokmaal', 'nynorsk' or 'combined' * 'text' ('str'), plain text * 'tokens' ('List[str]'), list of tokens extracted from 'text' * 'lemmas' ('List[str]'), list of lemmas extracted from 'tokens' * 'ner\_tags' ('List[int]'), list of numeric NER tags for each token in 'tokens' * 'pos\_tags' ('List[int]'), list of numeric PoS tags for each token in 'tokens' An example DataFrame obtained from the dataset: | | idx | lang | text | tokens | lemmas | ner\_tags | pos\_tags | | --- | --- | --- | --- | --- | --- | --- | --- | | 0 | 000001 | bokmaal | Lam og piggvar på bryllupsmenyen | [Lam, og, piggvar, på, bryllupsmenyen] | [lam, og, piggvar, på, bryllupsmeny] | [0, 0, 0, 0, 0] | [0, 9, 0, 5, 0] | | 1 | 000002 | bokmaal | Kamskjell, piggvar og lammefilet sto på menyen... | [Kamskjell, ,, piggvar, og, lammefilet, sto, p... | [kamskjell, $,, piggvar, og, lammefilet, stå, ... | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | [0, 1, 0, 9, 0, 15, 2, 0, 2, 8, 6, 0, 1] | | 2 | 000003 | bokmaal | Og til dessert: Parfait à la Mette-Marit. | [Og, til, dessert, :, Parfait, à, la, Mette-Ma... | [og, til, dessert, $:, Parfait, à, la, Mette-M... | [0, 0, 0, 0, 7, 8, 8, 8, 0] | [9, 2, 0, 1, 10, 12, 12, 10, 1] | ### Data Splits There are three splits: 'train', 'validation' and 'test'. Dataset Creation ---------------- ### Curation Rationale 1. A *name* in this context is close to Saul Kripke's definition of a name, in that a name has a unique reference and its meaning is constant (there are exceptions in the annotations, e.g. "Regjeringen" (en. "Government")). 2. It is the usage of a name that determines the entity type, not the default/literal sense of the name, 3. If there is an ambiguity in the type/sense of a name, then the the default/literal sense of the name is chosen (following Markert and Nissim, 2002). For more details, see the "Annotation URL" distributed with the corpus. ### Source Data Data was collected using blogs and newspapers in Norwegian, as well as parliament speeches and governamental reports. #### Initial Data Collection and Normalization The texts in the Norwegian Dependency Treebank (NDT) are manually annotated with morphological features, syntactic functions and hierarchical structure. The formalism used for the syntactic annotation is dependency grammar. The treebanks consists of two parts, one part in Norwegian Bokmål ('nob') and one part in Norwegian Nynorsk ('nno'). Both parts contain around 300.000 tokens, and are a mix of different non-fictional genres. See the NDT webpage for more details. ### Annotations The following types of entities are annotated: * Person ('PER'): Real or fictional characters and animals * Organization ('ORG'): Any collection of people, such as firms, institutions, organizations, music groups, sports teams, unions, political parties etc. * Location ('LOC'): Geographical places, buildings and facilities * Geo-political entity ('GPE'): Geographical regions defined by political and/or social groups. A GPE entity subsumes and does not distinguish between a nation, its region, its government, or its people * Product ('PROD'): Artificially produced entities are regarded products. This may include more abstract entities, such as speeches, radio shows, programming languages, contracts, laws and ideas. * Event ('EVT'): Festivals, cultural events, sports events, weather phenomena, wars, etc. Events are bounded in time and space. * Derived ('DRV'): Words (and phrases?) that are dervied from a name, but not a name in themselves. They typically contain a full name and are capitalized, but are not proper nouns. Examples (fictive) are "Brann-treneren" ("the Brann coach") or "Oslo-mannen" ("the man from Oslo"). * Miscellaneous ('MISC'): Names that do not belong in the other categories. Examples are animals species and names of medical conditions. Entities that are manufactured or produced are of type Products, whereas thing naturally or spontaneously occurring are of type Miscellaneous. Furthermore, all 'GPE' entities are additionally sub-categorized as being either 'ORG' or 'LOC', with the two annotation levels separated by an underscore: * 'GPE\_LOC': Geo-political entity, with a locative sense (e.g. "John lives in *Spain*") * 'GPE\_ORG': Geo-political entity, with an organisation sense (e.g. "*Spain* declined to meet with Belgium") The two special types 'GPE\_LOC' and 'GPE\_ORG' can easily be altered depending on the task, choosing either the more general 'GPE' tag or the more specific 'LOC'/'ORG' tags, conflating them with the other annotations of the same type. This means that the following sets of entity types can be derived: * 7 types, deleting '\_GPE': 'ORG', 'LOC', 'PER', 'PROD', 'EVT', 'DRV', 'MISC' * 8 types, deleting 'LOC\_' and 'ORG\_': 'ORG', 'LOC', 'GPE', 'PER', 'PROD', 'EVT', 'DRV', 'MISC' * 9 types, keeping all types: 'ORG', 'LOC', 'GPE\_LOC', 'GPE\_ORG', 'PER', 'PROD', 'EVT', 'DRV', 'MISC' The class distribution is as follows, broken down across the data splits of the UD version of NDT, and sorted by total counts (i.e. the number of examples, not tokens within the spans of the annotatons): To access these reduced versions of the dataset, you can use the configs 'bokmaal-7', 'nynorsk-7', 'combined-7' for the NER tag set with 7 tags ( 'ORG', 'LOC', 'PER', 'PROD', 'EVT', 'DRV', 'MISC'), and 'bokmaal-8', 'nynorsk-8', 'combined-8' for the NER tag set with 8 tags ('LOC\_' and 'ORG\_': 'ORG', 'LOC', 'GPE', 'PER', 'PROD', 'EVT', 'DRV', 'MISC'). By default, the full set (9 tags) will be used. Additional Information ---------------------- ### Dataset Curators NorNE was created as a collaboration between Schibsted Media Group, Språkbanken at the National Library of Norway and the Language Technology Group at the University of Oslo. NorNE was added to Datasets by the AI-Lab at the National Library of Norway. ### Licensing Information The NorNE corpus is published under the same license as the Norwegian Dependency Treebank This dataset is described in the paper *NorNE: Annotating Named Entities for Norwegian* by Fredrik Jørgensen, Tobias Aasmoe, Anne-Stine Ruud Husevåg, Lilja Øvrelid, and Erik Velldal, accepted for LREC 2020 and available as pre-print here: URL ### Contributions Thanks to @versae for adding this dataset.

8ff0d63b2ea853e95e449effff9e23cf8a5e8c8e

# Dataset Card for Norwegian NER ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Github](https://github.com/ljos/navnkjenner) - **Repository:** [Github](https://github.com/ljos/navnkjenner) - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@jplu](https://github.com/jplu) for adding this dataset.

norwegian_ner

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["crowdsourced"], "language": ["no"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "Norwegian NER", "dataset_info": [{"config_name": "bokmaal", "features": [{"name": "idx", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "lemmas", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "NOUN", "1": "PUNCT", "2": "ADP", "3": "NUM", "4": "SYM", "5": "SCONJ", "6": "ADJ", "7": "PART", "8": "DET", "9": "CCONJ", "10": "PROPN", "11": "PRON", "12": "X", "13": "ADV", "14": "INTJ", "15": "VERB", "16": "AUX"}}}}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-OTH", "2": "I-OTH", "3": "E-OTH", "4": "S-OTH", "5": "B-ORG", "6": "I-ORG", "7": "E-ORG", "8": "S-ORG", "9": "B-PRS", "10": "I-PRS", "11": "E-PRS", "12": "S-PRS", "13": "B-GEO", "14": "I-GEO", "15": "E-GEO", "16": "S-GEO"}}}}], "splits": [{"name": "train", "num_bytes": 9859760, "num_examples": 15696}, {"name": "validation", "num_bytes": 1475216, "num_examples": 2410}, {"name": "test", "num_bytes": 1212939, "num_examples": 1939}], "download_size": 8747760, "dataset_size": 12547915}, {"config_name": "nynorsk", "features": [{"name": "idx", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "lemmas", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "NOUN", "1": "PUNCT", "2": "ADP", "3": "NUM", "4": "SYM", "5": "SCONJ", "6": "ADJ", "7": "PART", "8": "DET", "9": "CCONJ", "10": "PROPN", "11": "PRON", "12": "X", "13": "ADV", "14": "INTJ", "15": "VERB", "16": "AUX"}}}}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-OTH", "2": "I-OTH", "3": "E-OTH", "4": "S-OTH", "5": "B-ORG", "6": "I-ORG", "7": "E-ORG", "8": "S-ORG", "9": "B-PRS", "10": "I-PRS", "11": "E-PRS", "12": "S-PRS", "13": "B-GEO", "14": "I-GEO", "15": "E-GEO", "16": "S-GEO"}}}}], "splits": [{"name": "train", "num_bytes": 9916338, "num_examples": 14174}, {"name": "validation", "num_bytes": 1257235, "num_examples": 1890}, {"name": "test", "num_bytes": 1006733, "num_examples": 1511}], "download_size": 8484545, "dataset_size": 12180306}, {"config_name": "samnorsk", "features": [{"name": "idx", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "lemmas", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "NOUN", "1": "PUNCT", "2": "ADP", "3": "NUM", "4": "SYM", "5": "SCONJ", "6": "ADJ", "7": "PART", "8": "DET", "9": "CCONJ", "10": "PROPN", "11": "PRON", "12": "X", "13": "ADV", "14": "INTJ", "15": "VERB", "16": "AUX"}}}}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-OTH", "2": "I-OTH", "3": "E-OTH", "4": "S-OTH", "5": "B-ORG", "6": "I-ORG", "7": "E-ORG", "8": "S-ORG", "9": "B-PRS", "10": "I-PRS", "11": "E-PRS", "12": "S-PRS", "13": "B-GEO", "14": "I-GEO", "15": "E-GEO", "16": "S-GEO"}}}}], "splits": [{"name": "train", "num_bytes": 22508485, "num_examples": 34170}, {"name": "validation", "num_bytes": 2732419, "num_examples": 4300}, {"name": "test", "num_bytes": 2219640, "num_examples": 3450}], "download_size": 19133049, "dataset_size": 27460544}]}

2024-01-18T11:10:45+00:00

TAGS #task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-expert-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Norwegian #license-unknown #region-us

# Dataset Card for Norwegian NER ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: Github - Repository: Github - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @jplu for adding this dataset.

3e24b5c209e8f578bd6f5ee795167a3577674383

# Dataset Card for nq_open ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://efficientqa.github.io/ - **Repository:** https://github.com/google-research-datasets/natural-questions/tree/master/nq_open - **Paper:** https://www.aclweb.org/anthology/P19-1612.pdf - **Leaderboard:** https://ai.google.com/research/NaturalQuestions/efficientqa - **Point of Contact:** [Mailing List](efficientqa@googlegroups.com) ### Dataset Summary The NQ-Open task, introduced by Lee et.al. 2019, is an open domain question answering benchmark that is derived from Natural Questions. The goal is to predict an English answer string for an input English question. All questions can be answered using the contents of English Wikipedia. ### Supported Tasks and Leaderboards Open Domain Question-Answering, EfficientQA Leaderboard: https://ai.google.com/research/NaturalQuestions/efficientqa ### Languages English (`en`) ## Dataset Structure ### Data Instances ``` { "question": "names of the metropolitan municipalities in south africa", "answer": [ "Mangaung Metropolitan Municipality", "Nelson Mandela Bay Metropolitan Municipality", "eThekwini Metropolitan Municipality", "City of Tshwane Metropolitan Municipality", "City of Johannesburg Metropolitan Municipality", "Buffalo City Metropolitan Municipality", "City of Ekurhuleni Metropolitan Municipality" ] } ``` ### Data Fields - `question` - Input open domain question. - `answer` - List of possible answers to the question ### Data Splits - Train : 87925 - validation : 1800 ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization Natural Questions contains question from aggregated queries to Google Search (Kwiatkowski et al., 2019). To gather an open version of this dataset, we only keep questions with short answers and discard the given evidence document. Answers with many tokens often resemble extractive snippets rather than canonical answers, so we discard answers with more than 5 tokens. #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases Evaluating on this diverse set of question-answer pairs is crucial, because all existing datasets have inherent biases that are problematic for open domain QA systems with learned retrieval. In the Natural Questions dataset the question askers do not already know the answer. This accurately reflects a distribution of genuine information-seeking questions. However, annotators must separately find correct answers, which requires assistance from automatic tools and can introduce a moderate bias towards results from the tool. ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information All of the Natural Questions data is released under the [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/) license. ### Citation Information ``` @article{doi:10.1162/tacl\_a\_00276, author = {Kwiatkowski, Tom and Palomaki, Jennimaria and Redfield, Olivia and Collins, Michael and Parikh, Ankur and Alberti, Chris and Epstein, Danielle and Polosukhin, Illia and Devlin, Jacob and Lee, Kenton and Toutanova, Kristina and Jones, Llion and Kelcey, Matthew and Chang, Ming-Wei and Dai, Andrew M. and Uszkoreit, Jakob and Le, Quoc and Petrov, Slav}, title = {Natural Questions: A Benchmark for Question Answering Research}, journal = {Transactions of the Association for Computational Linguistics}, volume = {7}, number = {}, pages = {453-466}, year = {2019}, doi = {10.1162/tacl\_a\_00276}, URL = { https://doi.org/10.1162/tacl_a_00276 }, eprint = { https://doi.org/10.1162/tacl_a_00276 }, abstract = { We present the Natural Questions corpus, a question answering data set. Questions consist of real anonymized, aggregated queries issued to the Google search engine. An annotator is presented with a question along with a Wikipedia page from the top 5 search results, and annotates a long answer (typically a paragraph) and a short answer (one or more entities) if present on the page, or marks null if no long/short answer is present. The public release consists of 307,373 training examples with single annotations; 7,830 examples with 5-way annotations for development data; and a further 7,842 examples with 5-way annotated sequestered as test data. We present experiments validating quality of the data. We also describe analysis of 25-way annotations on 302 examples, giving insights into human variability on the annotation task. We introduce robust metrics for the purposes of evaluating question answering systems; demonstrate high human upper bounds on these metrics; and establish baseline results using competitive methods drawn from related literature. } } @inproceedings{lee-etal-2019-latent, title = "Latent Retrieval for Weakly Supervised Open Domain Question Answering", author = "Lee, Kenton and Chang, Ming-Wei and Toutanova, Kristina", booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/P19-1612", doi = "10.18653/v1/P19-1612", pages = "6086--6096", abstract = "Recent work on open domain question answering (QA) assumes strong supervision of the supporting evidence and/or assumes a blackbox information retrieval (IR) system to retrieve evidence candidates. We argue that both are suboptimal, since gold evidence is not always available, and QA is fundamentally different from IR. We show for the first time that it is possible to jointly learn the retriever and reader from question-answer string pairs and without any IR system. In this setting, evidence retrieval from all of Wikipedia is treated as a latent variable. Since this is impractical to learn from scratch, we pre-train the retriever with an Inverse Cloze Task. We evaluate on open versions of five QA datasets. On datasets where the questioner already knows the answer, a traditional IR system such as BM25 is sufficient. On datasets where a user is genuinely seeking an answer, we show that learned retrieval is crucial, outperforming BM25 by up to 19 points in exact match.", } ``` ### Contributions Thanks to [@Nilanshrajput](https://github.com/Nilanshrajput) for adding this dataset.

nq_open

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["other"], "language": ["en"], "license": ["cc-by-sa-3.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["extended|natural_questions"], "task_categories": ["question-answering"], "task_ids": ["open-domain-qa"], "pretty_name": "NQ-Open", "dataset_info": {"config_name": "nq_open", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 6651236, "num_examples": 87925}, {"name": "validation", "num_bytes": 313829, "num_examples": 3610}], "download_size": 4678245, "dataset_size": 6965065}, "configs": [{"config_name": "nq_open", "data_files": [{"split": "train", "path": "nq_open/train-*"}, {"split": "validation", "path": "nq_open/validation-*"}], "default": true}]}

2024-01-04T16:07:17+00:00

TAGS #task_categories-question-answering #task_ids-open-domain-qa #annotations_creators-expert-generated #language_creators-other #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|natural_questions #language-English #license-cc-by-sa-3.0 #region-us

# Dataset Card for nq_open ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: URL - Paper: URL - Leaderboard: URL - Point of Contact: Mailing List ### Dataset Summary The NQ-Open task, introduced by Lee URL. 2019, is an open domain question answering benchmark that is derived from Natural Questions. The goal is to predict an English answer string for an input English question. All questions can be answered using the contents of English Wikipedia. ### Supported Tasks and Leaderboards Open Domain Question-Answering, EfficientQA Leaderboard: URL ### Languages English ('en') ## Dataset Structure ### Data Instances ### Data Fields - 'question' - Input open domain question. - 'answer' - List of possible answers to the question ### Data Splits - Train : 87925 - validation : 1800 ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization Natural Questions contains question from aggregated queries to Google Search (Kwiatkowski et al., 2019). To gather an open version of this dataset, we only keep questions with short answers and discard the given evidence document. Answers with many tokens often resemble extractive snippets rather than canonical answers, so we discard answers with more than 5 tokens. #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases Evaluating on this diverse set of question-answer pairs is crucial, because all existing datasets have inherent biases that are problematic for open domain QA systems with learned retrieval. In the Natural Questions dataset the question askers do not already know the answer. This accurately reflects a distribution of genuine information-seeking questions. However, annotators must separately find correct answers, which requires assistance from automatic tools and can introduce a moderate bias towards results from the tool. ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information All of the Natural Questions data is released under the CC BY-SA 3.0 license. ### Contributions Thanks to @Nilanshrajput for adding this dataset.

e8ac4f539e5604fec0b3c67ed040728e0862b5e1

# Dataset Card for Naver sentiment movie corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Github](https://github.com/e9t/nsmc/) - **Repository:** [Github](https://github.com/e9t/nsmc/) - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields Each instance is a movie review written by Korean internet users on Naver, the most commonly used search engine in Korea. Each row can be broken down into the following fields: - `id`: A unique review ID, provided by Naver - `document`: The actual movie review - `label`: Binary labels for sentiment analysis, where `0` denotes negative, and `1`, positive ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @InProceedings{Park:2016, title = "Naver Sentiment Movie Corpus", author = "Lucy Park", year = "2016", howpublished = {\\url{https://github.com/e9t/nsmc}} } ``` ### Contributions Thanks to [@jaketae](https://github.com/jaketae) for adding this dataset.

nsmc

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["ko"], "license": ["cc-by-2.0"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "paperswithcode_id": "nsmc", "pretty_name": "Naver Sentiment Movie Corpus", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "document", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "negative", "1": "positive"}}}}], "splits": [{"name": "train", "num_bytes": 16423803, "num_examples": 150000}, {"name": "test", "num_bytes": 5491417, "num_examples": 50000}], "download_size": 19522142, "dataset_size": 21915220}}

2024-01-18T11:10:49+00:00

TAGS #task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-Korean #license-cc-by-2.0 #region-us

# Dataset Card for Naver sentiment movie corpus ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: Github - Repository: Github - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields Each instance is a movie review written by Korean internet users on Naver, the most commonly used search engine in Korea. Each row can be broken down into the following fields: - 'id': A unique review ID, provided by Naver - 'document': The actual movie review - 'label': Binary labels for sentiment analysis, where '0' denotes negative, and '1', positive ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @jaketae for adding this dataset.

230af8cf25465f32f9b45351cc50bd4d4dbc7b9f

# Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://inklab.usc.edu/NumerSense/ - **Repository:** https://github.com/INK-USC/NumerSense - **Paper:** https://arxiv.org/abs/2005.00683 - **Leaderboard:** https://inklab.usc.edu/NumerSense/#exp - **Point of Contact:** Author emails listed in [paper](https://arxiv.org/abs/2005.00683) ### Dataset Summary NumerSense is a new numerical commonsense reasoning probing task, with a diagnostic dataset consisting of 3,145 masked-word-prediction probes. The general idea is to mask numbers between 0-10 in sentences mined from a commonsense corpus and evaluate whether a language model can correctly predict the masked value. ### Supported Tasks and Leaderboards The dataset supports the task of slot-filling, specifically as an evaluation of numerical common sense. A leaderboard is included on the [dataset webpage](https://inklab.usc.edu/NumerSense/#exp) with included benchmarks for GPT-2, RoBERTa, BERT, and human performance. Leaderboards are included for both the core set and the adversarial set discussed below. ### Languages This dataset is in English. ## Dataset Structure ### Data Instances Each instance consists of a sentence with a masked numerical value between 0-10 and (in the train set) a target. Example from the training set: ``` sentence: Black bears are about <mask> metres tall. target: two ``` ### Data Fields Each value of the training set consists of: - `sentence`: The sentence with a number masked out with the `<mask>` token. - `target`: The ground truth target value. Since the test sets do not include the ground truth, the `target` field values are empty strings in the `test_core` and `test_all` splits. ### Data Splits The dataset includes the following pre-defined data splits: - A train set with >10K labeled examples (i.e. containing a ground truth value) - A core test set (`test_core`) with 1,132 examples (no ground truth provided) - An expanded test set (`test_all`) encompassing `test_core` with the addition of adversarial examples for a total of 3,146 examples. See section 2.2 of [the paper] for a discussion of how these examples are constructed. ## Dataset Creation ### Curation Rationale The purpose of this dataset is "to study whether PTLMs capture numerical commonsense knowledge, i.e., commonsense knowledge that provides an understanding of the numeric relation between entities." This work is motivated by the prior research exploring whether language models possess _commonsense knowledge_. ### Source Data #### Initial Data Collection and Normalization The dataset is an extension of the [Open Mind Common Sense](https://huggingface.co/datasets/open_mind_common_sense) corpus. A query was performed to discover sentences containing numbers between 0-12, after which the resulting sentences were manually evaluated for inaccuracies, typos, and the expression of commonsense knowledge. The numerical values were then masked. #### Who are the source language producers? The [Open Mind Common Sense](https://huggingface.co/datasets/open_mind_common_sense) corpus, from which this dataset is sourced, is a crowdsourced dataset maintained by the MIT Media Lab. ### Annotations #### Annotation process No annotations are present in this dataset beyond the `target` values automatically sourced from the masked sentences, as discussed above. #### Who are the annotators? The curation and inspection was done in two rounds by graduate students. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset The motivation of measuring a model's ability to associate numerical values with real-world concepts appears relatively innocuous. However, as discussed in the following section, the source dataset may well have biases encoded from crowdworkers, particularly in terms of factoid coverage. A model's ability to perform well on this benchmark should therefore not be considered evidence that it is more unbiased or objective than a human performing similar tasks. [More Information Needed] ### Discussion of Biases This dataset is sourced from a crowdsourced commonsense knowledge base. While the information contained in the graph is generally considered to be of high quality, the coverage is considered to very low as a representation of all possible commonsense knowledge. The representation of certain factoids may also be skewed by the demographics of the crowdworkers. As one possible example, the term "homophobia" is connected with "Islam" in the ConceptNet knowledge base, but not with any other religion or group, possibly due to the biases of crowdworkers contributing to the project. ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators This dataset was collected by Bill Yuchen Lin, Seyeon Lee, Rahul Khanna, and Xiang Ren, Computer Science researchers at the at the University of Southern California. ### Licensing Information The data is hosted in a GitHub repositor with the [MIT License](https://github.com/INK-USC/NumerSense/blob/main/LICENSE). ### Citation Information ``` @inproceedings{lin2020numersense, title={Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-trained Language Models}, author={Bill Yuchen Lin and Seyeon Lee and Rahul Khanna and Xiang Ren}, booktitle={Proceedings of EMNLP}, year={2020}, note={to appear} } ``` ### Contributions Thanks to [@joeddav](https://github.com/joeddav) for adding this dataset.

numer_sense

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["extended|other"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["slot-filling"], "paperswithcode_id": "numersense", "pretty_name": "NumerSense", "dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 825865, "num_examples": 10444}, {"name": "test_core", "num_bytes": 62652, "num_examples": 1132}, {"name": "test_all", "num_bytes": 184180, "num_examples": 3146}], "download_size": 985463, "dataset_size": 1072697}}

2024-01-18T11:10:51+00:00

TAGS #task_categories-text-generation #task_categories-fill-mask #task_ids-slot-filling #annotations_creators-expert-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|other #language-English #license-mit #arxiv-2005.00683 #region-us

# Dataset Card for [Dataset Name] ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: URL - Paper: URL - Leaderboard: URL - Point of Contact: Author emails listed in paper ### Dataset Summary NumerSense is a new numerical commonsense reasoning probing task, with a diagnostic dataset consisting of 3,145 masked-word-prediction probes. The general idea is to mask numbers between 0-10 in sentences mined from a commonsense corpus and evaluate whether a language model can correctly predict the masked value. ### Supported Tasks and Leaderboards The dataset supports the task of slot-filling, specifically as an evaluation of numerical common sense. A leaderboard is included on the dataset webpage with included benchmarks for GPT-2, RoBERTa, BERT, and human performance. Leaderboards are included for both the core set and the adversarial set discussed below. ### Languages This dataset is in English. ## Dataset Structure ### Data Instances Each instance consists of a sentence with a masked numerical value between 0-10 and (in the train set) a target. Example from the training set: ### Data Fields Each value of the training set consists of: - 'sentence': The sentence with a number masked out with the '<mask>' token. - 'target': The ground truth target value. Since the test sets do not include the ground truth, the 'target' field values are empty strings in the 'test_core' and 'test_all' splits. ### Data Splits The dataset includes the following pre-defined data splits: - A train set with >10K labeled examples (i.e. containing a ground truth value) - A core test set ('test_core') with 1,132 examples (no ground truth provided) - An expanded test set ('test_all') encompassing 'test_core' with the addition of adversarial examples for a total of 3,146 examples. See section 2.2 of [the paper] for a discussion of how these examples are constructed. ## Dataset Creation ### Curation Rationale The purpose of this dataset is "to study whether PTLMs capture numerical commonsense knowledge, i.e., commonsense knowledge that provides an understanding of the numeric relation between entities." This work is motivated by the prior research exploring whether language models possess _commonsense knowledge_. ### Source Data #### Initial Data Collection and Normalization The dataset is an extension of the Open Mind Common Sense corpus. A query was performed to discover sentences containing numbers between 0-12, after which the resulting sentences were manually evaluated for inaccuracies, typos, and the expression of commonsense knowledge. The numerical values were then masked. #### Who are the source language producers? The Open Mind Common Sense corpus, from which this dataset is sourced, is a crowdsourced dataset maintained by the MIT Media Lab. ### Annotations #### Annotation process No annotations are present in this dataset beyond the 'target' values automatically sourced from the masked sentences, as discussed above. #### Who are the annotators? The curation and inspection was done in two rounds by graduate students. ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset The motivation of measuring a model's ability to associate numerical values with real-world concepts appears relatively innocuous. However, as discussed in the following section, the source dataset may well have biases encoded from crowdworkers, particularly in terms of factoid coverage. A model's ability to perform well on this benchmark should therefore not be considered evidence that it is more unbiased or objective than a human performing similar tasks. ### Discussion of Biases This dataset is sourced from a crowdsourced commonsense knowledge base. While the information contained in the graph is generally considered to be of high quality, the coverage is considered to very low as a representation of all possible commonsense knowledge. The representation of certain factoids may also be skewed by the demographics of the crowdworkers. As one possible example, the term "homophobia" is connected with "Islam" in the ConceptNet knowledge base, but not with any other religion or group, possibly due to the biases of crowdworkers contributing to the project. ### Other Known Limitations ## Additional Information ### Dataset Curators This dataset was collected by Bill Yuchen Lin, Seyeon Lee, Rahul Khanna, and Xiang Ren, Computer Science researchers at the at the University of Southern California. ### Licensing Information The data is hosted in a GitHub repositor with the MIT License. ### Contributions Thanks to @joeddav for adding this dataset.

031890085f75fa8401ecb95e44e096ee3c28d11d

# Dataset Card for Numeric Fused Heads ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [The Numeric Fused-Head demo](https://nlp.biu.ac.il/~lazary/fh/) - **Repository:** [Github Repo](https://github.com/yanaiela/num_fh) - **Paper:** [Where’s My Head? Definition, Dataset and Models for Numeric Fused-Heads Identification and Resolution](https://www.mitpressjournals.org/doi/full/10.1162/tacl_a_00280) - **Leaderboard:** [NLP Progress](http://nlpprogress.com/english/missing_elements.html) - **Point of Contact:** [Yanai Elazar](https://yanaiela.github.io), [Yoav Goldberg](https://www.cs.bgu.ac.il/~yoavg/uni/) ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards - Numeric Fused Head Identification - Numeric Fused Head Resolution ### Languages English ## Dataset Structure ### Data Instances ## Identification ``` { "tokens": ["It", "’s", "a", "curious", "thing", ",", "the", "death", "of", "a", "loved", "one", "."] "start_index": 11 "end_index": 12 "label": 1 } ``` ## Resolution ``` { "tokens": ["I", "'m", "eighty", "tomorrow", ".", "Are", "you", "sure", "?"], "line_indices": [0, 0, 0, 0, 0, 1, 1, 1, 1], "head": ["AGE"], "speakers": ["John Doe", "John Doe", "John Doe", "John Doe", "John Doe", "Joe Bloggs", "Joe Bloggs", "Joe Bloggs", "Joe Bloggs"], "anchors_indices": [2] } ``` ### Data Fields ## Identification - `tokens` - List of token strings as tokenized with [Spacy](spacy.io). - `start_index` - Start index of the anchor. - `end_index` - End index of the anchor. - `label` - "pos" or "neg" depending on whether this example contains a numeric fused head. ## Resolution - `tokens` - List of token strings as tokenized with [Spacy](spacy.io) - `line_indices` - List of indices indicating line number (one for each token) - `head` - Reference to the missing head. If the head exists elsewhere in the sentence this is given as a token index. - `speakers` - List of speaker names (one for each token) - `anchors_indices` - Index to indicate which token is the anchor (the visible number) ### Data Splits Train, Test, Dev [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information MIT License ### Citation Information ``` @article{doi:10.1162/tacl\_a\_00280, author = {Elazar, Yanai and Goldberg, Yoav}, title = {Where’s My Head? Definition, Data Set, and Models for Numeric Fused-Head Identification and Resolution}, journal = {Transactions of the Association for Computational Linguistics}, volume = {7}, number = {}, pages = {519-535}, year = {2019}, doi = {10.1162/tacl\_a\_00280}, } ``` ### Contributions Thanks to [@ghomasHudson](https://github.com/ghomasHudson) for adding this dataset.

numeric_fused_head

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced", "expert-generated", "machine-generated"], "language_creators": ["found"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M", "1K<n<10K"], "source_datasets": ["original"], "task_categories": ["token-classification"], "task_ids": [], "paperswithcode_id": "numeric-fused-head", "pretty_name": "Numeric Fused Heads", "config_names": ["identification", "resolution"], "tags": ["fused-head-identification"], "dataset_info": [{"config_name": "identification", "features": [{"name": "tokens", "sequence": "string"}, {"name": "start_index", "dtype": "int32"}, {"name": "end_index", "dtype": "int32"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "neg", "1": "pos"}}}}], "splits": [{"name": "train", "num_bytes": 22290345, "num_examples": 165606}, {"name": "test", "num_bytes": 68282, "num_examples": 500}, {"name": "validation", "num_bytes": 2474528, "num_examples": 18401}], "download_size": 24407520, "dataset_size": 24833155}, {"config_name": "resolution", "features": [{"name": "tokens", "sequence": "string"}, {"name": "line_indices", "sequence": "int32"}, {"name": "head", "sequence": "string"}, {"name": "speakers", "sequence": "string"}, {"name": "anchors_indices", "sequence": "int32"}], "splits": [{"name": "train", "num_bytes": 19766437, "num_examples": 7412}, {"name": "test", "num_bytes": 2743071, "num_examples": 1000}, {"name": "validation", "num_bytes": 2633549, "num_examples": 1000}], "download_size": 24923403, "dataset_size": 25143057}]}

2024-01-18T11:10:59+00:00

TAGS #task_categories-token-classification #annotations_creators-crowdsourced #annotations_creators-expert-generated #annotations_creators-machine-generated #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #size_categories-1K<n<10K #source_datasets-original #language-English #license-mit #fused-head-identification #region-us

# Dataset Card for Numeric Fused Heads ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: The Numeric Fused-Head demo - Repository: Github Repo - Paper: Where’s My Head? Definition, Dataset and Models for Numeric Fused-Heads Identification and Resolution - Leaderboard: NLP Progress - Point of Contact: Yanai Elazar, Yoav Goldberg ### Dataset Summary ### Supported Tasks and Leaderboards - Numeric Fused Head Identification - Numeric Fused Head Resolution ### Languages English ## Dataset Structure ### Data Instances ## Identification ## Resolution ### Data Fields ## Identification - 'tokens' - List of token strings as tokenized with Spacy. - 'start_index' - Start index of the anchor. - 'end_index' - End index of the anchor. - 'label' - "pos" or "neg" depending on whether this example contains a numeric fused head. ## Resolution - 'tokens' - List of token strings as tokenized with Spacy - 'line_indices' - List of indices indicating line number (one for each token) - 'head' - Reference to the missing head. If the head exists elsewhere in the sentence this is given as a token index. - 'speakers' - List of speaker names (one for each token) - 'anchors_indices' - Index to indicate which token is the anchor (the visible number) ### Data Splits Train, Test, Dev ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information MIT License ### Contributions Thanks to @ghomasHudson for adding this dataset.

53695c264229228e15f28f0a5bd9e249fa42bd43

# Dataset Card for OCLAR ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [OCLAR homepage](http://archive.ics.uci.edu/ml/datasets/Opinion+Corpus+for+Lebanese+Arabic+Reviews+%28OCLAR%29#) - **Paper:** [paper link](https://www.semanticscholar.org/paper/Sentiment-Classifier%3A-Logistic-Regression-for-in-Omari-Al-Hajj/9319f4d9e8b3b7bfd0d214314911c071ba7ce1a0) - **Point of Contact:** [Marwan Al Omari](marwanalomari@yahoo.com) ### Dataset Summary The researchers of OCLAR Marwan et al. (2019), they gathered Arabic costumer reviews [Zomato website](https://www.zomato.com/lebanon) on wide scope of domain, including restaurants, hotels, hospitals, local shops, etc. The corpus finally contains 3916 reviews in 5-rating scale. For this research purpose, the positive class considers rating stars from 5 to 3 of 3465 reviews, and the negative class is represented from values of 1 and 2 of about 451 texts. ### Supported Tasks and Leaderboards Opinion Corpus for Lebanese Arabic Reviews (OCLAR) corpus is utilizable for Arabic sentiment classification on services reviews, including hotels, restaurants, shops, and others. ### Languages The text in the dataset is in Arabic, mainly in Lebanese (LB). The associated BCP-47 code is `ar-LB`. ## Dataset Structure ### Data Instances A typical data point comprises a `pagename` which is the name of service / location being reviewed, a `review` which is the review left by the user / client , and a `rating` which is a score between 1 and 5. The authors consider a review to be positive if the score is greater or equal than `3`, else it is considered negative. An example from the OCLAR data set looks as follows: ``` "pagename": 'Ramlet Al Baida Beirut Lebanon', "review": 'مكان يطير العقل ويساعد على الاسترخاء', "rating": 5, ``` ### Data Fields - `pagename`: string name of the service / location being reviewed - `review`: string review left by the user / costumer - `rating`: number of stars left by the reviewer. It ranges from 1 to 5. ### Data Splits The data set comes in a single csv file of a total `3916` reviews : - `3465` are considered positive (a rating of 3 to 5) - `451` are considered negative (a rating of 1 or 2) ## Dataset Creation ### Curation Rationale This dataset was created for Arabic sentiment classification on services’ reviews in Lebanon country. Reviews are about public services, including hotels, restaurants, shops, and others. ### Source Data #### Initial Data Collection and Normalization The data was collected from Google Reviews and [Zomato website](https://www.zomato.com/lebanon) #### Who are the source language producers? The source language producers are people who posted their reviews on Google Reviews or [Zomato website](https://www.zomato.com/lebanon). They're mainly Arabic speaking Lebanese people. ### Annotations #### Annotation process The dataset does not contain any additional annotations #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset The author's research has tackled a highly important task of sentiment analysis for Arabic language in the Lebanese context on 3916 reviews’ services from Google and Zomato. Experiments show three main findings: 1) The classifier is confident when used to predict positive reviews, 2) while it is biased on predicting reviews with negative sentiment, and finally 3) the low percentage of negative reviews in the corpus contributes to the diffidence of LR. ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators This dataset was curated by Marwan Al Omari, Moustafa Al-Hajj from Centre for Language Sciences and Communication, Lebanese University, Beirut, Lebanon; Nacereddine Hammami from college of Computer and Information Sciences, Jouf University, Aljouf, KSA; and Amani Sabra from Centre for Language Sciences and Communication, Lebanese University, Beirut, Lebanon. ### Licensing Information [More Information Needed] ### Citation Information - Marwan Al Omari, Centre for Language Sciences and Communication, Lebanese University, Beirut, Lebanon, marwanalomari '@' yahoo.com - Moustafa Al-Hajj, Centre for Language Sciences and Communication, Lebanese University, Beirut, Lebanon, moustafa.alhajj '@' ul.edu.lb - Nacereddine Hammami, college of Computer and Information Sciences, Jouf University, Aljouf, KSA, n.hammami '@' ju.edu.sa - Amani Sabra, Centre for Language Sciences and Communication, Lebanese University, Beirut, Lebanon, amani.sabra '@' ul.edu.lb ``` @misc{Dua:2019 , author = "Dua, Dheeru and Graff, Casey", year = "2017", title = "{UCI} Machine Learning Repository", url = "http://archive.ics.uci.edu/ml", institution = "University of California, Irvine, School of Information and Computer Sciences" } @InProceedings{AlOmari2019oclar, title = {Sentiment Classifier: Logistic Regression for Arabic Services Reviews in Lebanon}, authors={Al Omari, M., Al-Hajj, M., Hammami, N., & Sabra, A.}, year={2019} } ``` ### Contributions Thanks to [@alaameloh](https://github.com/alaameloh) for adding this dataset.

oclar

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["ar"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["text-scoring", "sentiment-classification", "sentiment-scoring"], "pretty_name": "OCLAR", "dataset_info": {"features": [{"name": "pagename", "dtype": "string"}, {"name": "review", "dtype": "string"}, {"name": "rating", "dtype": "int8"}], "splits": [{"name": "train", "num_bytes": 398204, "num_examples": 3916}], "download_size": 382976, "dataset_size": 398204}}

2024-01-18T11:11:01+00:00

TAGS #task_categories-text-classification #task_ids-text-scoring #task_ids-sentiment-classification #task_ids-sentiment-scoring #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Arabic #license-unknown #region-us

# Dataset Card for OCLAR ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: OCLAR homepage - Paper: paper link - Point of Contact: Marwan Al Omari ### Dataset Summary The researchers of OCLAR Marwan et al. (2019), they gathered Arabic costumer reviews Zomato website on wide scope of domain, including restaurants, hotels, hospitals, local shops, etc. The corpus finally contains 3916 reviews in 5-rating scale. For this research purpose, the positive class considers rating stars from 5 to 3 of 3465 reviews, and the negative class is represented from values of 1 and 2 of about 451 texts. ### Supported Tasks and Leaderboards Opinion Corpus for Lebanese Arabic Reviews (OCLAR) corpus is utilizable for Arabic sentiment classification on services reviews, including hotels, restaurants, shops, and others. ### Languages The text in the dataset is in Arabic, mainly in Lebanese (LB). The associated BCP-47 code is 'ar-LB'. ## Dataset Structure ### Data Instances A typical data point comprises a 'pagename' which is the name of service / location being reviewed, a 'review' which is the review left by the user / client , and a 'rating' which is a score between 1 and 5. The authors consider a review to be positive if the score is greater or equal than '3', else it is considered negative. An example from the OCLAR data set looks as follows: ### Data Fields - 'pagename': string name of the service / location being reviewed - 'review': string review left by the user / costumer - 'rating': number of stars left by the reviewer. It ranges from 1 to 5. ### Data Splits The data set comes in a single csv file of a total '3916' reviews : - '3465' are considered positive (a rating of 3 to 5) - '451' are considered negative (a rating of 1 or 2) ## Dataset Creation ### Curation Rationale This dataset was created for Arabic sentiment classification on services’ reviews in Lebanon country. Reviews are about public services, including hotels, restaurants, shops, and others. ### Source Data #### Initial Data Collection and Normalization The data was collected from Google Reviews and Zomato website #### Who are the source language producers? The source language producers are people who posted their reviews on Google Reviews or Zomato website. They're mainly Arabic speaking Lebanese people. ### Annotations #### Annotation process The dataset does not contain any additional annotations #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset The author's research has tackled a highly important task of sentiment analysis for Arabic language in the Lebanese context on 3916 reviews’ services from Google and Zomato. Experiments show three main findings: 1) The classifier is confident when used to predict positive reviews, 2) while it is biased on predicting reviews with negative sentiment, and finally 3) the low percentage of negative reviews in the corpus contributes to the diffidence of LR. ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators This dataset was curated by Marwan Al Omari, Moustafa Al-Hajj from Centre for Language Sciences and Communication, Lebanese University, Beirut, Lebanon; Nacereddine Hammami from college of Computer and Information Sciences, Jouf University, Aljouf, KSA; and Amani Sabra from Centre for Language Sciences and Communication, Lebanese University, Beirut, Lebanon. ### Licensing Information - Marwan Al Omari, Centre for Language Sciences and Communication, Lebanese University, Beirut, Lebanon, marwanalomari '@' URL - Moustafa Al-Hajj, Centre for Language Sciences and Communication, Lebanese University, Beirut, Lebanon, URL '@' URL - Nacereddine Hammami, college of Computer and Information Sciences, Jouf University, Aljouf, KSA, n.hammami '@' URL - Amani Sabra, Centre for Language Sciences and Communication, Lebanese University, Beirut, Lebanon, URL '@' URL ### Contributions Thanks to @alaameloh for adding this dataset.

b62bda4a182ce0ba7a64cb6459ec3962795e7caa

# Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://www.inf.ufrgs.br/~rppelle/hatedetector/ - **Repository:** https://github.com/rogersdepelle/OffComBR - **Paper:** https://sol.sbc.org.br/index.php/brasnam/article/view/3260/3222 - **Leaderboard:** - **Point of Contact:** ### Dataset Summary OffComBR: an annotated dataset containing for hate speech detection in Portuguese composed of news comments on the Brazilian Web. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@hugoabonizio](https://github.com/hugoabonizio) for adding this dataset.

offcombr

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["pt"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": [], "paperswithcode_id": "offcombr", "pretty_name": "Offensive Comments in the Brazilian Web", "tags": ["hate-speech-detection"], "dataset_info": [{"config_name": "offcombr-2", "features": [{"name": "label", "dtype": {"class_label": {"names": {"0": "no", "1": "yes"}}}}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 105703, "num_examples": 1250}], "download_size": 99956, "dataset_size": 105703}, {"config_name": "offcombr-3", "features": [{"name": "label", "dtype": {"class_label": {"names": {"0": "no", "1": "yes"}}}}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 90094, "num_examples": 1033}], "download_size": 85215, "dataset_size": 90094}]}

2024-01-18T11:11:02+00:00

TAGS #task_categories-text-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Portuguese #license-unknown #hate-speech-detection #region-us

# Dataset Card for [Dataset Name] ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: URL - Paper: URL - Leaderboard: - Point of Contact: ### Dataset Summary OffComBR: an annotated dataset containing for hate speech detection in Portuguese composed of news comments on the Brazilian Web. ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @hugoabonizio for adding this dataset.

f5929ce78946319ba26935319f0738b62bb4d79c

# Dataset Card for OffensEval-TR 2020 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [offensive-turkish](https://coltekin.github.io/offensive-turkish/) - **Paper:** [A Corpus of Turkish Offensive Language on Social Media](https://coltekin.github.io/offensive-turkish/troff.pdf) - **Point of Contact:** [Çağrı Çöltekin](ccoltekin@sfs.uni-tuebingen.de) ### Dataset Summary The file offenseval-tr-training-v1.tsv contains 31,756 annotated tweets. The file offenseval-annotation.txt contains a short summary of the annotation guidelines. Twitter user mentions were substituted by @USER and URLs have been substitute by URL. Each instance contains up to 1 labels corresponding to one of the following sub-task: - Sub-task A: Offensive language identification; ### Supported Tasks and Leaderboards The dataset was published on this [paper](https://coltekin.github.io/offensive-turkish/troff.pdf). ### Languages The dataset is based on Turkish. ## Dataset Structure ### Data Instances A binary dataset with with (NOT) Not Offensive and (OFF) Offensive tweets. ### Data Fields Instances are included in TSV format as follows: ID INSTANCE SUBA The column names in the file are the following: id tweet subtask_a The labels used in the annotation are listed below. #### Task and Labels (A) Sub-task A: Offensive language identification - (NOT) Not Offensive - This post does not contain offense or profanity. - (OFF) Offensive - This post contains offensive language or a targeted (veiled or direct) offense In our annotation, we label a post as offensive (OFF) if it contains any form of non-acceptable language (profanity) or a targeted offense, which can be veiled or direct. ### Data Splits | train | test | |------:|-----:| | 31756 | 3528 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? From tweeter. ### Annotations [More Information Needed] #### Annotation process We describe the labels above in a “flat” manner. However, the annotation process we follow is hierarchical. The following QA pairs give a more flowchart-like procedure to follow 1. Is the tweet in Turkish and understandable? * No: mark tweet X for exclusion, and go to next tweet * Yes: continue to step 2 2. Is the tweet include offensive/inappropriate language? * No: mark the tweet non go to step 4 * Yes: continue to step 3 3. Is the offense in the tweet targeted? * No: mark the tweet prof go to step 4 * Yes: chose one (or more) of grp, ind, *oth based on the definitions above. Please try to limit the number of labels unless it is clear that the tweet includes offense against multiple categories. 4. Was the labeling decision difficult (precise answer needs more context, tweets includes irony, or for another reason)? * No: go to next tweet * Yes: add the label X, go to next tweet #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information The annotations are distributed under the terms of [Creative Commons Attribution License (CC-BY)](https://creativecommons.org/licenses/by/2.0/). Please cite the following paper, if you use this resource. ### Citation Information ``` @inproceedings{coltekin2020lrec, author = {\c{C}\"{o}ltekin, \c{C}a\u{g}r{\i}}, year = {2020}, title = {A Corpus of Turkish Offensive Language on Social Media}, booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference}, pages = {6174--6184}, address = {Marseille, France}, url = {https://www.aclweb.org/anthology/2020.lrec-1.758}, } ``` ### Contributions Thanks to [@yavuzKomecoglu](https://github.com/yavuzKomecoglu) for adding this dataset.

offenseval2020_tr

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["tr"], "license": ["cc-by-2.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": [], "pretty_name": "OffensEval-TR 2020", "tags": ["offensive-language-classification"], "dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "tweet", "dtype": "string"}, {"name": "subtask_a", "dtype": {"class_label": {"names": {"0": "NOT", "1": "OFF"}}}}], "config_name": "offenseval2020-turkish", "splits": [{"name": "train", "num_bytes": 4260505, "num_examples": 31756}, {"name": "test", "num_bytes": 481300, "num_examples": 3528}], "download_size": 2048258, "dataset_size": 4741805}}

2024-01-18T11:11:04+00:00

TAGS #task_categories-text-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Turkish #license-cc-by-2.0 #offensive-language-classification #region-us

Dataset Card for OffensEval-TR 2020 =================================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: offensive-turkish * Paper: A Corpus of Turkish Offensive Language on Social Media * Point of Contact: Çağrı Çöltekin ### Dataset Summary The file URL contains 31,756 annotated tweets. The file URL contains a short summary of the annotation guidelines. Twitter user mentions were substituted by @USER and URLs have been substitute by URL. Each instance contains up to 1 labels corresponding to one of the following sub-task: * Sub-task A: Offensive language identification; ### Supported Tasks and Leaderboards The dataset was published on this paper. ### Languages The dataset is based on Turkish. Dataset Structure ----------------- ### Data Instances A binary dataset with with (NOT) Not Offensive and (OFF) Offensive tweets. ### Data Fields Instances are included in TSV format as follows: ID INSTANCE SUBA The column names in the file are the following: id tweet subtask\_a The labels used in the annotation are listed below. #### Task and Labels (A) Sub-task A: Offensive language identification * (NOT) Not Offensive - This post does not contain offense or profanity. * (OFF) Offensive - This post contains offensive language or a targeted (veiled or direct) offense In our annotation, we label a post as offensive (OFF) if it contains any form of non-acceptable language (profanity) or a targeted offense, which can be veiled or direct. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? From tweeter. ### Annotations #### Annotation process We describe the labels above in a “flat” manner. However, the annotation process we follow is hierarchical. The following QA pairs give a more flowchart-like procedure to follow 1. Is the tweet in Turkish and understandable? * No: mark tweet X for exclusion, and go to next tweet * Yes: continue to step 2 2. Is the tweet include offensive/inappropriate language? * No: mark the tweet non go to step 4 * Yes: continue to step 3 3. Is the offense in the tweet targeted? * No: mark the tweet prof go to step 4 * Yes: chose one (or more) of grp, ind, \*oth based on the definitions above. Please try to limit the number of labels unless it is clear that the tweet includes offense against multiple categories. 4. Was the labeling decision difficult (precise answer needs more context, tweets includes irony, or for another reason)? * No: go to next tweet * Yes: add the label X, go to next tweet #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information The annotations are distributed under the terms of Creative Commons Attribution License (CC-BY). Please cite the following paper, if you use this resource. ### Contributions Thanks to @yavuzKomecoglu for adding this dataset.

1b118fb5d1a44c0abeff296fddee5e51cebd256f

# Dataset Card for Offenseval Dravidian ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://competitions.codalab.org/competitions/27654#learn_the_details - **Repository:** https://competitions.codalab.org/competitions/27654#participate-get_data - **Paper:** Findings of the Shared Task on {O}ffensive {L}anguage {I}dentification in {T}amil, {M}alayalam, and {K}annada - **Leaderboard:** https://competitions.codalab.org/competitions/27654#results - **Point of Contact:** [Bharathi Raja Chakravarthi](mailto:bharathiraja.akr@gmail.com) ### Dataset Summary Offensive language identification is classification task in natural language processing (NLP) where the aim is to moderate and minimise offensive content in social media. It has been an active area of research in both academia and industry for the past two decades. There is an increasing demand for offensive language identification on social media texts which are largely code-mixed. Code-mixing is a prevalent phenomenon in a multilingual community and the code-mixed texts are sometimes written in non-native scripts. Systems trained on monolingual data fail on code-mixed data due to the complexity of code-switching at different linguistic levels in the text. This shared task presents a new gold standard corpus for offensive language identification of code-mixed text in Dravidian languages (Tamil-English, Malayalam-English, and Kannada-English). ### Supported Tasks and Leaderboards The goal of this task is to identify offensive language content of the code-mixed dataset of comments/posts in Dravidian Languages ( (Tamil-English, Malayalam-English, and Kannada-English)) collected from social media. The comment/post may contain more than one sentence but the average sentence length of the corpora is 1. Each comment/post is annotated at the comment/post level. This dataset also has class imbalance problems depicting real-world scenarios. ### Languages Code-mixed text in Dravidian languages (Tamil-English, Malayalam-English, and Kannada-English). ## Dataset Structure ### Data Instances An example from the Tamil dataset looks as follows: | text | label | | :------ | :----- | | படம் கண்டிப்பாக வெற்றி பெற வேண்டும் செம்ம vara level | Not_offensive | | Avasara patutiya editor uhh antha bullet sequence aa nee soliruka kudathu, athu sollama iruntha movie ku konjam support aa surprise element aa irunthurukum | Not_offensive | An example from the Malayalam dataset looks as follows: | text | label | | :------ | :----- | | ഷൈലോക്ക് ന്റെ നല്ല ടീസർ ആയിട്ട് പോലും ട്രോളി നടന്ന ലാലേട്ടൻ ഫാൻസിന് കിട്ടിയൊരു നല്ലൊരു തിരിച്ചടി തന്നെ ആയിരിന്നു ബിഗ് ബ്രദർ ന്റെ ട്രെയ്‌ലർ | Not_offensive | | Marana mass Ekka kku kodukku oru | Not_offensive | An example from the Kannada dataset looks as follows: | text | label | | :------ | :----- | | ನಿಜವಾಗಿಯೂ ಅದ್ಭುತ heartly heltidini... plz avrigella namma nimmellara supprt beku | Not_offensive | | Next song gu kuda alru andre evaga yar comment madidera alla alrru like madi share madi nam industry na next level ge togond hogaona. | Not_offensive | ### Data Fields Tamil - `text`: Tamil-English code mixed comment. - `label`: integer from 0 to 5 that corresponds to these values: "Not_offensive", "Offensive_Untargetede", "Offensive_Targeted_Insult_Individual", "Offensive_Targeted_Insult_Group", "Offensive_Targeted_Insult_Other", "not-Tamil" Malayalam - `text`: Malayalam-English code mixed comment. - `label`: integer from 0 to 5 that corresponds to these values: "Not_offensive", "Offensive_Untargetede", "Offensive_Targeted_Insult_Individual", "Offensive_Targeted_Insult_Group", "Offensive_Targeted_Insult_Other", "not-malayalam" Kannada - `text`: Kannada-English code mixed comment. - `label`: integer from 0 to 5 that corresponds to these values: "Not_offensive", "Offensive_Untargetede", "Offensive_Targeted_Insult_Individual", "Offensive_Targeted_Insult_Group", "Offensive_Targeted_Insult_Other", "not-Kannada" ### Data Splits | | train | validation | |-----------|------:|-----------:| | Tamil | 35139 | 4388 | | Malayalam | 16010 | 1999 | | Kannada | 6217 | 777 | ## Dataset Creation ### Curation Rationale There is an increasing demand for offensive language identification on social media texts which are largely code-mixed. Code-mixing is a prevalent phenomenon in a multilingual community and the code-mixed texts are sometimes written in non-native scripts. Systems trained on monolingual data fail on code-mixed data due to the complexity of code-switching at different linguistic levels in the text. ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? Youtube users ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information This work is licensed under a [Creative Commons Attribution 4.0 International Licence](http://creativecommons.org/licenses/by/4.0/.) ### Citation Information ``` @article{chakravarthi-etal-2021-lre, title = "DravidianCodeMix: Sentiment Analysis and Offensive Language Identification Dataset for Dravidian Languages in Code-Mixed Text", author = "Chakravarthi, Bharathi Raja and Priyadharshini, Ruba and Muralidaran, Vigneshwaran and Jose, Navya and Suryawanshi, Shardul and Sherly, Elizabeth and McCrae, John P", journal={Language Resources and Evaluation}, publisher={Springer} } ``` ``` @inproceedings{dravidianoffensive-eacl, title={Findings of the Shared Task on {O}ffensive {L}anguage {I}dentification in {T}amil, {M}alayalam, and {K}annada}, author={Chakravarthi, Bharathi Raja and Priyadharshini, Ruba and Jose, Navya and M, Anand Kumar and Mandl, Thomas and Kumaresan, Prasanna Kumar and Ponnsamy, Rahul and V,Hariharan and Sherly, Elizabeth and McCrae, John Philip }, booktitle = "Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages", month = April, year = "2021", publisher = "Association for Computational Linguistics", year={2021} } ``` ``` @inproceedings{hande-etal-2020-kancmd, title = "{K}an{CMD}: {K}annada {C}ode{M}ixed Dataset for Sentiment Analysis and Offensive Language Detection", author = "Hande, Adeep and Priyadharshini, Ruba and Chakravarthi, Bharathi Raja", booktitle = "Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media", month = dec, year = "2020", address = "Barcelona, Spain (Online)", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.peoples-1.6", pages = "54--63", abstract = "We introduce Kannada CodeMixed Dataset (KanCMD), a multi-task learning dataset for sentiment analysis and offensive language identification. The KanCMD dataset highlights two real-world issues from the social media text. First, it contains actual comments in code mixed text posted by users on YouTube social media, rather than in monolingual text from the textbook. Second, it has been annotated for two tasks, namely sentiment analysis and offensive language detection for under-resourced Kannada language. Hence, KanCMD is meant to stimulate research in under-resourced Kannada language on real-world code-mixed social media text and multi-task learning. KanCMD was obtained by crawling the YouTube, and a minimum of three annotators annotates each comment. We release KanCMD 7,671 comments for multitask learning research purpose.", } ``` ``` @inproceedings{chakravarthi-etal-2020-corpus, title = "Corpus Creation for Sentiment Analysis in Code-Mixed {T}amil-{E}nglish Text", author = "Chakravarthi, Bharathi Raja and Muralidaran, Vigneshwaran and Priyadharshini, Ruba and McCrae, John Philip", booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)", month = may, year = "2020", address = "Marseille, France", publisher = "European Language Resources association", url = "https://www.aclweb.org/anthology/2020.sltu-1.28", pages = "202--210", abstract = "Understanding the sentiment of a comment from a video or an image is an essential task in many applications. Sentiment analysis of a text can be useful for various decision-making processes. One such application is to analyse the popular sentiments of videos on social media based on viewer comments. However, comments from social media do not follow strict rules of grammar, and they contain mixing of more than one language, often written in non-native scripts. Non-availability of annotated code-mixed data for a low-resourced language like Tamil also adds difficulty to this problem. To overcome this, we created a gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube. In this paper, we describe the process of creating the corpus and assigning polarities. We present inter-annotator agreement and show the results of sentiment analysis trained on this corpus as a benchmark.", language = "English", ISBN = "979-10-95546-35-1", } ``` ``` @inproceedings{chakravarthi-etal-2020-sentiment, title = "A Sentiment Analysis Dataset for Code-Mixed {M}alayalam-{E}nglish", author = "Chakravarthi, Bharathi Raja and Jose, Navya and Suryawanshi, Shardul and Sherly, Elizabeth and McCrae, John Philip", booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)", month = may, year = "2020", address = "Marseille, France", publisher = "European Language Resources association", url = "https://www.aclweb.org/anthology/2020.sltu-1.25", pages = "177--184", abstract = "There is an increasing demand for sentiment analysis of text from social media which are mostly code-mixed. Systems trained on monolingual data fail for code-mixed data due to the complexity of mixing at different levels of the text. However, very few resources are available for code-mixed data to create models specific for this data. Although much research in multilingual and cross-lingual sentiment analysis has used semi-supervised or unsupervised methods, supervised methods still performs better. Only a few datasets for popular languages such as English-Spanish, English-Hindi, and English-Chinese are available. There are no resources available for Malayalam-English code-mixed data. This paper presents a new gold standard corpus for sentiment analysis of code-mixed text in Malayalam-English annotated by voluntary annotators. This gold standard corpus obtained a Krippendorff{'}s alpha above 0.8 for the dataset. We use this new corpus to provide the benchmark for sentiment analysis in Malayalam-English code-mixed texts.", language = "English", ISBN = "979-10-95546-35-1", } ``` ### Contributions Thanks to [@jamespaultg](https://github.com/jamespaultg) for adding this dataset.

offenseval_dravidian

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["crowdsourced"], "language": ["en", "kn", "ml", "ta"], "license": ["cc-by-4.0"], "multilinguality": ["multilingual"], "size_categories": ["10K<n<100K", "1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": [], "pretty_name": "Offenseval Dravidian", "config_names": ["kannada", "malayalam", "tamil"], "tags": ["offensive-language"], "dataset_info": [{"config_name": "tamil", "features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Not_offensive", "1": "Offensive_Untargetede", "2": "Offensive_Targeted_Insult_Individual", "3": "Offensive_Targeted_Insult_Group", "4": "Offensive_Targeted_Insult_Other", "5": "not-Tamil"}}}}], "splits": [{"name": "train", "num_bytes": 4214801, "num_examples": 35139}, {"name": "validation", "num_bytes": 526108, "num_examples": 4388}], "download_size": 5040217, "dataset_size": 4740909}, {"config_name": "malayalam", "features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Not_offensive", "1": "Offensive_Untargetede", "2": "Offensive_Targeted_Insult_Individual", "3": "Offensive_Targeted_Insult_Group", "4": "Offensive_Targeted_Insult_Other", "5": "not-malayalam"}}}}], "splits": [{"name": "train", "num_bytes": 1944857, "num_examples": 16010}, {"name": "validation", "num_bytes": 249364, "num_examples": 1999}], "download_size": 2276736, "dataset_size": 2194221}, {"config_name": "kannada", "features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Not_offensive", "1": "Offensive_Untargetede", "2": "Offensive_Targeted_Insult_Individual", "3": "Offensive_Targeted_Insult_Group", "4": "Offensive_Targeted_Insult_Other", "5": "not-Kannada"}}}}], "splits": [{"name": "train", "num_bytes": 567119, "num_examples": 6217}, {"name": "validation", "num_bytes": 70147, "num_examples": 777}], "download_size": 678727, "dataset_size": 637266}]}

2024-01-18T11:11:06+00:00

TAGS #task_categories-text-classification #annotations_creators-expert-generated #language_creators-crowdsourced #multilinguality-multilingual #size_categories-10K<n<100K #size_categories-1K<n<10K #source_datasets-original #language-English #language-Kannada #language-Malayalam #language-Tamil #license-cc-by-4.0 #offensive-language #region-us

Dataset Card for Offenseval Dravidian ===================================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: URL * Paper: Findings of the Shared Task on {O}ffensive {L}anguage {I}dentification in {T}amil, {M}alayalam, and {K}annada * Leaderboard: URL * Point of Contact: Bharathi Raja Chakravarthi ### Dataset Summary Offensive language identification is classification task in natural language processing (NLP) where the aim is to moderate and minimise offensive content in social media. It has been an active area of research in both academia and industry for the past two decades. There is an increasing demand for offensive language identification on social media texts which are largely code-mixed. Code-mixing is a prevalent phenomenon in a multilingual community and the code-mixed texts are sometimes written in non-native scripts. Systems trained on monolingual data fail on code-mixed data due to the complexity of code-switching at different linguistic levels in the text. This shared task presents a new gold standard corpus for offensive language identification of code-mixed text in Dravidian languages (Tamil-English, Malayalam-English, and Kannada-English). ### Supported Tasks and Leaderboards The goal of this task is to identify offensive language content of the code-mixed dataset of comments/posts in Dravidian Languages ( (Tamil-English, Malayalam-English, and Kannada-English)) collected from social media. The comment/post may contain more than one sentence but the average sentence length of the corpora is 1. Each comment/post is annotated at the comment/post level. This dataset also has class imbalance problems depicting real-world scenarios. ### Languages Code-mixed text in Dravidian languages (Tamil-English, Malayalam-English, and Kannada-English). Dataset Structure ----------------- ### Data Instances An example from the Tamil dataset looks as follows: An example from the Malayalam dataset looks as follows: An example from the Kannada dataset looks as follows: ### Data Fields Tamil * 'text': Tamil-English code mixed comment. * 'label': integer from 0 to 5 that corresponds to these values: "Not\_offensive", "Offensive\_Untargetede", "Offensive\_Targeted\_Insult\_Individual", "Offensive\_Targeted\_Insult\_Group", "Offensive\_Targeted\_Insult\_Other", "not-Tamil" Malayalam * 'text': Malayalam-English code mixed comment. * 'label': integer from 0 to 5 that corresponds to these values: "Not\_offensive", "Offensive\_Untargetede", "Offensive\_Targeted\_Insult\_Individual", "Offensive\_Targeted\_Insult\_Group", "Offensive\_Targeted\_Insult\_Other", "not-malayalam" Kannada * 'text': Kannada-English code mixed comment. * 'label': integer from 0 to 5 that corresponds to these values: "Not\_offensive", "Offensive\_Untargetede", "Offensive\_Targeted\_Insult\_Individual", "Offensive\_Targeted\_Insult\_Group", "Offensive\_Targeted\_Insult\_Other", "not-Kannada" ### Data Splits Dataset Creation ---------------- ### Curation Rationale There is an increasing demand for offensive language identification on social media texts which are largely code-mixed. Code-mixing is a prevalent phenomenon in a multilingual community and the code-mixed texts are sometimes written in non-native scripts. Systems trained on monolingual data fail on code-mixed data due to the complexity of code-switching at different linguistic levels in the text. ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? Youtube users ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information This work is licensed under a Creative Commons Attribution 4.0 International Licence ### Contributions Thanks to @jamespaultg for adding this dataset.

e3f157bc3fde5a5a0c22790ea0bdaf9bce101495

# Dataset Card for OfisPublik ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://opus.nlpl.eu/OfisPublik.php - **Repository:** None - **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf - **Leaderboard:** [More Information Needed] - **Point of Contact:** [More Information Needed] ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.

ofis_publik

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["br", "fr"], "license": ["unknown"], "multilinguality": ["multilingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["translation"], "task_ids": [], "pretty_name": "OfisPublik", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["br", "fr"]}}}], "config_name": "br-fr", "splits": [{"name": "train", "num_bytes": 12256825, "num_examples": 63422}], "download_size": 3856983, "dataset_size": 12256825}}

2024-01-18T11:11:08+00:00

TAGS #task_categories-translation #annotations_creators-found #language_creators-found #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #language-Breton #language-French #license-unknown #region-us

# Dataset Card for OfisPublik ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: None - Paper: URL - Leaderboard: - Point of Contact: ### Dataset Summary ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @abhishekkrthakur for adding this dataset.

a41ac096c6480817b5c0d6ccff0555017e94b7a1

# Dataset Card for ohsumed ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://davis.wpi.edu/xmdv/datasets/ohsumed.html - **Repository:** https://trec.nist.gov/data/filtering/t9.filtering.tar.gz - **Paper:** https://link.springer.com/chapter/10.1007/978-1-4471-2099-5_20 - **Leaderboard:** - **Point of Contact:** [William Hersh](mailto:hersh@OHSU.EDU) [Aakash Gupta](mailto:aakashg80@gmail.com) ### Dataset Summary The OHSUMED test collection is a set of 348,566 references from MEDLINE, the on-line medical information database, consisting of titles and/or abstracts from 270 medical journals over a five-year period (1987-1991). The available fields are title, abstract, MeSH indexing terms, author, source, and publication type. The National Library of Medicine has agreed to make the MEDLINE references in the test database available for experimentation, restricted to the following conditions: 1. The data will not be used in any non-experimental clinical, library, or other setting. 2. Any human users of the data will explicitly be told that the data is incomplete and out-of-date. Please check this [readme](https://trec.nist.gov/data/filtering/README.t9.filtering) for more details ### Supported Tasks and Leaderboards [Text Classification](https://paperswithcode.com/sota/text-classification-on-ohsumed) ### Languages The text is primarily in English. The BCP 47 code is `en` ## Dataset Structure ### Data Instances ``` {'seq_id': 7770, 'medline_ui': 87120420, 'mesh_terms': 'Adult; Aged; Aneurysm/CO; Arteriovenous Fistula/*TH; Carotid Arteries; Case Report; Female; Human; Jugular Veins; Male; Methods; Middle Age; Neck/*BS; Vertebral Artery.', 'title': 'Arteriovenous fistulas of the large vessels of the neck: nonsurgical percutaneous occlusion.', 'publication_type': 'JOURNAL ARTICLE.', 'abstract': 'We describe the nonsurgical treatment of arteriovenous fistulas of the large vessels in the neck using three different means of endovascular occlusion of these large lesions, which are surgically difficult to approach and treat.', 'author': 'Vitek JJ; Keller FS.', 'source': 'South Med J 8705; 80(2):196-200'} ``` ### Data Fields Here are the field definitions: - seg_id: sequential identifier (important note: documents should be processed in this order) - medline_ui: MEDLINE identifier (UI) (<DOCNO> used for relevance judgements) - mesh_terms: Human-assigned MeSH terms (MH) - title: Title (TI) - publication_type : Publication type (PT) - abstract: Abstract (AB) - author: Author (AU) - source: Source (SO) Note: some abstracts are truncated at 250 words and some references have no abstracts at all (titles only). We do not have access to the full text of the documents. ### Data Splits The files are Train/ Test. Where the training has files from 1987 while the test files has abstracts from 1988-91 Total number of files: Train: 54710 Test: 348567 ## Dataset Creation ### Curation Rationale The OHSUMED document collection was obtained by William Hersh (hersh@OHSU.EDU) and colleagues for the experiments described in the papers below. [Check citation](#citation-information) ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? The test collection was built as part of a study assessing the use of MEDLINE by physicians in a clinical setting (Hersh and Hickam, above). Novice physicians using MEDLINE generated 106 queries. Only a subset of these queries were used in the TREC-9 Filtering Track. Before they searched, they were asked to provide a statement of information about their patient as well as their information need. The data was collected by William Hersh & colleagues ### Annotations #### Annotation process The existing OHSUMED topics describe actual information needs, but the relevance judgements probably do not have the same coverage provided by the TREC pooling process. The MeSH terms do not directly represent information needs, rather they are controlled indexing terms. However, the assessment should be more or less complete and there are a lot of them, so this provides an unusual opportunity to work with a very large topic sample. The topic statements are provided in the standard TREC format #### Who are the annotators? Each query was replicated by four searchers, two physicians experienced in searching and two medical librarians. The results were assessed for relevance by a different group of physicians, using a three point scale: definitely, possibly, or not relevant. The list of documents explicitly judged to be not relevant is not provided here. Over 10% of the query-document pairs were judged in duplicate to assess inter-observer reliability. For evaluation, all documents judged here as either possibly or definitely relevant were considered relevant. TREC-9 systems were allowed to distinguish between these two categories during the learning process if desired. ### Personal and Sensitive Information No PII data is present in the train, test or query files. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators [Aakash Gupta](mailto:aakashg80@gmail.com) *Th!nkEvolve Consulting* and Researcher at CoronaWhy ### Licensing Information CC BY-NC 4.0 ### Citation Information Hersh WR, Buckley C, Leone TJ, Hickam DH, OHSUMED: An interactive retrieval evaluation and new large test collection for research, Proceedings of the 17th Annual ACM SIGIR Conference, 1994, 192-201. Hersh WR, Hickam DH, Use of a multi-application computer workstation in a clinical setting, Bulletin of the Medical Library Association, 1994, 82: 382-389. ### Contributions Thanks to [@skyprince999](https://github.com/skyprince999) for adding this dataset.

ohsumed

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["cc-by-nc-4.0"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["multi-label-classification"], "pretty_name": "Ohsumed", "dataset_info": {"features": [{"name": "seq_id", "dtype": "int64"}, {"name": "medline_ui", "dtype": "int64"}, {"name": "mesh_terms", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "publication_type", "dtype": "string"}, {"name": "abstract", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "source", "dtype": "string"}], "config_name": "ohsumed", "splits": [{"name": "train", "num_bytes": 60117860, "num_examples": 54709}, {"name": "test", "num_bytes": 338533901, "num_examples": 293855}], "download_size": 139454017, "dataset_size": 398651761}}

2024-01-18T11:11:11+00:00

TAGS #task_categories-text-classification #task_ids-multi-label-classification #annotations_creators-expert-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-cc-by-nc-4.0 #region-us

# Dataset Card for ohsumed ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: URL - Paper: URL - Leaderboard: - Point of Contact: William Hersh Aakash Gupta ### Dataset Summary The OHSUMED test collection is a set of 348,566 references from MEDLINE, the on-line medical information database, consisting of titles and/or abstracts from 270 medical journals over a five-year period (1987-1991). The available fields are title, abstract, MeSH indexing terms, author, source, and publication type. The National Library of Medicine has agreed to make the MEDLINE references in the test database available for experimentation, restricted to the following conditions: 1. The data will not be used in any non-experimental clinical, library, or other setting. 2. Any human users of the data will explicitly be told that the data is incomplete and out-of-date. Please check this readme for more details ### Supported Tasks and Leaderboards Text Classification ### Languages The text is primarily in English. The BCP 47 code is 'en' ## Dataset Structure ### Data Instances ### Data Fields Here are the field definitions: - seg_id: sequential identifier (important note: documents should be processed in this order) - medline_ui: MEDLINE identifier (UI) (<DOCNO> used for relevance judgements) - mesh_terms: Human-assigned MeSH terms (MH) - title: Title (TI) - publication_type : Publication type (PT) - abstract: Abstract (AB) - author: Author (AU) - source: Source (SO) Note: some abstracts are truncated at 250 words and some references have no abstracts at all (titles only). We do not have access to the full text of the documents. ### Data Splits The files are Train/ Test. Where the training has files from 1987 while the test files has abstracts from 1988-91 Total number of files: Train: 54710 Test: 348567 ## Dataset Creation ### Curation Rationale The OHSUMED document collection was obtained by William Hersh (hersh@OHSU.EDU) and colleagues for the experiments described in the papers below. Check citation ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? The test collection was built as part of a study assessing the use of MEDLINE by physicians in a clinical setting (Hersh and Hickam, above). Novice physicians using MEDLINE generated 106 queries. Only a subset of these queries were used in the TREC-9 Filtering Track. Before they searched, they were asked to provide a statement of information about their patient as well as their information need. The data was collected by William Hersh & colleagues ### Annotations #### Annotation process The existing OHSUMED topics describe actual information needs, but the relevance judgements probably do not have the same coverage provided by the TREC pooling process. The MeSH terms do not directly represent information needs, rather they are controlled indexing terms. However, the assessment should be more or less complete and there are a lot of them, so this provides an unusual opportunity to work with a very large topic sample. The topic statements are provided in the standard TREC format #### Who are the annotators? Each query was replicated by four searchers, two physicians experienced in searching and two medical librarians. The results were assessed for relevance by a different group of physicians, using a three point scale: definitely, possibly, or not relevant. The list of documents explicitly judged to be not relevant is not provided here. Over 10% of the query-document pairs were judged in duplicate to assess inter-observer reliability. For evaluation, all documents judged here as either possibly or definitely relevant were considered relevant. TREC-9 systems were allowed to distinguish between these two categories during the learning process if desired. ### Personal and Sensitive Information No PII data is present in the train, test or query files. ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators Aakash Gupta *Th!nkEvolve Consulting* and Researcher at CoronaWhy ### Licensing Information CC BY-NC 4.0 Hersh WR, Buckley C, Leone TJ, Hickam DH, OHSUMED: An interactive retrieval evaluation and new large test collection for research, Proceedings of the 17th Annual ACM SIGIR Conference, 1994, 192-201. Hersh WR, Hickam DH, Use of a multi-application computer workstation in a clinical setting, Bulletin of the Medical Library Association, 1994, 82: 382-389. ### Contributions Thanks to @skyprince999 for adding this dataset.

b80689d295ca5c211632e15f8b4867a3ccbf0e0c

# Dataset Card for Ollie ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Ollie](https://knowitall.github.io/ollie/) - **Repository:** [Github](https://github.com/knowitall/ollie) - **Paper:** [Aclweb](https://www.aclweb.org/anthology/D12-1048/) ### Dataset Summary The Ollie dataset includes two configs for the data used to train the Ollie informatation extraction algorithm, for 18M sentences and 3M sentences respectively. This data is for academic use only. From the authors: Ollie is a program that automatically identifies and extracts binary relationships from English sentences. Ollie is designed for Web-scale information extraction, where target relations are not specified in advance. Ollie is our second-generation information extraction system . Whereas ReVerb operates on flat sequences of tokens, Ollie works with the tree-like (graph with only small cycles) representation using Stanford's compression of the dependencies. This allows Ollie to capture expression that ReVerb misses, such as long-range relations. Ollie also captures context that modifies a binary relation. Presently Ollie handles attribution (He said/she believes) and enabling conditions (if X then). More information is available at the Ollie homepage: https://knowitall.github.io/ollie/ ### Supported Tasks and Leaderboards [More Information Needed] ### Languages en ## Dataset Structure ### Data Instances There are two configurations for the dataset: ollie_lemmagrep which are 18M sentences from web searches for a subset of the Reverb relationships (110,000 relationships), and the 3M sentences for ollie_patterned which is a subset of the ollie_lemmagrep dataset derived from patterns according to the Ollie paper. An example of an ollie_lemmagrep record: `` {'arg1': 'adobe reader', 'arg2': 'pdf', 'chunk': 'B-NP I-NP I-NP I-NP B-PP B-NP I-NP B-VP B-PP B-NP I-NP O B-VP B-NP I-NP I-NP I-NP B-VP I-VP I-VP O', 'pos': 'JJ NNS CC NNS IN PRP$ NN VBP IN NNP NN CC VB DT NNP NNP NNP TO VB VBN .', 'rel': 'be require to view', 'search_query': 'require reader pdf adobe view', 'sentence': 'Many documents and reports on our site are in PDF format and require the Adobe Acrobat Reader to be viewed .', 'sentence_cnt': '9', 'words': 'many,document,and,report,on,our,site,be,in,pdf,format,and,require,the,adobe,acrobat,reader,to,be,view'} `` An example of an ollie_patterned record: `` {'arg1': 'english', 'arg2': 'internet', 'parse': '(in_IN_6), advmod(important_JJ_4, most_RBS_3); nsubj(language_NN_5, English_NNP_0); cop(language_NN_5, being_VBG_1); det(language_NN_5, the_DT_2); amod(language_NN_5, important_JJ_4); prep_in(language_NN_5, era_NN_9); punct(language_NN_5, ,_,_10); conj(language_NN_5, education_NN_12); det(era_NN_9, the_DT_7); nn(era_NN_9, Internet_NNP_8); amod(education_NN_12, English_JJ_11); nsubjpass(enriched_VBN_15, language_NN_5); aux(enriched_VBN_15, should_MD_13); auxpass(enriched_VBN_15, be_VB_14); punct(enriched_VBN_15, ._._16)', 'pattern': '{arg1} <nsubj< {rel:NN} >prep_in> {slot0:NN} >nn> {arg2}', 'rel': 'be language of', 'search_query': 'english language internet', 'sentence': 'English being the most important language in the Internet era , English education should be enriched .', 'slot0': 'era'} `` ### Data Fields For ollie_lemmagrep: * rel: the relationship phrase/verb phrase. This may be empty, which represents the "be" relationship. * arg1: the first argument in the relationship * arg2: the second argument in the relationship. * chunk: a tag of each token in the sentence, showing the pos chunks * pos: part of speech tagging of the sentence * sentence: the sentence * sentence_cnt: the number of copies of this sentence encountered * search_query: a combintion of rel, arg1, arg2 * words: the lemma of the words of the sentence separated by commas For ollie_patterned: * rel: the relationship phrase/verb phrase. * arg1: the first argument in the relationship * arg2: the second argument in the relationship. * slot0: the third argument in the relationship, which might be empty. * pattern: a parse pattern for the relationship * parse: a dependency parse forthe sentence * search_query: a combintion of rel, arg1, arg2 * sentence: the senence ### Data Splits There are no splits. ## Dataset Creation ### Curation Rationale This dataset was created as part of research on open information extraction. ### Source Data #### Initial Data Collection and Normalization See the research paper on OLlie. The training data is extracted from web pages (Cluebweb09). #### Who are the source language producers? The Ollie authors at the Univeristy of Washington and data from Cluebweb09 and the open web. ### Annotations #### Annotation process The various parsers and code from the Ollie alogrithm. #### Who are the annotators? Machine annotated. ### Personal and Sensitive Information Unkown, but likely there are names of famous individuals. ## Considerations for Using the Data ### Social Impact of Dataset The goal for the work is to help machines learn to extract information form open domains. ### Discussion of Biases Since the data is gathered from the web, there is likely to be biased text and relationships. [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The authors of Ollie at The University of Washington ### Licensing Information The University of Washington academic license: https://raw.githubusercontent.com/knowitall/ollie/master/LICENSE ### Citation Information ``` @inproceedings{ollie-emnlp12, author = {Mausam and Michael Schmitz and Robert Bart and Stephen Soderland and Oren Etzioni}, title = {Open Language Learning for Information Extraction}, booktitle = {Proceedings of Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CONLL)}, year = {2012} } ``` ### Contributions Thanks to [@ontocord](https://github.com/ontocord) for adding this dataset.

ollie

2022-03-02T23:29:22+00:00

{"annotations_creators": ["machine-generated"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["10M<n<100M", "1M<n<10M"], "source_datasets": ["original"], "task_categories": [], "task_ids": [], "pretty_name": "Ollie", "config_names": ["ollie_lemmagrep", "ollie_patterned"], "tags": ["relation-extraction", "text-to-structured"], "dataset_info": [{"config_name": "ollie_lemmagrep", "features": [{"name": "arg1", "dtype": "string"}, {"name": "arg2", "dtype": "string"}, {"name": "rel", "dtype": "string"}, {"name": "search_query", "dtype": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "words", "dtype": "string"}, {"name": "pos", "dtype": "string"}, {"name": "chunk", "dtype": "string"}, {"name": "sentence_cnt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 12324648919, "num_examples": 18674630}], "download_size": 1789363108, "dataset_size": 12324648919}, {"config_name": "ollie_patterned", "features": [{"name": "rel", "dtype": "string"}, {"name": "arg1", "dtype": "string"}, {"name": "arg2", "dtype": "string"}, {"name": "slot0", "dtype": "string"}, {"name": "search_query", "dtype": "string"}, {"name": "pattern", "dtype": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "parse", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2930309084, "num_examples": 3048961}], "download_size": 387514061, "dataset_size": 2930309084}]}

2024-01-18T11:11:13+00:00

TAGS #annotations_creators-machine-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10M<n<100M #size_categories-1M<n<10M #source_datasets-original #language-English #license-other #relation-extraction #text-to-structured #region-us

# Dataset Card for Ollie ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: Ollie - Repository: Github - Paper: Aclweb ### Dataset Summary The Ollie dataset includes two configs for the data used to train the Ollie informatation extraction algorithm, for 18M sentences and 3M sentences respectively. This data is for academic use only. From the authors: Ollie is a program that automatically identifies and extracts binary relationships from English sentences. Ollie is designed for Web-scale information extraction, where target relations are not specified in advance. Ollie is our second-generation information extraction system . Whereas ReVerb operates on flat sequences of tokens, Ollie works with the tree-like (graph with only small cycles) representation using Stanford's compression of the dependencies. This allows Ollie to capture expression that ReVerb misses, such as long-range relations. Ollie also captures context that modifies a binary relation. Presently Ollie handles attribution (He said/she believes) and enabling conditions (if X then). More information is available at the Ollie homepage: URL ### Supported Tasks and Leaderboards ### Languages en ## Dataset Structure ### Data Instances There are two configurations for the dataset: ollie_lemmagrep which are 18M sentences from web searches for a subset of the Reverb relationships (110,000 relationships), and the 3M sentences for ollie_patterned which is a subset of the ollie_lemmagrep dataset derived from patterns according to the Ollie paper. An example of an ollie_lemmagrep record: '' {'arg1': 'adobe reader', 'arg2': 'pdf', 'chunk': 'B-NP I-NP I-NP I-NP B-PP B-NP I-NP B-VP B-PP B-NP I-NP O B-VP B-NP I-NP I-NP I-NP B-VP I-VP I-VP O', 'pos': 'JJ NNS CC NNS IN PRP$ NN VBP IN NNP NN CC VB DT NNP NNP NNP TO VB VBN .', 'rel': 'be require to view', 'search_query': 'require reader pdf adobe view', 'sentence': 'Many documents and reports on our site are in PDF format and require the Adobe Acrobat Reader to be viewed .', 'sentence_cnt': '9', 'words': 'many,document,and,report,on,our,site,be,in,pdf,format,and,require,the,adobe,acrobat,reader,to,be,view'} '' An example of an ollie_patterned record: '' {'arg1': 'english', 'arg2': 'internet', 'parse': '(in_IN_6), advmod(important_JJ_4, most_RBS_3); nsubj(language_NN_5, English_NNP_0); cop(language_NN_5, being_VBG_1); det(language_NN_5, the_DT_2); amod(language_NN_5, important_JJ_4); prep_in(language_NN_5, era_NN_9); punct(language_NN_5, ,_,_10); conj(language_NN_5, education_NN_12); det(era_NN_9, the_DT_7); nn(era_NN_9, Internet_NNP_8); amod(education_NN_12, English_JJ_11); nsubjpass(enriched_VBN_15, language_NN_5); aux(enriched_VBN_15, should_MD_13); auxpass(enriched_VBN_15, be_VB_14); punct(enriched_VBN_15, ._._16)', 'pattern': '{arg1} <nsubj< {rel:NN} >prep_in> {slot0:NN} >nn> {arg2}', 'rel': 'be language of', 'search_query': 'english language internet', 'sentence': 'English being the most important language in the Internet era , English education should be enriched .', 'slot0': 'era'} '' ### Data Fields For ollie_lemmagrep: * rel: the relationship phrase/verb phrase. This may be empty, which represents the "be" relationship. * arg1: the first argument in the relationship * arg2: the second argument in the relationship. * chunk: a tag of each token in the sentence, showing the pos chunks * pos: part of speech tagging of the sentence * sentence: the sentence * sentence_cnt: the number of copies of this sentence encountered * search_query: a combintion of rel, arg1, arg2 * words: the lemma of the words of the sentence separated by commas For ollie_patterned: * rel: the relationship phrase/verb phrase. * arg1: the first argument in the relationship * arg2: the second argument in the relationship. * slot0: the third argument in the relationship, which might be empty. * pattern: a parse pattern for the relationship * parse: a dependency parse forthe sentence * search_query: a combintion of rel, arg1, arg2 * sentence: the senence ### Data Splits There are no splits. ## Dataset Creation ### Curation Rationale This dataset was created as part of research on open information extraction. ### Source Data #### Initial Data Collection and Normalization See the research paper on OLlie. The training data is extracted from web pages (Cluebweb09). #### Who are the source language producers? The Ollie authors at the Univeristy of Washington and data from Cluebweb09 and the open web. ### Annotations #### Annotation process The various parsers and code from the Ollie alogrithm. #### Who are the annotators? Machine annotated. ### Personal and Sensitive Information Unkown, but likely there are names of famous individuals. ## Considerations for Using the Data ### Social Impact of Dataset The goal for the work is to help machines learn to extract information form open domains. ### Discussion of Biases Since the data is gathered from the web, there is likely to be biased text and relationships. ### Other Known Limitations ## Additional Information ### Dataset Curators The authors of Ollie at The University of Washington ### Licensing Information The University of Washington academic license: URL ### Contributions Thanks to @ontocord for adding this dataset.

de148f3e180cfd5218843f301272307de4ee9772

# Dataset Card for One Million Posts Corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://ofai.github.io/million-post-corpus/ - **Repository:** https://github.com/OFAI/million-post-corpus - **Paper:** https://dl.acm.org/doi/10.1145/3077136.3080711 - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The “One Million Posts” corpus is an annotated data set consisting of user comments posted to an Austrian newspaper website (in German language). DER STANDARD is an Austrian daily broadsheet newspaper. On the newspaper’s website, there is a discussion section below each news article where readers engage in online discussions. The data set contains a selection of user posts from the 12 month time span from 2015-06-01 to 2016-05-31. There are 11,773 labeled and 1,000,000 unlabeled posts in the data set. The labeled posts were annotated by professional forum moderators employed by the newspaper. The data set contains the following data for each post: * Post ID * Article ID * Headline (max. 250 characters) * Main Body (max. 750 characters) * User ID (the user names used by the website have been re-mapped to new numeric IDs) * Time stamp * Parent post (replies give rise to tree-like discussion thread structures) * Status (online or deleted by a moderator) * Number of positive votes by other community members * Number of negative votes by other community members For each article, the data set contains the following data: * Article ID * Publishing date * Topic Path (e.g.: Newsroom / Sports / Motorsports / Formula 1) * Title * Body Detailed descriptions of the post selection and annotation procedures are given in the paper. #### Annotated Categories Potentially undesirable content: * Sentiment (negative/neutral/positive) An important goal is to detect changes in the prevalent sentiment in a discussion, e.g., the location within the fora and the point in time where a turn from positive/neutral sentiment to negative sentiment takes place. * Off-Topic (yes/no) Posts which digress too far from the topic of the corresponding article. * Inappropriate (yes/no) Swearwords, suggestive and obscene language, insults, threats etc. * Discriminating (yes/no) Racist, sexist, misogynistic, homophobic, antisemitic and other misanthropic content. Neutral content that requires a reaction: * Feedback (yes/no) Sometimes users ask questions or give feedback to the author of the article or the newspaper in general, which may require a reply/reaction. Potentially desirable content: * Personal Stories (yes/no) In certain fora, users are encouraged to share their personal stories, experiences, anecdotes etc. regarding the respective topic. * Arguments Used (yes/no) It is desirable for users to back their statements with rational argumentation, reasoning and sources. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Austrian German ## Dataset Structure ### Data Instances An example from the `posts_labeled` config: ```json { "ID_Post": "79", "ID_Parent_Post": "", "ID_Article": "1", "ID_User": "12071", "CreatedAt": "2015-06-01 08:58:32.363", "Status": "online", "Headline": "", "Body": "ich kann keinen hinweis finden, wo man sich hinwenden muss, sollte man als abonnent des standard, die zeitung nicht bekommt, ist dass bewusst so arrangiert?", "PositiveVotes": 0, "NegativeVotes": 0, "Category": 5, "Value": 1, "Fold": 1 } ``` An example from the `posts_unlabeled` config: ```json { "ID_Post": "51", "ID_Parent_Post": "", "ID_Article": "1", "ID_User": "11125", "CreatedAt": "2011-05-15 08:37:11.313", "Status": "online", "Headline": "Ich würde es sehr begrüßen, wenn", "Body": "Antworten erst beim Erscheinen als e-Mail dem Poster zugestellt würden.\r\n\r\nEs gibt User, die ihre Kommentare sofort nach Mail-Eingang irgendwo hinposten. Dadurch wird \r\n1. vor allem für andere Unser die Lesbarkeit wesentlich beeinträchtigt,\r\n2. kann das Post verdreht wiedergegeben werden,\r\n3. man ist immer wieder gezwungen die Antwort richtig zu stellen.\r\n\r\nPrivatfehden von Usern sollten, wenn schon zugelassen, für alle User nachvollziehbar sein.\r\n\r\nDanke!", "PositiveVotes": 1, "NegativeVotes": 0 } ``` An example from the `articles` config: ```json { "ID_Article": "41", "Path": "Newsroom/Wirtschaft/Wirtschaftpolitik/Energiemarkt", "publishingDate": "2015-06-01 12:39:35.00", "Title": "Öl- und Gas-Riesen fordern weltweite CO2-Preise", "Body": '<div class="section" id="content-main" itemprop="articleBody"><div class="copytext"><h2 itemprop="description">Brief von BP, Total, Shell, Statoil, BG Group und Eni unterzeichnet</h2><p>Paris/London/La Defense - Sechs große Öl- und Gaskonzerne haben mit Blick auf die Verhandlungen über einen neuen Welt-Klimavertrag ein globales Preissystem für CO2-Emissionen gefordert. Wenn der Ausstoß von CO2 Geld kostet, sei dies ein Anreiz für die Nutzung von Erdgas statt Kohle, mehr Energieeffizienz und Investitionen zur Vermeidung des Treibhausgases, heißt es in einem am Montag veröffentlichten Brief.</p>\n<p>Das Schreiben ist unterzeichnet von BP, Total, Shell, Statoil, BG Group und Eni. Die Unternehmen versicherten, sie seien bereit, ihren Teil zum Kampf gegen den <a href="/r1937/Klimawandel">Klimawandel</a> beizutragen. Dafür sei aber ein klarer und verlässlicher Politik-Rahmen nötig. (APA, 1.6.2015)</p> </div></div>' } ``` ### Data Fields The data set contains the following data for each post: * **ID_Post**: Post ID * **ID_Parent_Post**: Parent post (replies give rise to tree-like discussion thread structures) * **ID_Article**: Article ID * **ID_User**: User ID (the user names used by the website have been re-mapped to new numeric IDs) * **Headline**: Headline (max. 250 characters) * **Body**: Main Body (max. 750 characters) * **CreatedAt**: Time stamp * **Status**: Status (online or deleted by a moderator) * **PositiveVotes**: Number of positive votes by other community members * **NegativeVotes**: Number of negative votes by other community members Labeled posts also contain: * **Category**: The category of the annotation, one of: ArgumentsUsed, Discriminating, Inappropriate, OffTopic, PersonalStories, PossiblyFeedback, SentimentNegative, SentimentNeutral, SentimentPositive * **Value**: either 0 or 1, explicitly indicating whether or not the post has the specified category as a label (i.e. a category of `ArgumentsUsed` with value of `0` means that an annotator explicitly labeled that this post doesn't use arguments, as opposed to the mere absence of a positive label). * **Fold**: a number between [0-9] from a 10-fold split by the authors For each article, the data set contains the following data: * **ID_Article**: Article ID * **publishingDate**: Publishing date * **Path**: Topic Path (e.g.: Newsroom / Sports / Motorsports / Formula 1) * **Title**: Title * **Body**: Body ### Data Splits Training split only. | name | train | |-----------------|--------:| | posts_labeled | 40567 | | posts_unlabeled | 1000000 | | articles | 12087 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information This data set is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. ### Citation Information ``` @InProceedings{Schabus2018, author = {Dietmar Schabus and Marcin Skowron}, title = {Academic-Industrial Perspective on the Development and Deployment of a Moderation System for a Newspaper Website}, booktitle = {Proceedings of the 11th International Conference on Language Resources and Evaluation (LREC)}, year = {2018}, address = {Miyazaki, Japan}, month = may, pages = {1602-1605}, abstract = {This paper describes an approach and our experiences from the development, deployment and usability testing of a Natural Language Processing (NLP) and Information Retrieval system that supports the moderation of user comments on a large newspaper website. We highlight some of the differences between industry-oriented and academic research settings and their influence on the decisions made in the data collection and annotation processes, selection of document representation and machine learning methods. We report on classification results, where the problems to solve and the data to work with come from a commercial enterprise. In this context typical for NLP research, we discuss relevant industrial aspects. We believe that the challenges faced as well as the solutions proposed for addressing them can provide insights to others working in a similar setting.}, url = {http://www.lrec-conf.org/proceedings/lrec2018/summaries/8885.html}, } ``` ### Contributions Thanks to [@aseifert](https://github.com/aseifert) for adding this dataset.

omp

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["crowdsourced"], "language": ["de"], "license": ["cc-by-nc-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "paperswithcode_id": "one-million-posts-corpus", "pretty_name": "One Million Posts", "dataset_info": [{"config_name": "posts_labeled", "features": [{"name": "ID_Post", "dtype": "string"}, {"name": "ID_Parent_Post", "dtype": "string"}, {"name": "ID_Article", "dtype": "string"}, {"name": "ID_User", "dtype": "string"}, {"name": "CreatedAt", "dtype": "string"}, {"name": "Status", "dtype": "string"}, {"name": "Headline", "dtype": "string"}, {"name": "Body", "dtype": "string"}, {"name": "PositiveVotes", "dtype": "int32"}, {"name": "NegativeVotes", "dtype": "int32"}, {"name": "Category", "dtype": {"class_label": {"names": {"0": "ArgumentsUsed", "1": "Discriminating", "2": "Inappropriate", "3": "OffTopic", "4": "PersonalStories", "5": "PossiblyFeedback", "6": "SentimentNegative", "7": "SentimentNeutral", "8": "SentimentPositive"}}}}, {"name": "Value", "dtype": "int32"}, {"name": "Fold", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 13955964, "num_examples": 40567}], "download_size": 1329892, "dataset_size": 13955964}, {"config_name": "posts_unlabeled", "features": [{"name": "ID_Post", "dtype": "string"}, {"name": "ID_Parent_Post", "dtype": "string"}, {"name": "ID_Article", "dtype": "string"}, {"name": "ID_User", "dtype": "string"}, {"name": "CreatedAt", "dtype": "string"}, {"name": "Status", "dtype": "string"}, {"name": "Headline", "dtype": "string"}, {"name": "Body", "dtype": "string"}, {"name": "PositiveVotes", "dtype": "int32"}, {"name": "NegativeVotes", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 305770324, "num_examples": 1000000}], "download_size": 79296188, "dataset_size": 305770324}, {"config_name": "articles", "features": [{"name": "ID_Article", "dtype": "string"}, {"name": "Path", "dtype": "string"}, {"name": "publishingDate", "dtype": "string"}, {"name": "Title", "dtype": "string"}, {"name": "Body", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 43529400, "num_examples": 12087}], "download_size": 10681288, "dataset_size": 43529400}]}

2024-01-18T11:11:14+00:00

TAGS #task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-expert-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-German #license-cc-by-nc-sa-4.0 #region-us

Dataset Card for One Million Posts Corpus ========================================= Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: URL * Paper: URL * Leaderboard: * Point of Contact: ### Dataset Summary The “One Million Posts” corpus is an annotated data set consisting of user comments posted to an Austrian newspaper website (in German language). DER STANDARD is an Austrian daily broadsheet newspaper. On the newspaper’s website, there is a discussion section below each news article where readers engage in online discussions. The data set contains a selection of user posts from the 12 month time span from 2015-06-01 to 2016-05-31. There are 11,773 labeled and 1,000,000 unlabeled posts in the data set. The labeled posts were annotated by professional forum moderators employed by the newspaper. The data set contains the following data for each post: * Post ID * Article ID * Headline (max. 250 characters) * Main Body (max. 750 characters) * User ID (the user names used by the website have been re-mapped to new numeric IDs) * Time stamp * Parent post (replies give rise to tree-like discussion thread structures) * Status (online or deleted by a moderator) * Number of positive votes by other community members * Number of negative votes by other community members For each article, the data set contains the following data: * Article ID * Publishing date * Topic Path (e.g.: Newsroom / Sports / Motorsports / Formula 1) * Title * Body Detailed descriptions of the post selection and annotation procedures are given in the paper. #### Annotated Categories Potentially undesirable content: * Sentiment (negative/neutral/positive) An important goal is to detect changes in the prevalent sentiment in a discussion, e.g., the location within the fora and the point in time where a turn from positive/neutral sentiment to negative sentiment takes place. * Off-Topic (yes/no) Posts which digress too far from the topic of the corresponding article. * Inappropriate (yes/no) Swearwords, suggestive and obscene language, insults, threats etc. * Discriminating (yes/no) Racist, sexist, misogynistic, homophobic, antisemitic and other misanthropic content. Neutral content that requires a reaction: * Feedback (yes/no) Sometimes users ask questions or give feedback to the author of the article or the newspaper in general, which may require a reply/reaction. Potentially desirable content: * Personal Stories (yes/no) In certain fora, users are encouraged to share their personal stories, experiences, anecdotes etc. regarding the respective topic. * Arguments Used (yes/no) It is desirable for users to back their statements with rational argumentation, reasoning and sources. ### Supported Tasks and Leaderboards ### Languages Austrian German Dataset Structure ----------------- ### Data Instances An example from the 'posts\_labeled' config: An example from the 'posts\_unlabeled' config: An example from the 'articles' config: ### Data Fields The data set contains the following data for each post: * ID\_Post: Post ID * ID\_Parent\_Post: Parent post (replies give rise to tree-like discussion thread structures) * ID\_Article: Article ID * ID\_User: User ID (the user names used by the website have been re-mapped to new numeric IDs) * Headline: Headline (max. 250 characters) * Body: Main Body (max. 750 characters) * CreatedAt: Time stamp * Status: Status (online or deleted by a moderator) * PositiveVotes: Number of positive votes by other community members * NegativeVotes: Number of negative votes by other community members Labeled posts also contain: * Category: The category of the annotation, one of: ArgumentsUsed, Discriminating, Inappropriate, OffTopic, PersonalStories, PossiblyFeedback, SentimentNegative, SentimentNeutral, SentimentPositive * Value: either 0 or 1, explicitly indicating whether or not the post has the specified category as a label (i.e. a category of 'ArgumentsUsed' with value of '0' means that an annotator explicitly labeled that this post doesn't use arguments, as opposed to the mere absence of a positive label). * Fold: a number between [0-9] from a 10-fold split by the authors For each article, the data set contains the following data: * ID\_Article: Article ID * publishingDate: Publishing date * Path: Topic Path (e.g.: Newsroom / Sports / Motorsports / Formula 1) * Title: Title * Body: Body ### Data Splits Training split only. Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information This data set is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. ### Contributions Thanks to @aseifert for adding this dataset.

09de2383dec57f235e40792d44e45b69f8be33bb

# Dataset Card for OneStopEnglish corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/nishkalavallabhi/OneStopEnglishCorpus - **Repository:** https://github.com/purvimisal/OneStopCorpus-Compiled/raw/main/Texts-SeparatedByReadingLevel.zip - **Paper:** https://www.aclweb.org/anthology/W18-0535.pdf - **Leaderboard:** - **Point of Contact:** ### Dataset Summary OneStopEnglish is a corpus of texts written at three reading levels, and demonstrates its usefulness for through two applications - automatic readability assessment and automatic text simplification. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances An instance example: ``` { "text": "When you see the word Amazon, what’s the first thing you think...", "label": 0 } ``` Note that each instance contains the full text of the document. ### Data Fields - `text`: Full document text. - `label`: Reading level of the document- ele/int/adv (Elementary/Intermediate/Advance). ### Data Splits The OneStopEnglish dataset has a single _train_ split. | Split | Number of instances | |-------|--------------------:| | train | 567 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Creative Commons Attribution-ShareAlike 4.0 International License ### Citation Information [More Information Needed] ### Contributions Thanks to [@purvimisal](https://github.com/purvimisal) for adding this dataset.

onestop_english

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "source_datasets": ["original"], "task_categories": ["text2text-generation", "text-classification"], "task_ids": ["multi-class-classification", "text-simplification"], "paperswithcode_id": "onestopenglish", "pretty_name": "OneStopEnglish corpus", "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "ele", "1": "int", "2": "adv"}}}}], "splits": [{"name": "train", "num_bytes": 2278043, "num_examples": 567}], "download_size": 1228804, "dataset_size": 2278043}}

2024-01-18T11:11:15+00:00

TAGS #task_categories-text2text-generation #task_categories-text-classification #task_ids-multi-class-classification #task_ids-text-simplification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-cc-by-sa-4.0 #region-us

Dataset Card for OneStopEnglish corpus ====================================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: URL * Paper: URL * Leaderboard: * Point of Contact: ### Dataset Summary OneStopEnglish is a corpus of texts written at three reading levels, and demonstrates its usefulness for through two applications - automatic readability assessment and automatic text simplification. ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances An instance example: Note that each instance contains the full text of the document. ### Data Fields * 'text': Full document text. * 'label': Reading level of the document- ele/int/adv (Elementary/Intermediate/Advance). ### Data Splits The OneStopEnglish dataset has a single *train* split. Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information Creative Commons Attribution-ShareAlike 4.0 International License ### Contributions Thanks to @purvimisal for adding this dataset.

09ccbe0ab925a88c6c5ba3418d6a02fe886dd54e

# Dataset Card for OneStopQA ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [OneStopQA repository](https://github.com/berzak/onestop-qa) - **Repository:** [OneStopQA repository](https://github.com/berzak/onestop-qa) - **Paper:** [STARC: Structured Annotations for Reading Comprehension](https://arxiv.org/abs/2004.14797) - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary OneStopQA is a multiple choice reading comprehension dataset annotated according to the STARC (Structured Annotations for Reading Comprehension) scheme. The reading materials are Guardian articles taken from the [OneStopEnglish corpus](https://github.com/nishkalavallabhi/OneStopEnglishCorpus). Each article comes in three difficulty levels, Elementary, Intermediate and Advanced. Each paragraph is annotated with three multiple choice reading comprehension questions. The reading comprehension questions can be answered based on any of the three paragraph levels. ### Supported Tasks and Leaderboards [Needs More Information] ### Languages English (`en-US`). The original Guardian articles were manually converted from British to American English. ## Dataset Structure ### Data Instances An example of instance looks as follows. ```json { "title": "101-Year-Old Bottle Message", "paragraph": "Angela Erdmann never knew her grandfather. He died in 1946, six years before she was born. But, on Tuesday 8th April, 2014, she described the extraordinary moment when she received a message in a bottle, 101 years after he had lobbed it into the Baltic Sea. Thought to be the world’s oldest message in a bottle, it was presented to Erdmann by the museum that is now exhibiting it in Germany.", "paragraph_index": 1, "level": "Adv", "question": "How did Angela Erdmann find out about the bottle?", "answers": ["A museum told her that they had it", "She coincidentally saw it at the museum where it was held", "She found it in her basement on April 28th, 2014", "A friend told her about it"], "a_span": [56, 70], "d_span": [16, 34] } ``` Where, | Answer | Description | Textual Span | |--------|------------------------------------------------------------|-----------------| | a | Correct answer. | Critical Span | | b | Incorrect answer. A miscomprehension of the critical span. | Critical Span | | c | Incorrect answer. Refers to an additional span. | Distractor Span | | d | Incorrect answer. Has no textual support. | - | The order of the answers in the `answers` list corresponds to the order of the answers in the table. ### Data Fields - `title`: A `string` feature. The article title. - `paragraph`: A `string` feature. The paragraph from the article. - `paragraph_index`: An `int` feature. Corresponds to the paragraph index in the article. - `question`: A `string` feature. The given question. - `answers`: A list of `string` feature containing the four possible answers. - `a_span`: A list of start and end indices (inclusive) of the critical span. - `d_span`: A list of start and end indices (inclusive) of the distractor span. *Span indices are according to word positions after whitespace tokenization. **In the rare case where a span is spread over multiple sections, the span list will contain multiple instances of start and stop indices in the format: [start_1, stop_1, start_2, stop_2,...]. ### Data Splits Articles: 30 Paragraphs: 162 Questions: 486 Question-Paragraph Level pairs: 1,458 No preconfigured split is currently provided. ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process The annotation and piloting process of the dataset is described in Appendix A in [STARC: Structured Annotations for Reading Comprehension](https://aclanthology.org/2020.acl-main.507.pdf). #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>. ### Citation Information [STARC: Structured Annotations for Reading Comprehension](http://people.csail.mit.edu/berzak/papers/acl2020.pdf) ``` @inproceedings{starc2020, author = {Berzak, Yevgeni and Malmaud, Jonathan and Levy, Roger}, title = {STARC: Structured Annotations for Reading Comprehension}, booktitle = {ACL}, year = {2020}, publisher = {Association for Computational Linguistics} } ``` ### Contributions Thanks to [@scaperex](https://github.com/scaperex) for adding this dataset.

onestop_qa

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original", "extended|onestop_english"], "task_categories": ["question-answering"], "task_ids": ["multiple-choice-qa"], "paperswithcode_id": "onestopqa", "pretty_name": "OneStopQA", "language_bcp47": ["en-US"], "dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "paragraph", "dtype": "string"}, {"name": "level", "dtype": {"class_label": {"names": {"0": "Adv", "1": "Int", "2": "Ele"}}}}, {"name": "question", "dtype": "string"}, {"name": "paragraph_index", "dtype": "int32"}, {"name": "answers", "sequence": "string", "length": 4}, {"name": "a_span", "sequence": "int32"}, {"name": "d_span", "sequence": "int32"}], "splits": [{"name": "train", "num_bytes": 1423090, "num_examples": 1458}], "download_size": 118173, "dataset_size": 1423090}}

2024-01-18T11:11:17+00:00

TAGS #task_categories-question-answering #task_ids-multiple-choice-qa #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #source_datasets-extended|onestop_english #language-English #license-cc-by-sa-4.0 #arxiv-2004.14797 #region-us

Dataset Card for OneStopQA ========================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: OneStopQA repository * Repository: OneStopQA repository * Paper: STARC: Structured Annotations for Reading Comprehension * Leaderboard: * Point of Contact: ### Dataset Summary OneStopQA is a multiple choice reading comprehension dataset annotated according to the STARC (Structured Annotations for Reading Comprehension) scheme. The reading materials are Guardian articles taken from the OneStopEnglish corpus. Each article comes in three difficulty levels, Elementary, Intermediate and Advanced. Each paragraph is annotated with three multiple choice reading comprehension questions. The reading comprehension questions can be answered based on any of the three paragraph levels. ### Supported Tasks and Leaderboards ### Languages English ('en-US'). The original Guardian articles were manually converted from British to American English. Dataset Structure ----------------- ### Data Instances An example of instance looks as follows. Where, Answer: a, Description: Correct answer., Textual Span: Critical Span Answer: b, Description: Incorrect answer. A miscomprehension of the critical span., Textual Span: Critical Span Answer: c, Description: Incorrect answer. Refers to an additional span., Textual Span: Distractor Span Answer: d, Description: Incorrect answer. Has no textual support., Textual Span: - The order of the answers in the 'answers' list corresponds to the order of the answers in the table. ### Data Fields * 'title': A 'string' feature. The article title. * 'paragraph': A 'string' feature. The paragraph from the article. * 'paragraph\_index': An 'int' feature. Corresponds to the paragraph index in the article. * 'question': A 'string' feature. The given question. * 'answers': A list of 'string' feature containing the four possible answers. * 'a\_span': A list of start and end indices (inclusive) of the critical span. * 'd\_span': A list of start and end indices (inclusive) of the distractor span. \*Span indices are according to word positions after whitespace tokenization. In the rare case where a span is spread over multiple sections, the span list will contain multiple instances of start and stop indices in the format: [start\_1, stop\_1, start\_2, stop\_2,...]. ### Data Splits Articles: 30 Paragraphs: 162 Questions: 486 Question-Paragraph Level pairs: 1,458 No preconfigured split is currently provided. Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process The annotation and piloting process of the dataset is described in Appendix A in STARC: Structured Annotations for Reading Comprehension. #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information <a rel="license" href="URL alt="Creative Commons License" style="border-width:0" src="https://i.URL /> This work is licensed under a <a rel="license" href="URL Commons Attribution-ShareAlike 4.0 International License. STARC: Structured Annotations for Reading Comprehension ### Contributions Thanks to @scaperex for adding this dataset.

6a1ddce6f2173f08c7d316736086d411fb155e65

# Dataset Card for OpenSubtitles ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://opus.nlpl.eu/OpenSubtitles.php - **Repository:** None - **Paper:** http://www.lrec-conf.org/proceedings/lrec2016/pdf/62_Paper.pdf - **Leaderboard:** [More Information Needed] - **Point of Contact:** [More Information Needed] ### Dataset Summary To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs. You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/OpenSubtitles.php E.g. `dataset = load_dataset("open_subtitles", lang1="fi", lang2="hi")` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The languages in the dataset are: - af - ar - bg - bn - br - bs - ca - cs - da - de - el - en - eo - es - et - eu - fa - fi - fr - gl - he - hi - hr - hu - hy - id - is - it - ja - ka - kk - ko - lt - lv - mk - ml - ms - nl - no - pl - pt - pt_br: Portuguese (Brazil) (pt-BR) - ro - ru - si - sk - sl - sq - sr - sv - ta - te - th - tl - tr - uk - ur - vi - ze_en: English constituent of Bilingual Chinese-English (subtitles displaying two languages at once, one per line) - ze_zh: Chinese constituent of Bilingual Chinese-English (subtitles displaying two languages at once, one per line) - zh_cn: Simplified Chinese (zh-CN, `zh-Hans`) - zh_tw: Traditional Chinese (zh-TW, `zh-Hant`) ## Dataset Structure ### Data Instances Here are some examples of questions and facts: ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.

open_subtitles

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["af", "ar", "bg", "bn", "br", "bs", "ca", "cs", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "gl", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "ja", "ka", "kk", "ko", "lt", "lv", "mk", "ml", "ms", "nl", "no", "pl", "pt", "ro", "ru", "si", "sk", "sl", "sq", "sr", "sv", "ta", "te", "th", "tl", "tr", "uk", "ur", "vi", "zh"], "license": ["unknown"], "multilinguality": ["multilingual"], "size_categories": ["10K<n<100K", "1M<n<10M", "n<1K"], "source_datasets": ["original"], "task_categories": ["translation"], "task_ids": [], "paperswithcode_id": "opensubtitles", "pretty_name": "OpenSubtitles", "config_names": ["bn-is", "bs-eo", "da-ru", "en-hi", "fr-hy"], "language_bcp47": ["pt-BR", "ze-EN", "ze-ZH", "zh-CN", "zh-TW"], "dataset_info": [{"config_name": "bs-eo", "features": [{"name": "id", "dtype": "string"}, {"name": "meta", "struct": [{"name": "year", "dtype": "uint32"}, {"name": "imdbId", "dtype": "uint32"}, {"name": "subtitleId", "struct": [{"name": "bs", "dtype": "uint32"}, {"name": "eo", "dtype": "uint32"}]}, {"name": "sentenceIds", "struct": [{"name": "bs", "sequence": "uint32"}, {"name": "eo", "sequence": "uint32"}]}]}, {"name": "translation", "dtype": {"translation": {"languages": ["bs", "eo"]}}}], "splits": [{"name": "train", "num_bytes": 1204266, "num_examples": 10989}], "download_size": 333050, "dataset_size": 1204266}, {"config_name": "fr-hy", "features": [{"name": "id", "dtype": "string"}, {"name": "meta", "struct": [{"name": "year", "dtype": "uint32"}, {"name": "imdbId", "dtype": "uint32"}, {"name": "subtitleId", "struct": [{"name": "fr", "dtype": "uint32"}, {"name": "hy", "dtype": "uint32"}]}, {"name": "sentenceIds", "struct": [{"name": "fr", "sequence": "uint32"}, {"name": "hy", "sequence": "uint32"}]}]}, {"name": "translation", "dtype": {"translation": {"languages": ["fr", "hy"]}}}], "splits": [{"name": "train", "num_bytes": 132450, "num_examples": 668}], "download_size": 41861, "dataset_size": 132450}, {"config_name": "da-ru", "features": [{"name": "id", "dtype": "string"}, {"name": "meta", "struct": [{"name": "year", "dtype": "uint32"}, {"name": "imdbId", "dtype": "uint32"}, {"name": "subtitleId", "struct": [{"name": "da", "dtype": "uint32"}, {"name": "ru", "dtype": "uint32"}]}, {"name": "sentenceIds", "struct": [{"name": "da", "sequence": "uint32"}, {"name": "ru", "sequence": "uint32"}]}]}, {"name": "translation", "dtype": {"translation": {"languages": ["da", "ru"]}}}], "splits": [{"name": "train", "num_bytes": 1082649105, "num_examples": 7543012}], "download_size": 267995167, "dataset_size": 1082649105}, {"config_name": "en-hi", "features": [{"name": "id", "dtype": "string"}, {"name": "meta", "struct": [{"name": "year", "dtype": "uint32"}, {"name": "imdbId", "dtype": "uint32"}, {"name": "subtitleId", "struct": [{"name": "en", "dtype": "uint32"}, {"name": "hi", "dtype": "uint32"}]}, {"name": "sentenceIds", "struct": [{"name": "en", "sequence": "uint32"}, {"name": "hi", "sequence": "uint32"}]}]}, {"name": "translation", "dtype": {"translation": {"languages": ["en", "hi"]}}}], "splits": [{"name": "train", "num_bytes": 13845544, "num_examples": 93016}], "download_size": 2967295, "dataset_size": 13845544}, {"config_name": "bn-is", "features": [{"name": "id", "dtype": "string"}, {"name": "meta", "struct": [{"name": "year", "dtype": "uint32"}, {"name": "imdbId", "dtype": "uint32"}, {"name": "subtitleId", "struct": [{"name": "bn", "dtype": "uint32"}, {"name": "is", "dtype": "uint32"}]}, {"name": "sentenceIds", "struct": [{"name": "bn", "sequence": "uint32"}, {"name": "is", "sequence": "uint32"}]}]}, {"name": "translation", "dtype": {"translation": {"languages": ["bn", "is"]}}}], "splits": [{"name": "train", "num_bytes": 6371251, "num_examples": 38272}], "download_size": 1411625, "dataset_size": 6371251}]}

2024-01-18T11:11:17+00:00

TAGS #task_categories-translation #annotations_creators-found #language_creators-found #multilinguality-multilingual #size_categories-10K<n<100K #size_categories-1M<n<10M #size_categories-n<1K #source_datasets-original #language-Afrikaans #language-Arabic #language-Bulgarian #language-Bengali #language-Breton #language-Bosnian #language-Catalan #language-Czech #language-Danish #language-German #language-Modern Greek (1453-) #language-English #language-Esperanto #language-Spanish #language-Estonian #language-Basque #language-Persian #language-Finnish #language-French #language-Galician #language-Hebrew #language-Hindi #language-Croatian #language-Hungarian #language-Armenian #language-Indonesian #language-Icelandic #language-Italian #language-Japanese #language-Georgian #language-Kazakh #language-Korean #language-Lithuanian #language-Latvian #language-Macedonian #language-Malayalam #language-Malay (macrolanguage) #language-Dutch #language-Norwegian #language-Polish #language-Portuguese #language-Romanian #language-Russian #language-Sinhala #language-Slovak #language-Slovenian #language-Albanian #language-Serbian #language-Swedish #language-Tamil #language-Telugu #language-Thai #language-Tagalog #language-Turkish #language-Ukrainian #language-Urdu #language-Vietnamese #language-Chinese #license-unknown #region-us

# Dataset Card for OpenSubtitles ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: None - Paper: URL - Leaderboard: - Point of Contact: ### Dataset Summary To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs. You can find the valid pairs in Homepage section of Dataset Description: URL E.g. 'dataset = load_dataset("open_subtitles", lang1="fi", lang2="hi")' ### Supported Tasks and Leaderboards ### Languages The languages in the dataset are: - af - ar - bg - bn - br - bs - ca - cs - da - de - el - en - eo - es - et - eu - fa - fi - fr - gl - he - hi - hr - hu - hy - id - is - it - ja - ka - kk - ko - lt - lv - mk - ml - ms - nl - no - pl - pt - pt_br: Portuguese (Brazil) (pt-BR) - ro - ru - si - sk - sl - sq - sr - sv - ta - te - th - tl - tr - uk - ur - vi - ze_en: English constituent of Bilingual Chinese-English (subtitles displaying two languages at once, one per line) - ze_zh: Chinese constituent of Bilingual Chinese-English (subtitles displaying two languages at once, one per line) - zh_cn: Simplified Chinese (zh-CN, 'zh-Hans') - zh_tw: Traditional Chinese (zh-TW, 'zh-Hant') ## Dataset Structure ### Data Instances Here are some examples of questions and facts: ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @abhishekkrthakur for adding this dataset.

7dce6050a7d6d172f3cc5c32aa97f52fa1a2e544

# Dataset Card for OpenAI HumanEval ## Table of Contents - [OpenAI HumanEval](#openai-humaneval) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** [GitHub Repository](https://github.com/openai/human-eval) - **Paper:** [Evaluating Large Language Models Trained on Code](https://arxiv.org/abs/2107.03374) ### Dataset Summary The HumanEval dataset released by OpenAI includes 164 programming problems with a function sig- nature, docstring, body, and several unit tests. They were handwritten to ensure not to be included in the training set of code generation models. ### Supported Tasks and Leaderboards ### Languages The programming problems are written in Python and contain English natural text in comments and docstrings. ## Dataset Structure ```python from datasets import load_dataset load_dataset("openai_humaneval") DatasetDict({ test: Dataset({ features: ['task_id', 'prompt', 'canonical_solution', 'test', 'entry_point'], num_rows: 164 }) }) ``` ### Data Instances An example of a dataset instance: ``` { "task_id": "test/0", "prompt": "def return1():\n", "canonical_solution": " return 1", "test": "def check(candidate):\n assert candidate() == 1", "entry_point": "return1" } ``` ### Data Fields - `task_id`: identifier for the data sample - `prompt`: input for the model containing function header and docstrings - `canonical_solution`: solution for the problem in the `prompt` - `test`: contains function to test generated code for correctness - `entry_point`: entry point for test ### Data Splits The dataset only consists of a test split with 164 samples. ## Dataset Creation ### Curation Rationale Since code generation models are often trained on dumps of GitHub a dataset not included in the dump was necessary to properly evaluate the model. However, since this dataset was published on GitHub it is likely to be included in future dumps. ### Source Data The dataset was handcrafted by engineers and researchers at OpenAI. #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information None. ## Considerations for Using the Data Make sure you execute generated Python code in a safe environment when evauating against this dataset as generated code could be harmful. ### Social Impact of Dataset With this dataset code generating models can be better evaluated which leads to fewer issues introduced when using such models. ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators OpenAI ### Licensing Information MIT License ### Citation Information ``` @misc{chen2021evaluating, title={Evaluating Large Language Models Trained on Code}, author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan and Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards and Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray and Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf and Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray and Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser and Mohammad Bavarian and Clemens Winter and Philippe Tillet and Felipe Petroski Such and Dave Cummings and Matthias Plappert and Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss and William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak and Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain and William Saunders and Christopher Hesse and Andrew N. Carr and Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa and Alec Radford and Matthew Knight and Miles Brundage and Mira Murati and Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei and Sam McCandlish and Ilya Sutskever and Wojciech Zaremba}, year={2021}, eprint={2107.03374}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` ### Contributions Thanks to [@lvwerra](https://github.com/lvwerra) for adding this dataset.

openai_humaneval

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "source_datasets": ["original"], "task_categories": ["text2text-generation"], "task_ids": [], "paperswithcode_id": "humaneval", "pretty_name": "OpenAI HumanEval", "tags": ["code-generation"], "dataset_info": {"config_name": "openai_humaneval", "features": [{"name": "task_id", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "canonical_solution", "dtype": "string"}, {"name": "test", "dtype": "string"}, {"name": "entry_point", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 194394, "num_examples": 164}], "download_size": 83920, "dataset_size": 194394}, "configs": [{"config_name": "openai_humaneval", "data_files": [{"split": "test", "path": "openai_humaneval/test-*"}], "default": true}]}

2024-01-04T16:08:05+00:00

TAGS #task_categories-text2text-generation #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-mit #code-generation #arxiv-2107.03374 #region-us

# Dataset Card for OpenAI HumanEval ## Table of Contents - OpenAI HumanEval - Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Initial Data Collection and Normalization - Who are the source language producers? - Annotations - Annotation process - Who are the annotators? - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Repository: GitHub Repository - Paper: Evaluating Large Language Models Trained on Code ### Dataset Summary The HumanEval dataset released by OpenAI includes 164 programming problems with a function sig- nature, docstring, body, and several unit tests. They were handwritten to ensure not to be included in the training set of code generation models. ### Supported Tasks and Leaderboards ### Languages The programming problems are written in Python and contain English natural text in comments and docstrings. ## Dataset Structure ### Data Instances An example of a dataset instance: ### Data Fields - 'task_id': identifier for the data sample - 'prompt': input for the model containing function header and docstrings - 'canonical_solution': solution for the problem in the 'prompt' - 'test': contains function to test generated code for correctness - 'entry_point': entry point for test ### Data Splits The dataset only consists of a test split with 164 samples. ## Dataset Creation ### Curation Rationale Since code generation models are often trained on dumps of GitHub a dataset not included in the dump was necessary to properly evaluate the model. However, since this dataset was published on GitHub it is likely to be included in future dumps. ### Source Data The dataset was handcrafted by engineers and researchers at OpenAI. #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information None. ## Considerations for Using the Data Make sure you execute generated Python code in a safe environment when evauating against this dataset as generated code could be harmful. ### Social Impact of Dataset With this dataset code generating models can be better evaluated which leads to fewer issues introduced when using such models. ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators OpenAI ### Licensing Information MIT License ### Contributions Thanks to @lvwerra for adding this dataset.

388097ea7776314e93a529163e0fea805b8a6454

# Dataset Card for OpenBookQA ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://allenai.org/data/open-book-qa](https://allenai.org/data/open-book-qa) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 2.89 MB - **Size of the generated dataset:** 2.88 MB - **Total amount of disk used:** 5.78 MB ### Dataset Summary OpenBookQA aims to promote research in advanced question-answering, probing a deeper understanding of both the topic (with salient facts summarized as an open book, also provided with the dataset) and the language it is expressed in. In particular, it contains questions that require multi-step reasoning, use of additional common and commonsense knowledge, and rich text comprehension. OpenBookQA is a new kind of question-answering dataset modeled after open book exams for assessing human understanding of a subject. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### main - **Size of downloaded dataset files:** 1.45 MB - **Size of the generated dataset:** 1.45 MB - **Total amount of disk used:** 2.88 MB An example of 'train' looks as follows: ``` {'id': '7-980', 'question_stem': 'The sun is responsible for', 'choices': {'text': ['puppies learning new tricks', 'children growing up and getting old', 'flowers wilting in a vase', 'plants sprouting, blooming and wilting'], 'label': ['A', 'B', 'C', 'D']}, 'answerKey': 'D'} ``` #### additional - **Size of downloaded dataset files:** 1.45 MB - **Size of the generated dataset:** 1.45 MB - **Total amount of disk used:** 2.88 MB An example of 'train' looks as follows: ``` {'id': '7-980', 'question_stem': 'The sun is responsible for', 'choices': {'text': ['puppies learning new tricks', 'children growing up and getting old', 'flowers wilting in a vase', 'plants sprouting, blooming and wilting'], 'label': ['A', 'B', 'C', 'D']}, 'answerKey': 'D', 'fact1': 'the sun is the source of energy for physical cycles on Earth', 'humanScore': 1.0, 'clarity': 2.0, 'turkIdAnonymized': 'b356d338b7'} ``` ### Data Fields The data fields are the same among all splits. #### main - `id`: a `string` feature. - `question_stem`: a `string` feature. - `choices`: a dictionary feature containing: - `text`: a `string` feature. - `label`: a `string` feature. - `answerKey`: a `string` feature. #### additional - `id`: a `string` feature. - `question_stem`: a `string` feature. - `choices`: a dictionary feature containing: - `text`: a `string` feature. - `label`: a `string` feature. - `answerKey`: a `string` feature. - `fact1` (`str`): oOriginating common knowledge core fact associated to the question. - `humanScore` (`float`): Human accuracy score. - `clarity` (`float`): Clarity score. - `turkIdAnonymized` (`str`): Anonymized crowd-worker ID. ### Data Splits | name | train | validation | test | |------------|------:|-----------:|-----:| | main | 4957 | 500 | 500 | | additional | 4957 | 500 | 500 | ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{OpenBookQA2018, title={Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering}, author={Todor Mihaylov and Peter Clark and Tushar Khot and Ashish Sabharwal}, booktitle={EMNLP}, year={2018} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset.

openbookqa

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced", "expert-generated"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["open-domain-qa"], "paperswithcode_id": "openbookqa", "pretty_name": "OpenBookQA", "dataset_info": [{"config_name": "additional", "features": [{"name": "id", "dtype": "string"}, {"name": "question_stem", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "string"}]}, {"name": "answerKey", "dtype": "string"}, {"name": "fact1", "dtype": "string"}, {"name": "humanScore", "dtype": "float32"}, {"name": "clarity", "dtype": "float32"}, {"name": "turkIdAnonymized", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1288577, "num_examples": 4957}, {"name": "validation", "num_bytes": 135916, "num_examples": 500}, {"name": "test", "num_bytes": 130701, "num_examples": 500}], "download_size": 783789, "dataset_size": 1555194}, {"config_name": "main", "features": [{"name": "id", "dtype": "string"}, {"name": "question_stem", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "string"}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 895386, "num_examples": 4957}, {"name": "validation", "num_bytes": 95428, "num_examples": 500}, {"name": "test", "num_bytes": 91759, "num_examples": 500}], "download_size": 609613, "dataset_size": 1082573}], "configs": [{"config_name": "additional", "data_files": [{"split": "train", "path": "additional/train-*"}, {"split": "validation", "path": "additional/validation-*"}, {"split": "test", "path": "additional/test-*"}]}, {"config_name": "main", "data_files": [{"split": "train", "path": "main/train-*"}, {"split": "validation", "path": "main/validation-*"}, {"split": "test", "path": "main/test-*"}], "default": true}]}

2024-01-04T16:09:20+00:00

TAGS #task_categories-question-answering #task_ids-open-domain-qa #annotations_creators-crowdsourced #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-unknown #region-us

Dataset Card for OpenBookQA =========================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: * Point of Contact: * Size of downloaded dataset files: 2.89 MB * Size of the generated dataset: 2.88 MB * Total amount of disk used: 5.78 MB ### Dataset Summary OpenBookQA aims to promote research in advanced question-answering, probing a deeper understanding of both the topic (with salient facts summarized as an open book, also provided with the dataset) and the language it is expressed in. In particular, it contains questions that require multi-step reasoning, use of additional common and commonsense knowledge, and rich text comprehension. OpenBookQA is a new kind of question-answering dataset modeled after open book exams for assessing human understanding of a subject. ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### main * Size of downloaded dataset files: 1.45 MB * Size of the generated dataset: 1.45 MB * Total amount of disk used: 2.88 MB An example of 'train' looks as follows: #### additional * Size of downloaded dataset files: 1.45 MB * Size of the generated dataset: 1.45 MB * Total amount of disk used: 2.88 MB An example of 'train' looks as follows: ### Data Fields The data fields are the same among all splits. #### main * 'id': a 'string' feature. * 'question\_stem': a 'string' feature. * 'choices': a dictionary feature containing: + 'text': a 'string' feature. + 'label': a 'string' feature. * 'answerKey': a 'string' feature. #### additional * 'id': a 'string' feature. * 'question\_stem': a 'string' feature. * 'choices': a dictionary feature containing: + 'text': a 'string' feature. + 'label': a 'string' feature. * 'answerKey': a 'string' feature. * 'fact1' ('str'): oOriginating common knowledge core fact associated to the question. * 'humanScore' ('float'): Human accuracy score. * 'clarity' ('float'): Clarity score. * 'turkIdAnonymized' ('str'): Anonymized crowd-worker ID. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @thomwolf, @patrickvonplaten, @lewtun for adding this dataset.

a5fae0f756e1e740c67e045d9bd58f19c0e073b2

# Dataset Card for openslr ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://www.openslr.org/ - **Repository:** [Needs More Information] - **Paper:** [Needs More Information] - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary OpenSLR is a site devoted to hosting speech and language resources, such as training corpora for speech recognition, and software related to speech recognition. Currently, following resources are available: #### SLR32: High quality TTS data for four South African languages (af, st, tn, xh). This data set contains multi-speaker high quality transcribed audio data for four languages of South Africa. The data set consists of wave files, and a TSV file transcribing the audio. In each folder, the file line_index.tsv contains a FileID, which in turn contains the UserID and the Transcription of audio in the file. The data set has had some quality checks, but there might still be errors. This data set was collected by as a collaboration between North West University and Google. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See https://github.com/google/language-resources#license for license information. Copyright 2017 Google, Inc. #### SLR35: Large Javanese ASR training data set. This data set contains transcribed audio data for Javanese (~185K utterances). The data set consists of wave files, and a TSV file. The file utt_spk_text.tsv contains a FileID, UserID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. This dataset was collected by Google in collaboration with Reykjavik University and Universitas Gadjah Mada in Indonesia. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/35/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2016, 2017 Google, Inc. #### SLR36: Large Sundanese ASR training data set. This data set contains transcribed audio data for Sundanese (~220K utterances). The data set consists of wave files, and a TSV file. The file utt_spk_text.tsv contains a FileID, UserID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. This dataset was collected by Google in Indonesia. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/36/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2016, 2017 Google, Inc. #### SLR41: High quality TTS data for Javanese. This data set contains high-quality transcribed audio data for Javanese. The data set consists of wave files, and a TSV file. The file line_index.tsv contains a filename and the transcription of audio in the file. Each filename is prepended with a speaker identification number. The data set has been manually quality checked, but there might still be errors. This dataset was collected by Google in collaboration with Gadjah Mada University in Indonesia. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/41/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2016, 2017, 2018 Google LLC #### SLR42: High quality TTS data for Khmer. This data set contains high-quality transcribed audio data for Khmer. The data set consists of wave files, and a TSV file. The file line_index.tsv contains a filename and the transcription of audio in the file. Each filename is prepended with a speaker identification number. The data set has been manually quality checked, but there might still be errors. This dataset was collected by Google. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/42/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2016, 2017, 2018 Google LLC #### SLR43: High quality TTS data for Nepali. This data set contains high-quality transcribed audio data for Nepali. The data set consists of wave files, and a TSV file. The file line_index.tsv contains a filename and the transcription of audio in the file. Each filename is prepended with a speaker identification number. The data set has been manually quality checked, but there might still be errors. This dataset was collected by Google in Nepal. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/43/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2016, 2017, 2018 Google LLC #### SLR44: High quality TTS data for Sundanese. This data set contains high-quality transcribed audio data for Sundanese. The data set consists of wave files, and a TSV file. The file line_index.tsv contains a filename and the transcription of audio in the file. Each filename is prepended with a speaker identification number. The data set has been manually quality checked, but there might still be errors. This dataset was collected by Google in collaboration with Universitas Pendidikan Indonesia. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/44/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2016, 2017, 2018 Google LLC #### SLR52: Large Sinhala ASR training data set. This data set contains transcribed audio data for Sinhala (~185K utterances). The data set consists of wave files, and a TSV file. The file utt_spk_text.tsv contains a FileID, UserID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/52/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2016, 2017, 2018 Google, Inc. #### SLR53: Large Bengali ASR training data set. This data set contains transcribed audio data for Bengali (~196K utterances). The data set consists of wave files, and a TSV file. The file utt_spk_text.tsv contains a FileID, UserID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/53/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2016, 2017, 2018 Google, Inc. #### SLR54: Large Nepali ASR training data set. This data set contains transcribed audio data for Nepali (~157K utterances). The data set consists of wave files, and a TSV file. The file utt_spk_text.tsv contains a FileID, UserID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/54/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2016, 2017, 2018 Google, Inc. #### SLR63: Crowdsourced high-quality Malayalam multi-speaker speech data set This data set contains transcribed high-quality audio of Malayalam sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/63/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR64: Crowdsourced high-quality Marathi multi-speaker speech data set This data set contains transcribed high-quality audio of Marathi sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/64/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR65: Crowdsourced high-quality Tamil multi-speaker speech data set This data set contains transcribed high-quality audio of Tamil sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/65/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR66: Crowdsourced high-quality Telugu multi-speaker speech data set This data set contains transcribed high-quality audio of Telugu sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/66/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR69: Crowdsourced high-quality Catalan multi-speaker speech data set This data set contains transcribed high-quality audio of Catalan sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/69/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR70: Crowdsourced high-quality Nigerian English speech data set This data set contains transcribed high-quality audio of Nigerian English sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/70/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR71: Crowdsourced high-quality Chilean Spanish speech data set This data set contains transcribed high-quality audio of Chilean Spanish sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/71/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR72: Crowdsourced high-quality Colombian Spanish speech data set This data set contains transcribed high-quality audio of Colombian Spanish sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/72/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR73: Crowdsourced high-quality Peruvian Spanish speech data set This data set contains transcribed high-quality audio of Peruvian Spanish sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/73/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR74: Crowdsourced high-quality Puerto Rico Spanish speech data set This data set contains transcribed high-quality audio of Puerto Rico Spanish sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/74/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR75: Crowdsourced high-quality Venezuelan Spanish speech data set This data set contains transcribed high-quality audio of Venezuelan Spanish sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/75/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR76: Crowdsourced high-quality Basque speech data set This data set contains transcribed high-quality audio of Basque sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/76/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR77: Crowdsourced high-quality Galician speech data set This data set contains transcribed high-quality audio of Galician sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/77/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR78: Crowdsourced high-quality Gujarati multi-speaker speech data set This data set contains transcribed high-quality audio of Gujarati sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/78/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR79: Crowdsourced high-quality Kannada multi-speaker speech data set This data set contains transcribed high-quality audio of Kannada sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/79/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR80: Crowdsourced high-quality Burmese speech data set This data set contains transcribed high-quality audio of Burmese sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/80/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR83: Crowdsourced high-quality UK and Ireland English Dialect speech data set This data set contains transcribed high-quality audio of English sentences recorded by volunteers speaking different dialects of the language. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.csv contains a line id, an anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. The recordings from the Welsh English speakers were collected in collaboration with Cardiff University. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/83/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR86: Crowdsourced high-quality multi-speaker speech data set This data set contains transcribed high-quality audio of sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/86/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019, 2020 Google, Inc. ### Supported Tasks and Leaderboards [Needs More Information] ### Languages Javanese, Khmer, Nepali, Sundanese, Malayalam, Marathi, Tamil, Telugu, Catalan, Nigerian English, Chilean Spanish, Columbian Spanish, Peruvian Spanish, Puerto Rico Spanish, Venezuelan Spanish, Basque, Galician, Gujarati, Kannada, Afrikaans, Sesotho, Setswana and isiXhosa. ## Dataset Structure ### Data Instances A typical data point comprises the path to the audio file, called path and its sentence. #### SLR32, SLR35, SLR36, SLR41, SLR42, SLR43, SLR44, SLR52, SLR53, SLR54, SLR63, SLR64, SLR65, SLR66, SLR69, SLR70, SLR71, SLR72, SLR73, SLR74, SLR75, SLR76, SLR77, SLR78, SLR79, SLR80, SLR86 ``` { 'path': '/home/cahya/.cache/huggingface/datasets/downloads/extracted/4d9cf915efc21110199074da4d492566dee6097068b07a680f670fcec9176e62/su_id_female/wavs/suf_00297_00037352660.wav' 'audio': {'path': '/home/cahya/.cache/huggingface/datasets/downloads/extracted/4d9cf915efc21110199074da4d492566dee6097068b07a680f670fcec9176e62/su_id_female/wavs/suf_00297_00037352660.wav', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 16000}, 'sentence': 'Panonton ting haruleng ningali Kelly Clarkson keur nyanyi di tipi', } ``` ### Data Fields - `path`: The path to the audio file. - `audio`: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`. - `sentence`: The sentence the user was prompted to speak. ### Data Splits There is only one "train" split for all configurations and the number of examples are: | | Number of examples | |:------|---------------------:| | SLR41 | 5822 | | SLR42 | 2906 | | SLR43 | 2064 | | SLR44 | 4213 | | SLR63 | 4126 | | SLR64 | 1569 | | SLR65 | 4284 | | SLR66 | 4448 | | SLR69 | 4240 | | SLR35 | 185076 | | SLR36 | 219156 | | SLR70 | 3359 | | SLR71 | 4374 | | SLR72 | 4903 | | SLR73 | 5447 | | SLR74 | 617 | | SLR75 | 3357 | | SLR76 | 7136 | | SLR77 | 5587 | | SLR78 | 4272 | | SLR79 | 4400 | | SLR80 | 2530 | | SLR86 | 3583 | | SLR32 | 9821 | | SLR52 | 185293 | | SLR53 | 218703 | | SLR54 | 157905 | | SLR83 | 17877 | ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset. ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Each dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License ([CC-BY-SA-4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode)). See https://github.com/google/language-resources#license or the resource page on [OpenSLR](https://openslr.org/resources.php) for more information. ### Citation Information #### SLR32 ``` @inproceedings{van-niekerk-etal-2017, title = {{Rapid development of TTS corpora for four South African languages}}, author = {Daniel van Niekerk and Charl van Heerden and Marelie Davel and Neil Kleynhans and Oddur Kjartansson and Martin Jansche and Linne Ha}, booktitle = {Proc. Interspeech 2017}, pages = {2178--2182}, address = {Stockholm, Sweden}, month = aug, year = {2017}, URL = {https://dx.doi.org/10.21437/Interspeech.2017-1139} } ``` #### SLR35, SLR36, SLR52, SLR53, SLR54 ``` @inproceedings{kjartansson-etal-sltu2018, title = {{Crowd-Sourced Speech Corpora for Javanese, Sundanese, Sinhala, Nepali, and Bangladeshi Bengali}}, author = {Oddur Kjartansson and Supheakmungkol Sarin and Knot Pipatsrisawat and Martin Jansche and Linne Ha}, booktitle = {Proc. The 6th Intl. Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU)}, year = {2018}, address = {Gurugram, India}, month = aug, pages = {52--55}, URL = {https://dx.doi.org/10.21437/SLTU.2018-11}, } ``` #### SLR41, SLR42, SLR43, SLR44 ``` @inproceedings{kjartansson-etal-tts-sltu2018, title = {{A Step-by-Step Process for Building TTS Voices Using Open Source Data and Framework for Bangla, Javanese, Khmer, Nepali, Sinhala, and Sundanese}}, author = {Keshan Sodimana and Knot Pipatsrisawat and Linne Ha and Martin Jansche and Oddur Kjartansson and Pasindu De Silva and Supheakmungkol Sarin}, booktitle = {Proc. The 6th Intl. Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU)}, year = {2018}, address = {Gurugram, India}, month = aug, pages = {66--70}, URL = {https://dx.doi.org/10.21437/SLTU.2018-14} } ``` #### SLR63, SLR64, SLR65, SLR66, SLR78, SLR79 ``` @inproceedings{he-etal-2020-open, title = {{Open-source Multi-speaker Speech Corpora for Building Gujarati, Kannada, Malayalam, Marathi, Tamil and Telugu Speech Synthesis Systems}}, author = {He, Fei and Chu, Shan-Hui Cathy and Kjartansson, Oddur and Rivera, Clara and Katanova, Anna and Gutkin, Alexander and Demirsahin, Isin and Johny, Cibu and Jansche, Martin and Sarin, Supheakmungkol and Pipatsrisawat, Knot}, booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference (LREC)}, month = may, year = {2020}, address = {Marseille, France}, publisher = {European Language Resources Association (ELRA)}, pages = {6494--6503}, url = {https://www.aclweb.org/anthology/2020.lrec-1.800}, ISBN = "{979-10-95546-34-4}, } ``` #### SLR69, SLR76, SLR77 ``` @inproceedings{kjartansson-etal-2020-open, title = {{Open-Source High Quality Speech Datasets for Basque, Catalan and Galician}}, author = {Kjartansson, Oddur and Gutkin, Alexander and Butryna, Alena and Demirsahin, Isin and Rivera, Clara}, booktitle = {Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)}, year = {2020}, pages = {21--27}, month = may, address = {Marseille, France}, publisher = {European Language Resources association (ELRA)}, url = {https://www.aclweb.org/anthology/2020.sltu-1.3}, ISBN = {979-10-95546-35-1}, } ``` #### SLR70, SLR71, SLR72, SLR73, SLR74, SLR75 ``` @inproceedings{guevara-rukoz-etal-2020-crowdsourcing, title = {{Crowdsourcing Latin American Spanish for Low-Resource Text-to-Speech}}, author = {Guevara-Rukoz, Adriana and Demirsahin, Isin and He, Fei and Chu, Shan-Hui Cathy and Sarin, Supheakmungkol and Pipatsrisawat, Knot and Gutkin, Alexander and Butryna, Alena and Kjartansson, Oddur}, booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference (LREC)}, year = {2020}, month = may, address = {Marseille, France}, publisher = {European Language Resources Association (ELRA)}, url = {https://www.aclweb.org/anthology/2020.lrec-1.801}, pages = {6504--6513}, ISBN = {979-10-95546-34-4}, } ``` #### SLR80 ``` @inproceedings{oo-etal-2020-burmese, title = {{Burmese Speech Corpus, Finite-State Text Normalization and Pronunciation Grammars with an Application to Text-to-Speech}}, author = {Oo, Yin May and Wattanavekin, Theeraphol and Li, Chenfang and De Silva, Pasindu and Sarin, Supheakmungkol and Pipatsrisawat, Knot and Jansche, Martin and Kjartansson, Oddur and Gutkin, Alexander}, booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference (LREC)}, month = may, year = {2020}, pages = "6328--6339", address = {Marseille, France}, publisher = {European Language Resources Association (ELRA)}, url = {https://www.aclweb.org/anthology/2020.lrec-1.777}, ISBN = {979-10-95546-34-4}, } ``` #### SLR86 ``` @inproceedings{gutkin-et-al-yoruba2020, title = {{Developing an Open-Source Corpus of Yoruba Speech}}, author = {Alexander Gutkin and I{\c{s}}{\i}n Demir{\c{s}}ahin and Oddur Kjartansson and Clara Rivera and K\d{\'o}lá Túb\d{\`o}sún}, booktitle = {Proceedings of Interspeech 2020}, pages = {404--408}, month = {October}, year = {2020}, address = {Shanghai, China}, publisher = {International Speech and Communication Association (ISCA)}, doi = {10.21437/Interspeech.2020-1096}, url = {https://dx.doi.org/10.21437/Interspeech.2020-1096}, } ``` ### Contributions Thanks to [@cahya-wirawan](https://github.com/cahya-wirawan) for adding this dataset.

openslr

2022-03-02T23:29:22+00:00

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2024-01-18T11:11:19+00:00

TAGS #task_categories-automatic-speech-recognition #annotations_creators-found #language_creators-found #multilinguality-multilingual #size_categories-1K<n<10K #source_datasets-original #language-Afrikaans #language-Bengali #language-Catalan #language-English #language-Spanish #language-Basque #language-Galician #language-Gujarati #language-Javanese #language-Khmer #language-Kannada #language-Malayalam #language-Marathi #language-Burmese #language-Nepali (macrolanguage) #language-Sinhala #language-Southern Sotho #language-Sundanese #language-Tamil #language-Telugu #language-Tswana #language-Venda #language-Xhosa #language-Yoruba #license-cc-by-sa-4.0 #region-us

Dataset Card for openslr ======================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: * Leaderboard: * Point of Contact: ### Dataset Summary OpenSLR is a site devoted to hosting speech and language resources, such as training corpora for speech recognition, and software related to speech recognition. Currently, following resources are available: #### SLR32: High quality TTS data for four South African languages (af, st, tn, xh). This data set contains multi-speaker high quality transcribed audio data for four languages of South Africa. The data set consists of wave files, and a TSV file transcribing the audio. In each folder, the file line\_index.tsv contains a FileID, which in turn contains the UserID and the Transcription of audio in the file. The data set has had some quality checks, but there might still be errors. This data set was collected by as a collaboration between North West University and Google. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See URL for license information. Copyright 2017 Google, Inc. #### SLR35: Large Javanese ASR training data set. This data set contains transcribed audio data for Javanese (~185K utterances). The data set consists of wave files, and a TSV file. The file utt\_spk\_text.tsv contains a FileID, UserID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. This dataset was collected by Google in collaboration with Reykjavik University and Universitas Gadjah Mada in Indonesia. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See LICENSE file and URL for license information. Copyright 2016, 2017 Google, Inc. #### SLR36: Large Sundanese ASR training data set. This data set contains transcribed audio data for Sundanese (~220K utterances). The data set consists of wave files, and a TSV file. The file utt\_spk\_text.tsv contains a FileID, UserID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. This dataset was collected by Google in Indonesia. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See LICENSE file and URL for license information. Copyright 2016, 2017 Google, Inc. #### SLR41: High quality TTS data for Javanese. This data set contains high-quality transcribed audio data for Javanese. The data set consists of wave files, and a TSV file. The file line\_index.tsv contains a filename and the transcription of audio in the file. Each filename is prepended with a speaker identification number. The data set has been manually quality checked, but there might still be errors. This dataset was collected by Google in collaboration with Gadjah Mada University in Indonesia. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See LICENSE file and URL for license information. Copyright 2016, 2017, 2018 Google LLC #### SLR42: High quality TTS data for Khmer. This data set contains high-quality transcribed audio data for Khmer. The data set consists of wave files, and a TSV file. The file line\_index.tsv contains a filename and the transcription of audio in the file. Each filename is prepended with a speaker identification number. The data set has been manually quality checked, but there might still be errors. This dataset was collected by Google. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See LICENSE file and URL for license information. Copyright 2016, 2017, 2018 Google LLC #### SLR43: High quality TTS data for Nepali. This data set contains high-quality transcribed audio data for Nepali. The data set consists of wave files, and a TSV file. The file line\_index.tsv contains a filename and the transcription of audio in the file. Each filename is prepended with a speaker identification number. The data set has been manually quality checked, but there might still be errors. This dataset was collected by Google in Nepal. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See LICENSE file and URL for license information. Copyright 2016, 2017, 2018 Google LLC #### SLR44: High quality TTS data for Sundanese. This data set contains high-quality transcribed audio data for Sundanese. The data set consists of wave files, and a TSV file. The file line\_index.tsv contains a filename and the transcription of audio in the file. Each filename is prepended with a speaker identification number. The data set has been manually quality checked, but there might still be errors. This dataset was collected by Google in collaboration with Universitas Pendidikan Indonesia. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See LICENSE file and URL for license information. Copyright 2016, 2017, 2018 Google LLC #### SLR52: Large Sinhala ASR training data set. This data set contains transcribed audio data for Sinhala (~185K utterances). The data set consists of wave files, and a TSV file. The file utt\_spk\_text.tsv contains a FileID, UserID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See LICENSE file and URL for license information. Copyright 2016, 2017, 2018 Google, Inc. #### SLR53: Large Bengali ASR training data set. This data set contains transcribed audio data for Bengali (~196K utterances). The data set consists of wave files, and a TSV file. The file utt\_spk\_text.tsv contains a FileID, UserID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See LICENSE file and URL for license information. Copyright 2016, 2017, 2018 Google, Inc. #### SLR54: Large Nepali ASR training data set. This data set contains transcribed audio data for Nepali (~157K utterances). The data set consists of wave files, and a TSV file. The file utt\_spk\_text.tsv contains a FileID, UserID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See LICENSE file and URL for license information. Copyright 2016, 2017, 2018 Google, Inc. #### SLR63: Crowdsourced high-quality Malayalam multi-speaker speech data set This data set contains transcribed high-quality audio of Malayalam sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line\_index.tsv). The file line\_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. URL The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See LICENSE file and URL for license information. Copyright 2018, 2019 Google, Inc. #### SLR64: Crowdsourced high-quality Marathi multi-speaker speech data set This data set contains transcribed high-quality audio of Marathi sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line\_index.tsv). The file line\_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. URL The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See LICENSE file and URL for license information. Copyright 2018, 2019 Google, Inc. #### SLR65: Crowdsourced high-quality Tamil multi-speaker speech data set This data set contains transcribed high-quality audio of Tamil sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line\_index.tsv). The file line\_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. URL The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See LICENSE file and URL for license information. Copyright 2018, 2019 Google, Inc. #### SLR66: Crowdsourced high-quality Telugu multi-speaker speech data set This data set contains transcribed high-quality audio of Telugu sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line\_index.tsv). The file line\_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. URL The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See LICENSE file and URL for license information. Copyright 2018, 2019 Google, Inc. #### SLR69: Crowdsourced high-quality Catalan multi-speaker speech data set This data set contains transcribed high-quality audio of Catalan sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line\_index.tsv). The file line\_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. URL The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See LICENSE file and URL for license information. Copyright 2018, 2019 Google, Inc. #### SLR70: Crowdsourced high-quality Nigerian English speech data set This data set contains transcribed high-quality audio of Nigerian English sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line\_index.tsv). The file line\_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. URL The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See LICENSE file and URL for license information. Copyright 2018, 2019 Google, Inc. #### SLR71: Crowdsourced high-quality Chilean Spanish speech data set This data set contains transcribed high-quality audio of Chilean Spanish sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line\_index.tsv). The file line\_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. URL The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See LICENSE file and URL for license information. Copyright 2018, 2019 Google, Inc. #### SLR72: Crowdsourced high-quality Colombian Spanish speech data set This data set contains transcribed high-quality audio of Colombian Spanish sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line\_index.tsv). The file line\_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. URL The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See LICENSE file and URL for license information. Copyright 2018, 2019 Google, Inc. #### SLR73: Crowdsourced high-quality Peruvian Spanish speech data set This data set contains transcribed high-quality audio of Peruvian Spanish sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line\_index.tsv). The file line\_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. URL The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See LICENSE file and URL for license information. Copyright 2018, 2019 Google, Inc. #### SLR74: Crowdsourced high-quality Puerto Rico Spanish speech data set This data set contains transcribed high-quality audio of Puerto Rico Spanish sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line\_index.tsv). The file line\_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. URL The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See LICENSE file and URL for license information. Copyright 2018, 2019 Google, Inc. #### SLR75: Crowdsourced high-quality Venezuelan Spanish speech data set This data set contains transcribed high-quality audio of Venezuelan Spanish sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line\_index.tsv). The file line\_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. URL The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See LICENSE file and URL for license information. Copyright 2018, 2019 Google, Inc. #### SLR76: Crowdsourced high-quality Basque speech data set This data set contains transcribed high-quality audio of Basque sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line\_index.tsv). The file line\_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. URL The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See LICENSE file and URL for license information. Copyright 2018, 2019 Google, Inc. #### SLR77: Crowdsourced high-quality Galician speech data set This data set contains transcribed high-quality audio of Galician sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line\_index.tsv). The file line\_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. URL The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See LICENSE file and URL for license information. Copyright 2018, 2019 Google, Inc. #### SLR78: Crowdsourced high-quality Gujarati multi-speaker speech data set This data set contains transcribed high-quality audio of Gujarati sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line\_index.tsv). The file line\_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. URL The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See LICENSE file and URL for license information. Copyright 2018, 2019 Google, Inc. #### SLR79: Crowdsourced high-quality Kannada multi-speaker speech data set This data set contains transcribed high-quality audio of Kannada sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line\_index.tsv). The file line\_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. URL The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See LICENSE file and URL for license information. Copyright 2018, 2019 Google, Inc. #### SLR80: Crowdsourced high-quality Burmese speech data set This data set contains transcribed high-quality audio of Burmese sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line\_index.tsv). The file line\_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. URL The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See LICENSE file and URL for license information. Copyright 2018, 2019 Google, Inc. #### SLR83: Crowdsourced high-quality UK and Ireland English Dialect speech data set This data set contains transcribed high-quality audio of English sentences recorded by volunteers speaking different dialects of the language. The data set consists of wave files, and a TSV file (line\_index.tsv). The file line\_index.csv contains a line id, an anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. The recordings from the Welsh English speakers were collected in collaboration with Cardiff University. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See LICENSE file and URL for license information. Copyright 2018, 2019 Google, Inc. #### SLR86: Crowdsourced high-quality multi-speaker speech data set This data set contains transcribed high-quality audio of sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line\_index.tsv). The file line\_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. URL The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See LICENSE file and URL for license information. Copyright 2018, 2019, 2020 Google, Inc. ### Supported Tasks and Leaderboards ### Languages Javanese, Khmer, Nepali, Sundanese, Malayalam, Marathi, Tamil, Telugu, Catalan, Nigerian English, Chilean Spanish, Columbian Spanish, Peruvian Spanish, Puerto Rico Spanish, Venezuelan Spanish, Basque, Galician, Gujarati, Kannada, Afrikaans, Sesotho, Setswana and isiXhosa. Dataset Structure ----------------- ### Data Instances A typical data point comprises the path to the audio file, called path and its sentence. #### SLR32, SLR35, SLR36, SLR41, SLR42, SLR43, SLR44, SLR52, SLR53, SLR54, SLR63, SLR64, SLR65, SLR66, SLR69, SLR70, SLR71, SLR72, SLR73, SLR74, SLR75, SLR76, SLR77, SLR78, SLR79, SLR80, SLR86 ### Data Fields * 'path': The path to the audio file. * 'audio': A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0]["audio"]' the audio file is automatically decoded and resampled to 'dataset.features["audio"].sampling\_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '"audio"' column, *i.e.* 'dataset[0]["audio"]' should always be preferred over 'dataset["audio"][0]'. * 'sentence': The sentence the user was prompted to speak. ### Data Splits There is only one "train" split for all configurations and the number of examples are: Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset. Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information Each dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License (CC-BY-SA-4.0). See URL or the resource page on OpenSLR for more information. #### SLR32 #### SLR35, SLR36, SLR52, SLR53, SLR54 #### SLR41, SLR42, SLR43, SLR44 #### SLR63, SLR64, SLR65, SLR66, SLR78, SLR79 #### SLR69, SLR76, SLR77 #### SLR70, SLR71, SLR72, SLR73, SLR74, SLR75 #### SLR80 #### SLR86 ### Contributions Thanks to @cahya-wirawan for adding this dataset.

f2cb45cb946a0126a94a7b141b3376ceb527518f

# Dataset Card for "openwebtext" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://skylion007.github.io/OpenWebTextCorpus/](https://skylion007.github.io/OpenWebTextCorpus/) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 13.51 GB - **Size of the generated dataset:** 41.70 GB - **Total amount of disk used:** 55.21 GB ### Dataset Summary An open-source replication of the WebText dataset from OpenAI, that was used to train GPT-2. This distribution was created by Aaron Gokaslan and Vanya Cohen of Brown University. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### plain_text - **Size of downloaded dataset files:** 13.51 GB - **Size of the generated dataset:** 41.70 GB - **Total amount of disk used:** 55.21 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "text": "\"A magazine supplement with an image of Adolf Hitler and the title 'The Unreadable Book' is pictured in Berlin. No law bans “Mei..." } ``` ### Data Fields The data fields are the same among all splits. #### plain_text - `text`: a `string` feature. ### Data Splits | name | train | |------------|--------:| | plain_text | 8013769 | ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization The authors started by extracting all Reddit post urls from the Reddit submissions dataset. These links were deduplicated, filtered to exclude non-html content, and then shuffled randomly. The links were then distributed to several machines in parallel for download, and all web pages were extracted using the newspaper python package. Using Facebook FastText, non-English web pages were filtered out. Subsequently, near-duplicate documents were identified using local-sensitivity hashing (LSH). Documents were hashed into sets of 5-grams and all documents that had a similarity threshold of greater than 0.5 were removed. The the remaining documents were tokenized, and documents with fewer than 128 tokens were removed. This left 38GB of text data (40GB using SI units) from 8,013,769 documents. #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations The dataset doesn't contain annotations. ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information These data are released under this licensing scheme from the original authors ([source](https://skylion007.github.io/OpenWebTextCorpus/)): ``` We do not own any of the text from which these data has been extracted. We license the actual packaging of these parallel data under the [Creative Commons CC0 license (“no rights reserved”)](https://creativecommons.org/share-your-work/public-domain/cc0/) ``` #### Notice policy Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please: Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted. Clearly identify the copyrighted work claimed to be infringed. Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material. And contact us at the following email address: openwebtext at gmail.com and datasets at huggingface.co #### Take down policy The original authors will comply to legitimate requests by removing the affected sources from the next release of the corpus. Hugging Face will also update this repository accordingly. ### Citation Information ``` @misc{Gokaslan2019OpenWeb, title={OpenWebText Corpus}, author={Aaron Gokaslan*, Vanya Cohen*, Ellie Pavlick, Stefanie Tellex}, howpublished{\url{http://Skylion007.github.io/OpenWebTextCorpus}}, year={2019} } ``` ### Contributions Thanks to [@richarddwang](https://github.com/richarddwang) for adding this dataset.

Skylion007/openwebtext

2022-03-02T23:29:22+00:00

{"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["en"], "license": ["cc0-1.0"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"], "paperswithcode_id": "openwebtext", "pretty_name": "OpenWebText", "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "config_name": "plain_text", "splits": [{"name": "train", "num_bytes": 39769491688, "num_examples": 8013769}], "download_size": 12880189440, "dataset_size": 39769491688}}

2023-04-05T12:36:17+00:00

TAGS #task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-English #license-cc0-1.0 #region-us

Dataset Card for "openwebtext" ============================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: * Point of Contact: * Size of downloaded dataset files: 13.51 GB * Size of the generated dataset: 41.70 GB * Total amount of disk used: 55.21 GB ### Dataset Summary An open-source replication of the WebText dataset from OpenAI, that was used to train GPT-2. This distribution was created by Aaron Gokaslan and Vanya Cohen of Brown University. ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### plain\_text * Size of downloaded dataset files: 13.51 GB * Size of the generated dataset: 41.70 GB * Total amount of disk used: 55.21 GB An example of 'train' looks as follows. ### Data Fields The data fields are the same among all splits. #### plain\_text * 'text': a 'string' feature. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization The authors started by extracting all Reddit post urls from the Reddit submissions dataset. These links were deduplicated, filtered to exclude non-html content, and then shuffled randomly. The links were then distributed to several machines in parallel for download, and all web pages were extracted using the newspaper python package. Using Facebook FastText, non-English web pages were filtered out. Subsequently, near-duplicate documents were identified using local-sensitivity hashing (LSH). Documents were hashed into sets of 5-grams and all documents that had a similarity threshold of greater than 0.5 were removed. The the remaining documents were tokenized, and documents with fewer than 128 tokens were removed. This left 38GB of text data (40GB using SI units) from 8,013,769 documents. #### Who are the source language producers? ### Annotations The dataset doesn't contain annotations. ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information These data are released under this licensing scheme from the original authors (source): #### Notice policy Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please: Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted. Clearly identify the copyrighted work claimed to be infringed. Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material. And contact us at the following email address: openwebtext at URL and datasets at URL #### Take down policy The original authors will comply to legitimate requests by removing the affected sources from the next release of the corpus. Hugging Face will also update this repository accordingly. ### Contributions Thanks to @richarddwang for adding this dataset.

9cf0ab7e644a8f2664be1d79757427b8489e6f6f

# Dataset Card for "opinosis" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://kavita-ganesan.com/opinosis-opinion-dataset/ - **Repository:** https://github.com/kavgan/opinosis-summarization - **Paper:** [Opinosis: A Graph Based Approach to Abstractive Summarization of Highly Redundant Opinions](https://aclanthology.org/C10-1039/) - **Point of Contact:** [Kavita Ganesan](mailto:kavita@opinosis.ai) - **Size of downloaded dataset files:** 0.75 MB - **Size of the generated dataset:** 0.74 MB - **Total amount of disk used:** 1.50 MB ### Dataset Summary The Opinosis Opinion Dataset consists of sentences extracted from reviews for 51 topics. Topics and opinions are obtained from Tripadvisor, Edmunds.com and Amazon.com. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 0.75 MB - **Size of the generated dataset:** 0.74 MB - **Total amount of disk used:** 1.50 MB An example of 'train' looks as follows. ``` { "review_sents": "This is a fake topic. \nThe topics have multiple sentence inputs. \n", "summaries": ["This is a gold summary for topic 1. \nSentences in gold summaries are separated by newlines.", "This is another gold summary for topic 1. \nSentences in gold summaries are separated by newlines."] } ``` ### Data Fields The data fields are the same among all splits. #### default - `review_sents`: a `string` feature. - `summaries`: a `list` of `string` features. ### Data Splits | name |train| |-------|----:| |default| 51| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information The license for this dataset is Apache License 2.0 and can be found [here](https://github.com/kavgan/opinosis-summarization/blob/master/LICENSE). ### Citation Information ``` @inproceedings{ganesan2010opinosis, title={Opinosis: a graph-based approach to abstractive summarization of highly redundant opinions}, author={Ganesan, Kavita and Zhai, ChengXiang and Han, Jiawei}, booktitle={Proceedings of the 23rd International Conference on Computational Linguistics}, pages={340--348}, year={2010}, organization={Association for Computational Linguistics} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

opinosis

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "source_datasets": ["original"], "task_categories": ["summarization"], "task_ids": [], "paperswithcode_id": "opinosis", "pretty_name": "Opinosis", "tags": ["abstractive-summarization"], "dataset_info": {"features": [{"name": "review_sents", "dtype": "string"}, {"name": "summaries", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 741270, "num_examples": 51}], "download_size": 757398, "dataset_size": 741270}}

2024-01-18T11:11:20+00:00

TAGS #task_categories-summarization #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-apache-2.0 #abstractive-summarization #region-us

Dataset Card for "opinosis" =========================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: URL * Paper: Opinosis: A Graph Based Approach to Abstractive Summarization of Highly Redundant Opinions * Point of Contact: Kavita Ganesan * Size of downloaded dataset files: 0.75 MB * Size of the generated dataset: 0.74 MB * Total amount of disk used: 1.50 MB ### Dataset Summary The Opinosis Opinion Dataset consists of sentences extracted from reviews for 51 topics. Topics and opinions are obtained from Tripadvisor, URL and URL. ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### default * Size of downloaded dataset files: 0.75 MB * Size of the generated dataset: 0.74 MB * Total amount of disk used: 1.50 MB An example of 'train' looks as follows. ### Data Fields The data fields are the same among all splits. #### default * 'review\_sents': a 'string' feature. * 'summaries': a 'list' of 'string' features. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information The license for this dataset is Apache License 2.0 and can be found here. ### Contributions Thanks to @thomwolf, @patrickvonplaten for adding this dataset.

95696bf5ec259450db818a5a5b2de705f38d1b91

# Dataset Card for Opus100 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Link](http://opus.nlpl.eu/opus-100.php) - **Repository:** [GitHub](https://github.com/EdinburghNLP/opus-100-corpus) - **Paper:** [ARXIV](https://arxiv.org/abs/2004.11867) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary OPUS-100 is English-centric, meaning that all training pairs include English on either the source or target side. The corpus covers 100 languages (including English). Selected the languages based on the volume of parallel data available in OPUS. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages OPUS-100 contains approximately 55M sentence pairs. Of the 99 language pairs, 44 have 1M sentence pairs of training data, 73 have at least 100k, and 95 have at least 10k. ## Dataset Structure ### Data Instances ``` { "ca": "El departament de bombers té el seu propi equip d'investigació.", "en": "Well, the fire department has its own investigative unit." } ``` ### Data Fields - `src_tag`: `string` text in source language - `tgt_tag`: `string` translation of source language in target language ### Data Splits The dataset is split into training, development, and test portions. Data was prepared by randomly sampled up to 1M sentence pairs per language pair for training and up to 2000 each for development and test. To ensure that there was no overlap (at the monolingual sentence level) between the training and development/test data, they applied a filter during sampling to exclude sentences that had already been sampled. Note that this was done cross-lingually so that, for instance, an English sentence in the Portuguese-English portion of the training data could not occur in the Hindi-English test set. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @misc{zhang2020improving, title={Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation}, author={Biao Zhang and Philip Williams and Ivan Titov and Rico Sennrich}, year={2020}, eprint={2004.11867}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@vasudevgupta7](https://github.com/vasudevgupta7) for adding this dataset.

opus100

2022-03-02T23:29:22+00:00

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"am-en", "an-en", "ar-de", "ar-en", "ar-fr", "ar-nl", "ar-ru", "ar-zh", "as-en", "az-en", "be-en", "bg-en", "bn-en", "br-en", "bs-en", "ca-en", "cs-en", "cy-en", "da-en", "de-en", "de-fr", "de-nl", "de-ru", "de-zh", "dz-en", "el-en", "en-eo", "en-es", "en-et", "en-eu", "en-fa", "en-fi", "en-fr", "en-fy", "en-ga", "en-gd", "en-gl", "en-gu", "en-ha", "en-he", "en-hi", "en-hr", "en-hu", "en-hy", "en-id", "en-ig", "en-is", "en-it", "en-ja", "en-ka", "en-kk", "en-km", "en-kn", "en-ko", "en-ku", "en-ky", "en-li", "en-lt", "en-lv", "en-mg", "en-mk", "en-ml", "en-mn", "en-mr", "en-ms", "en-mt", "en-my", "en-nb", "en-ne", "en-nl", "en-nn", "en-no", "en-oc", "en-or", "en-pa", "en-pl", "en-ps", "en-pt", "en-ro", "en-ru", "en-rw", "en-se", "en-sh", "en-si", "en-sk", "en-sl", "en-sq", "en-sr", "en-sv", "en-ta", "en-te", "en-tg", "en-th", "en-tk", "en-tr", "en-tt", "en-ug", "en-uk", "en-ur", "en-uz", "en-vi", "en-wa", "en-xh", "en-yi", "en-yo", "en-zh", "en-zu", "fr-nl", "fr-ru", "fr-zh", "nl-ru", 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2024-02-14T10:15:10+00:00

TAGS #task_categories-translation #annotations_creators-no-annotation #language_creators-found #multilinguality-translation #size_categories-100K<n<1M #size_categories-10K<n<100K #size_categories-1K<n<10K #size_categories-1M<n<10M #size_categories-n<1K #source_datasets-extended #language-Afrikaans #language-Amharic #language-Aragonese #language-Arabic #language-Assamese #language-Azerbaijani #language-Belarusian #language-Bulgarian #language-Bengali #language-Breton #language-Bosnian #language-Catalan #language-Czech #language-Welsh #language-Danish #language-German #language-Dzongkha #language-Modern Greek (1453-) #language-English #language-Esperanto #language-Spanish #language-Estonian #language-Basque #language-Persian #language-Finnish #language-French #language-Western Frisian #language-Irish #language-Scottish Gaelic #language-Galician #language-Gujarati #language-Hausa #language-Hebrew #language-Hindi #language-Croatian #language-Hungarian #language-Armenian #language-Indonesian #language-Igbo #language-Icelandic #language-Italian #language-Japanese #language-Georgian #language-Kazakh #language-Khmer #language-Kannada #language-Korean #language-Kurdish #language-Kirghiz #language-Limburgan #language-Lithuanian #language-Latvian #language-Malagasy #language-Macedonian #language-Malayalam #language-Mongolian #language-Marathi #language-Malay (macrolanguage) #language-Maltese #language-Burmese #language-Norwegian Bokmål #language-Nepali (macrolanguage) #language-Dutch #language-Norwegian Nynorsk #language-Norwegian #language-Occitan (post 1500) #language-Oriya (macrolanguage) #language-Panjabi #language-Polish #language-Pushto #language-Portuguese #language-Romanian #language-Russian #language-Kinyarwanda #language-Northern Sami #language-Serbo-Croatian #language-Sinhala #language-Slovak #language-Slovenian #language-Albanian #language-Serbian #language-Swedish #language-Tamil #language-Telugu #language-Tajik #language-Thai #language-Turkmen #language-Turkish #language-Tatar #language-Uighur #language-Ukrainian #language-Urdu #language-Uzbek #language-Vietnamese #language-Walloon #language-Xhosa #language-Yiddish #language-Yoruba #language-Chinese #language-Zulu #license-unknown #arxiv-2004.11867 #region-us

# Dataset Card for Opus100 ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: Link - Repository: GitHub - Paper: ARXIV - Leaderboard: - Point of Contact: ### Dataset Summary OPUS-100 is English-centric, meaning that all training pairs include English on either the source or target side. The corpus covers 100 languages (including English). Selected the languages based on the volume of parallel data available in OPUS. ### Supported Tasks and Leaderboards ### Languages OPUS-100 contains approximately 55M sentence pairs. Of the 99 language pairs, 44 have 1M sentence pairs of training data, 73 have at least 100k, and 95 have at least 10k. ## Dataset Structure ### Data Instances ### Data Fields - 'src_tag': 'string' text in source language - 'tgt_tag': 'string' translation of source language in target language ### Data Splits The dataset is split into training, development, and test portions. Data was prepared by randomly sampled up to 1M sentence pairs per language pair for training and up to 2000 each for development and test. To ensure that there was no overlap (at the monolingual sentence level) between the training and development/test data, they applied a filter during sampling to exclude sentences that had already been sampled. Note that this was done cross-lingually so that, for instance, an English sentence in the Portuguese-English portion of the training data could not occur in the Hindi-English test set. ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @vasudevgupta7 for adding this dataset.

23c020fc7575df3f1516087b457f182e3624e769

# Dataset Card for OpusBooks ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://opus.nlpl.eu/Books.php - **Repository:** None - **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf - **Leaderboard:** [More Information Needed] - **Point of Contact:** [More Information Needed] ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances Here are some examples of questions and facts: ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.

opus_books

2022-03-02T23:29:22+00:00

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"path": "ca-en/train-*"}]}, {"config_name": "ca-hu", "data_files": [{"split": "train", "path": "ca-hu/train-*"}]}, {"config_name": "ca-nl", "data_files": [{"split": "train", "path": "ca-nl/train-*"}]}, {"config_name": "de-en", "data_files": [{"split": "train", "path": "de-en/train-*"}]}, {"config_name": "de-eo", "data_files": [{"split": "train", "path": "de-eo/train-*"}]}, {"config_name": "de-es", "data_files": [{"split": "train", "path": "de-es/train-*"}]}, {"config_name": "de-fr", "data_files": [{"split": "train", "path": "de-fr/train-*"}]}, {"config_name": "de-hu", "data_files": [{"split": "train", "path": "de-hu/train-*"}]}, {"config_name": "de-it", "data_files": [{"split": "train", "path": "de-it/train-*"}]}, {"config_name": "de-nl", "data_files": [{"split": "train", "path": "de-nl/train-*"}]}, {"config_name": "de-pt", "data_files": [{"split": "train", "path": "de-pt/train-*"}]}, {"config_name": "de-ru", "data_files": [{"split": "train", "path": "de-ru/train-*"}]}, {"config_name": "el-en", "data_files": [{"split": "train", "path": "el-en/train-*"}]}, {"config_name": "el-es", "data_files": [{"split": "train", "path": "el-es/train-*"}]}, {"config_name": "el-fr", "data_files": [{"split": "train", "path": "el-fr/train-*"}]}, {"config_name": "el-hu", "data_files": [{"split": "train", "path": "el-hu/train-*"}]}, {"config_name": "en-eo", "data_files": [{"split": "train", "path": "en-eo/train-*"}]}, {"config_name": "en-es", "data_files": [{"split": "train", "path": "en-es/train-*"}]}, {"config_name": "en-fi", "data_files": [{"split": "train", "path": "en-fi/train-*"}]}, {"config_name": "en-fr", "data_files": [{"split": "train", "path": "en-fr/train-*"}]}, {"config_name": "en-hu", "data_files": [{"split": "train", "path": "en-hu/train-*"}]}, {"config_name": "en-it", "data_files": [{"split": "train", "path": "en-it/train-*"}]}, {"config_name": "en-nl", "data_files": [{"split": "train", "path": "en-nl/train-*"}]}, {"config_name": "en-no", "data_files": [{"split": "train", "path": "en-no/train-*"}]}, {"config_name": "en-pl", "data_files": [{"split": "train", "path": "en-pl/train-*"}]}, {"config_name": "en-pt", "data_files": [{"split": "train", "path": "en-pt/train-*"}]}, {"config_name": "en-ru", "data_files": [{"split": "train", "path": "en-ru/train-*"}]}, {"config_name": "en-sv", "data_files": [{"split": "train", "path": "en-sv/train-*"}]}, {"config_name": "eo-es", "data_files": [{"split": "train", "path": "eo-es/train-*"}]}, {"config_name": "eo-fr", "data_files": [{"split": "train", "path": "eo-fr/train-*"}]}, {"config_name": "eo-hu", "data_files": [{"split": "train", "path": "eo-hu/train-*"}]}, {"config_name": "eo-it", "data_files": [{"split": "train", "path": "eo-it/train-*"}]}, {"config_name": "eo-pt", "data_files": [{"split": "train", "path": "eo-pt/train-*"}]}, {"config_name": "es-fi", "data_files": [{"split": "train", "path": "es-fi/train-*"}]}, {"config_name": "es-fr", "data_files": [{"split": "train", "path": "es-fr/train-*"}]}, {"config_name": "es-it", "data_files": [{"split": "train", "path": "es-it/train-*"}]}, {"config_name": "es-nl", "data_files": [{"split": "train", "path": "es-nl/train-*"}]}, {"config_name": "es-no", "data_files": [{"split": "train", "path": "es-no/train-*"}]}, {"config_name": "es-pt", "data_files": [{"split": "train", "path": "es-pt/train-*"}]}, {"config_name": "es-ru", "data_files": [{"split": "train", "path": "es-ru/train-*"}]}, {"config_name": "fi-fr", "data_files": [{"split": "train", "path": "fi-fr/train-*"}]}, {"config_name": "fi-no", "data_files": [{"split": "train", "path": "fi-no/train-*"}]}, {"config_name": "fi-pl", "data_files": [{"split": "train", "path": "fi-pl/train-*"}]}, {"config_name": "fr-hu", "data_files": [{"split": "train", "path": "fr-hu/train-*"}]}, {"config_name": "fr-it", "data_files": [{"split": "train", "path": "fr-it/train-*"}]}, {"config_name": "fr-nl", "data_files": [{"split": "train", "path": "fr-nl/train-*"}]}, {"config_name": "fr-no", "data_files": [{"split": "train", "path": "fr-no/train-*"}]}, {"config_name": "fr-pl", "data_files": [{"split": "train", "path": "fr-pl/train-*"}]}, {"config_name": "fr-pt", "data_files": [{"split": "train", "path": "fr-pt/train-*"}]}, {"config_name": "fr-ru", "data_files": [{"split": "train", "path": "fr-ru/train-*"}]}, {"config_name": "fr-sv", "data_files": [{"split": "train", "path": "fr-sv/train-*"}]}, {"config_name": "hu-it", "data_files": [{"split": "train", "path": "hu-it/train-*"}]}, {"config_name": "hu-nl", "data_files": [{"split": "train", "path": "hu-nl/train-*"}]}, {"config_name": "hu-no", "data_files": [{"split": "train", "path": "hu-no/train-*"}]}, {"config_name": "hu-pl", "data_files": [{"split": "train", "path": "hu-pl/train-*"}]}, {"config_name": "hu-pt", "data_files": [{"split": "train", "path": "hu-pt/train-*"}]}, {"config_name": "hu-ru", "data_files": [{"split": "train", "path": "hu-ru/train-*"}]}, {"config_name": "it-nl", "data_files": [{"split": "train", "path": "it-nl/train-*"}]}, {"config_name": "it-pt", "data_files": [{"split": "train", "path": "it-pt/train-*"}]}, {"config_name": "it-ru", "data_files": [{"split": "train", "path": "it-ru/train-*"}]}, {"config_name": "it-sv", "data_files": [{"split": "train", "path": "it-sv/train-*"}]}]}

2024-02-14T10:34:21+00:00

TAGS #task_categories-translation #annotations_creators-found #language_creators-found #multilinguality-multilingual #size_categories-1K<n<10K #source_datasets-original #language-Catalan #language-German #language-Modern Greek (1453-) #language-English #language-Esperanto #language-Spanish #language-Finnish #language-French #language-Hungarian #language-Italian #language-Dutch #language-Norwegian #language-Polish #language-Portuguese #language-Russian #language-Swedish #license-unknown #region-us

# Dataset Card for OpusBooks ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: None - Paper: URL - Leaderboard: - Point of Contact: ### Dataset Summary ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances Here are some examples of questions and facts: ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @abhishekkrthakur for adding this dataset.

0d6f4cba364bcd0877669cd6e87dda425b1b03f3

# Dataset Card for OpusDgt ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://opus.nlpl.eu/DGT.php - **Repository:** None - **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf - **Leaderboard:** [More Information Needed] - **Point of Contact:** [More Information Needed] ### Dataset Summary A collection of translation memories provided by the Joint Research Centre (JRC) Directorate-General for Translation (DGT): https://ec.europa.eu/jrc/en/language-technologies/dgt-translation-memory Tha dataset contains 25 languages and 299 bitexts. To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs, e.g. ```python dataset = load_dataset("opus_dgt", lang1="it", lang2="pl") ``` You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/DGT.php ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The languages in the dataset are: - bg - cs - da - de - el - en - es - et - fi - fr - ga - hr - hu - it - lt - lv - mt - nl - pl - pt - ro - sh - sk - sl - sv ## Dataset Structure ### Data Instances ``` { 'id': '0', 'translation': { "bg": "Протокол за поправка на Конвенцията относно компетентността, признаването и изпълнението на съдебни решения по граждански и търговски дела, подписана в Лугано на 30 октомври 2007 г.", "ga": "Miontuairisc cheartaitheach maidir le Coinbhinsiún ar dhlínse agus ar aithint agus ar fhorghníomhú breithiúnas in ábhair shibhialta agus tráchtála, a siníodh in Lugano an 30 Deireadh Fómhair 2007" } } ``` ### Data Fields - `id` (`str`): Unique identifier of the parallel sentence for the pair of languages. - `translation` (`dict`): Parallel sentences for the pair of languages. ### Data Splits The dataset contains a single `train` split. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ```bibtex @InProceedings{TIEDEMANN12.463, author = {J{\"o}rg Tiedemann}, title = {Parallel Data, Tools and Interfaces in OPUS}, booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, month = {may}, date = {23-25}, address = {Istanbul, Turkey}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-7-7}, language = {english} } ``` ### Contributions Thanks to [@rkc007](https://github.com/rkc007) for adding this dataset.

opus_dgt

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["bg", "cs", "da", "de", "el", "en", "es", "et", "fi", "fr", "ga", "hr", "hu", "it", "lt", "lv", "mt", "nl", "pl", "pt", "ro", "sh", "sk", "sl", "sv"], "license": ["unknown"], "multilinguality": ["multilingual"], "size_categories": ["100K<n<1M", "10K<n<100K", "1M<n<10M"], "source_datasets": ["original"], "task_categories": ["translation"], "task_ids": [], "pretty_name": "OpusDgt", "config_names": ["bg-ga", "bg-hr", "bg-sh", "es-ga", "fi-ga", "ga-nl", "ga-sh", "hr-sk", "hr-sv", "mt-sh"], "dataset_info": [{"config_name": "bg-ga", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["bg", "ga"]}}}], "splits": [{"name": "train", "num_bytes": 82972212, "num_examples": 179142}], "download_size": 32909143, "dataset_size": 82972212}, {"config_name": "bg-hr", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["bg", "hr"]}}}], "splits": [{"name": "train", "num_bytes": 239827799, "num_examples": 701572}], "download_size": 95163332, "dataset_size": 239827799}, {"config_name": "bg-sh", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["bg", "sh"]}}}], "splits": [{"name": "train", "num_bytes": 498883117, "num_examples": 1488507}], "download_size": 197907658, "dataset_size": 498883117}, {"config_name": "es-ga", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["es", "ga"]}}}], "splits": [{"name": "train", "num_bytes": 63115450, "num_examples": 178696}], "download_size": 27625395, "dataset_size": 63115450}, {"config_name": "fi-ga", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["fi", "ga"]}}}], "splits": [{"name": "train", "num_bytes": 61312920, "num_examples": 178619}], "download_size": 27498616, "dataset_size": 61312920}, {"config_name": "ga-nl", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["ga", "nl"]}}}], "splits": [{"name": "train", "num_bytes": 59065358, "num_examples": 170644}], "download_size": 26024485, "dataset_size": 59065358}, {"config_name": "ga-sh", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["ga", "sh"]}}}], "splits": [{"name": "train", "num_bytes": 28666465, "num_examples": 91613}], "download_size": 13309478, "dataset_size": 28666465}, {"config_name": "hr-sk", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["hr", "sk"]}}}], "splits": [{"name": "train", "num_bytes": 170717543, "num_examples": 689263}], "download_size": 79828239, "dataset_size": 170717543}, {"config_name": "hr-sv", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["hr", "sv"]}}}], "splits": [{"name": "train", "num_bytes": 171857552, "num_examples": 696334}], "download_size": 77567933, "dataset_size": 171857552}, {"config_name": "mt-sh", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["mt", "sh"]}}}], "splits": [{"name": "train", "num_bytes": 368560691, "num_examples": 1450424}], "download_size": 166554923, "dataset_size": 368560691}], "configs": [{"config_name": "bg-ga", "data_files": [{"split": "train", "path": "bg-ga/train-*"}]}, {"config_name": "bg-hr", "data_files": [{"split": "train", "path": "bg-hr/train-*"}]}, {"config_name": "bg-sh", "data_files": [{"split": "train", "path": "bg-sh/train-*"}]}, {"config_name": "es-ga", "data_files": [{"split": "train", "path": "es-ga/train-*"}]}, {"config_name": "fi-ga", "data_files": [{"split": "train", "path": "fi-ga/train-*"}]}, {"config_name": "ga-nl", "data_files": [{"split": "train", "path": "ga-nl/train-*"}]}, {"config_name": "ga-sh", "data_files": [{"split": "train", "path": "ga-sh/train-*"}]}, {"config_name": "hr-sk", "data_files": [{"split": "train", "path": "hr-sk/train-*"}]}, {"config_name": "hr-sv", "data_files": [{"split": "train", "path": "hr-sv/train-*"}]}, {"config_name": "mt-sh", "data_files": [{"split": "train", "path": "mt-sh/train-*"}]}]}

2024-02-14T11:22:31+00:00

TAGS #task_categories-translation #annotations_creators-found #language_creators-found #multilinguality-multilingual #size_categories-100K<n<1M #size_categories-10K<n<100K #size_categories-1M<n<10M #source_datasets-original #language-Bulgarian #language-Czech #language-Danish #language-German #language-Modern Greek (1453-) #language-English #language-Spanish #language-Estonian #language-Finnish #language-French #language-Irish #language-Croatian #language-Hungarian #language-Italian #language-Lithuanian #language-Latvian #language-Maltese #language-Dutch #language-Polish #language-Portuguese #language-Romanian #language-Serbo-Croatian #language-Slovak #language-Slovenian #language-Swedish #license-unknown #region-us

# Dataset Card for OpusDgt ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: None - Paper: URL - Leaderboard: - Point of Contact: ### Dataset Summary A collection of translation memories provided by the Joint Research Centre (JRC) Directorate-General for Translation (DGT): URL Tha dataset contains 25 languages and 299 bitexts. To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs, e.g. You can find the valid pairs in Homepage section of Dataset Description: URL ### Supported Tasks and Leaderboards ### Languages The languages in the dataset are: - bg - cs - da - de - el - en - es - et - fi - fr - ga - hr - hu - it - lt - lv - mt - nl - pl - pt - ro - sh - sk - sl - sv ## Dataset Structure ### Data Instances ### Data Fields - 'id' ('str'): Unique identifier of the parallel sentence for the pair of languages. - 'translation' ('dict'): Parallel sentences for the pair of languages. ### Data Splits The dataset contains a single 'train' split. ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @rkc007 for adding this dataset.

095a1f29f0181654ffda557a76cebc3612c3417e

# Dataset Card for OPUS DOGC ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://opus.nlpl.eu/DOGC/corpus/version/DOGC - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary OPUS DOGC is a collection of documents from the Official Journal of the Government of Catalonia, in Catalan and Spanish languages, provided by Antoni Oliver Gonzalez from the Universitat Oberta de Catalunya. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Dataset is multilingual with parallel text in: - Catalan - Spanish ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields A data instance contains the following fields: - `ca`: the Catalan text - `es`: the aligned Spanish text ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Dataset is in the Public Domain under [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/). ### Citation Information ``` @inproceedings{tiedemann-2012-parallel, title = "Parallel Data, Tools and Interfaces in {OPUS}", author = {Tiedemann, J{\"o}rg}, booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation ({LREC}'12)", month = may, year = "2012", address = "Istanbul, Turkey", publisher = "European Language Resources Association (ELRA)", url = "http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf", pages = "2214--2218", abstract = "This paper presents the current status of OPUS, a growing language resource of parallel corpora and related tools. The focus in OPUS is to provide freely available data sets in various formats together with basic annotation to be useful for applications in computational linguistics, translation studies and cross-linguistic corpus studies. In this paper, we report about new data sets and their features, additional annotation tools and models provided from the website and essential interfaces and on-line services included in the project.", } ``` ### Contributions Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.

opus_dogc

2022-03-02T23:29:22+00:00

{"annotations_creators": ["no-annotation"], "language_creators": ["expert-generated"], "language": ["ca", "es"], "license": ["cc0-1.0"], "multilinguality": ["translation"], "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["translation"], "task_ids": [], "paperswithcode_id": "dogc", "pretty_name": "OPUS DOGC", "dataset_info": {"config_name": "tmx", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["ca", "es"]}}}], "splits": [{"name": "train", "num_bytes": 1258920648, "num_examples": 4763575}], "download_size": 599902063, "dataset_size": 1258920648}, "configs": [{"config_name": "tmx", "data_files": [{"split": "train", "path": "tmx/train-*"}], "default": true}]}

2024-02-14T13:45:04+00:00

TAGS #task_categories-translation #annotations_creators-no-annotation #language_creators-expert-generated #multilinguality-translation #size_categories-1M<n<10M #source_datasets-original #language-Catalan #language-Spanish #license-cc0-1.0 #region-us

# Dataset Card for OPUS DOGC ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary OPUS DOGC is a collection of documents from the Official Journal of the Government of Catalonia, in Catalan and Spanish languages, provided by Antoni Oliver Gonzalez from the Universitat Oberta de Catalunya. ### Supported Tasks and Leaderboards ### Languages Dataset is multilingual with parallel text in: - Catalan - Spanish ## Dataset Structure ### Data Instances ### Data Fields A data instance contains the following fields: - 'ca': the Catalan text - 'es': the aligned Spanish text ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information Dataset is in the Public Domain under CC0 1.0. ### Contributions Thanks to @albertvillanova for adding this dataset.

ac9fb28c758994b6b2e709b9a92b8eb9481744c0

# Dataset Card for [opus_elhuyar] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:**[Opus Elhuyar](http://opus.nlpl.eu/Elhuyar.php) - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary Dataset provided by the foundation Elhuyar (http://webcorpusak.elhuyar.eus/sarrera_paraleloa.html) and submitted to OPUS by Joseba Garcia Beaumont ### Supported Tasks and Leaderboards The underlying task is machine translation from Spanish to Basque ### Languages Spanish to Basque ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012) ### Contributions Thanks to [@spatil6](https://github.com/spatil6) for adding this dataset.

opus_elhuyar

2022-03-02T23:29:22+00:00

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2024-02-14T14:00:47+00:00

TAGS #task_categories-translation #annotations_creators-found #language_creators-found #multilinguality-translation #size_categories-100K<n<1M #source_datasets-original #language-Spanish #language-Basque #license-unknown #region-us

# Dataset Card for [opus_elhuyar] ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage:Opus Elhuyar - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary Dataset provided by the foundation Elhuyar (URL and submitted to OPUS by Joseba Garcia Beaumont ### Supported Tasks and Leaderboards The underlying task is machine translation from Spanish to Basque ### Languages Spanish to Basque ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012) ### Contributions Thanks to @spatil6 for adding this dataset.

e39b9dfeead9541ff545ea6355cbc0b88396245d

# Dataset Card for EUconst ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://opus.nlpl.eu/EUconst/corpus/version/EUconst - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Leaderboard:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Dataset Summary A parallel corpus collected from the European Constitution. 21 languages, 210 bitexts ### Supported Tasks and Leaderboards The underlying task is machine translation. ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information If you use any part of the corpus in your own work, please cite the following article: ``` @inproceedings{tiedemann-2012-parallel, title = "Parallel Data, Tools and Interfaces in {OPUS}", author = {Tiedemann, J{\"o}rg}, editor = "Calzolari, Nicoletta and Choukri, Khalid and Declerck, Thierry and Do{\u{g}}an, Mehmet U{\u{g}}ur and Maegaard, Bente and Mariani, Joseph and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios", booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation ({LREC}'12)", month = may, year = "2012", address = "Istanbul, Turkey", publisher = "European Language Resources Association (ELRA)", url = "http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf", pages = "2214--2218", abstract = "This paper presents the current status of OPUS, a growing language resource of parallel corpora and related tools. The focus in OPUS is to provide freely available data sets in various formats together with basic annotation to be useful for applications in computational linguistics, translation studies and cross-linguistic corpus studies. In this paper, we report about new data sets and their features, additional annotation tools and models provided from the website and essential interfaces and on-line services included in the project.", } ``` ### Contributions Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.

opus_euconst

2022-03-02T23:29:22+00:00

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2024-02-15T08:32:49+00:00

TAGS #task_categories-translation #annotations_creators-found #language_creators-found #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #language-Czech #language-Danish #language-German #language-Modern Greek (1453-) #language-English #language-Spanish #language-Estonian #language-Finnish #language-French #language-Irish #language-Hungarian #language-Italian #language-Lithuanian #language-Latvian #language-Maltese #language-Dutch #language-Polish #language-Portuguese #language-Slovak #language-Slovenian #language-Swedish #license-unknown #region-us

# Dataset Card for EUconst ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary A parallel corpus collected from the European Constitution. 21 languages, 210 bitexts ### Supported Tasks and Leaderboards The underlying task is machine translation. ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information If you use any part of the corpus in your own work, please cite the following article: ### Contributions Thanks to @patil-suraj for adding this dataset.

9468830608e94635b6161d5add2667574ecb21b8

# Dataset Card for [opus_finlex] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:**[Finlex](http://opus.nlpl.eu/Finlex.php) - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The Finlex Data Base is a comprehensive collection of legislative and other judicial information of Finland, which is available in Finnish, Swedish and partially in English. This corpus is taken from the Semantic Finlex serice that provides the Finnish and Swedish data as linked open data and also raw XML files. ### Supported Tasks and Leaderboards The underlying task is machine translation for language pair Swedish and Finnish. ### Languages Swedish and Finnish ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012) ### Contributions Thanks to [@spatil6](https://github.com/spatil6) for adding this dataset.

opus_finlex

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["fi", "sv"], "license": ["unknown"], "multilinguality": ["translation"], "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["translation"], "task_ids": [], "pretty_name": "OpusFinlex", "dataset_info": {"config_name": "fi-sv", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["fi", "sv"]}}}], "splits": [{"name": "train", "num_bytes": 610547719, "num_examples": 3114141}], "download_size": 269359572, "dataset_size": 610547719}, "configs": [{"config_name": "fi-sv", "data_files": [{"split": "train", "path": "fi-sv/train-*"}], "default": true}]}

2024-02-15T11:57:27+00:00

TAGS #task_categories-translation #annotations_creators-found #language_creators-found #multilinguality-translation #size_categories-1M<n<10M #source_datasets-original #language-Finnish #language-Swedish #license-unknown #region-us

# Dataset Card for [opus_finlex] ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage:Finlex - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary The Finlex Data Base is a comprehensive collection of legislative and other judicial information of Finland, which is available in Finnish, Swedish and partially in English. This corpus is taken from the Semantic Finlex serice that provides the Finnish and Swedish data as linked open data and also raw XML files. ### Supported Tasks and Leaderboards The underlying task is machine translation for language pair Swedish and Finnish. ### Languages Swedish and Finnish ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012) ### Contributions Thanks to @spatil6 for adding this dataset.

f5abcaf7e58bde5303cbd87484b3ae234b7d0f5c

# Dataset Card for [opus_fiskmo] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:**[fiskmo](http://opus.nlpl.eu/fiskmo.php) - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary fiskmo, a massive parallel corpus for Finnish and Swedish. ### Supported Tasks and Leaderboards The underlying task is machine translation for language pair Finnish and Swedish. ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012) ### Contributions Thanks to [@spatil6](https://github.com/spatil6) for adding this dataset.

opus_fiskmo

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["fi", "sv"], "license": ["unknown"], "multilinguality": ["translation"], "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["translation"], "task_ids": [], "pretty_name": "OpusFiskmo", "dataset_info": {"features": [{"name": "translation", "dtype": {"translation": {"languages": ["fi", "sv"]}}}], "config_name": "fi-sv", "splits": [{"name": "train", "num_bytes": 326528834, "num_examples": 2100001}], "download_size": 144858927, "dataset_size": 326528834}}

2024-01-18T11:11:29+00:00

TAGS #task_categories-translation #annotations_creators-found #language_creators-found #multilinguality-translation #size_categories-1M<n<10M #source_datasets-original #language-Finnish #language-Swedish #license-unknown #region-us

# Dataset Card for [opus_fiskmo] ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage:fiskmo - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary fiskmo, a massive parallel corpus for Finnish and Swedish. ### Supported Tasks and Leaderboards The underlying task is machine translation for language pair Finnish and Swedish. ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012) ### Contributions Thanks to @spatil6 for adding this dataset.

d458f795db46d97ffcd9a768213af99aaed5e005

# Dataset Card for Opus Gnome ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://opus.nlpl.eu/GNOME.php - **Repository:** None - **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf - **Leaderboard:** [More Information Needed] - **Point of Contact:** [More Information Needed] ### Dataset Summary To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs. You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/GNOME.php E.g. `dataset = load_dataset("opus_gnome", lang1="it", lang2="pl")` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances ``` { 'id': '0', 'translation': { 'ar': 'إعداد سياسة القفل', 'bal': 'تنظیم کتن سیاست کبل' } } ``` ### Data Fields Each instance has two fields: - **id**: the id of the example - **translation**: a dictionary containing translated texts in two languages. ### Data Splits Each subset simply consists in a train set. We provide the number of examples for certain language pairs: | | train | |:---------|--------:| | ar-bal | 60 | | bg-csb | 10 | | ca-en_GB | 7982 | | cs-eo | 73 | | de-ha | 216 | | cs-tk | 18686 | | da-vi | 149 | | en_GB-my | 28232 | | el-sk | 150 | | de-tt | 2169 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information @InProceedings{TIEDEMANN12.463, author = {J{\"o}rg Tiedemann}, title = {Parallel Data, Tools and Interfaces in OPUS}, booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, month = {may}, date = {23-25}, address = {Istanbul, Turkey}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-7-7}, language = {english} } ### Contributions Thanks to [@rkc007](https://github.com/rkc007) for adding this dataset.

opus_gnome

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["af", "am", "an", "ang", "ar", "as", "ast", "az", "bal", "be", "bem", "bg", "bn", "bo", "br", "brx", "bs", "ca", "crh", "cs", "csb", "cy", "da", "de", "dv", "dz", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fo", "fr", "fur", "fy", "ga", "gd", "gl", "gn", "gu", "gv", "ha", "he", "hi", "hr", "hu", "hy", "ia", "id", "ig", "io", "is", "it", "ja", "jbo", "ka", "kg", "kk", "km", "kn", "ko", "kr", "ks", "ku", "ky", "la", "lg", "li", "lo", "lt", "lv", "mai", "mg", "mi", "mk", "ml", "mn", "mr", "ms", "mt", "mus", "my", "nb", "nds", "ne", "nhn", "nl", "nn", "no", "nqo", "nr", "nso", "oc", "or", "os", "pa", "pl", "ps", "pt", "quz", "ro", "ru", "rw", "si", "sk", "sl", "so", "sq", "sr", "st", "sv", "sw", "szl", "ta", "te", "tg", "th", "tk", "tl", "tr", "ts", "tt", "tyj", "ug", "uk", "ur", "uz", "vi", "wa", "xh", "yi", "yo", "zh", "zu"], "license": ["unknown"], "multilinguality": ["multilingual"], "size_categories": ["10K<n<100K", "1K<n<10K", "n<1K"], "source_datasets": ["original"], "task_categories": ["translation"], "task_ids": [], "pretty_name": "OpusGnome", "config_names": ["ar-bal", "bg-csb", "ca-en_GB", "cs-eo", "cs-tk", "da-vi", "de-ha", "de-tt", "el-sk", "en_GB-my"], "language_bcp47": ["ar-TN", "az-IR", "bg-BG", "bn-IN", "da-DK", "de-CH", "en-AU", "en-CA", "en-GB", "en-NZ", "en-US", "en-ZA", "es-AR", "es-CL", "es-CO", "es-CR", "es-DO", "es-EC", "es-ES", "es-GT", "es-HN", "es-MX", "es-NI", "es-PA", "es-PE", "es-PR", "es-SV", "es-UY", "es-VE", "fa-IR", "hi-IN", "it-IT", "ms-MY", "nb-NO", "nn-NO", "no-NB", "pt-BR", "pt-PT", "sr-ME", "tg-TJ", "tl-PH", "tr-TR", "ur-PK", "vi-VN", "zh-CN", "zh-HK", "zh-TW"], "dataset_info": [{"config_name": "ar-bal", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["ar", "bal"]}}}], "splits": [{"name": "train", "num_bytes": 5150, "num_examples": 60}], "download_size": 2503, "dataset_size": 5150}, {"config_name": "bg-csb", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["bg", "csb"]}}}], "splits": [{"name": "train", "num_bytes": 172545, "num_examples": 1768}], "download_size": 29706, "dataset_size": 172545}, {"config_name": "ca-en_GB", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["ca", "en_GB"]}}}], "splits": [{"name": "train", "num_bytes": 1007488, "num_examples": 7982}], "download_size": 188727, "dataset_size": 1007488}, {"config_name": "cs-eo", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["cs", "eo"]}}}], "splits": [{"name": "train", "num_bytes": 2895, "num_examples": 73}], "download_size": 3055, "dataset_size": 2895}, {"config_name": "de-ha", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["de", "ha"]}}}], "splits": [{"name": "train", "num_bytes": 22899, "num_examples": 216}], "download_size": 5287, "dataset_size": 22899}, {"config_name": "cs-tk", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["cs", "tk"]}}}], "splits": [{"name": "train", "num_bytes": 1197731, "num_examples": 18686}], "download_size": 98044, "dataset_size": 1197731}, {"config_name": "da-vi", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["da", "vi"]}}}], "splits": [{"name": "train", "num_bytes": 9372, "num_examples": 149}], "download_size": 5432, "dataset_size": 9372}, {"config_name": "en_GB-my", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["en_GB", "my"]}}}], "splits": [{"name": "train", "num_bytes": 3298074, "num_examples": 28232}], "download_size": 362750, "dataset_size": 3298074}, {"config_name": "el-sk", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["el", "sk"]}}}], "splits": [{"name": "train", "num_bytes": 12121, "num_examples": 150}], "download_size": 6116, "dataset_size": 12121}, {"config_name": "de-tt", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["de", "tt"]}}}], "splits": [{"name": "train", "num_bytes": 134978, "num_examples": 2169}], "download_size": 15891, "dataset_size": 134978}]}

2024-01-18T11:11:32+00:00

TAGS #task_categories-translation #annotations_creators-found #language_creators-found #multilinguality-multilingual #size_categories-10K<n<100K #size_categories-1K<n<10K #size_categories-n<1K #source_datasets-original #language-Afrikaans #language-Amharic #language-Aragonese #language-Old English (ca. 450-1100) #language-Arabic #language-Assamese #language-Asturian #language-Azerbaijani #language-Baluchi #language-Belarusian #language-Bemba (Zambia) #language-Bulgarian #language-Bengali #language-Tibetan #language-Breton #language-Bodo (India) #language-Bosnian #language-Catalan #language-Crimean Tatar #language-Czech #language-Kashubian #language-Welsh #language-Danish #language-German #language-Dhivehi #language-Dzongkha #language-Modern Greek (1453-) #language-English #language-Esperanto #language-Spanish #language-Estonian #language-Basque #language-Persian #language-Finnish #language-Faroese #language-French #language-Friulian #language-Western Frisian #language-Irish #language-Scottish Gaelic #language-Galician #language-Guarani #language-Gujarati #language-Manx #language-Hausa #language-Hebrew #language-Hindi #language-Croatian #language-Hungarian #language-Armenian #language-Interlingua (International Auxiliary Language Association) #language-Indonesian #language-Igbo #language-Ido #language-Icelandic #language-Italian #language-Japanese #language-Lojban #language-Georgian #language-Kongo #language-Kazakh #language-Khmer #language-Kannada #language-Korean #language-Kanuri #language-Kashmiri #language-Kurdish #language-Kirghiz #language-Latin #language-Ganda #language-Limburgan #language-Lao #language-Lithuanian #language-Latvian #language-Maithili #language-Malagasy #language-Maori #language-Macedonian #language-Malayalam #language-Mongolian #language-Marathi #language-Malay (macrolanguage) #language-Maltese #language-Creek #language-Burmese #language-Norwegian Bokmål #language-Low German #language-Nepali (macrolanguage) #language-Central Nahuatl #language-Dutch #language-Norwegian Nynorsk #language-Norwegian #language-N'Ko #language-South Ndebele #language-Pedi #language-Occitan (post 1500) #language-Oriya (macrolanguage) #language-Ossetian #language-Panjabi #language-Polish #language-Pushto #language-Portuguese #language-Cusco Quechua #language-Romanian #language-Russian #language-Kinyarwanda #language-Sinhala #language-Slovak #language-Slovenian #language-Somali #language-Albanian #language-Serbian #language-Southern Sotho #language-Swedish #language-Swahili (macrolanguage) #language-Silesian #language-Tamil #language-Telugu #language-Tajik #language-Thai #language-Turkmen #language-Tagalog #language-Turkish #language-Tsonga #language-Tatar #language-Tai Do #language-Uighur #language-Ukrainian #language-Urdu #language-Uzbek #language-Vietnamese #language-Walloon #language-Xhosa #language-Yiddish #language-Yoruba #language-Chinese #language-Zulu #license-unknown #region-us

Dataset Card for Opus Gnome =========================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: None * Paper: URL * Leaderboard: * Point of Contact: ### Dataset Summary To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs. You can find the valid pairs in Homepage section of Dataset Description: URL E.g. 'dataset = load\_dataset("opus\_gnome", lang1="it", lang2="pl")' ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances ### Data Fields Each instance has two fields: * id: the id of the example * translation: a dictionary containing translated texts in two languages. ### Data Splits Each subset simply consists in a train set. We provide the number of examples for certain language pairs: Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information @InProceedings{TIEDEMANN12.463, author = {J{"o}rg Tiedemann}, title = {Parallel Data, Tools and Interfaces in OPUS}, booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, month = {may}, date = {23-25}, address = {Istanbul, Turkey}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-7-7}, language = {english} } ### Contributions Thanks to @rkc007 for adding this dataset.

6786f66cec48f88c85d91c7840607d6bf0ba30f6

# Dataset Card for infopankki ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://opus.nlpl.eu/infopankki/corpus/version/infopankki - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Leaderboard:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Dataset Summary A parallel corpus of 12 languages, 66 bitexts. ### Supported Tasks and Leaderboards The underlying task is machine translation. ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization Source: http://www.infopankki.fi via the Open Data API #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Licensed under [CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/). ### Citation Information If you use any part of the corpus in your own work, please cite the following article: ``` @inproceedings{tiedemann-2012-parallel, title = "Parallel Data, Tools and Interfaces in {OPUS}", author = {Tiedemann, J{\"o}rg}, editor = "Calzolari, Nicoletta and Choukri, Khalid and Declerck, Thierry and Do{\u{g}}an, Mehmet U{\u{g}}ur and Maegaard, Bente and Mariani, Joseph and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios", booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation ({LREC}'12)", month = may, year = "2012", address = "Istanbul, Turkey", publisher = "European Language Resources Association (ELRA)", url = "http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf", pages = "2214--2218", abstract = "This paper presents the current status of OPUS, a growing language resource of parallel corpora and related tools. The focus in OPUS is to provide freely available data sets in various formats together with basic annotation to be useful for applications in computational linguistics, translation studies and cross-linguistic corpus studies. In this paper, we report about new data sets and their features, additional annotation tools and models provided from the website and essential interfaces and on-line services included in the project.", } ``` ### Contributions Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.

opus_infopankki

2022-03-02T23:29:22+00:00

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2024-02-13T17:14:24+00:00

TAGS #task_categories-translation #annotations_creators-found #language_creators-found #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #language-Arabic #language-English #language-Spanish #language-Estonian #language-Persian #language-Finnish #language-French #language-Russian #language-Somali #language-Swedish #language-Turkish #language-Chinese #license-cc-by-4.0 #region-us

# Dataset Card for infopankki ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary A parallel corpus of 12 languages, 66 bitexts. ### Supported Tasks and Leaderboards The underlying task is machine translation. ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization Source: URL via the Open Data API #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information Licensed under CC-BY 4.0. If you use any part of the corpus in your own work, please cite the following article: ### Contributions Thanks to @patil-suraj for adding this dataset.

5ac10423fffff4e606d909ecb494f27f8e58bdb5

# Dataset Card for [opus_memat] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:**[memat](http://opus.nlpl.eu/memat.php) - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary Xhosa-English parallel corpora, funded by EPSRC, the Medical Machine Translation project worked on machine translation between ixiXhosa and English, with a focus on the medical domain. ### Supported Tasks and Leaderboards The underlying task is machine translation from Xhosa to English ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012) ### Contributions Thanks to [@spatil6](https://github.com/spatil6) for adding this dataset.

opus_memat

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["en", "xh"], "license": ["unknown"], "multilinguality": ["translation"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["translation"], "task_ids": [], "pretty_name": "OpusMemat", "dataset_info": {"features": [{"name": "translation", "dtype": {"translation": {"languages": ["xh", "en"]}}}], "config_name": "xh-en", "splits": [{"name": "train", "num_bytes": 25400570, "num_examples": 154764}], "download_size": 8382865, "dataset_size": 25400570}}

2024-01-18T11:11:37+00:00

TAGS #task_categories-translation #annotations_creators-found #language_creators-found #multilinguality-translation #size_categories-100K<n<1M #source_datasets-original #language-English #language-Xhosa #license-unknown #region-us

# Dataset Card for [opus_memat] ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage:memat - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary Xhosa-English parallel corpora, funded by EPSRC, the Medical Machine Translation project worked on machine translation between ixiXhosa and English, with a focus on the medical domain. ### Supported Tasks and Leaderboards The underlying task is machine translation from Xhosa to English ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012) ### Contributions Thanks to @spatil6 for adding this dataset.

ffc2073aa5321bbc3accd3d37565acc07ca698fe

# Dataset Card for [opus_montenegrinsubs] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:**[opus MontenegrinSubs ](http://opus.nlpl.eu/MontenegrinSubs.php) - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary Opus MontenegrinSubs dataset for machine translation task, for language pair en-me: english and montenegrin ### Supported Tasks and Leaderboards The underlying task is machine translation from en to me ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012) ### Contributions Thanks to [@spatil6](https://github.com/spatil6) for adding this dataset.

opus_montenegrinsubs

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["cnr", "en"], "license": ["unknown"], "multilinguality": ["translation"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["translation"], "task_ids": [], "pretty_name": "OpusMontenegrinsubs", "dataset_info": {"features": [{"name": "translation", "dtype": {"translation": {"languages": ["en", "me"]}}}], "config_name": "en-me", "splits": [{"name": "train", "num_bytes": 4896403, "num_examples": 65043}], "download_size": 1990570, "dataset_size": 4896403}}

2024-01-18T11:11:44+00:00

TAGS #task_categories-translation #annotations_creators-found #language_creators-found #multilinguality-translation #size_categories-10K<n<100K #source_datasets-original #language-Montenegrin #language-English #license-unknown #region-us

# Dataset Card for [opus_montenegrinsubs] ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage:opus MontenegrinSubs - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary Opus MontenegrinSubs dataset for machine translation task, for language pair en-me: english and montenegrin ### Supported Tasks and Leaderboards The underlying task is machine translation from en to me ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012) ### Contributions Thanks to @spatil6 for adding this dataset.

bb69626945f673a97295878096b022518eea5c9b

# Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://opus.nlpl.eu/OpenOffice/corpus/version/OpenOffice - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary A collection of documents from http://www.openoffice.org/. 8 languages, 28 bitexts ### Supported Tasks and Leaderboards The underlying task is machine translation. ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @InProceedings{TIEDEMANN12.463, author = {J�rg Tiedemann}, title = {Parallel Data, Tools and Interfaces in OPUS}, booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, month = {may}, date = {23-25}, address = {Istanbul, Turkey}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-7-7}, language = {english} } ``` ### Contributions Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.

opus_openoffice

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["de", "en", "es", "fr", "ja", "ru", "sv", "zh"], "license": ["unknown"], "multilinguality": ["multilingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["translation"], "task_ids": [], "pretty_name": "OpusOpenoffice", "config_names": ["de-en_GB", "de-es", "de-fr", "de-ja", "de-ru", "de-sv", "de-zh_CN", "en_GB-es", "en_GB-fr", "en_GB-ja", "en_GB-ru", "en_GB-sv", "en_GB-zh_CN", "es-fr", "es-ja", "es-ru", "es-sv", "es-zh_CN", "fr-ja", "fr-ru", "fr-sv", "fr-zh_CN", "ja-ru", "ja-sv", "ja-zh_CN", "ru-sv", "ru-zh_CN", "sv-zh_CN"], "language_bcp47": ["en-GB", "zh-CN"], "dataset_info": [{"config_name": "de-en_GB", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["de", "en_GB"]}}}], "splits": [{"name": "train", "num_bytes": 6201077, "num_examples": 77052}], "download_size": 2030226, "dataset_size": 6201077}, {"config_name": "de-es", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["de", "es"]}}}], "splits": [{"name": "train", "num_bytes": 6571615, "num_examples": 77000}], "download_size": 2100214, "dataset_size": 6571615}, {"config_name": "de-fr", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["de", "fr"]}}}], "splits": [{"name": "train", "num_bytes": 6715805, "num_examples": 76684}], "download_size": 2111078, "dataset_size": 6715805}, {"config_name": "de-ja", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["de", "ja"]}}}], "splits": [{"name": "train", "num_bytes": 7084951, "num_examples": 69396}], "download_size": 2112771, "dataset_size": 7084951}, {"config_name": "de-ru", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["de", "ru"]}}}], "splits": [{"name": "train", "num_bytes": 8333241, "num_examples": 75511}], "download_size": 2267499, "dataset_size": 8333241}, {"config_name": "de-sv", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["de", "sv"]}}}], "splits": [{"name": "train", "num_bytes": 6288962, "num_examples": 77366}], "download_size": 2056115, "dataset_size": 6288962}, {"config_name": "de-zh_CN", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["de", "zh_CN"]}}}], "splits": [{"name": "train", "num_bytes": 5836628, "num_examples": 68712}], "download_size": 2006818, "dataset_size": 5836628}, {"config_name": "en_GB-es", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["en_GB", "es"]}}}], "splits": [{"name": "train", "num_bytes": 6147581, "num_examples": 77646}], "download_size": 1978922, "dataset_size": 6147581}, {"config_name": "en_GB-fr", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["en_GB", "fr"]}}}], "splits": [{"name": "train", "num_bytes": 6297779, "num_examples": 77696}], "download_size": 1987317, "dataset_size": 6297779}, {"config_name": "en_GB-ja", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["en_GB", "ja"]}}}], "splits": [{"name": "train", "num_bytes": 6636722, "num_examples": 69149}], "download_size": 1987255, "dataset_size": 6636722}, {"config_name": "en_GB-ru", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["en_GB", "ru"]}}}], "splits": [{"name": "train", "num_bytes": 7877970, "num_examples": 75401}], "download_size": 2137510, "dataset_size": 7877970}, {"config_name": "en_GB-sv", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["en_GB", "sv"]}}}], "splits": [{"name": "train", "num_bytes": 5861461, "num_examples": 77815}], "download_size": 1934619, "dataset_size": 5861461}, {"config_name": "en_GB-zh_CN", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["en_GB", "zh_CN"]}}}], "splits": [{"name": "train", "num_bytes": 5424865, "num_examples": 69400}], "download_size": 1887600, "dataset_size": 5424865}, {"config_name": "es-fr", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["es", "fr"]}}}], "splits": [{"name": "train", "num_bytes": 6663092, "num_examples": 77417}], "download_size": 2059241, "dataset_size": 6663092}, {"config_name": "es-ja", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["es", "ja"]}}}], "splits": [{"name": "train", "num_bytes": 7005123, "num_examples": 68944}], "download_size": 2059072, "dataset_size": 7005123}, {"config_name": "es-ru", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["es", "ru"]}}}], "splits": [{"name": "train", "num_bytes": 8283703, "num_examples": 76461}], "download_size": 2214447, "dataset_size": 8283703}, {"config_name": "es-sv", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["es", "sv"]}}}], "splits": [{"name": "train", "num_bytes": 6232466, "num_examples": 77825}], "download_size": 2002804, "dataset_size": 6232466}, {"config_name": "es-zh_CN", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["es", "zh_CN"]}}}], "splits": [{"name": "train", "num_bytes": 5776827, "num_examples": 68583}], "download_size": 1958411, "dataset_size": 5776827}, {"config_name": "fr-ja", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["fr", "ja"]}}}], "splits": [{"name": "train", "num_bytes": 7160332, "num_examples": 69026}], "download_size": 2069621, "dataset_size": 7160332}, {"config_name": "fr-ru", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["fr", "ru"]}}}], "splits": [{"name": "train", "num_bytes": 8432061, "num_examples": 76464}], "download_size": 2222427, "dataset_size": 8432061}, {"config_name": "fr-sv", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["fr", "sv"]}}}], "splits": [{"name": "train", "num_bytes": 6373350, "num_examples": 77398}], "download_size": 2014028, "dataset_size": 6373350}, {"config_name": "fr-zh_CN", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["fr", "zh_CN"]}}}], "splits": [{"name": "train", "num_bytes": 5918482, "num_examples": 68723}], "download_size": 1966020, "dataset_size": 5918482}, {"config_name": "ja-ru", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["ja", "ru"]}}}], "splits": [{"name": "train", "num_bytes": 8781230, "num_examples": 68589}], "download_size": 2224576, "dataset_size": 8781230}, {"config_name": "ja-sv", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["ja", "sv"]}}}], "splits": [{"name": "train", "num_bytes": 6709627, "num_examples": 69154}], "download_size": 2012693, "dataset_size": 6709627}, {"config_name": "ja-zh_CN", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["ja", "zh_CN"]}}}], "splits": [{"name": "train", "num_bytes": 6397676, "num_examples": 68953}], "download_size": 1972833, "dataset_size": 6397676}, {"config_name": "ru-sv", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["ru", "sv"]}}}], "splits": [{"name": "train", "num_bytes": 7966150, "num_examples": 75560}], "download_size": 2167678, "dataset_size": 7966150}, {"config_name": "ru-zh_CN", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["ru", "zh_CN"]}}}], "splits": [{"name": "train", "num_bytes": 7393659, "num_examples": 66259}], "download_size": 2098229, "dataset_size": 7393659}, {"config_name": "sv-zh_CN", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["sv", "zh_CN"]}}}], "splits": [{"name": "train", "num_bytes": 5492902, "num_examples": 68846}], "download_size": 1914096, "dataset_size": 5492902}]}

2024-02-09T08:25:27+00:00

TAGS #task_categories-translation #annotations_creators-found #language_creators-found #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #language-German #language-English #language-Spanish #language-French #language-Japanese #language-Russian #language-Swedish #language-Chinese #license-unknown #region-us

# Dataset Card for [Dataset Name] ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary A collection of documents from URL 8 languages, 28 bitexts ### Supported Tasks and Leaderboards The underlying task is machine translation. ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @patil-suraj for adding this dataset.

1e6ba095c9b4c2e9d611944236d10055d804b663

# Dataset Card for OpusParaCrawl ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://opus.nlpl.eu/ParaCrawl.php - **Repository:** None - **Paper:** [ParaCrawl: Web-Scale Acquisition of Parallel Corpora](https://aclanthology.org/2020.acl-main.417/) - **Leaderboard:** [More Information Needed] - **Point of Contact:** [More Information Needed] ### Dataset Summary Parallel corpora from Web Crawls collected in the ParaCrawl project. Tha dataset contains: - 42 languages, 43 bitexts - total number of files: 59,996 - total number of tokens: 56.11G - total number of sentence fragments: 3.13G To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs, e.g. ```python dataset = load_dataset("opus_paracrawl", lang1="en", lang2="so") ``` You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/ParaCrawl.php ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The languages in the dataset are: - bg - ca - cs - da - de - el - en - es - et - eu - fi - fr - ga - gl - hr - hu - is - it - km - ko - lt - lv - mt - my - nb - ne - nl - nn - pl - pt - ro - ru - si - sk - sl - so - sv - sw - tl - uk - zh ## Dataset Structure ### Data Instances ``` { 'id': '0', 'translation': { "el": "Συνεχίστε ευθεία 300 μέτρα μέχρι να καταλήξουμε σε μια σωστή οδός (ul. Gagarina)? Περπατήστε περίπου 300 μέτρα μέχρι να φτάσετε το πρώτο ορθή οδός (ul Khotsa Namsaraeva)?", "en": "Go straight 300 meters until you come to a proper street (ul. Gagarina); Walk approximately 300 meters until you reach the first proper street (ul Khotsa Namsaraeva);" } } ``` ### Data Fields - `id` (`str`): Unique identifier of the parallel sentence for the pair of languages. - `translation` (`dict`): Parallel sentences for the pair of languages. ### Data Splits The dataset contains a single `train` split. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information - Creative commons CC0 (no rights reserved) ### Citation Information ```bibtex @inproceedings{banon-etal-2020-paracrawl, title = "{P}ara{C}rawl: Web-Scale Acquisition of Parallel Corpora", author = "Ba{\~n}{\'o}n, Marta and Chen, Pinzhen and Haddow, Barry and Heafield, Kenneth and Hoang, Hieu and Espl{\`a}-Gomis, Miquel and Forcada, Mikel L. and Kamran, Amir and Kirefu, Faheem and Koehn, Philipp and Ortiz Rojas, Sergio and Pla Sempere, Leopoldo and Ram{\'\i}rez-S{\'a}nchez, Gema and Sarr{\'\i}as, Elsa and Strelec, Marek and Thompson, Brian and Waites, William and Wiggins, Dion and Zaragoza, Jaume", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.acl-main.417", doi = "10.18653/v1/2020.acl-main.417", pages = "4555--4567", } ``` ```bibtex @InProceedings{TIEDEMANN12.463, author = {Jörg Tiedemann}, title = {Parallel Data, Tools and Interfaces in OPUS}, booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, month = {may}, date = {23-25}, address = {Istanbul, Turkey}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Uğur Doğan and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-7-7}, language = {english} } ``` ### Contributions Thanks to [@rkc007](https://github.com/rkc007) for adding this dataset.

opus_paracrawl

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["bg", "ca", "cs", "da", "de", "el", "en", "es", "et", "eu", "fi", "fr", "ga", "gl", "hr", "hu", "is", "it", "km", "ko", "lt", "lv", "mt", "my", "nb", "ne", "nl", "nn", "pl", "pt", "ro", "ru", "si", "sk", "sl", "so", "sv", "sw", "tl", "uk", "zh"], "license": ["cc0-1.0"], "multilinguality": ["multilingual"], "size_categories": ["100K<n<1M", "10K<n<100K", "1M<n<10M"], "source_datasets": ["original"], "task_categories": ["translation"], "task_ids": [], "pretty_name": "OpusParaCrawl", "config_names": ["de-pl", "el-en", "en-ha", "en-ig", "en-km", "en-so", "en-sw", "en-tl", "es-gl", "fr-nl"], "dataset_info": [{"config_name": "el-en", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["el", "en"]}}}], "splits": [{"name": "train", "num_bytes": 6760375061, "num_examples": 21402471}], "download_size": 2317102846, "dataset_size": 6760375061}, {"config_name": "en-ha", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["en", "ha"]}}}], "splits": [{"name": "train", "num_bytes": 4618460, "num_examples": 19694}], "download_size": 1757433, "dataset_size": 4618460}, {"config_name": "en-ig", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["en", "ig"]}}}], "splits": [{"name": "train", "num_bytes": 6709030, "num_examples": 28829}], "download_size": 2691716, "dataset_size": 6709030}, {"config_name": "en-km", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["en", "km"]}}}], "splits": [{"name": "train", "num_bytes": 31964493, "num_examples": 65115}], "download_size": 9907279, "dataset_size": 31964493}, {"config_name": "en-so", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["en", "so"]}}}], "splits": [{"name": "train", "num_bytes": 5791003, "num_examples": 14880}], "download_size": 2227727, "dataset_size": 5791003}, {"config_name": "de-pl", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["de", "pl"]}}}], "splits": [{"name": "train", "num_bytes": 298637031, "num_examples": 916643}], "download_size": 106891602, "dataset_size": 298637031}, {"config_name": "fr-nl", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["fr", "nl"]}}}], "splits": [{"name": "train", "num_bytes": 862303220, "num_examples": 2687673}], "download_size": 319804705, "dataset_size": 862303220}, {"config_name": "en-sw", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["en", "sw"]}}}], "splits": [{"name": "train", "num_bytes": 44264442, "num_examples": 132520}], "download_size": 18611087, "dataset_size": 44264442}, {"config_name": "en-tl", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["en", "tl"]}}}], "splits": [{"name": "train", "num_bytes": 82502798, "num_examples": 248689}], "download_size": 32933118, "dataset_size": 82502798}, {"config_name": "es-gl", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["es", "gl"]}}}], "splits": [{"name": "train", "num_bytes": 582660901, "num_examples": 1879689}], "download_size": 236696353, "dataset_size": 582660901}]}

2024-01-18T11:11:49+00:00

TAGS #task_categories-translation #annotations_creators-found #language_creators-found #multilinguality-multilingual #size_categories-100K<n<1M #size_categories-10K<n<100K #size_categories-1M<n<10M #source_datasets-original #language-Bulgarian #language-Catalan #language-Czech #language-Danish #language-German #language-Modern Greek (1453-) #language-English #language-Spanish #language-Estonian #language-Basque #language-Finnish #language-French #language-Irish #language-Galician #language-Croatian #language-Hungarian #language-Icelandic #language-Italian #language-Khmer #language-Korean #language-Lithuanian #language-Latvian #language-Maltese #language-Burmese #language-Norwegian Bokmål #language-Nepali (macrolanguage) #language-Dutch #language-Norwegian Nynorsk #language-Polish #language-Portuguese #language-Romanian #language-Russian #language-Sinhala #language-Slovak #language-Slovenian #language-Somali #language-Swedish #language-Swahili (macrolanguage) #language-Tagalog #language-Ukrainian #language-Chinese #license-cc0-1.0 #region-us

# Dataset Card for OpusParaCrawl ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: None - Paper: ParaCrawl: Web-Scale Acquisition of Parallel Corpora - Leaderboard: - Point of Contact: ### Dataset Summary Parallel corpora from Web Crawls collected in the ParaCrawl project. Tha dataset contains: - 42 languages, 43 bitexts - total number of files: 59,996 - total number of tokens: 56.11G - total number of sentence fragments: 3.13G To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs, e.g. You can find the valid pairs in Homepage section of Dataset Description: URL ### Supported Tasks and Leaderboards ### Languages The languages in the dataset are: - bg - ca - cs - da - de - el - en - es - et - eu - fi - fr - ga - gl - hr - hu - is - it - km - ko - lt - lv - mt - my - nb - ne - nl - nn - pl - pt - ro - ru - si - sk - sl - so - sv - sw - tl - uk - zh ## Dataset Structure ### Data Instances ### Data Fields - 'id' ('str'): Unique identifier of the parallel sentence for the pair of languages. - 'translation' ('dict'): Parallel sentences for the pair of languages. ### Data Splits The dataset contains a single 'train' split. ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information - Creative commons CC0 (no rights reserved) ### Contributions Thanks to @rkc007 for adding this dataset.

2b1969cf946ea2dc0104548c5fd2f8691a5d3773

# Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://opus.nlpl.eu/RF.php - **Repository:** - **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf - **Leaderboard:** [More Information Needed] - **Point of Contact:** [More Information Needed] ### Dataset Summary RF is a tiny parallel corpus of the Declarations of the Swedish Government and its translations. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English (en), Spanish (es), German (de), French (fr), Swedish (sv) ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @InProceedings{TIEDEMANN12.463, author = {J{\"o}rg Tiedemann}, title = {Parallel Data, Tools and Interfaces in OPUS}, booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, month = {may}, date = {23-25}, address = {Istanbul, Turkey}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-7-7}, language = {english} } ``` ### Contributions Thanks to [@akshayb7](https://github.com/akshayb7) for adding this dataset.

opus_rf

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["expert-generated"], "language": ["de", "en", "es", "fr", "sv"], "license": ["unknown"], "multilinguality": ["multilingual"], "size_categories": ["n<1K"], "source_datasets": ["original"], "task_categories": ["translation"], "task_ids": [], "pretty_name": "OpusRf", "config_names": ["de-en", "de-es", "de-fr", "de-sv", "en-es", "en-fr", "en-sv", "es-fr", "es-sv", "fr-sv"], "dataset_info": [{"config_name": "de-en", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["de", "en"]}}}], "splits": [{"name": "train", "num_bytes": 38683, "num_examples": 177}], "download_size": 16029, "dataset_size": 38683}, {"config_name": "de-es", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["de", "es"]}}}], "splits": [{"name": "train", "num_bytes": 2316, "num_examples": 24}], "download_size": 2403, "dataset_size": 2316}, {"config_name": "de-fr", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["de", "fr"]}}}], "splits": [{"name": "train", "num_bytes": 41300, "num_examples": 173}], "download_size": 16720, "dataset_size": 41300}, {"config_name": "de-sv", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["de", "sv"]}}}], "splits": [{"name": "train", "num_bytes": 37414, "num_examples": 178}], "download_size": 15749, "dataset_size": 37414}, {"config_name": "en-es", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["en", "es"]}}}], "splits": [{"name": "train", "num_bytes": 2600, "num_examples": 25}], "download_size": 2485, "dataset_size": 2600}, {"config_name": "en-fr", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["en", "fr"]}}}], "splits": [{"name": "train", "num_bytes": 39503, "num_examples": 175}], "download_size": 16038, "dataset_size": 39503}, {"config_name": "en-sv", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["en", "sv"]}}}], "splits": [{"name": "train", "num_bytes": 35778, "num_examples": 180}], "download_size": 15147, "dataset_size": 35778}, {"config_name": "es-fr", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["es", "fr"]}}}], "splits": [{"name": "train", "num_bytes": 2519, "num_examples": 21}], "download_size": 2469, "dataset_size": 2519}, {"config_name": "es-sv", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["es", "sv"]}}}], "splits": [{"name": "train", "num_bytes": 3110, "num_examples": 28}], "download_size": 2726, "dataset_size": 3110}, {"config_name": "fr-sv", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["fr", "sv"]}}}], "splits": [{"name": "train", "num_bytes": 38627, "num_examples": 175}], "download_size": 15937, "dataset_size": 38627}]}

2024-01-18T11:11:58+00:00

TAGS #task_categories-translation #annotations_creators-found #language_creators-expert-generated #multilinguality-multilingual #size_categories-n<1K #source_datasets-original #language-German #language-English #language-Spanish #language-French #language-Swedish #license-unknown #region-us

# Dataset Card for [Dataset Name] ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: - Paper: URL - Leaderboard: - Point of Contact: ### Dataset Summary RF is a tiny parallel corpus of the Declarations of the Swedish Government and its translations. ### Supported Tasks and Leaderboards ### Languages English (en), Spanish (es), German (de), French (fr), Swedish (sv) ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @akshayb7 for adding this dataset.

8b6c7102f60f76a8eb9e351e50ba06aa24275ba0

# Dataset Card for OpusTedtalks ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://opus.nlpl.eu/TedTalks.php - **Repository:** None - **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf - **Leaderboard:** [More Information Needed] - **Point of Contact:** [More Information Needed] ### Dataset Summary This is a Croatian-English parallel corpus of transcribed and translated TED talks, originally extracted from https://wit3.fbk.eu. The corpus is compiled by Željko Agić and is taken from http://lt.ffzg.hr/zagic provided under the CC-BY-NC-SA license. This corpus is sentence aligned for both language pairs. The documents were collected and aligned using the Hunalign algorithm. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [CC-BY-NC-SA license]<http://creativecommons.org/licenses/by-sa/3.0/> ### Citation Information @InProceedings{TIEDEMANN12.463, author = {J{\"o}rg Tiedemann}, title = {Parallel Data, Tools and Interfaces in OPUS}, booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, month = {may}, date = {23-25}, address = {Istanbul, Turkey}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-7-7}, language = {english} } ### Contributions Thanks to [@rkc007](https://github.com/rkc007) for adding this dataset.

opus_tedtalks

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["en", "hr"], "license": ["unknown"], "multilinguality": ["multilingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["translation"], "task_ids": [], "pretty_name": "OpusTedtalks", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["en", "hr"]}}}], "config_name": "en-hr", "splits": [{"name": "train", "num_bytes": 15249417, "num_examples": 86348}], "download_size": 5639306, "dataset_size": 15249417}}

2024-01-18T11:12:07+00:00

TAGS #task_categories-translation #annotations_creators-found #language_creators-found #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #language-English #language-Croatian #license-unknown #region-us

# Dataset Card for OpusTedtalks ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: None - Paper: URL - Leaderboard: - Point of Contact: ### Dataset Summary This is a Croatian-English parallel corpus of transcribed and translated TED talks, originally extracted from URL. The corpus is compiled by Željko Agić and is taken from URL provided under the CC-BY-NC-SA license. This corpus is sentence aligned for both language pairs. The documents were collected and aligned using the Hunalign algorithm. ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators ### Licensing Information [CC-BY-NC-SA license]<URL @InProceedings{TIEDEMANN12.463, author = {J{\"o}rg Tiedemann}, title = {Parallel Data, Tools and Interfaces in OPUS}, booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, month = {may}, date = {23-25}, address = {Istanbul, Turkey}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-7-7}, language = {english} } ### Contributions Thanks to @rkc007 for adding this dataset.

cbf068b5a764df5725cc923132698f0ebb028785

# Dataset Card for Opus Ubuntu ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://opus.nlpl.eu/Ubuntu.php - **Repository:** None - **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf - **Leaderboard:** [More Information Needed] - **Point of Contact:** [More Information Needed] ### Dataset Summary These are translations of the Ubuntu software package messages, donated by the Ubuntu community. To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs. You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/Ubuntu.php E.g. `dataset = load_dataset("opus_ubuntu", lang1="it", lang2="pl")` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances Example instance: ``` { 'id': '0', 'translation': { 'it': 'Comprende Gmail, Google Docs, Google+, YouTube e Picasa', 'pl': 'Zawiera Gmail, Google Docs, Google+, YouTube oraz Picasa' } } ``` ### Data Fields Each instance has two fields: - **id**: the id of the example - **translation**: a dictionary containing translated texts in two languages. ### Data Splits Each subset simply consists in a train set. We provide the number of examples for certain language pairs: | | train | |:---------|--------:| | as-bs | 8583 | | az-cs | 293 | | bg-de | 184 | | br-es_PR | 125 | | bn-ga | 7324 | | br-hi | 15551 | | br-la | 527 | | bs-szl | 646 | | br-uz | 1416 | | br-yi | 2799 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information BSD "Revised" license (see (https://help.launchpad.net/Legal#Translations_copyright)[https://help.launchpad.net/Legal#Translations_copyright]) ### Citation Information ```bibtex @InProceedings{TIEDEMANN12.463, author = {J{\"o}rg Tiedemann}, title = {Parallel Data, Tools and Interfaces in OPUS}, booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, month = {may}, date = {23-25}, address = {Istanbul, Turkey}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-7-7}, language = {english} } ``` ### Contributions Thanks to [@rkc007](https://github.com/rkc007) for adding this dataset.

opus_ubuntu

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced", "expert-generated"], "language_creators": ["found"], "language": ["ace", "af", "ak", "am", "an", "ang", "ar", "ary", "as", "ast", "az", "ba", "bal", "be", "bem", "ber", "bg", "bho", "bn", "bo", "br", "brx", "bs", "bua", "byn", "ca", "ce", "ceb", "chr", "ckb", "co", "crh", "cs", "csb", "cv", "cy", "da", "de", "dsb", "dv", "dz", "el", "en", "eo", "es", "et", "eu", "fa", "ff", "fi", "fil", "fo", "fr", "frm", "frp", "fur", "fy", "ga", "gd", "gl", "gn", "grc", "gu", "guc", "gv", "ha", "haw", "he", "hi", "hil", "hne", "hr", "hsb", "ht", "hu", "hy", "ia", "id", "ig", "io", "is", "it", "iu", "ja", "jbo", "jv", "ka", "kab", "kg", "kk", "kl", "km", "kn", "ko", "kok", "ks", "ksh", "ku", "kw", "ky", "la", "lb", "lg", "li", "lij", "lld", "ln", "lo", "lt", "ltg", "lv", "mai", "mg", "mh", "mhr", "mi", "miq", "mk", "ml", "mn", "mr", "ms", "mt", "mus", "my", "nan", "nap", "nb", "nds", "ne", "nhn", "nl", "nn", "no", "nso", "ny", "oc", "om", "or", "os", "pa", "pam", "pap", "pl", "pms", "pmy", "ps", "pt", "qu", "rm", "ro", "rom", "ru", "rw", "sa", "sc", "sco", "sd", "se", "shn", "shs", "si", "sk", "sl", "sm", "sml", "sn", "so", "son", "sq", "sr", "st", "sv", "sw", "syr", "szl", "ta", "te", "tet", "tg", "th", "ti", "tk", "tl", "tlh", "tr", "trv", "ts", "tt", "ug", "uk", "ur", "uz", "ve", "vec", "vi", "wa", "wae", "wo", "xal", "xh", "yi", "yo", "zh", "zu", "zza"], "license": ["bsd-3-clause"], "multilinguality": ["multilingual"], "size_categories": ["10K<n<100K", "1K<n<10K", "n<1K"], "source_datasets": ["original"], "task_categories": ["translation"], "task_ids": [], "pretty_name": "Opus Ubuntu", "config_names": ["as-bs", "az-cs", "bg-de", "bn-ga", "br-es_PR", "br-hi", "br-la", "br-uz", "br-yi", "bs-szl"], "language_bcp47": ["ar-SY", "bn-IN", "de-AT", "de-DE", "en-AU", "en-CA", "en-GB", "en-NZ", "en-US", "es-AR", "es-CL", "es-CO", "es-CR", "es-DO", "es-EC", "es-ES", "es-GT", "es-HN", "es-MX", "es-NI", "es-PA", "es-PE", "es-PR", "es-SV", "es-UY", "es-VE", "fa-AF", "fr-CA", "fr-FR", "nl-NL", "pt-BR", "pt-PT", "ta-LK", "zh-CN", "zh-HK", "zh-TW"], "dataset_info": [{"config_name": "as-bs", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["as", "bs"]}}}], "splits": [{"name": "train", "num_bytes": 1037811, "num_examples": 8583}], "download_size": 229723, "dataset_size": 1037811}, {"config_name": "az-cs", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["az", "cs"]}}}], "splits": [{"name": "train", "num_bytes": 17821, "num_examples": 293}], "download_size": 9501, "dataset_size": 17821}, {"config_name": "bg-de", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["bg", "de"]}}}], "splits": [{"name": "train", "num_bytes": 27627, "num_examples": 184}], "download_size": 9994, "dataset_size": 27627}, {"config_name": "br-es_PR", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["br", "es_PR"]}}}], "splits": [{"name": "train", "num_bytes": 8875, "num_examples": 125}], "download_size": 5494, "dataset_size": 8875}, {"config_name": "bn-ga", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["bn", "ga"]}}}], "splits": [{"name": "train", "num_bytes": 584629, "num_examples": 7324}], "download_size": 142710, "dataset_size": 584629}, {"config_name": "br-hi", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["br", "hi"]}}}], "splits": [{"name": "train", "num_bytes": 1300081, "num_examples": 15551}], "download_size": 325415, "dataset_size": 1300081}, {"config_name": "br-la", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["br", "la"]}}}], "splits": [{"name": "train", "num_bytes": 29341, "num_examples": 527}], "download_size": 11565, "dataset_size": 29341}, {"config_name": "bs-szl", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["bs", "szl"]}}}], "splits": [{"name": "train", "num_bytes": 41116, "num_examples": 646}], "download_size": 18134, "dataset_size": 41116}, {"config_name": "br-uz", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["br", "uz"]}}}], "splits": [{"name": "train", "num_bytes": 110278, "num_examples": 1416}], "download_size": 33595, "dataset_size": 110278}, {"config_name": "br-yi", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["br", "yi"]}}}], "splits": [{"name": "train", "num_bytes": 172846, "num_examples": 2799}], "download_size": 41956, "dataset_size": 172846}]}

2024-01-18T11:12:08+00:00

TAGS #task_categories-translation #annotations_creators-crowdsourced #annotations_creators-expert-generated #language_creators-found #multilinguality-multilingual #size_categories-10K<n<100K #size_categories-1K<n<10K #size_categories-n<1K #source_datasets-original #language-Achinese #language-Afrikaans #language-Akan #language-Amharic #language-Aragonese #language-Old English (ca. 450-1100) #language-Arabic #language-Moroccan Arabic #language-Assamese #language-Asturian #language-Azerbaijani #language-Bashkir #language-Baluchi #language-Belarusian #language-Bemba (Zambia) #language-ber #language-Bulgarian #language-Bhojpuri #language-Bengali #language-Tibetan #language-Breton #language-Bodo (India) #language-Bosnian #language-Buriat #language-Bilin #language-Catalan #language-Chechen #language-Cebuano #language-Cherokee #language-Central Kurdish #language-Corsican #language-Crimean Tatar #language-Czech #language-Kashubian #language-Chuvash #language-Welsh #language-Danish #language-German #language-Lower Sorbian #language-Dhivehi #language-Dzongkha #language-Modern Greek (1453-) #language-English #language-Esperanto #language-Spanish #language-Estonian #language-Basque #language-Persian #language-Fulah #language-Finnish #language-Filipino #language-Faroese #language-French #language-Middle French (ca. 1400-1600) #language-Arpitan #language-Friulian #language-Western Frisian #language-Irish #language-Scottish Gaelic #language-Galician #language-Guarani #language-Ancient Greek (to 1453) #language-Gujarati #language-Wayuu #language-Manx #language-Hausa #language-Hawaiian #language-Hebrew #language-Hindi #language-Hiligaynon #language-Chhattisgarhi #language-Croatian #language-Upper Sorbian #language-Haitian #language-Hungarian #language-Armenian #language-Interlingua (International Auxiliary Language Association) #language-Indonesian #language-Igbo #language-Ido #language-Icelandic #language-Italian #language-Inuktitut #language-Japanese #language-Lojban #language-Javanese #language-Georgian #language-Kabyle #language-Kongo #language-Kazakh #language-Kalaallisut #language-Khmer #language-Kannada #language-Korean #language-Konkani (macrolanguage) #language-Kashmiri #language-Kölsch #language-Kurdish #language-Cornish #language-Kirghiz #language-Latin #language-Luxembourgish #language-Ganda #language-Limburgan #language-Ligurian #language-Ladin #language-Lingala #language-Lao #language-Lithuanian #language-Latgalian #language-Latvian #language-Maithili #language-Malagasy #language-Marshallese #language-Eastern Mari #language-Maori #language-Mískito #language-Macedonian #language-Malayalam #language-Mongolian #language-Marathi #language-Malay (macrolanguage) #language-Maltese #language-Creek #language-Burmese #language-Min Nan Chinese #language-Neapolitan #language-Norwegian Bokmål #language-Low German #language-Nepali (macrolanguage) #language-Central Nahuatl #language-Dutch #language-Norwegian Nynorsk #language-Norwegian #language-Pedi #language-Nyanja #language-Occitan (post 1500) #language-Oromo #language-Oriya (macrolanguage) #language-Ossetian #language-Panjabi #language-Pampanga #language-Papiamento #language-Polish #language-Piemontese #language-Papuan Malay #language-Pushto #language-Portuguese #language-Quechua #language-Romansh #language-Romanian #language-Romany #language-Russian #language-Kinyarwanda #language-Sanskrit #language-Sardinian #language-Scots #language-Sindhi #language-Northern Sami #language-Shan #language-Shuswap #language-Sinhala #language-Slovak #language-Slovenian #language-Samoan #language-Central Sama #language-Shona #language-Somali #language-son #language-Albanian #language-Serbian #language-Southern Sotho #language-Swedish #language-Swahili (macrolanguage) #language-Syriac #language-Silesian #language-Tamil #language-Telugu #language-Tetum #language-Tajik #language-Thai #language-Tigrinya #language-Turkmen #language-Tagalog #language-Klingon #language-Turkish #language-Sediq #language-Tsonga #language-Tatar #language-Uighur #language-Ukrainian #language-Urdu #language-Uzbek #language-Venda #language-Venetian #language-Vietnamese #language-Walloon #language-Walser #language-Wolof #language-Kalmyk #language-Xhosa #language-Yiddish #language-Yoruba #language-Chinese #language-Zulu #language-Zaza #license-bsd-3-clause #region-us

Dataset Card for Opus Ubuntu ============================ Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: None * Paper: URL * Leaderboard: * Point of Contact: ### Dataset Summary These are translations of the Ubuntu software package messages, donated by the Ubuntu community. To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs. You can find the valid pairs in Homepage section of Dataset Description: URL E.g. 'dataset = load\_dataset("opus\_ubuntu", lang1="it", lang2="pl")' ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances Example instance: ### Data Fields Each instance has two fields: * id: the id of the example * translation: a dictionary containing translated texts in two languages. ### Data Splits Each subset simply consists in a train set. We provide the number of examples for certain language pairs: Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information BSD "Revised" license (see (URL/URL ### Contributions Thanks to @rkc007 for adding this dataset.

92a3ce9c82b2c84a525fabbb2f3028c5571ee7a9

# Dataset Card for OpusWikipedia ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://opus.nlpl.eu/Wikipedia.php - **Repository:** None - **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf - **Leaderboard:** [More Information Needed] - **Point of Contact:** [More Information Needed] ### Dataset Summary This is a corpus of parallel sentences extracted from Wikipedia by Krzysztof Wołk and Krzysztof Marasek. Tha dataset contains 20 languages and 36 bitexts. To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs, e.g. ```python dataset = load_dataset("opus_wikipedia", lang1="it", lang2="pl") ``` You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/Wikipedia.php ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The languages in the dataset are: - ar - bg - cs - de - el - en - es - fa - fr - he - hu - it - nl - pl - pt - ro - ru - sl - tr - vi ## Dataset Structure ### Data Instances ``` { 'id': '0', 'translation': { "ar": "* Encyclopaedia of Mathematics online encyclopaedia from Springer, Graduate-level reference work with over 8,000 entries, illuminating nearly 50,000 notions in mathematics.", "en": "*Encyclopaedia of Mathematics online encyclopaedia from Springer, Graduate-level reference work with over 8,000 entries, illuminating nearly 50,000 notions in mathematics." } } ``` ### Data Fields - `id` (`str`): Unique identifier of the parallel sentence for the pair of languages. - `translation` (`dict`): Parallel sentences for the pair of languages. ### Data Splits The dataset contains a single `train` split. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ```bibtex @article{WOLK2014126, title = {Building Subject-aligned Comparable Corpora and Mining it for Truly Parallel Sentence Pairs}, journal = {Procedia Technology}, volume = {18}, pages = {126-132}, year = {2014}, note = {International workshop on Innovations in Information and Communication Science and Technology, IICST 2014, 3-5 September 2014, Warsaw, Poland}, issn = {2212-0173}, doi = {https://doi.org/10.1016/j.protcy.2014.11.024}, url = {https://www.sciencedirect.com/science/article/pii/S2212017314005453}, author = {Krzysztof Wołk and Krzysztof Marasek}, keywords = {Comparable corpora, machine translation, NLP}, } ``` ```bibtex @InProceedings{TIEDEMANN12.463, author = {J{\"o}rg Tiedemann}, title = {Parallel Data, Tools and Interfaces in OPUS}, booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, month = {may}, date = {23-25}, address = {Istanbul, Turkey}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-7-7}, language = {english} } ``` ### Contributions Thanks to [@rkc007](https://github.com/rkc007) for adding this dataset.

opus_wikipedia

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["ar", "bg", "cs", "de", "el", "en", "es", "fa", "fr", "he", "hu", "it", "nl", "pl", "pt", "ro", "ru", "sl", "tr", "vi"], "license": ["unknown"], "multilinguality": ["multilingual"], "size_categories": ["100K<n<1M", "10K<n<100K"], "source_datasets": ["original"], "task_categories": ["translation"], "task_ids": [], "pretty_name": "OpusWikipedia", "config_names": ["ar-en", "ar-pl", "en-ru", "en-sl", "en-vi"], "dataset_info": [{"config_name": "ar-en", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["ar", "en"]}}}], "splits": [{"name": "train", "num_bytes": 45207715, "num_examples": 151136}], "download_size": 16097997, "dataset_size": 45207715}, {"config_name": "ar-pl", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["ar", "pl"]}}}], "splits": [{"name": "train", "num_bytes": 304851676, "num_examples": 823715}], "download_size": 104585718, "dataset_size": 304851676}, {"config_name": "en-sl", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["en", "sl"]}}}], "splits": [{"name": "train", "num_bytes": 30479739, "num_examples": 140124}], "download_size": 11727538, "dataset_size": 30479739}, {"config_name": "en-ru", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["en", "ru"]}}}], "splits": [{"name": "train", "num_bytes": 167649057, "num_examples": 572717}], "download_size": 57356138, "dataset_size": 167649057}, {"config_name": "en-vi", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["en", "vi"]}}}], "splits": [{"name": "train", "num_bytes": 7571598, "num_examples": 58116}], "download_size": 2422413, "dataset_size": 7571598}]}

2024-01-18T11:12:09+00:00

TAGS #task_categories-translation #annotations_creators-found #language_creators-found #multilinguality-multilingual #size_categories-100K<n<1M #size_categories-10K<n<100K #source_datasets-original #language-Arabic #language-Bulgarian #language-Czech #language-German #language-Modern Greek (1453-) #language-English #language-Spanish #language-Persian #language-French #language-Hebrew #language-Hungarian #language-Italian #language-Dutch #language-Polish #language-Portuguese #language-Romanian #language-Russian #language-Slovenian #language-Turkish #language-Vietnamese #license-unknown #region-us

# Dataset Card for OpusWikipedia ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: None - Paper: URL - Leaderboard: - Point of Contact: ### Dataset Summary This is a corpus of parallel sentences extracted from Wikipedia by Krzysztof Wołk and Krzysztof Marasek. Tha dataset contains 20 languages and 36 bitexts. To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs, e.g. You can find the valid pairs in Homepage section of Dataset Description: URL ### Supported Tasks and Leaderboards ### Languages The languages in the dataset are: - ar - bg - cs - de - el - en - es - fa - fr - he - hu - it - nl - pl - pt - ro - ru - sl - tr - vi ## Dataset Structure ### Data Instances ### Data Fields - 'id' ('str'): Unique identifier of the parallel sentence for the pair of languages. - 'translation' ('dict'): Parallel sentences for the pair of languages. ### Data Splits The dataset contains a single 'train' split. ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @rkc007 for adding this dataset.

3bf070e433369efccf31285b46ea167e91d73407

# Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:**[XhosaNavy](http://opus.nlpl.eu/XhosaNavy-v1.php) - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This corpus is part of OPUS - the open collection of parallel corpora OPUS Website: http://opus.nlpl.eu ### Supported Tasks and Leaderboards The underlying task is machine translation from English to Xhosa ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012) ### Contributions Thanks to [@lhoestq](https://github.com/lhoestq), [@spatil6](https://github.com/spatil6) for adding this dataset.

opus_xhosanavy

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["en", "xh"], "license": ["unknown"], "multilinguality": ["translation"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["translation"], "task_ids": [], "pretty_name": "OpusXhosanavy", "dataset_info": {"features": [{"name": "translation", "dtype": {"translation": {"languages": ["en", "xh"]}}}], "config_name": "en-xh", "splits": [{"name": "train", "num_bytes": 9654422, "num_examples": 49982}], "download_size": 3263865, "dataset_size": 9654422}}

2024-01-18T11:12:11+00:00

TAGS #task_categories-translation #annotations_creators-found #language_creators-found #multilinguality-translation #size_categories-10K<n<100K #source_datasets-original #language-English #language-Xhosa #license-unknown #region-us

# Dataset Card for [Dataset Name] ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage:XhosaNavy - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary This corpus is part of OPUS - the open collection of parallel corpora OPUS Website: URL ### Supported Tasks and Leaderboards The underlying task is machine translation from English to Xhosa ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012) ### Contributions Thanks to @lhoestq, @spatil6 for adding this dataset.

64fa97f854ca7ca41f23a4b3815c449d83b53513

# Dataset Card for OrangeSum ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** [OrangeSum repository](https://github.com/Tixierae/OrangeSum) - **Paper:** [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321) - **Point of Contact:** [Antoine J.-P. Tixier](Antoine.Tixier-1@colorado.edu) ### Dataset Summary The OrangeSum dataset was inspired by the XSum dataset. It was created by scraping the "Orange Actu" website: https://actu.orange.fr/. Orange S.A. is a large French multinational telecommunications corporation, with 266M customers worldwide. Scraped pages cover almost a decade from Feb 2011 to Sep 2020. They belong to five main categories: France, world, politics, automotive, and society. The society category is itself divided into 8 subcategories: health, environment, people, culture, media, high-tech, unsual ("insolite" in French), and miscellaneous. Each article featured a single-sentence title as well as a very brief abstract, both professionally written by the author of the article. These two fields were extracted from each page, thus creating two summarization tasks: OrangeSum Title and OrangeSum Abstract. ### Supported Tasks and Leaderboards **Tasks:** OrangeSum Title and OrangeSum Abstract. To this day, there is no Leaderboard for this dataset. ### Languages The text in the dataset is in French. ## Dataset Structure ### Data Instances A data instance consists of a news article and a summary. The summary can be a short abstract or a title depending on the configuration. Example: **Document:** Le temps sera pluvieux sur huit départements de la France ces prochaines heures : outre les trois départements bretons placés en vigilance orange jeudi matin, cinq autres départements du sud du Massif Central ont été à leur tour placés en alerte orange pluie et inondation. Il s'agit de l'Aveyron, du Cantal, du Gard, de la Lozère, et de la Haute-Loire. Sur l'ensemble de l'épisode, les cumuls de pluies attendus en Bretagne sont compris entre 40 et 60 mm en 24 heures et peuvent atteindre localement les 70 mm en 24 heures.Par la suite, la dégradation qui va se mettre en place cette nuit sur le Languedoc et le sud du Massif Central va donner sur l'Aveyron une première salve intense de pluie. Des cumuls entre 70 et 100 mm voir 120 mm localement sont attendus sur une durée de 24 heures. Sur le relief des Cévennes on attend de 150 à 200 mm, voire 250 mm très ponctuellement sur l'ouest du Gard et l'est de la Lozère. Cet épisode va s'estomper dans la soirée avec le décalage des orages vers les régions plus au nord. Un aspect orageux se mêlera à ces précipitations, avec de la grêle possible, des rafales de vent et une forte activité électrique. **Abstract:** Outre les trois départements bretons, cinq autres départements du centre de la France ont été placés en vigilance orange pluie-inondation. **Title:** Pluie-inondations : 8 départements en alerte orange. ### Data Fields `text`: the document to be summarized. \ `summary`: the summary of the source document. ### Data Splits The data is split into a training, validation and test in both configuration. | | train | validation | test | |----------|------:|-----------:|-----:| | Abstract | 21400 | 1500 | 1500 | | Title | 30658 | 1500 | 1500 | ## Dataset Creation ### Curation Rationale The goal here was to create a French equivalent of the recently introduced [XSum](https://github.com/EdinburghNLP/XSum/tree/master/XSum-Dataset) dataset. Unlike the historical summarization datasets, CNN, DailyMail, and NY Times, which favor extractive strategies, XSum, as well as OrangeSum require the models to display a high degree of abstractivity to perform well. The summaries in OrangeSum are not catchy headlines, but rather capture the gist of the articles. ### Source Data #### Initial Data Collection and Normalization Each article features a single-sentence title as well as a very brief abstract. Extracting these two fields from each news article page, creates two summarization tasks: OrangeSum Title and OrangeSum Abstract. As a post-processing step, all empty articles and those whose summaries were shorter than 5 words were removed. For OrangeSum Abstract, the top 10% articles in terms of proportion of novel unigrams in the abstracts were removed, as it was observed that such abstracts tend to be introductions rather than real abstracts. This corresponded to a threshold of 57% novel unigrams. For both OrangeSum Title and OrangeSum Abstract, 1500 pairs for testing and 1500 for validation are set aside, and all the remaining ones are used for training. #### Who are the source language producers? The authors of the artiles. ### Annotations #### Annotation process The smmaries are professionally written by the author of the articles. #### Who are the annotators? The authors of the artiles. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The dataset was initially created by Antoine J.-P. Tixier. ### Licensing Information [More Information Needed] ### Citation Information ``` @article{eddine2020barthez, title={BARThez: a Skilled Pretrained French Sequence-to-Sequence Model}, author={Eddine, Moussa Kamal and Tixier, Antoine J-P and Vazirgiannis, Michalis}, journal={arXiv preprint arXiv:2010.12321}, year={2020} } ``` ### Contributions Thanks to [@moussaKam](https://github.com/moussaKam) for adding this dataset.

orange_sum

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["fr"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["summarization"], "task_ids": ["news-articles-headline-generation", "news-articles-summarization"], "paperswithcode_id": "orangesum", "pretty_name": "OrangeSum", "dataset_info": [{"config_name": "abstract", "features": [{"name": "text", "dtype": "string"}, {"name": "summary", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 53531651, "num_examples": 21401}, {"name": "test", "num_bytes": 3785207, "num_examples": 1500}, {"name": "validation", "num_bytes": 3698650, "num_examples": 1500}], "download_size": 23058350, "dataset_size": 61015508}, {"config_name": "title", "features": [{"name": "text", "dtype": "string"}, {"name": "summary", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 65225136, "num_examples": 30659}, {"name": "test", "num_bytes": 3176690, "num_examples": 1500}, {"name": "validation", "num_bytes": 3276713, "num_examples": 1500}], "download_size": 27321627, "dataset_size": 71678539}]}

2024-01-18T11:12:19+00:00

TAGS #task_categories-summarization #task_ids-news-articles-headline-generation #task_ids-news-articles-summarization #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-French #license-unknown #arxiv-2010.12321 #region-us

Dataset Card for OrangeSum ========================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Repository: OrangeSum repository * Paper: BARThez: a Skilled Pretrained French Sequence-to-Sequence Model * Point of Contact: Antoine J.-P. Tixier ### Dataset Summary The OrangeSum dataset was inspired by the XSum dataset. It was created by scraping the "Orange Actu" website: URL Orange S.A. is a large French multinational telecommunications corporation, with 266M customers worldwide. Scraped pages cover almost a decade from Feb 2011 to Sep 2020. They belong to five main categories: France, world, politics, automotive, and society. The society category is itself divided into 8 subcategories: health, environment, people, culture, media, high-tech, unsual ("insolite" in French), and miscellaneous. Each article featured a single-sentence title as well as a very brief abstract, both professionally written by the author of the article. These two fields were extracted from each page, thus creating two summarization tasks: OrangeSum Title and OrangeSum Abstract. ### Supported Tasks and Leaderboards Tasks: OrangeSum Title and OrangeSum Abstract. To this day, there is no Leaderboard for this dataset. ### Languages The text in the dataset is in French. Dataset Structure ----------------- ### Data Instances A data instance consists of a news article and a summary. The summary can be a short abstract or a title depending on the configuration. Example: Document: Le temps sera pluvieux sur huit départements de la France ces prochaines heures : outre les trois départements bretons placés en vigilance orange jeudi matin, cinq autres départements du sud du Massif Central ont été à leur tour placés en alerte orange pluie et inondation. Il s'agit de l'Aveyron, du Cantal, du Gard, de la Lozère, et de la Haute-Loire. Sur l'ensemble de l'épisode, les cumuls de pluies attendus en Bretagne sont compris entre 40 et 60 mm en 24 heures et peuvent atteindre localement les 70 mm en 24 heures.Par la suite, la dégradation qui va se mettre en place cette nuit sur le Languedoc et le sud du Massif Central va donner sur l'Aveyron une première salve intense de pluie. Des cumuls entre 70 et 100 mm voir 120 mm localement sont attendus sur une durée de 24 heures. Sur le relief des Cévennes on attend de 150 à 200 mm, voire 250 mm très ponctuellement sur l'ouest du Gard et l'est de la Lozère. Cet épisode va s'estomper dans la soirée avec le décalage des orages vers les régions plus au nord. Un aspect orageux se mêlera à ces précipitations, avec de la grêle possible, des rafales de vent et une forte activité électrique. Abstract: Outre les trois départements bretons, cinq autres départements du centre de la France ont été placés en vigilance orange pluie-inondation. Title: Pluie-inondations : 8 départements en alerte orange. ### Data Fields 'text': the document to be summarized. 'summary': the summary of the source document. ### Data Splits The data is split into a training, validation and test in both configuration. Dataset Creation ---------------- ### Curation Rationale The goal here was to create a French equivalent of the recently introduced XSum dataset. Unlike the historical summarization datasets, CNN, DailyMail, and NY Times, which favor extractive strategies, XSum, as well as OrangeSum require the models to display a high degree of abstractivity to perform well. The summaries in OrangeSum are not catchy headlines, but rather capture the gist of the articles. ### Source Data #### Initial Data Collection and Normalization Each article features a single-sentence title as well as a very brief abstract. Extracting these two fields from each news article page, creates two summarization tasks: OrangeSum Title and OrangeSum Abstract. As a post-processing step, all empty articles and those whose summaries were shorter than 5 words were removed. For OrangeSum Abstract, the top 10% articles in terms of proportion of novel unigrams in the abstracts were removed, as it was observed that such abstracts tend to be introductions rather than real abstracts. This corresponded to a threshold of 57% novel unigrams. For both OrangeSum Title and OrangeSum Abstract, 1500 pairs for testing and 1500 for validation are set aside, and all the remaining ones are used for training. #### Who are the source language producers? The authors of the artiles. ### Annotations #### Annotation process The smmaries are professionally written by the author of the articles. #### Who are the annotators? The authors of the artiles. ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators The dataset was initially created by Antoine J.-P. Tixier. ### Licensing Information ### Contributions Thanks to @moussaKam for adding this dataset.

6bfe45dd2b8ca760439e74ebc1f4f617781afc9b

# Dataset Card for "oscar" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://oscar-corpus.com](https://oscar-corpus.com) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Dataset Summary OSCAR or **O**pen **S**uper-large **C**rawled [**A**LMAnaCH](https://team.inria.fr/almanach/) co**R**pus is a huge multilingual corpus obtained by language classification and filtering of the [Common Crawl](https://commoncrawl.org/) corpus using the [goclassy](https://github.com/pjox/goclassy) architecture. Data is distributed by language in both original and deduplicated form. The version here is the original OSCAR 2019 release: https://oscar-project.org/post/oscar-2019/ For more recent versions, visit the [oscar-corpus](https://huggingface.co/oscar-corpus) organization on the Hub: - OSCAR 22.01 (released in January 2022): [oscar-corpus/OSCAR-2201](https://huggingface.co/datasets/oscar-corpus/OSCAR-2201) - OSCAR 21.09 (released in September 2021): [oscar-corpus/OSCAR-2109](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109) ### Supported Tasks and Leaderboards OSCAR is mainly inteded to pretrain language models and word represantations. ### Languages All the data is distributed by language, both the original and the deduplicated versions of the data are available. 166 different languages are available. The table in subsection [Data Splits Sample Size](#data-splits-sample-size) provides the language code for each subcorpus as well as the number of words (space separated tokens), lines and sizes for both the original and the deduplicated versions of OSCAR. ## Dataset Structure We show detailed information for all the configurations of the dataset. ### Data Instances <details> <summary>Click to expand the Data/size information for each language (deduplicated)</summary> #### unshuffled_deduplicated_af - **Size of downloaded dataset files:** 65.99 MB - **Size of the generated dataset:** 172.30 MB - **Total amount of disk used:** 238.29 MB An example of 'train' looks as follows. ``` { "id": 0, "text": "aanlyn markte as gevolg van ons voortgesette 'n begrip opsie handel sakeplan pdf terwyl ons steeds die gereelde ons binêre opsies handel" } ``` #### unshuffled_deduplicated_als - **Size of downloaded dataset files:** 1.26 MB - **Size of the generated dataset:** 2.96 MB - **Total amount of disk used:** 4.22 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"De Nazionalpark hät e Flächi vo 170,3 km² und isch dodemit s grösti Naturschutzgebiet vo de Schwiz. Er ligt uf em Gebiet vo de ..." } ``` #### unshuffled_deduplicated_am - **Size of downloaded dataset files:** 61.35 MB - **Size of the generated dataset:** 216.15 MB - **Total amount of disk used:** 277.50 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"አየር መንገዱ ከአዲስ አበባ ወደ ሮም ጣሊያን በማምራት ላይ በነበረበት ጊዜ ረዳት አብራሪው የጉዞውን አቅጣጫ በመቀየር ጄኔቭ አውሮፓላን ማረፊያ በማሳረፍ እጁን ለፖሊስ ሰጥቷል።\\nየኢትዮጵያ መንግስት የ..." } ``` #### unshuffled_deduplicated_an - **Size of downloaded dataset files:** 0.14 MB - **Size of the generated dataset:** 0.85 MB - **Total amount of disk used:** 0.99 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"واااااااأسفاه الأمم تفتخر ب 0 أمي ووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووو..." } ``` #### unshuffled_deduplicated_ar - **Size of downloaded dataset files:** 9.67 GB - **Size of the generated dataset:** 33.57 GB - **Total amount of disk used:** 43.23 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"مرحبا بك عزيز الزائر نتمنى لك أوقاتاً سعيدة معنا وأن نزداد شرفا بخدمتك ولا تنسى التسجيل معنا لتستفيد بكل جديد\\nأهلا وسهلا بك زا..." } ``` #### unshuffled_deduplicated_arz - **Size of downloaded dataset files:** 10.02 MB - **Size of the generated dataset:** 35.91 MB - **Total amount of disk used:** 45.94 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"بنى عجل : قبيلة من عجل بن لجيم بن صعب بن على بن بكر بن وائل انتقل اغلبهم الى البصرة فى العراق و اصفهان و خراسان فى ايران و اذرب..." } ``` #### unshuffled_deduplicated_as - **Size of downloaded dataset files:** 15.51 MB - **Size of the generated dataset:** 74.07 MB - **Total amount of disk used:** 89.58 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"আমি, এই সংগঠনৰ সদস্য সকলে একেলগ হৈ অসমকে ধৰি ভাৰতৰ উত্তৰ পূৰ্বাঞ্চলৰ অমূল্য কলা-সাংস্কৃতিক সম্পদৰাজি বৃহত্তৰ অষ্ট্ৰেলিয়াৰ সন্মু..." } ``` #### unshuffled_deduplicated_ast - **Size of downloaded dataset files:** 0.86 MB - **Size of the generated dataset:** 2.17 MB - **Total amount of disk used:** 3.03 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"The Killers llanzaron el so álbum debú, Hot Fuss, en xunu de 2004 nel Reinu Xuníu, al traviés de la discográfica Lizard King, y..." } ``` #### unshuffled_deduplicated_av - **Size of downloaded dataset files:** 0.07 MB - **Size of the generated dataset:** 0.34 MB - **Total amount of disk used:** 0.41 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Жинда малъараб ва божизе бегьулеб рагІудаса кьуризе бегьуларо гьев. Гьес насихІат гьабизе кколелъул бацІцІадаб диналъул рахъалъ..." } ``` #### unshuffled_deduplicated_az - **Size of downloaded dataset files:** 521.74 MB - **Size of the generated dataset:** 1.53 GB - **Total amount of disk used:** 2.05 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"AZTV-Artıq 7 ildir ki, Abşeron rayonu dotasiya almadan bütün xərclərini yerli daxilolmalar hesabına maliyyələşdirir.\\nDünən, 10..." } ``` #### unshuffled_deduplicated_azb - **Size of downloaded dataset files:** 5.19 MB - **Size of the generated dataset:** 20.08 MB - **Total amount of disk used:** 25.27 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"لعلی ١٣-جو عصرده یاشاییب یاراتمیش گؤرکملی آذربایجان شاعرلریندندیر. ١٢٢٤-جی ایلده تبریزده آنادان اولموشدور، گنج یاشلاریندا تیجار..." } ``` #### unshuffled_deduplicated_ba - **Size of downloaded dataset files:** 25.98 MB - **Size of the generated dataset:** 93.84 MB - **Total amount of disk used:** 119.82 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Күҙәтеү ҡуласаһы моделен хәҙер Мифтахетдин Аҡмулла исемендәге Башҡорт дәүләт педагогия университетында ла эшләргә мөмкин\\t\\nКүҙ..." } ``` #### unshuffled_deduplicated_bar - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` { "id": 0, "text": " vo" } ``` #### unshuffled_deduplicated_bcl - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"& ÿ ó / í 0 - ø û ù ö ú ð ï ú \\u0014 ù þ ô ö í ÷ ò \\u0014 ÷ í ù û ö í \\u0001 û ñ ç þ \\u0001 ð \\u0007 þ ò ñ ñ ò ô \\u0017 û ö ô ÷..." } ``` #### unshuffled_deduplicated_be - **Size of downloaded dataset files:** 306.70 MB - **Size of the generated dataset:** 1.08 GB - **Total amount of disk used:** 1.39 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Брэсцкія ўлады не дазволілі прафсаюзу РЭП правесці пікетаванне ў парку Воінаў-інтэрнацыяналістаў 30 мая 2018 года.\\nСітуацыю пр..." } ``` #### unshuffled_deduplicated_bg - **Size of downloaded dataset files:** 3.85 GB - **Size of the generated dataset:** 14.45 GB - **Total amount of disk used:** 18.30 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ЖАЛБОПОДАТЕЛЯТ директор на Дирекция „ Обжалване и данъчно-осигурителна практика“- Бургас, редовно призован, се представлява от ..." } ``` #### unshuffled_deduplicated_bh - **Size of downloaded dataset files:** 0.01 MB - **Size of the generated dataset:** 0.04 MB - **Total amount of disk used:** 0.04 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"सुकमा जिला भारत के छत्तीसगढ़ राज्य में एगो जिला बाटे। एकर मुख्यालय सुकमा शहर बाटे। एकर कुल रकबा 5636 वर्ग कि॰मी॰ बाटे।\"..." } ``` #### unshuffled_deduplicated_bn - **Size of downloaded dataset files:** 1.26 GB - **Size of the generated dataset:** 6.24 GB - **Total amount of disk used:** 7.50 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ভড়ং সর্বস্ব বাংলা আর্ট অ্যান্ড কালচারের হিসাব গুলিয়ে দেওয়ার ম্যাজিকের নাম ব্রাত্য রাইসু November 23, 2017\\nTagged with ডায়োজিনি..." } ``` #### unshuffled_deduplicated_bo - **Size of downloaded dataset files:** 22.37 MB - **Size of the generated dataset:** 144.65 MB - **Total amount of disk used:** 167.02 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"བོད་མི་འདི་དག་ནི་རང་རྒྱུད་སྒོ་རུ་ཕུད་དེ་གཞན་རྒྱུད་པང་དུ་ཉར་ནས་གསོ་སྐྱོང་བྱེད་དགོས་ཟེར་བ་དང་གཅིག་མཚུངས་རེད།\\nཚན་རིག་ནི་དང་ཐོག་རང..." } ``` #### unshuffled_deduplicated_bpy - **Size of downloaded dataset files:** 0.19 MB - **Size of the generated dataset:** 1.78 MB - **Total amount of disk used:** 1.97 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"পৌরসভা এহার আয়তন (লয়াহান) ২,৭৩০,.৬৩ বর্গ কিলোমিটার। পৌরসভা এহার মাপাহানর অক্ষাংশ বারো দ্রাঘিমাংশ ইলতাই 18.63° S 48.18° W ।[১]..." } ``` #### unshuffled_deduplicated_br - **Size of downloaded dataset files:** 6.47 MB - **Size of the generated dataset:** 17.00 MB - **Total amount of disk used:** 23.47 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Ar mank Magalhães(Daveoù a vank) a zo ur spesad evned, Spheniscus magellanicus an anv skiantel anezhañ.\\nGallout a reer implijo..." } ``` #### unshuffled_deduplicated_bs - **Size of downloaded dataset files:** 0.04 MB - **Size of the generated dataset:** 0.15 MB - **Total amount of disk used:** 0.18 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ž šř é ú šř šř ě šř ž é č ě ž ů ě ď éé ýš ě ě Ž č š ý ě ď é ýš ě ď ě éé ýš ě č ž ě š ý ď ě ýš é ú č ž č š ý ď ý ž é éě ď é č ýš..." } ``` #### unshuffled_deduplicated_bxr - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.01 MB - **Total amount of disk used:** 0.01 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"2002 оной хабар буряад хэлэ бэшэгэй һалбари Үндэһэтэнэй хүмүүнлиг ухаанай дээдэ һургуули болгогдожо өөршэлэгдөө.\\nХарин мүнөө б..." } ``` #### unshuffled_deduplicated_ca - **Size of downloaded dataset files:** 1.73 GB - **Size of the generated dataset:** 4.57 GB - **Total amount of disk used:** 6.30 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Daniel Vendrell, conegut com Vandrell, ha sigut un dels il•lustradors contemporanis més influents, representant a la nova onada..." } ``` #### unshuffled_deduplicated_cbk - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano..." } ``` #### unshuffled_deduplicated_ce - **Size of downloaded dataset files:** 1.87 MB - **Size of the generated dataset:** 7.04 MB - **Total amount of disk used:** 8.90 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Шаьш анархисташ ду бохучу жигархойн дIахьедарехь дуьйцу, оьрсийн ницкъаллийн структурийн а, федералан каналан а Iалашонаш \\\"мар..." } ``` #### unshuffled_deduplicated_ceb - **Size of downloaded dataset files:** 7.12 MB - **Size of the generated dataset:** 24.83 MB - **Total amount of disk used:** 31.95 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Si Isko walay pupamilok nga nagtan-aw sa unahan, natugaw. “Naunsa ka gud diha Isko nga layo man kaayo ang imong panan-aw?” ni I..." } ``` #### unshuffled_deduplicated_ckb - **Size of downloaded dataset files:** 60.32 MB - **Size of the generated dataset:** 237.72 MB - **Total amount of disk used:** 298.05 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"رسی رۆژ - ساڵێک دوای بومەلەرزەی کرماشان میوانی بەرنامە : کاک سیاوەش حەیاتی چالاکی مەدەنی -قەسری شیرین\\nپارچە موزیک 30 / 10 / 20..." } ``` #### unshuffled_deduplicated_cs - **Size of downloaded dataset files:** 10.49 GB - **Size of the generated dataset:** 25.71 GB - **Total amount of disk used:** 36.20 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Akce anarchistů proti připravovanému novému služební řádu a nízkým mzdám 1903 – Historie českého anarchismu (1880 – 1939)\\nRost..." } ``` #### unshuffled_deduplicated_cv - **Size of downloaded dataset files:** 7.47 MB - **Size of the generated dataset:** 27.49 MB - **Total amount of disk used:** 34.95 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Шыранӑ чухне ӑнсӑртран латин кирилл саспаллисем вырӑнне латин саспаллисене ҫырсан, сайт эсир ҫырнине юсама тӑрӑшӗ.\\nКу сайтра ч..." } ``` #### unshuffled_deduplicated_cy - **Size of downloaded dataset files:** 53.63 MB - **Size of the generated dataset:** 141.22 MB - **Total amount of disk used:** 194.86 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Mae capeli Cymreig yr Andes ym Mhatagonia wedi cyhoeddi na fydd gwasanaethau yno weddill y mis, oherwydd yr eira trwm sydd wedi..." } ``` #### unshuffled_deduplicated_da - **Size of downloaded dataset files:** 3.82 GB - **Size of the generated dataset:** 10.24 GB - **Total amount of disk used:** 14.06 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Den 2.-5. februar 2016 løb det tredje kursus i uddannelsen af 4kommunesamarbejdets Local Impact Coaches, af stablen i Gentofte ..." } ``` #### unshuffled_deduplicated_de - **Size of downloaded dataset files:** 60.80 GB - **Size of the generated dataset:** 156.30 GB - **Total amount of disk used:** 217.10 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Auf dieser Seite gibt es mind. ein YouTube Video. Cookies für diese Website wurden abgelehnt. Dadurch können keine YouTube Vide..." } ``` #### unshuffled_deduplicated_diq - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` { "id": 0, "text": "Zıwanê Slawki, zıwano merdumanê Slawano. Zıwanê Slawki yew lızgeyê Zıwananê Hind u Ewropao. Keyeyê Zıwananê Slawki beno hirê letey:" } ``` #### unshuffled_deduplicated_dsb - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.01 MB - **Total amount of disk used:** 0.01 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "Pśiklaskaju južo pśed pśedstajenim... 1500 źiśi njamóžo wěcej docakaś, měsćańska hala w Chóśebuzu - wupśedana." } ``` #### unshuffled_deduplicated_dv - **Size of downloaded dataset files:** 16.84 MB - **Size of the generated dataset:** 82.19 MB - **Total amount of disk used:** 99.03 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ބ. އަތޮޅުގައި ހުޅުވަން ތައްޔާރުވަމުން އަންނަ ވައްކަރު ރިސޯޓުގައި ވަޒީފާ އަދާކުރަން ޝައުގުވެރިވާ ފަރާތްތަކަށް ކުރިމަތިލުމުގެ ފުރ..." } ``` #### unshuffled_deduplicated_el - **Size of downloaded dataset files:** 7.91 GB - **Size of the generated dataset:** 28.74 GB - **Total amount of disk used:** 36.65 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Νεκρός εντοπίστηκε μέσα στο σπίτι του στην οδό Ηρώδου Αττικού στον αριθμό 7 ο επικεφαλής του προξενικού τμήματος της Ρωσικής πρ..." } ``` #### unshuffled_deduplicated_eml - **Size of downloaded dataset files:** 0.01 MB - **Size of the generated dataset:** 0.02 MB - **Total amount of disk used:** 0.03 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"A séguit dal prucès ad rubutiśasiòṅ di abitànt dal pòpul ad Mikenes, Angoras 'l è finî dènt'r a 'n robot cun la tèsta dna rana ..." } ``` #### unshuffled_deduplicated_en - **Size of downloaded dataset files:** 496.50 GB - **Size of the generated dataset:** 1299.75 GB - **Total amount of disk used:** 1796.24 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Mtendere Village was inspired by the vision of Chief Napoleon Dzombe, which he shared with John Blanchard during his first visi..." } ``` #### unshuffled_deduplicated_eo - **Size of downloaded dataset files:** 92.86 MB - **Size of the generated dataset:** 240.12 MB - **Total amount of disk used:** 332.99 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Ĉu ... preĝi | mediti | ricevi instigojn || kanti | muziki || informiĝi | legi | studi || prepari Diservon\\nTemas pri kolekto d..." } ``` #### unshuffled_deduplicated_es - **Size of downloaded dataset files:** 60.46 GB - **Size of the generated dataset:** 160.86 GB - **Total amount of disk used:** 221.32 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Como se librará de la celulitis en el gimnasio La piel superflua en las manos después del adelgazamiento, Los bailes fáciles pa..." } ``` #### unshuffled_deduplicated_et - **Size of downloaded dataset files:** 966.79 MB - **Size of the generated dataset:** 2.45 GB - **Total amount of disk used:** 3.41 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"MTÜ AB Video järgib oma tegevuses kodanikuühenduste eetilise tegevuse üldtunnustatud põhimõtteid, mis on lühidalt kokkuvõetud 7..." } ``` #### unshuffled_deduplicated_eu - **Size of downloaded dataset files:** 134.68 MB - **Size of the generated dataset:** 363.93 MB - **Total amount of disk used:** 498.61 MB An example of 'train' looks as follows. ``` { "id": 0, "text": "Gure jarduerek eraikuntzarekin, elkarbizitzarekin, hirigintzarekin eta ekologiarekin dute harremana, baita ideia eta konponbideak irudikatu eta garatzearekin ere, eraikuntza sektorea hobetuz, pertsonen erosotasuna eta bizi-kalitatea hobetzeko." } ``` #### unshuffled_deduplicated_fa - **Size of downloaded dataset files:** 10.46 GB - **Size of the generated dataset:** 40.06 GB - **Total amount of disk used:** 50.52 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"قـــــــــــــــــرار بود با هم کنـــــــــــــار بیایم نه اینکه از کنــــــــــــار هم رد بشیم...!!!\\nاگر روزی دلت لبریز غم بو..." } ``` #### unshuffled_deduplicated_fi - **Size of downloaded dataset files:** 5.38 GB - **Size of the generated dataset:** 13.99 GB - **Total amount of disk used:** 19.37 GB An example of 'train' looks as follows. ``` { "id": 1, "text": "Kiitos Deelle kaikesta - 1,5 viikkoa kulunut, kun Dee ei ole enää ollut omani. Reilu viikko sitten sunnuntaina vein Deen uuteen kotiinsa. Itselläni on ollut niin ristiriitaiset t..." } ``` #### unshuffled_deduplicated_fr - **Size of downloaded dataset files:** 55.46 GB - **Size of the generated dataset:** 148.28 GB - **Total amount of disk used:** 203.75 GB An example of 'train' looks as follows. ``` { "id": 0, "text": "Média de débat d'idées, de culture et de littérature. Récits, décryptages, analyses, portraits et critiques autour de la vie des idées. Magazine engagé, ouvert aux autres et au monde.. Bring up to date in french" } ``` #### unshuffled_deduplicated_frr - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Hiragana’ Practice’Sheet’1’(A -O)’ ’ Name:’________ __________________________’Section:’_______________ _’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ..." } ``` #### unshuffled_deduplicated_fy - **Size of downloaded dataset files:** 10.27 MB - **Size of the generated dataset:** 26.73 MB - **Total amount of disk used:** 37.00 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "Nim in sêfte ride op Holmsjön, yn ien fan 'e lytse marren yn de omkriten, of nim se op avontueren lykas nonresidential. lâns Indalsälven wetter. Holm Sportklubb hawwe kano 's te huur, yn gearwurking mei de Baltyske Power konferinsje." } ``` #### unshuffled_deduplicated_ga - **Size of downloaded dataset files:** 22.22 MB - **Size of the generated dataset:** 63.86 MB - **Total amount of disk used:** 86.08 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Is fóram é seo chun plé a dhéanamh ar an leabhar atá roghnaithe do mhí na Samhna 2013 amháin. Ní féidir ach le baill chláraithe..." } ``` #### unshuffled_deduplicated_gd - **Size of downloaded dataset files:** 0.42 MB - **Size of the generated dataset:** 1.36 MB - **Total amount of disk used:** 1.78 MB An example of 'train' looks as follows. ``` { "id": 0, "text": "Zhou Yujun, a 'phàrtaidh Rùnaire Comataidh Sgìre Yanfeng ann Hengyang bhaile agus a Sgìre pàrtaidh agus an riaghaltas a' bhuidheann-riochdachaidh a 'tighinn a chèilidh air ar companaidh air Apr. 14, 2017." } ``` #### unshuffled_deduplicated_gl - **Size of downloaded dataset files:** 155.85 MB - **Size of the generated dataset:** 408.34 MB - **Total amount of disk used:** 564.19 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"O persoal de Inditex da provincia de Pontevedra segue a reclamar iguais condicións laborais no conxunto do país - CIG: Confeder..." } ``` #### unshuffled_deduplicated_gn - **Size of downloaded dataset files:** 0.01 MB - **Size of the generated dataset:** 0.02 MB - **Total amount of disk used:** 0.03 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"º ѐÆÚÓ À Ã Ð É Æ ¾ Ä ΠÀ ¼ Æ É ÄÛ = Ü Ý\\\"Þ ß†à á â ã ä å æçè ã é ê â å àë ì æê íî é á ë ï í çì àð í Ü à ñ ê é ò ä ì\"..." } ``` #### unshuffled_deduplicated_gom - **Size of downloaded dataset files:** 0.38 MB - **Size of the generated dataset:** 1.87 MB - **Total amount of disk used:** 2.24 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"दुष्ट शीळ हें कौरवांचें । रामें सविस्तर देखूनि साचें । बोलिले वचनें जें दुर्वाचे । करी तयांचें अनुस्मरण ॥२२०॥\"..." } ``` #### unshuffled_deduplicated_gu - **Size of downloaded dataset files:** 162.97 MB - **Size of the generated dataset:** 759.34 MB - **Total amount of disk used:** 922.32 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"અધિક માસ ચાલે છે. સમગ્ર ભારતમાં અને તેમાંય ખાસ કરીને પવિત્ર કે ધાર્મિક કહેવાય છે તેવા સ્થાનક પર કથાનો દોર ચાલે છે. ઉનાળાની કાળઝ..." } ``` #### unshuffled_deduplicated_he - **Size of downloaded dataset files:** 3.04 GB - **Size of the generated dataset:** 10.47 GB - **Total amount of disk used:** 13.51 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"זקוקים לרשתות נגד יתושים? מחפשים רשת מתאימה לחלון צר וקטן? רשתות נגד יתושים אקורדיון של חברת קליר-מש הן הפתרון.\\nרשתות לחלונות ..." } ``` #### unshuffled_deduplicated_hi - **Size of downloaded dataset files:** 2.01 GB - **Size of the generated dataset:** 9.57 GB - **Total amount of disk used:** 11.58 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"'आइटम गर्ल' बनकर हिट हुई थीं राखी सावंत, आज करीना-कटरीना तक फॉलो कर रही हैं ट्रेंड नक्‍सलियों का दम निकालेगा बाइक ग्रेनेड लॉन्च..." } ``` #### unshuffled_deduplicated_hr - **Size of downloaded dataset files:** 46.74 MB - **Size of the generated dataset:** 121.50 MB - **Total amount of disk used:** 168.23 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"U raspravi je sudjelovao i HSS-ov saborski zastupnik rekavši kako poljoprivrednici ne osjete mjere o kojima ministar govori jer..." } ``` #### unshuffled_deduplicated_hsb - **Size of downloaded dataset files:** 0.72 MB - **Size of the generated dataset:** 1.89 MB - **Total amount of disk used:** 2.61 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Budyšin (SN/BŠe). Elektronikarjo mějachu lětsa cyle hinaši zazběh do swojeho wukubłanja. Wokrjesne rjemjeslnistwo bě mjenujcy w..." } ``` #### unshuffled_deduplicated_ht - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan..." } ``` #### unshuffled_deduplicated_hu - **Size of downloaded dataset files:** 7.37 GB - **Size of the generated dataset:** 19.09 GB - **Total amount of disk used:** 26.46 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"monster - Amatőr, házi szex videók és kezdő csjaok pornó filmjei. - Free amateur, home made sex videos and online porn movies. ..." } ``` #### unshuffled_deduplicated_hy - **Size of downloaded dataset files:** 393.62 MB - **Size of the generated dataset:** 1.56 GB - **Total amount of disk used:** 1.96 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Արցախի Հանրապետության հռչակման 26-րդ տարեդարձի կապակցությամբ Շուշիի Արվեստի կենտրոնում կազմակերպվել է մոսկվաբնակ նկարիչներ՝ հայ..." } ``` #### unshuffled_deduplicated_ia - **Size of downloaded dataset files:** 0.05 MB - **Size of the generated dataset:** 0.38 MB - **Total amount of disk used:** 0.43 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha h..." } ``` #### unshuffled_deduplicated_id - **Size of downloaded dataset files:** 6.00 GB - **Size of the generated dataset:** 17.05 GB - **Total amount of disk used:** 23.05 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Perihal dari itu, kalau kunci hal yang demikian hilang, pemilik wajib melapor ke bengkel sah untuk dibuatkan kunci baru dengan ..." } ``` #### unshuffled_deduplicated_ie - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` { "id": 0, "text": "Plastic Yo Yo Metal Yo Yos Wooden Yo Yo Keychain Yo Yo Translucent Yo Yo Light Up Yo Yo Globe Yo Yo Stress Reliever Yo Yo Jellyfish Yo Yo Sports Ball Yo Yo Sound Yo Yo Miniature Yo Yo Promotional Yo Yo Novelty Yo Yo Video Game Yo Yo ECO Recycled Yo Yo" } ``` #### unshuffled_deduplicated_ilo - **Size of downloaded dataset files:** 0.23 MB - **Size of the generated dataset:** 0.68 MB - **Total amount of disk used:** 0.91 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Segun ken ni Ping-ay, ti yellow corn ti maysa kadagiti nadakamat a liberalized agricultural commodity iti daytoy a free trade k..." } ``` #### unshuffled_deduplicated_io - **Size of downloaded dataset files:** 0.04 MB - **Size of the generated dataset:** 0.14 MB - **Total amount of disk used:** 0.19 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Chekia esas parlamentala republiko. La chefo di stato esas la prezidanto. Til 2013 lu elektesis dal parlamento. Pos ta yaro, ol..." } ``` #### unshuffled_deduplicated_is - **Size of downloaded dataset files:** 332.87 MB - **Size of the generated dataset:** 894.28 MB - **Total amount of disk used:** 1.23 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Eyjar.net - upplýsinga- og fréttamiðill um Vestmannaeyjar - Fréttir - Nái núverandi stefna stjórnvalda fram að ganga mun það va..." } ``` #### unshuffled_deduplicated_it - **Size of downloaded dataset files:** 27.93 GB - **Size of the generated dataset:** 74.09 GB - **Total amount of disk used:** 102.03 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Jaundice - causes, treatment & pathology massaggio a osteochondrosis dellindizio di una controindicazione\\nTrattamento su un co..." } ``` #### unshuffled_deduplicated_ja - **Size of downloaded dataset files:** 40.80 GB - **Size of the generated dataset:** 113.63 GB - **Total amount of disk used:** 154.44 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"神社などへ一緒に同行して、様々な角度のショットで家族写真やお子様の写真を撮影致します！お好みに合わせて様々な写真を取ることができますので、その場でカメラマンへのリクエストも可能です！お子様の晴れ姿を、緊張していない自然な笑顔で残しませんか？\\n※七五三の..." } ``` #### unshuffled_deduplicated_jbo - **Size of downloaded dataset files:** 0.20 MB - **Size of the generated dataset:** 0.70 MB - **Total amount of disk used:** 0.91 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "ni'o 23 la cimast. cu 23moi djedi fi'o masti la cimast. noi ke'a cu cimoi masti .i 22 la cimast. cu purlamdei .ije 24 la cimast. cu bavlamdei" } ``` #### unshuffled_deduplicated_jv - **Size of downloaded dataset files:** 0.21 MB - **Size of the generated dataset:** 0.62 MB - **Total amount of disk used:** 0.82 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"José Mourinho (diwaca: [ʒuˈzɛ moˈɾiɲu]; lair ing Setubal, Portugal, 26 Januari 1963; umur 55 taun) iku salah siji pelatih bal k..." } ``` #### unshuffled_deduplicated_ka - **Size of downloaded dataset files:** 377.23 MB - **Size of the generated dataset:** 1.99 GB - **Total amount of disk used:** 2.36 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"წამიყვანე შენთან ერთად (ქართულად) / Возьми меня с собой (картулад) / (რუსული სერიალები ქართულად) (რუსების პორნო ონლაინში) (ruse..." } ``` #### unshuffled_deduplicated_kk - **Size of downloaded dataset files:** 389.12 MB - **Size of the generated dataset:** 1.59 GB - **Total amount of disk used:** 1.97 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Түлкібас ауданында «Латын негізді әліпби мен емле ережесі туралы насихат» жобасының тобы семинар өткізді\\nЕлорданың «Қазақстан»..." } ``` #### unshuffled_deduplicated_km - **Size of downloaded dataset files:** 114.48 MB - **Size of the generated dataset:** 610.61 MB - **Total amount of disk used:** 725.09 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ខ្សឹបដាក់ត្រចៀក៖ លោក សួស សុផានិត នាយផ្នែករដ្ឋបាលព្រៃឈើ ស្រុកភ្នំក្រវាញ់ ដែលទើបឡើងកាន់តំណែងថ្មី បើកដៃឲ្យឈ្នួញ ប្រព្រឹត្តបទល្មើស ..." } ``` #### unshuffled_deduplicated_kn - **Size of downloaded dataset files:** 215.52 MB - **Size of the generated dataset:** 1.08 GB - **Total amount of disk used:** 1.30 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ರಾಷ್ಟ್ರಪತಿ ಪ್ರಣಬ್ ಮುಖರ್ಜಿಯಿಂದ ಪದ್ಮ ಪ್ರಶಸ್ತಿ ಪ್ರದಾನ | President Pranab Mukherjee Confers Padma Awards | Photo Gallery on Kannada..." } ``` #### unshuffled_deduplicated_ko - **Size of downloaded dataset files:** 4.46 GB - **Size of the generated dataset:** 12.00 GB - **Total amount of disk used:** 16.47 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"CIA 프로젝트에서는 데이터베이스로 들어오는 요청을 중간에 수집(Sniffing)하고 수집한 데이터를 분석(Parsing)하여 그로 인한 결과를 판단하여 알릴 수 있는 시스템(Push Service)이 필요하다. 그리고 연구를 ..." } ``` #### unshuffled_deduplicated_krc - **Size of downloaded dataset files:** 0.62 MB - **Size of the generated dataset:** 2.41 MB - **Total amount of disk used:** 3.03 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Шамханланы, Бийлени къаршысына ябушуп, Батыр уланларыбызны къоллары булан «ортакъ ожакъ» къургъанбыз. Шо иш уллу зараллы иш бол..." } ``` #### unshuffled_deduplicated_ku - **Size of downloaded dataset files:** 23.34 MB - **Size of the generated dataset:** 63.09 MB - **Total amount of disk used:** 86.43 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Me di 114 bernameyên xwe yên berê da perçeyên ji berhemên zanyarî yên kurdzanên mezin bi wergera kurdî da ...\\nMe di 114 bernam..." } ``` #### unshuffled_deduplicated_kv - **Size of downloaded dataset files:** 0.33 MB - **Size of the generated dataset:** 1.21 MB - **Total amount of disk used:** 1.54 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Коми кытшыслӧн ыджытжык тор вӧр увтын куйлӧ, сійӧн и фаунасӧ татӧн аркмӧтӧны вӧрын олісь подаэз. Ассямаӧн лоӧ сія, мый кытшас с..." } ``` #### unshuffled_deduplicated_kw - **Size of downloaded dataset files:** 0.01 MB - **Size of the generated dataset:** 0.02 MB - **Total amount of disk used:** 0.02 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼Pray without ceasing🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏..." } ``` #### unshuffled_deduplicated_ky - **Size of downloaded dataset files:** 106.22 MB - **Size of the generated dataset:** 408.40 MB - **Total amount of disk used:** 514.61 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Turmush: Бишкек шаардык кеңешинин кезексиз отурумунда мэрге ишенбөөчүлүк көрсөтүү маселеси каралат, - депутат Т.Сагынов\\nБишкек..." } ``` #### unshuffled_deduplicated_la - **Size of downloaded dataset files:** 3.42 MB - **Size of the generated dataset:** 9.79 MB - **Total amount of disk used:** 13.22 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Hæ sunt generationes Noë: Noë vir justus atque perfectus fuit in generationibus suis; cum Deo ambulavit.\\nEcce ego adducam aqua..." } ``` #### unshuffled_deduplicated_lb - **Size of downloaded dataset files:** 8.30 MB - **Size of the generated dataset:** 21.42 MB - **Total amount of disk used:** 29.72 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Während dem Gaardefestival \\\"Ambiance Jardins\\\" vum 15. bis de 17. Mee huet den SNJ nees zesumme mam Groupe Animateur en Inform..." } ``` #### unshuffled_deduplicated_lez - **Size of downloaded dataset files:** 0.77 MB - **Size of the generated dataset:** 3.08 MB - **Total amount of disk used:** 3.84 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Ахцегь хуьр, виридалай ч1ехи лезги хуьрерикая я. Ам Урусатдин виридалай къиблепатавай хуьрерикай я. Ин хуьр...\"..." } ``` #### unshuffled_deduplicated_li - **Size of downloaded dataset files:** 0.01 MB - **Size of the generated dataset:** 0.03 MB - **Total amount of disk used:** 0.04 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"'t Good Goedenraad aan de Ezerbaek besjteit oet 'n kesjtièl mèt gesjlote haof en 'n park van 26 hectare. Hie in sjtoon väól beu..." } ``` #### unshuffled_deduplicated_lmo - **Size of downloaded dataset files:** 0.10 MB - **Size of the generated dataset:** 0.46 MB - **Total amount of disk used:** 0.57 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Serét (en tortonés: Sregh; en piemontés: Srèj) l'è 'n cümü italià, de la regiù del Piemónt, en Pruvìncia de Alessandria. El g'h..." } ``` #### unshuffled_deduplicated_lo - **Size of downloaded dataset files:** 23.63 MB - **Size of the generated dataset:** 119.29 MB - **Total amount of disk used:** 142.92 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"ຜູ້ພິພາກສາ ປະຈຳເຂດ ສຫລ ທ່ານນຶ່ງ ຕັດສິນວ່າ ໂຄງການເກັບກຳຂໍ້ມູນ ທາງໂທລະສັບ ຂອງອົງການ ຄວາມໝັ້ນຄົງແຫ່ງຊາດ ແມ່ນຖືກຕ້ອງ ຕາມກົດໝາຍ.\\nກະ..." } ``` #### unshuffled_deduplicated_lrc - **Size of downloaded dataset files:** 0.02 MB - **Size of the generated dataset:** 0.06 MB - **Total amount of disk used:** 0.08 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"آرلینگتون یئ گئل د شأریا ڤولاتچە ڤیرجینیا و یئ گئل د شأریا ڤولات ڤولاتچە یا یأکاگئرئتە ئمریکاە. ئی شأر دویومی کألوٙن شأر د راسا..." } ``` #### unshuffled_deduplicated_lt - **Size of downloaded dataset files:** 1.65 GB - **Size of the generated dataset:** 4.20 GB - **Total amount of disk used:** 5.86 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Čir vir vir pavasaris! Čia čia čia… dalinamės labai simpatiška video pamokėle, kurią pristato ab888art galerija.\\nBe galo papra..." } ``` #### unshuffled_deduplicated_lv - **Size of downloaded dataset files:** 710.45 MB - **Size of the generated dataset:** 1.91 GB - **Total amount of disk used:** 2.62 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Dekoratīvi sliekšņi MITSUBISHI OUTLANDER 2007, izgatavoti no ovālas formas, pulētas nerūsējošā tērauda caurules...\\ndažādas tūn..." } ``` #### unshuffled_deduplicated_mai - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.01 MB - **Total amount of disk used:** 0.01 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"१ · २ · ३ · ४ · ५ · ६ · ७ · ८ · ९ · १० · ११ · १२ · १३ · १४ · १५ · १६ · १७ · १८ · १९ · २० · २१ · २२ · २३ · २४ · २५ · २६ · २७ · २..." } ``` #### unshuffled_deduplicated_mg - **Size of downloaded dataset files:** 4.30 MB - **Size of the generated dataset:** 13.59 MB - **Total amount of disk used:** 17.89 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Nanamboatra taratasy apetaka sy soso-kevitra ho an'ny olona te-hanatevin-daharana ity fihetsiketsehana ity i Anocrena.\\nNosorat..." } ``` #### unshuffled_deduplicated_mhr - **Size of downloaded dataset files:** 1.63 MB - **Size of the generated dataset:** 6.26 MB - **Total amount of disk used:** 7.89 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Акрет жап годым Уганда кундемым Пигмей племена- влак айлен шогеныт. мемнан эран 1 курым гыч Банту племена влакат тиде кундемышк..." } ``` #### unshuffled_deduplicated_min - **Size of downloaded dataset files:** 0.01 MB - **Size of the generated dataset:** 0.31 MB - **Total amount of disk used:** 0.33 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ..." } ``` #### unshuffled_deduplicated_mk - **Size of downloaded dataset files:** 303.12 MB - **Size of the generated dataset:** 1.19 GB - **Total amount of disk used:** 1.49 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"„Филм плус“ е насловен првиот филмски месечник во Македонија, чиј прв број ќе биде промовиран вечер во „Менада“. Новото македон..." } ``` #### unshuffled_deduplicated_ml - **Size of downloaded dataset files:** 496.80 MB - **Size of the generated dataset:** 2.69 GB - **Total amount of disk used:** 3.18 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"സ്ത്രീ പ്രവേശനം സര്‍ക്കാര്‍ പൂര്‍ണമായും അംഗീകരിക്കുന്നുവെന്നും ശബരിമലയുടെ സുരക്ഷയില്‍ ഇടപെടുമെന്നും സര്‍ക്കാര്‍ ഹൈക്കോടതിയില്‍\\..." } ``` #### unshuffled_deduplicated_mn - **Size of downloaded dataset files:** 219.52 MB - **Size of the generated dataset:** 883.46 MB - **Total amount of disk used:** 1.10 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"МУБИС-ын багш мэргэжлийн хөрвөх сургалтыг төгссөн багшид багшлах эрх олгох тухай ~ БМДИ-ийн захирлын тушаал - Багшийн мэргэжил ..." } ``` #### unshuffled_deduplicated_mr - **Size of downloaded dataset files:** 299.68 MB - **Size of the generated dataset:** 1.49 GB - **Total amount of disk used:** 1.79 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Home / motivational marathi story / उद्योजकता (Entrepreneurship) / यांना हे जमलय, तर आपल्याला का नाही जमणार ?\\nयापैकी कोणाचीही ..." } ``` #### unshuffled_deduplicated_mrj - **Size of downloaded dataset files:** 0.29 MB - **Size of the generated dataset:** 1.10 MB - **Total amount of disk used:** 1.38 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Лӹпӹвлӓ (латинлӓ Lepidoptera ; алыкмарла лыве-влак) — капшангывлӓ йыхыш пырышы сӱмӓн нӹл шылдыран капшангывлӓ. Цилӓжӹ 180000 тӹ..." } ``` #### unshuffled_deduplicated_ms - **Size of downloaded dataset files:** 16.39 MB - **Size of the generated dataset:** 49.45 MB - **Total amount of disk used:** 65.85 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Sanad pertama daripada Zuhair bin Harb daripada ‘Affan daripada Hammad daripada Thabit daripada Anas.\\nSanad kedua daripada ‘Ab..." } ``` #### unshuffled_deduplicated_mt - **Size of downloaded dataset files:** 5.90 MB - **Size of the generated dataset:** 17.68 MB - **Total amount of disk used:** 23.58 MB An example of 'train' looks as follows. ``` { "id": 0, "text": "tibgħat il-kawża lura lill-Qorti Ġenerali għall-annullament jew għat-tnaqqis tal-penalità imposta mill-Kummissjoni bid-deċiżjoni inizjali kif emendata bid-deċiżjoni ta’ rettifika;" } ``` #### unshuffled_deduplicated_mwl - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Deciplina social i outónoma que angloba atebidades de ouserbaçon, de análeze, de çcriçon, cumparaçon, de sistematizaçon i de sp..." } ``` #### unshuffled_deduplicated_my - **Size of downloaded dataset files:** 207.14 MB - **Size of the generated dataset:** 1.11 GB - **Total amount of disk used:** 1.32 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ျမ၀တီ - ရန္ကုန္တိုင္းေဒသႀကီး ေျမာက္ဥကၠလာပႏွင္႕ ဗဟန္းၿမိဳ႔နယ္ မေကြးတိုင္း ေဒသႀကီး ပခုကၠဴၿမိဳ႔နယ္တို႔၌ ျမန္မာ႕တပ္မေတာ္အား ေထာက္ခံ..." } ``` #### unshuffled_deduplicated_myv - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"2018 иень умарьковонь 6-це чистэ сась паро куля! Россиянь культурань Министерствась макссь невтемань конёв (прокатной удостовер..." } ``` #### unshuffled_deduplicated_mzn - **Size of downloaded dataset files:** 0.16 MB - **Size of the generated dataset:** 0.63 MB - **Total amount of disk used:** 0.79 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"قرآن یا قوران اسلام ِآسمونی کتاب هسته. مسلمونون گانّّه قرآن ره خدا، وحی جه برسنی‌یه، «محمد معجزه» هسته و ثقلین حدیث دله ونه خَو..." } ``` #### unshuffled_deduplicated_nah - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.01 MB - **Total amount of disk used:** 0.01 MB An example of 'train' looks as follows. ``` { "id": 0, "text": "In mācuīlpōhualxihuitl VI (inic chicuacē) in mācuīlpōhualli xiuhitl cāhuitl īhuīcpa 501 xihuitl oc 600 xihuitl." } ``` #### unshuffled_deduplicated_nap - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.01 MB - **Total amount of disk used:** 0.02 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ò AUDIT í Ç è î ÿ å å 30 ò ÿ ÿ é, õ ñ ì ÿ, ê ã- ò à ì. å â å í ç â à à é ñ è å é ó ó ë. å å å û è å î é è à. à è à AUDIT 1-7 â ..." } ``` #### unshuffled_deduplicated_nds - **Size of downloaded dataset files:** 5.27 MB - **Size of the generated dataset:** 13.48 MB - **Total amount of disk used:** 18.76 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Dor kann sik vun nu af an de hele plattdüütsche Welt – vun Niebüll bit New York, vun Helgoland bit Honolulu – drapen. Allens, w..." } ``` #### unshuffled_deduplicated_ne - **Size of downloaded dataset files:** 240.63 MB - **Size of the generated dataset:** 1.24 GB - **Total amount of disk used:** 1.48 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"बर्दिबास नगरपालिकाको तेस्रो नगर परिषदबाट पारित आ.व.२०७३।७४ को संशोधित र २०७४।७५ को प्रस्तावित नीति, कार्यक्रम तथा बजेट\\nअार्थिक..." } ``` #### unshuffled_deduplicated_new - **Size of downloaded dataset files:** 0.83 MB - **Size of the generated dataset:** 4.26 MB - **Total amount of disk used:** 5.09 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"थ्व शहरयागु अक्षांश ३४.७००१६४ उत्तर व देशान्तर ८६.३७६४६९ पश्चिम खः (34.700164° N 86.376469° W)। थ्व थासे ७२२६७३२ वर्ग मिटर (२.७..." } ``` #### unshuffled_deduplicated_nl - **Size of downloaded dataset files:** 15.73 GB - **Size of the generated dataset:** 41.91 GB - **Total amount of disk used:** 57.65 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Op vrijdag 31 augustus wordt het nieuwe studiejaar van de masteropleiding architectuur geopend met een dagexcursie naar Venlo.\\..." } ``` #### unshuffled_deduplicated_nn - **Size of downloaded dataset files:** 23.58 MB - **Size of the generated dataset:** 58.32 MB - **Total amount of disk used:** 81.90 MB An example of 'train' looks as follows. ``` { "id": 0, "text": "Planomtale krav til innhald Bakgrunn: Spørsmål frå fleire kommunar om kva ein planomtale/planbeskrivelse bør innehalde Fylkeskommunen og fylkesmannen har i ein del saker reist motsegn på formelt grunnlag" } ``` #### unshuffled_deduplicated_no - **Size of downloaded dataset files:** 1.96 GB - **Size of the generated dataset:** 5.11 GB - **Total amount of disk used:** 7.07 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Ytterligere aktører i primærhelsetjenesten og andre NHS-virksomheter ble infisert, inkludert legekontor.Læreren vår er så attra..." } ``` #### unshuffled_deduplicated_oc - **Size of downloaded dataset files:** 1.34 MB - **Size of the generated dataset:** 4.00 MB - **Total amount of disk used:** 5.34 MB An example of 'train' looks as follows. ``` { "id": 1, "text": ".рф (rf, còdi punycode: .xn--p1ai)[1] es lo nom de domeni en rus per Russia. Foguèt activat lo 12 de mai de 2010. Lo còdi latin es .ru." } ``` #### unshuffled_deduplicated_or - **Size of downloaded dataset files:** 38.72 MB - **Size of the generated dataset:** 197.63 MB - **Total amount of disk used:** 236.36 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ଭୁବନେଶ୍ୱର, ୨୭/୧– (ଓଡ଼ିଆ ପୁଅ) ସିପିଆଇ ଜାତୀୟ ପରିଷଦର ଆହ୍ୱାନକ୍ରମେ ଗତକାଲି ଜାନୁୟାରୀ ୨୬ ସାଧାରଣତନ୍ତ୍ର ଦିବସକୁ ଦେଶ ବ୍ୟାପୀ ସମ୍ବିଧାନ ସୁରକ୍ଷା ..." } ``` #### unshuffled_deduplicated_os - **Size of downloaded dataset files:** 2.83 MB - **Size of the generated dataset:** 11.00 MB - **Total amount of disk used:** 13.83 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"1. Лæппу æмæ чызг казрæдзийы зæрдæмæ куы фæцæуынц æмæ, куы сфæнд кæнынц сæ цард баиу кæнын, уæд лæппу бар ракуры чызгæй, цæмæй ..." } ``` #### unshuffled_deduplicated_pa - **Size of downloaded dataset files:** 102.39 MB - **Size of the generated dataset:** 483.04 MB - **Total amount of disk used:** 585.42 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ਰਜਿ: ਨੰ: PB/JL-138/2018-20 ਜਿਲਦ 63, ਬਾਨੀ ਸੰਪਾਦਕ (ਸਵ:) ਡਾ: ਸਾਧੂ ਸਿੰਘ ਹਮਦਰਦ ਫ਼ੋਨ : 0181-2455961-62-63, 5032400, ਫੈਕਸ : 2455960, 2..." } ``` #### unshuffled_deduplicated_pam - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Áku pu i Anak ning Aláya at ngeni ipákit kó kékayu ngan nûng makanánu lang susúlat détinang kulit a mágkas. Lauan ya ing tarátu..." } ``` #### unshuffled_deduplicated_pl - **Size of downloaded dataset files:** 20.19 GB - **Size of the generated dataset:** 50.59 GB - **Total amount of disk used:** 70.78 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"System informatyczny - Załącznik nr 1 do zarządzenia Wójta Gminy Podegrodzie Nr 530/2013 z dnia 27 maja 2013 r\\nSystem informat..." } ``` #### unshuffled_deduplicated_pms - **Size of downloaded dataset files:** 0.71 MB - **Size of the generated dataset:** 2.00 MB - **Total amount of disk used:** 2.72 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Louvigné-du-Désert a l'é na comun-a fransèisa ant la region aministrativa dla Brëtagna, ant ël dipartiment d'Ille-et-Vilaine. A..." } ``` #### unshuffled_deduplicated_pnb - **Size of downloaded dataset files:** 2.58 MB - **Size of the generated dataset:** 9.44 MB - **Total amount of disk used:** 12.02 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"ایہ فائل Wikimedia Commons توں اے تے دوجیاں ویونتاں تے وی ورتی جاےکدی اے۔ گل بات اس دے فائل گل بات صفہ تے تھلے دتی گئی۔\"..." } ``` #### unshuffled_deduplicated_ps - **Size of downloaded dataset files:** 71.83 MB - **Size of the generated dataset:** 254.79 MB - **Total amount of disk used:** 326.61 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Many people usually use the time period ‘business to business (B2B) advertising,’ however most of them do not know precisely wh..." } ``` #### unshuffled_deduplicated_pt - **Size of downloaded dataset files:** 26.00 GB - **Size of the generated dataset:** 68.37 GB - **Total amount of disk used:** 94.37 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Você pode estar lendo este texto no sofá, levantar pra pegar uma breja na geladeira, dar uma cagada e sentar novamente, sem int..." } ``` #### unshuffled_deduplicated_qu - **Size of downloaded dataset files:** 0.02 MB - **Size of the generated dataset:** 0.07 MB - **Total amount of disk used:** 0.09 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "Warayu wichay (kastilla simipi: Ascensión de Guarayos) nisqaqa Buliwya mama llaqtapi, Santa Krus suyupi, huk llaqtam, Warayu pruwinsyap uma llaqtanmi." } ``` #### unshuffled_deduplicated_rm - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.01 MB - **Total amount of disk used:** 0.01 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"practicists agrars / practicistas agraras AFP pon far ina furmaziun da basa scursanida per cuntanscher in attestat federal da q..." } ``` #### unshuffled_deduplicated_ro - **Size of downloaded dataset files:** 4.48 GB - **Size of the generated dataset:** 11.66 GB - **Total amount of disk used:** 16.14 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"“În viață, oportunitatea nu este totul. Cine atrage Lumina, cineva bun în umbră. Timpul ne creează.” maestru\\nLyn.Evans: Ce mar..." } ``` #### unshuffled_deduplicated_ru - **Size of downloaded dataset files:** 166.68 GB - **Size of the generated dataset:** 611.70 GB - **Total amount of disk used:** 778.38 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Доступ к данному профилю для публичного просмотра закрыт администрацией сайта - профиль находится на модерации.\\nРазработчикам ..." } ``` #### unshuffled_deduplicated_sa - **Size of downloaded dataset files:** 7.27 MB - **Size of the generated dataset:** 38.33 MB - **Total amount of disk used:** 45.60 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"अनिरुद्धनगरे क्रीडिता रामलीला सम्‍प्रति समाप्‍ता अस्ति । तस्‍य कानिचन् चित्राणि पूर्वमेव प्रकाशितानि सन्ति । द्वौ चलचित्रौ अपि ..." } ``` #### unshuffled_deduplicated_sah - **Size of downloaded dataset files:** 7.01 MB - **Size of the generated dataset:** 27.46 MB - **Total amount of disk used:** 34.49 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████..." } ``` #### unshuffled_deduplicated_scn - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` { "id": 0, "text": "La gilusìa è nu sintimentu dulurusu ca nasci d'un disideriu di pussessu sclusivu ntê cunfrunti dâ pirsuna amata e dû timuri, dû suspettu o dâ cirtizza dâ sò nfidiltati." } ``` #### unshuffled_deduplicated_sd - **Size of downloaded dataset files:** 74.17 MB - **Size of the generated dataset:** 275.48 MB - **Total amount of disk used:** 349.66 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"هر ڪو ڄاڻي ٿو ته جڏهن توهان هڪ وڏي خريد ڪرڻ چاهيون ٿا, توهان پڄي ضروري حڪم ۾ ان جي ڪم ڪرڻ جي هٿ ۾ لاڳاپو ڪيو آهي. جي شيء آهي ته..." } ``` #### unshuffled_deduplicated_sh - **Size of downloaded dataset files:** 1.45 MB - **Size of the generated dataset:** 6.44 MB - **Total amount of disk used:** 7.87 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Opština Gornja Radgona se nalazi u sjeveroistočnoj Sloveniji i graniči s susjednom Austriji duž rijeke Mure. Sa tridesetim nase..." } ``` #### unshuffled_deduplicated_si - **Size of downloaded dataset files:** 175.62 MB - **Size of the generated dataset:** 842.57 MB - **Total amount of disk used:** 1.02 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"ලාංකීය සිතිවිලි සිංහල බ්ලොග් කියවනය කොත්තු සින්ඩිය ලංකා Blogger හත්මාළුව ලංකා බ්ලොග් කියවනය මාතලන්ගේ සින්ඩිය මොබයිල්lk\\nඅවකාශය ..." } ``` #### unshuffled_deduplicated_sk - **Size of downloaded dataset files:** 1.96 GB - **Size of the generated dataset:** 4.80 GB - **Total amount of disk used:** 6.76 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Aktivity | Agentúra podporovaného zamestnávania | vzdelávanie pre klientov, vzdelávanie pre odborníkov, kurzy\\nŠpecializované k..." } ``` #### unshuffled_deduplicated_sl - **Size of downloaded dataset files:** 523.22 MB - **Size of the generated dataset:** 1.32 GB - **Total amount of disk used:** 1.85 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Če Creatures, ki je želel, da pridejo na čas, predvsem je povedlo – razlikuje od ljubosumja začel grizenja kolen (ali zadnjica)..." } ``` #### unshuffled_deduplicated_so - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.02 MB - **Total amount of disk used:** 0.02 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"тттттттттттттттттттттттттттттттт тттттттттттттттттттттттттттттттт тттттттттттттттттттттттттттттттт ттттттттттттттттуууууууууууу..." } ``` #### unshuffled_deduplicated_sq - **Size of downloaded dataset files:** 445.36 MB - **Size of the generated dataset:** 1.21 GB - **Total amount of disk used:** 1.66 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Çfarë do të më pëlqente tek një femër ose çfarë do të më shndërronte në një shpërthim drite? – Albert Vataj\\nTë gjithëve një zo..." } ``` #### unshuffled_deduplicated_sr - **Size of downloaded dataset files:** 665.03 MB - **Size of the generated dataset:** 2.36 GB - **Total amount of disk used:** 3.03 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Корисни савети за сваки дан. На сајту су разне категорије, као што су љепота, мода, кување и поправка властитим рукама.\\nШколск..." } ``` #### unshuffled_deduplicated_su - **Size of downloaded dataset files:** 0.05 MB - **Size of the generated dataset:** 0.16 MB - **Total amount of disk used:** 0.21 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "Kartu krédit nyaéta \"duit plastik\" anu dikaluarkeun ku bank pikeun alat pambayaran di tempat-tempat nu tangtu samisal jiga di hotél, réstoran, tempat rékréasi jeung sajabana.[1]" } ``` #### unshuffled_deduplicated_sv - **Size of downloaded dataset files:** 10.19 GB - **Size of the generated dataset:** 26.33 GB - **Total amount of disk used:** 36.51 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"1783 är ett viktigt årtal i den nya tidens historia. Det året slöts en fred i Paris och därmed blev de 13 brittiska kolonierna ..." } ``` #### unshuffled_deduplicated_sw - **Size of downloaded dataset files:** 2.95 MB - **Size of the generated dataset:** 8.98 MB - **Total amount of disk used:** 11.92 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "Miripuko hiyo inakuja mwanzoni mwa Wiki Takatifu kuelekea Pasaka na ikiwa ni wiki chache tu kabla ya Papa Francis kuanza ziara yake katika nchi hiyo yenye idadi kubwa kabisa ya watu katika ulimwengu wa nchi za Kiarabu." } ``` #### unshuffled_deduplicated_ta - **Size of downloaded dataset files:** 971.12 MB - **Size of the generated dataset:** 5.48 GB - **Total amount of disk used:** 6.45 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"பொழுது சாய்ந்து வெகு நேரமாகிவிட்டது. கூலி வேலைக்குப் போயிருந்த 'சித்தாள் ' பெண்கள் எல்லோரும் வீடு திரும்பி விட்டார்கள். இன்னும்..." } ``` #### unshuffled_deduplicated_te - **Size of downloaded dataset files:** 342.43 MB - **Size of the generated dataset:** 1.70 GB - **Total amount of disk used:** 2.04 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"హర్యానాలో టోల్ దగ్గర సిబ్బంది.. స్థానిక ప్రజలు కొట్టుకున్నారు. కర్నాల్ అనే గ్రామానికి సమీపంలో టోల్ గేట్ ఉంది. అయితే సాధారణంగా స..." } ``` #### unshuffled_deduplicated_tg - **Size of downloaded dataset files:** 62.90 MB - **Size of the generated dataset:** 261.68 MB - **Total amount of disk used:** 324.60 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Ҳумайро гуфтааст, мухолифи низом аст, низоме, ки дар Тоҷикистон вуҷуд дорад. Ба ин маънӣ, худро мухолифи давлату ҳукумати Тоҷик..." } ``` #### unshuffled_deduplicated_th - **Size of downloaded dataset files:** 3.54 GB - **Size of the generated dataset:** 17.11 GB - **Total amount of disk used:** 20.65 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ฟันที่แลดูขาวสะอาดไม่มีเศษอาหารติดอยู่ เหงือกสีชมพู ไม่เจ็บ หรือมีเลือดออกเวลาแปรงฟันหรือขัดฟัน ไม่มีปัญหาเรื่องกลิ่นปาก ทำให้ก..." } ``` #### unshuffled_deduplicated_tk - **Size of downloaded dataset files:** 2.22 MB - **Size of the generated dataset:** 7.12 MB - **Total amount of disk used:** 9.34 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Türkmenistanyň Prezidenti agyr atletika boýunça dünýä çempionatyna taýýarlyk işleriniň barşy bilen tanyşdy\\nHalallykdan kemal t..." } ``` #### unshuffled_deduplicated_tl - **Size of downloaded dataset files:** 151.34 MB - **Size of the generated dataset:** 431.69 MB - **Total amount of disk used:** 583.04 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"“Gusto ko manawagan sa mga Unit Head ng Chanel 2 Salve. Kasi napapansin ko iyon mga alaga ko ang taping halos once a week lang,..." } ``` #### unshuffled_deduplicated_tr - **Size of downloaded dataset files:** 10.39 GB - **Size of the generated dataset:** 28.47 GB - **Total amount of disk used:** 38.86 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Son yıllarda görülen ay tutulmalarına göre daha etkili olacağı söylenen Kanlı veya Kırmızı Ay Tutulmasına saatler kaldı. Bu akş..." } ``` #### unshuffled_deduplicated_tt - **Size of downloaded dataset files:** 85.89 MB - **Size of the generated dataset:** 321.37 MB - **Total amount of disk used:** 407.26 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"\\\"Иремнең вафатына 40 көн узгач, Алмаз да безнең өйгә кереп үлде\\\". Арчада 35 яшьлек ир өстенә кондызлар ега башлаган агач төшк..." } ``` #### unshuffled_deduplicated_tyv - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.01 MB - **Total amount of disk used:** 0.01 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Экии, хүндүлуг аалчылар болгаш тыва дылдың деткикчилери! Тыва дылдың болгаш чогаалдың ховар бир башкызынга, Менги Ооржакка, ажы..." } ``` #### unshuffled_deduplicated_ug - **Size of downloaded dataset files:** 20.53 MB - **Size of the generated dataset:** 86.44 MB - **Total amount of disk used:** 106.97 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"زاڭ-ءتۇزىم | عىلىم-تەحنيكا | ءتىل-ادەبيەت | تۇرمىس | دەنە تاربيە | ساياحات-ورتا | سۋرەتتى حابار | سىر سۇحبات | ارناۋلى تاقىرىپ ..." } ``` #### unshuffled_deduplicated_uk - **Size of downloaded dataset files:** 8.04 GB - **Size of the generated dataset:** 29.86 GB - **Total amount of disk used:** 37.90 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Про надання роз'яснення (щодо форми письмового зобов'язання громадян про зворотне ввезення/вивезення товарів), Державна митна с..." } ``` #### unshuffled_deduplicated_ur - **Size of downloaded dataset files:** 483.59 MB - **Size of the generated dataset:** 1.82 GB - **Total amount of disk used:** 2.31 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"آئیے اہم اسلامی کتب کو یونیکوڈ میں انٹرنیٹ پر پیش کرنے کے لئے مل جل کر آن لائن ٹائپنگ کریں۔ محدث ٹائپنگ پراجیکٹ کے ذریعے آپ روز..." } ``` #### unshuffled_deduplicated_uz - **Size of downloaded dataset files:** 4.30 MB - **Size of the generated dataset:** 12.00 MB - **Total amount of disk used:** 16.29 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "Qurama tog'lari tizmasining Toshkentdan 154 km uzoqlikdagi Toshkent-Ush yo'li yeqasidaxushmanzara tabiat qo'ynida joylashgan maydoni 30 ga.\nBolalarni sog'lomlashtirish oromgohi Bo'stonliq tumani Oqtosh muntaqasining soy-salqin gushasida joylashgan." } ``` #### unshuffled_deduplicated_vec - **Size of downloaded dataset files:** 0.01 MB - **Size of the generated dataset:** 0.02 MB - **Total amount of disk used:** 0.02 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Par ogni pónto, ła derivada ła xe ła pendensa de ła reta tangente a ła curva de ła funsion f. Ła reta de cołor róso l'è senpre ..." } ``` #### unshuffled_deduplicated_vi - **Size of downloaded dataset files:** 10.71 GB - **Size of the generated dataset:** 33.60 GB - **Total amount of disk used:** 44.31 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Canh chua cá bông lau không chỉ là món ăn giải nhiệt, thanh mát ngày hè mà còn là món siêu bổ dưỡng, rất tốt cho người gầy ốm. ..." } ``` #### unshuffled_deduplicated_vo - **Size of downloaded dataset files:** 0.30 MB - **Size of the generated dataset:** 2.10 MB - **Total amount of disk used:** 2.40 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "Sarniguet binon zif in ziläk: Hautes-Pyrénées, in topäd: Midi-Pyrénées, in Fransän. Sarniguet topon videtü 43°19’ 7’’ N e lunetü 0°5’ 19’’ L." } ``` #### unshuffled_deduplicated_wa - **Size of downloaded dataset files:** 0.08 MB - **Size of the generated dataset:** 0.22 MB - **Total amount of disk used:** 0.29 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "Cisse pådje ci n' est co k' on djermon, dj' ô bén k' el pådje est djusse sibåtcheye, eyet co trop tene; et s' divreut ele ecråxhî ene miete." } ``` #### unshuffled_deduplicated_war - **Size of downloaded dataset files:** 0.55 MB - **Size of the generated dataset:** 2.36 MB - **Total amount of disk used:** 2.90 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "An Honce amo in usa ka baryo ngan munisipalidad ha distrito han Rožňava ha rehiyon han Košice ha nasod han Slovakia.\nAn Rumegies amo in usa ka komyun ha departamento han Nord ngan ha rehiyon han Nord-Pas-de-Calais ha nasod han Fransya." } ``` #### unshuffled_deduplicated_wuu - **Size of downloaded dataset files:** 0.01 MB - **Size of the generated dataset:** 0.03 MB - **Total amount of disk used:** 0.04 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"伊春元旦天气 伊春腊八天气 伊春春节天气 伊春情人节天气 伊春元宵节天气 伊春愚人节天气 伊春清明节天气 伊春劳动节天气 伊春母亲节天气 伊春端午节天气 伊春七夕节天气 伊春教师节天气 伊春中秋节天气 伊春国庆节天气 伊春重阳节天气 伊春万圣节天气 伊春..." } ``` #### unshuffled_deduplicated_xal - **Size of downloaded dataset files:** 0.03 MB - **Size of the generated dataset:** 0.12 MB - **Total amount of disk used:** 0.15 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Арнгудин Орн гисн Европд бәәдг һазр. 2007 җилин тooһaр эн орн нутгт 3,600,523 әмтн бәәдг билә. Арнгудин Орнин хотл балһсна нерн..." } ``` #### unshuffled_deduplicated_xmf - **Size of downloaded dataset files:** 0.94 MB - **Size of the generated dataset:** 4.63 MB - **Total amount of disk used:** 5.58 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"მოჩამილი ტექსტი წჷმორინელი რე Creative Commons Attribution-ShareAlike ლიცენზიათ; შილებე გეძინელი პირობეფიშ არსებუა. კილიშკილიშა..." } ``` #### unshuffled_deduplicated_yi - **Size of downloaded dataset files:** 22.20 MB - **Size of the generated dataset:** 88.29 MB - **Total amount of disk used:** 110.49 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ממשותדיק - חבֿרה, איך אַרבעט איצט אױף אַ זשורנאַל. טאָמער איר האָט עפּעס צוצוגעבן זאָלט איר שיקן מיר אַן אָנזאָג. ס'װעט הײסן \\\"..." } ``` #### unshuffled_deduplicated_yo - **Size of downloaded dataset files:** 0.01 MB - **Size of the generated dataset:** 0.03 MB - **Total amount of disk used:** 0.04 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Copyright © 2018 BBC. BBC kò mọ̀ nípa àwọn ohun tí ó wà ní àwọn ojú òpó tí ó wà ní ìta. Ọwọ́ tí a fi mú ìbáṣepọ̀ ti ìta.\"..." } ``` #### unshuffled_deduplicated_yue - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"我 灌 我 灌 我 灌 灌 灌 我 灌 我 灌 我 灌 灌 灌 我 灌 我 灌 我 灌 灌 灌 我 灌 我 灌 我 灌 灌 灌 我 灌 我 灌 我 灌 灌 灌 我 灌 我 灌 我 灌 灌 灌 你還不爆 我累了 投降輸一半可以嗎\"..." } ``` #### unshuffled_deduplicated_zh - **Size of downloaded dataset files:** 99.98 GB - **Size of the generated dataset:** 267.88 GB - **Total amount of disk used:** 367.86 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"中国铝灰网 中国有色金属矿产网 中国黄莲网 中国水轮发电机网 中国抽油泵网 中国数控雕刻机网 中国不锈钢抛光网 中国磨具加工网 中国压铸铝网 中国耐水腻子网 中国手机摄像头网 中国粗粮网 中国车门锁网 中国钛粉网 中国轮圈网\\n天天中奖彩票图 天天中彩票..." } ``` </details> <details> <summary>Click to expand the Data/size information for each language (original)</summary> #### unshuffled_original_af - **Size of downloaded dataset files:** 85.79 MB - **Size of the generated dataset:** 254.08 MB - **Total amount of disk used:** 339.87 MB An example of 'train' looks as follows. ``` { "id": 0, "text": "aanlyn markte as gevolg van ons voortgesette 'n begrip opsie handel sakeplan pdf terwyl ons steeds die gereelde ons binêre opsies handel" } ``` #### unshuffled_original_als - **Size of downloaded dataset files:** 1.49 MB - **Size of the generated dataset:** 5.30 MB - **Total amount of disk used:** 6.78 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"De Nazionalpark hät e Flächi vo 170,3 km² und isch dodemit s grösti Naturschutzgebiet vo de Schwiz. Er ligt uf em Gebiet vo de ..." } ``` #### unshuffled_original_am - **Size of downloaded dataset files:** 102.79 MB - **Size of the generated dataset:** 378.06 MB - **Total amount of disk used:** 480.85 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"አየር መንገዱ ከአዲስ አበባ ወደ ሮም ጣሊያን በማምራት ላይ በነበረበት ጊዜ ረዳት አብራሪው የጉዞውን አቅጣጫ በመቀየር ጄኔቭ አውሮፓላን ማረፊያ በማሳረፍ እጁን ለፖሊስ ሰጥቷል።\\nየኢትዮጵያ መንግስት የ..." } ``` #### unshuffled_original_an - **Size of downloaded dataset files:** 0.15 MB - **Size of the generated dataset:** 1.33 MB - **Total amount of disk used:** 1.48 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"واااااااأسفاه الأمم تفتخر ب 0 أمي ووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووو..." } ``` #### unshuffled_original_ar - **Size of downloaded dataset files:** 22.23 GB - **Size of the generated dataset:** 87.94 GB - **Total amount of disk used:** 110.17 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"مرحبا بك عزيز الزائر نتمنى لك أوقاتاً سعيدة معنا وأن نزداد شرفا بخدمتك ولا تنسى التسجيل معنا لتستفيد بكل جديد\\nأهلا وسهلا بك زا..." } ``` #### unshuffled_original_arz - **Size of downloaded dataset files:** 15.90 MB - **Size of the generated dataset:** 70.13 MB - **Total amount of disk used:** 86.03 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"بنى عجل : قبيلة من عجل بن لجيم بن صعب بن على بن بكر بن وائل انتقل اغلبهم الى البصرة فى العراق و اصفهان و خراسان فى ايران و اذرب..." } ``` #### unshuffled_original_as - **Size of downloaded dataset files:** 21.43 MB - **Size of the generated dataset:** 117.73 MB - **Total amount of disk used:** 139.17 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"আমি, এই সংগঠনৰ সদস্য সকলে একেলগ হৈ অসমকে ধৰি ভাৰতৰ উত্তৰ পূৰ্বাঞ্চলৰ অমূল্য কলা-সাংস্কৃতিক সম্পদৰাজি বৃহত্তৰ অষ্ট্ৰেলিয়াৰ সন্মু..." } ``` #### unshuffled_original_ast - **Size of downloaded dataset files:** 0.92 MB - **Size of the generated dataset:** 2.54 MB - **Total amount of disk used:** 3.46 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"The Killers llanzaron el so álbum debú, Hot Fuss, en xunu de 2004 nel Reinu Xuníu, al traviés de la discográfica Lizard King, y..." } ``` #### unshuffled_original_av - **Size of downloaded dataset files:** 0.08 MB - **Size of the generated dataset:** 0.42 MB - **Total amount of disk used:** 0.50 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Жинда малъараб ва божизе бегьулеб рагІудаса кьуризе бегьуларо гьев. Гьес насихІат гьабизе кколелъул бацІцІадаб диналъул рахъалъ..." } ``` #### unshuffled_original_az - **Size of downloaded dataset files:** 927.76 MB - **Size of the generated dataset:** 2.96 GB - **Total amount of disk used:** 3.89 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"AZTV-Artıq 7 ildir ki, Abşeron rayonu dotasiya almadan bütün xərclərini yerli daxilolmalar hesabına maliyyələşdirir.\\nDünən, 10..." } ``` #### unshuffled_original_azb - **Size of downloaded dataset files:** 6.64 MB - **Size of the generated dataset:** 28.47 MB - **Total amount of disk used:** 35.11 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"لعلی ١٣-جو عصرده یاشاییب یاراتمیش گؤرکملی آذربایجان شاعرلریندندیر. ١٢٢٤-جی ایلده تبریزده آنادان اولموشدور، گنج یاشلاریندا تیجار..." } ``` #### unshuffled_original_ba - **Size of downloaded dataset files:** 33.22 MB - **Size of the generated dataset:** 133.70 MB - **Total amount of disk used:** 166.92 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Күҙәтеү ҡуласаһы моделен хәҙер Мифтахетдин Аҡмулла исемендәге Башҡорт дәүләт педагогия университетында ла эшләргә мөмкин\\t\\nКүҙ..." } ``` #### unshuffled_original_bar - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` { "id": 0, "text": " vo" } ``` #### unshuffled_original_bcl - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"& ÿ ó / í 0 - ø û ù ö ú ð ï ú \\u0014 ù þ ô ö í ÷ ò \\u0014 ÷ í ù û ö í \\u0001 û ñ ç þ \\u0001 ð \\u0007 þ ò ñ ñ ò ô \\u0017 û ö ô ÷..." } ``` #### unshuffled_original_be - **Size of downloaded dataset files:** 498.29 MB - **Size of the generated dataset:** 1.88 GB - **Total amount of disk used:** 2.38 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Брэсцкія ўлады не дазволілі прафсаюзу РЭП правесці пікетаванне ў парку Воінаў-інтэрнацыяналістаў 30 мая 2018 года.\\nСітуацыю пр..." } ``` #### unshuffled_original_bg - **Size of downloaded dataset files:** 8.34 GB - **Size of the generated dataset:** 33.75 GB - **Total amount of disk used:** 42.09 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ЖАЛБОПОДАТЕЛЯТ директор на Дирекция „ Обжалване и данъчно-осигурителна практика“- Бургас, редовно призован, се представлява от ..." } ``` #### unshuffled_original_bh - **Size of downloaded dataset files:** 0.01 MB - **Size of the generated dataset:** 0.12 MB - **Total amount of disk used:** 0.13 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"सुकमा जिला भारत के छत्तीसगढ़ राज्य में एगो जिला बाटे। एकर मुख्यालय सुकमा शहर बाटे। एकर कुल रकबा 5636 वर्ग कि॰मी॰ बाटे।\"..." } ``` #### unshuffled_original_bn - **Size of downloaded dataset files:** 2.14 GB - **Size of the generated dataset:** 10.77 GB - **Total amount of disk used:** 12.91 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ভড়ং সর্বস্ব বাংলা আর্ট অ্যান্ড কালচারের হিসাব গুলিয়ে দেওয়ার ম্যাজিকের নাম ব্রাত্য রাইসু November 23, 2017\\nভড়ং সর্বস্ব বাংলা আর..." } ``` #### unshuffled_original_bo - **Size of downloaded dataset files:** 28.94 MB - **Size of the generated dataset:** 195.40 MB - **Total amount of disk used:** 224.34 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"བོད་མི་འདི་དག་ནི་རང་རྒྱུད་སྒོ་རུ་ཕུད་དེ་གཞན་རྒྱུད་པང་དུ་ཉར་ནས་གསོ་སྐྱོང་བྱེད་དགོས་ཟེར་བ་དང་གཅིག་མཚུངས་རེད།\\nཚན་རིག་ནི་དང་ཐོག་རང..." } ``` #### unshuffled_original_bpy - **Size of downloaded dataset files:** 0.34 MB - **Size of the generated dataset:** 4.35 MB - **Total amount of disk used:** 4.69 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"পৌরসভা এহার আয়তন (লয়াহান) ২,৭৩০,.৬৩ বর্গ কিলোমিটার। পৌরসভা এহার মাপাহানর অক্ষাংশ বারো দ্রাঘিমাংশ ইলতাই 18.63° S 48.18° W ।[১]..." } ``` #### unshuffled_original_br - **Size of downloaded dataset files:** 9.18 MB - **Size of the generated dataset:** 30.20 MB - **Total amount of disk used:** 39.38 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Ar mank Magalhães(Daveoù a vank) a zo ur spesad evned, Spheniscus magellanicus an anv skiantel anezhañ.\\nGallout a reer implijo..." } ``` #### unshuffled_original_bs - **Size of downloaded dataset files:** 0.05 MB - **Size of the generated dataset:** 0.48 MB - **Total amount of disk used:** 0.53 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ž šř é ú šř šř ě šř ž é č ě ž ů ě ď éé ýš ě ě Ž č š ý ě ď é ýš ě ď ě éé ýš ě č ž ě š ý ď ě ýš é ú č ž č š ý ď ý ž é éě ď é č ýš..." } ``` #### unshuffled_original_bxr - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.01 MB - **Total amount of disk used:** 0.02 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"2002 оной хабар буряад хэлэ бэшэгэй һалбари Үндэһэтэнэй хүмүүнлиг ухаанай дээдэ һургуули болгогдожо өөршэлэгдөө.\\nХарин мүнөө б..." } ``` #### unshuffled_original_ca - **Size of downloaded dataset files:** 3.10 GB - **Size of the generated dataset:** 8.62 GB - **Total amount of disk used:** 11.73 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Daniel Vendrell, conegut com Vandrell, ha sigut un dels il•lustradors contemporanis més influents, representant a la nova onada..." } ``` #### unshuffled_original_cbk - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano..." } ``` #### unshuffled_original_ce - **Size of downloaded dataset files:** 2.09 MB - **Size of the generated dataset:** 8.73 MB - **Total amount of disk used:** 10.82 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Шаьш анархисташ ду бохучу жигархойн дIахьедарехь дуьйцу, оьрсийн ницкъаллийн структурийн а, федералан каналан а Iалашонаш \\\"мар..." } ``` #### unshuffled_original_ceb - **Size of downloaded dataset files:** 11.07 MB - **Size of the generated dataset:** 40.97 MB - **Total amount of disk used:** 52.03 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Si Isko walay pupamilok nga nagtan-aw sa unahan, natugaw. “Naunsa ka gud diha Isko nga layo man kaayo ang imong panan-aw?” ni I..." } ``` #### unshuffled_original_ckb - **Size of downloaded dataset files:** 111.88 MB - **Size of the generated dataset:** 510.97 MB - **Total amount of disk used:** 622.85 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"رسی رۆژ - ساڵێک دوای بومەلەرزەی کرماشان میوانی بەرنامە : کاک سیاوەش حەیاتی چالاکی مەدەنی -قەسری شیرین\\nپارچە موزیک 30 / 10 / 20..." } ``` #### unshuffled_original_cs - **Size of downloaded dataset files:** 21.72 GB - **Size of the generated dataset:** 57.08 GB - **Total amount of disk used:** 78.80 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Akce anarchistů proti připravovanému novému služební řádu a nízkým mzdám 1903 – Historie českého anarchismu (1880 – 1939)\\nRost..." } ``` #### unshuffled_original_cv - **Size of downloaded dataset files:** 9.40 MB - **Size of the generated dataset:** 41.05 MB - **Total amount of disk used:** 50.45 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Шыранӑ чухне ӑнсӑртран латин кирилл саспаллисем вырӑнне латин саспаллисене ҫырсан, сайт эсир ҫырнине юсама тӑрӑшӗ.\\nКу сайтра ч..." } ``` #### unshuffled_original_cy - **Size of downloaded dataset files:** 81.74 MB - **Size of the generated dataset:** 224.93 MB - **Total amount of disk used:** 306.67 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Mae capeli Cymreig yr Andes ym Mhatagonia wedi cyhoeddi na fydd gwasanaethau yno weddill y mis, oherwydd yr eira trwm sydd wedi..." } ``` #### unshuffled_original_da - **Size of downloaded dataset files:** 6.00 GB - **Size of the generated dataset:** 16.76 GB - **Total amount of disk used:** 22.76 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Den 2.-5. februar 2016 løb det tredje kursus i uddannelsen af 4kommunesamarbejdets Local Impact Coaches, af stablen i Gentofte ..." } ``` #### unshuffled_original_de - **Size of downloaded dataset files:** 119.51 GB - **Size of the generated dataset:** 331.22 GB - **Total amount of disk used:** 450.73 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Auf dieser Seite gibt es mind. ein YouTube Video. Cookies für diese Website wurden abgelehnt. Dadurch können keine YouTube Vide..." } ``` #### unshuffled_original_diq - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` { "id": 0, "text": "Zıwanê Slawki, zıwano merdumanê Slawano. Zıwanê Slawki yew lızgeyê Zıwananê Hind u Ewropao. Keyeyê Zıwananê Slawki beno hirê letey:" } ``` #### unshuffled_original_dsb - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.01 MB - **Total amount of disk used:** 0.02 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "Pśiklaskaju južo pśed pśedstajenim... 1500 źiśi njamóžo wěcej docakaś, měsćańska hala w Chóśebuzu - wupśedana." } ``` #### unshuffled_original_dv - **Size of downloaded dataset files:** 24.91 MB - **Size of the generated dataset:** 131.63 MB - **Total amount of disk used:** 156.54 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ބ. އަތޮޅުގައި ހުޅުވަން ތައްޔާރުވަމުން އަންނަ ވައްކަރު ރިސޯޓުގައި ވަޒީފާ އަދާކުރަން ޝައުގުވެރިވާ ފަރާތްތަކަށް ކުރިމަތިލުމުގެ ފުރ..." } ``` #### unshuffled_original_el - **Size of downloaded dataset files:** 17.31 GB - **Size of the generated dataset:** 66.27 GB - **Total amount of disk used:** 83.58 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Νεκρός εντοπίστηκε μέσα στο σπίτι του στην οδό Ηρώδου Αττικού στον αριθμό 7 ο επικεφαλής του προξενικού τμήματος της Ρωσικής πρ..." } ``` #### unshuffled_original_eml - **Size of downloaded dataset files:** 0.01 MB - **Size of the generated dataset:** 0.02 MB - **Total amount of disk used:** 0.03 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"A séguit dal prucès ad rubutiśasiòṅ di abitànt dal pòpul ad Mikenes, Angoras 'l è finî dènt'r a 'n robot cun la tèsta dna rana ..." } ``` #### unshuffled_original_en - **Size of downloaded dataset files:** 903.83 GB - **Size of the generated dataset:** 2525.44 GB - **Total amount of disk used:** 3429.27 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Mtendere Village was inspired by the vision of Chief Napoleon Dzombe, which he shared with John Blanchard during his first visi..." } ``` #### unshuffled_original_eo - **Size of downloaded dataset files:** 117.07 MB - **Size of the generated dataset:** 314.18 MB - **Total amount of disk used:** 431.27 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Ĉu ... preĝi | mediti | ricevi instigojn || kanti | muziki || informiĝi | legi | studi || prepari Diservon\\nTemas pri kolekto d..." } ``` #### unshuffled_original_es - **Size of downloaded dataset files:** 106.04 GB - **Size of the generated dataset:** 298.49 GB - **Total amount of disk used:** 404.53 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Como se librará de la celulitis en el gimnasio La piel superflua en las manos después del adelgazamiento, Los bailes fáciles pa..." } ``` #### unshuffled_original_et - **Size of downloaded dataset files:** 1.88 GB - **Size of the generated dataset:** 5.17 GB - **Total amount of disk used:** 7.06 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"MTÜ AB Video järgib oma tegevuses kodanikuühenduste eetilise tegevuse üldtunnustatud põhimõtteid, mis on lühidalt kokkuvõetud 7..." } ``` #### unshuffled_original_eu - **Size of downloaded dataset files:** 248.19 MB - **Size of the generated dataset:** 894.83 MB - **Total amount of disk used:** 1.14 GB An example of 'train' looks as follows. ``` { "id": 0, "text": "Gure jarduerek eraikuntzarekin, elkarbizitzarekin, hirigintzarekin eta ekologiarekin dute harremana, baita ideia eta konponbideak irudikatu eta garatzearekin ere, eraikuntza sektorea hobetuz, pertsonen erosotasuna eta bizi-kalitatea hobetzeko." } ``` #### unshuffled_original_fa - **Size of downloaded dataset files:** 20.96 GB - **Size of the generated dataset:** 84.21 GB - **Total amount of disk used:** 105.17 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"قـــــــــــــــــرار بود با هم کنـــــــــــــار بیایم نه اینکه از کنــــــــــــار هم رد بشیم...!!!\\nاگر روزی دلت لبریز غم بو..." } ``` #### unshuffled_original_fi - **Size of downloaded dataset files:** 9.97 GB - **Size of the generated dataset:** 28.57 GB - **Total amount of disk used:** 38.54 GB An example of 'train' looks as follows. ``` { "id": 1, "text": "Kiitos Deelle kaikesta - 1,5 viikkoa kulunut, kun Dee ei ole enää ollut omani. Reilu viikko sitten sunnuntaina vein Deen uuteen kotiinsa. Itselläni on ollut niin ristiriitaiset t..." } ``` #### unshuffled_original_fr - **Size of downloaded dataset files:** 105.32 GB - **Size of the generated dataset:** 303.19 GB - **Total amount of disk used:** 408.51 GB An example of 'train' looks as follows. ``` { "id": 0, "text": "Média de débat d'idées, de culture et de littérature. Récits, décryptages, analyses, portraits et critiques autour de la vie des idées. Magazine engagé, ouvert aux autres et au monde.. Bring up to date in french" } ``` #### unshuffled_original_frr - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Hiragana’ Practice’Sheet’1’(A -O)’ ’ Name:’________ __________________________’Section:’_______________ _’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ..." } ``` #### unshuffled_original_fy - **Size of downloaded dataset files:** 12.40 MB - **Size of the generated dataset:** 36.24 MB - **Total amount of disk used:** 48.64 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "Nim in sêfte ride op Holmsjön, yn ien fan 'e lytse marren yn de omkriten, of nim se op avontueren lykas nonresidential. lâns Indalsälven wetter. Holm Sportklubb hawwe kano 's te huur, yn gearwurking mei de Baltyske Power konferinsje." } ``` #### unshuffled_original_ga - **Size of downloaded dataset files:** 29.27 MB - **Size of the generated dataset:** 92.37 MB - **Total amount of disk used:** 121.63 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Is fóram é seo chun plé a dhéanamh ar an leabhar atá roghnaithe do mhí na Samhna 2013 amháin. Ní féidir ach le baill chláraithe..." } ``` #### unshuffled_original_gd - **Size of downloaded dataset files:** 0.52 MB - **Size of the generated dataset:** 2.02 MB - **Total amount of disk used:** 2.55 MB An example of 'train' looks as follows. ``` { "id": 0, "text": "Zhou Yujun, a 'phàrtaidh Rùnaire Comataidh Sgìre Yanfeng ann Hengyang bhaile agus a Sgìre pàrtaidh agus an riaghaltas a' bhuidheann-riochdachaidh a 'tighinn a chèilidh air ar companaidh air Apr. 14, 2017." } ``` #### unshuffled_original_gl - **Size of downloaded dataset files:** 235.38 MB - **Size of the generated dataset:** 656.48 MB - **Total amount of disk used:** 891.87 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"O persoal de Inditex da provincia de Pontevedra segue a reclamar iguais condicións laborais no conxunto do país - CIG: Confeder..." } ``` #### unshuffled_original_gn - **Size of downloaded dataset files:** 0.01 MB - **Size of the generated dataset:** 0.04 MB - **Total amount of disk used:** 0.05 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"º ѐÆÚÓ À Ã Ð É Æ ¾ Ä ΠÀ ¼ Æ É ÄÛ = Ü Ý\\\"Þ ß†à á â ã ä å æçè ã é ê â å àë ì æê íî é á ë ï í çì àð í Ü à ñ ê é ò ä ì\"..." } ``` #### unshuffled_original_gom - **Size of downloaded dataset files:** 0.44 MB - **Size of the generated dataset:** 2.25 MB - **Total amount of disk used:** 2.71 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"दुष्ट शीळ हें कौरवांचें । रामें सविस्तर देखूनि साचें । बोलिले वचनें जें दुर्वाचे । करी तयांचें अनुस्मरण ॥२२०॥\"..." } ``` #### unshuffled_original_gu - **Size of downloaded dataset files:** 232.02 MB - **Size of the generated dataset:** 1.09 GB - **Total amount of disk used:** 1.33 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"અધિક માસ ચાલે છે. સમગ્ર ભારતમાં અને તેમાંય ખાસ કરીને પવિત્ર કે ધાર્મિક કહેવાય છે તેવા સ્થાનક પર કથાનો દોર ચાલે છે. ઉનાળાની કાળઝ..." } ``` #### unshuffled_original_he - **Size of downloaded dataset files:** 5.66 GB - **Size of the generated dataset:** 21.11 GB - **Total amount of disk used:** 26.77 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"זקוקים לרשתות נגד יתושים? מחפשים רשת מתאימה לחלון צר וקטן? רשתות נגד יתושים אקורדיון של חברת קליר-מש הן הפתרון.\\nרשתות לחלונות ..." } ``` #### unshuffled_original_hi - **Size of downloaded dataset files:** 3.66 GB - **Size of the generated dataset:** 17.93 GB - **Total amount of disk used:** 21.59 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"'आइटम गर्ल' बनकर हिट हुई थीं राखी सावंत, आज करीना-कटरीना तक फॉलो कर रही हैं ट्रेंड नक्‍सलियों का दम निकालेगा बाइक ग्रेनेड लॉन्च..." } ``` #### unshuffled_original_hr - **Size of downloaded dataset files:** 79.42 MB - **Size of the generated dataset:** 243.83 MB - **Total amount of disk used:** 323.24 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"U raspravi je sudjelovao i HSS-ov saborski zastupnik rekavši kako poljoprivrednici ne osjete mjere o kojima ministar govori jer..." } ``` #### unshuffled_original_hsb - **Size of downloaded dataset files:** 1.39 MB - **Size of the generated dataset:** 4.49 MB - **Total amount of disk used:** 5.87 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Budyšin (SN/BŠe). Elektronikarjo mějachu lětsa cyle hinaši zazběh do swojeho wukubłanja. Wokrjesne rjemjeslnistwo bě mjenujcy w..." } ``` #### unshuffled_original_ht - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan..." } ``` #### unshuffled_original_hu - **Size of downloaded dataset files:** 15.69 GB - **Size of the generated dataset:** 43.07 GB - **Total amount of disk used:** 58.77 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"monster - Amatőr, házi szex videók és kezdő csjaok pornó filmjei. - Free amateur, home made sex videos and online porn movies. ..." } ``` #### unshuffled_original_hy - **Size of downloaded dataset files:** 897.36 MB - **Size of the generated dataset:** 3.94 GB - **Total amount of disk used:** 4.84 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Արցախի Հանրապետության հռչակման 26-րդ տարեդարձի կապակցությամբ Շուշիի Արվեստի կենտրոնում կազմակերպվել է մոսկվաբնակ նկարիչներ՝ հայ..." } ``` #### unshuffled_original_ia - **Size of downloaded dataset files:** 0.08 MB - **Size of the generated dataset:** 0.69 MB - **Total amount of disk used:** 0.78 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha h..." } ``` #### unshuffled_original_id - **Size of downloaded dataset files:** 10.60 GB - **Size of the generated dataset:** 32.32 GB - **Total amount of disk used:** 42.91 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Perihal dari itu, kalau kunci hal yang demikian hilang, pemilik wajib melapor ke bengkel sah untuk dibuatkan kunci baru dengan ..." } ``` #### unshuffled_original_ie - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.02 MB - **Total amount of disk used:** 0.02 MB An example of 'train' looks as follows. ``` { "id": 0, "text": "Plastic Yo Yo Metal Yo Yos Wooden Yo Yo Keychain Yo Yo Translucent Yo Yo Light Up Yo Yo Globe Yo Yo Stress Reliever Yo Yo Jellyfish Yo Yo Sports Ball Yo Yo Sound Yo Yo Miniature Yo Yo Promotional Yo Yo Novelty Yo Yo Video Game Yo Yo ECO Recycled Yo Yo" } ``` #### unshuffled_original_ilo - **Size of downloaded dataset files:** 0.27 MB - **Size of the generated dataset:** 0.92 MB - **Total amount of disk used:** 1.20 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Segun ken ni Ping-ay, ti yellow corn ti maysa kadagiti nadakamat a liberalized agricultural commodity iti daytoy a free trade k..." } ``` #### unshuffled_original_io - **Size of downloaded dataset files:** 0.04 MB - **Size of the generated dataset:** 0.16 MB - **Total amount of disk used:** 0.20 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Chekia esas parlamentala republiko. La chefo di stato esas la prezidanto. Til 2013 lu elektesis dal parlamento. Pos ta yaro, ol..." } ``` #### unshuffled_original_is - **Size of downloaded dataset files:** 533.03 MB - **Size of the generated dataset:** 1.52 GB - **Total amount of disk used:** 2.06 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Eyjar.net - upplýsinga- og fréttamiðill um Vestmannaeyjar - Fréttir - Nái núverandi stefna stjórnvalda fram að ganga mun það va..." } ``` #### unshuffled_original_it - **Size of downloaded dataset files:** 52.16 GB - **Size of the generated dataset:** 147.38 GB - **Total amount of disk used:** 199.54 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Jaundice - causes, treatment & pathology massaggio a osteochondrosis dellindizio di una controindicazione\\nTrattamento su un co..." } ``` #### unshuffled_original_ja - **Size of downloaded dataset files:** 79.56 GB - **Size of the generated dataset:** 232.22 GB - **Total amount of disk used:** 311.78 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"神社などへ一緒に同行して、様々な角度のショットで家族写真やお子様の写真を撮影致します！お好みに合わせて様々な写真を取ることができますので、その場でカメラマンへのリクエストも可能です！お子様の晴れ姿を、緊張していない自然な笑顔で残しませんか？\\n※七五三の..." } ``` #### unshuffled_original_jbo - **Size of downloaded dataset files:** 0.21 MB - **Size of the generated dataset:** 0.77 MB - **Total amount of disk used:** 0.98 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "ni'o 23 la cimast. cu 23moi djedi fi'o masti la cimast. noi ke'a cu cimoi masti .i 22 la cimast. cu purlamdei .ije 24 la cimast. cu bavlamdei" } ``` #### unshuffled_original_jv - **Size of downloaded dataset files:** 0.22 MB - **Size of the generated dataset:** 0.69 MB - **Total amount of disk used:** 0.91 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"José Mourinho (diwaca: [ʒuˈzɛ moˈɾiɲu]; lair ing Setubal, Portugal, 26 Januari 1963; umur 55 taun) iku salah siji pelatih bal k..." } ``` #### unshuffled_original_ka - **Size of downloaded dataset files:** 680.74 MB - **Size of the generated dataset:** 3.77 GB - **Total amount of disk used:** 4.45 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"წამიყვანე შენთან ერთად (ქართულად) / Возьми меня с собой (картулад) / (რუსული სერიალები ქართულად) (რუსების პორნო ონლაინში) (ruse..." } ``` #### unshuffled_original_kk - **Size of downloaded dataset files:** 615.06 MB - **Size of the generated dataset:** 2.83 GB - **Total amount of disk used:** 3.45 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Түлкібас ауданында «Латын негізді әліпби мен емле ережесі туралы насихат» жобасының тобы семинар өткізді\\nЕлорданың «Қазақстан»..." } ``` #### unshuffled_original_km - **Size of downloaded dataset files:** 193.28 MB - **Size of the generated dataset:** 1.10 GB - **Total amount of disk used:** 1.30 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ខ្សឹបដាក់ត្រចៀក៖ លោក សួស សុផានិត នាយផ្នែករដ្ឋបាលព្រៃឈើ ស្រុកភ្នំក្រវាញ់ ដែលទើបឡើងកាន់តំណែងថ្មី បើកដៃឲ្យឈ្នួញ ប្រព្រឹត្តបទល្មើស ..." } ``` #### unshuffled_original_kn - **Size of downloaded dataset files:** 342.15 MB - **Size of the generated dataset:** 1.76 GB - **Total amount of disk used:** 2.11 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ರಾಷ್ಟ್ರಪತಿ ಪ್ರಣಬ್ ಮುಖರ್ಜಿಯಿಂದ ಪದ್ಮ ಪ್ರಶಸ್ತಿ ಪ್ರದಾನ | President Pranab Mukherjee Confers Padma Awards | Photo Gallery on Kannada..." } ``` #### unshuffled_original_ko - **Size of downloaded dataset files:** 8.81 GB - **Size of the generated dataset:** 25.29 GB - **Total amount of disk used:** 34.10 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"CIA 프로젝트에서는 데이터베이스로 들어오는 요청을 중간에 수집(Sniffing)하고 수집한 데이터를 분석(Parsing)하여 그로 인한 결과를 판단하여 알릴 수 있는 시스템(Push Service)이 필요하다. 그리고 연구를 ..." } ``` #### unshuffled_original_krc - **Size of downloaded dataset files:** 0.66 MB - **Size of the generated dataset:** 2.68 MB - **Total amount of disk used:** 3.34 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Шамханланы, Бийлени къаршысына ябушуп, Батыр уланларыбызны къоллары булан «ортакъ ожакъ» къургъанбыз. Шо иш уллу зараллы иш бол..." } ``` #### unshuffled_original_ku - **Size of downloaded dataset files:** 33.38 MB - **Size of the generated dataset:** 99.06 MB - **Total amount of disk used:** 132.44 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Me di 114 bernameyên xwe yên berê da perçeyên ji berhemên zanyarî yên kurdzanên mezin bi wergera kurdî da ...\\nMe di 114 bernam..." } ``` #### unshuffled_original_kv - **Size of downloaded dataset files:** 0.40 MB - **Size of the generated dataset:** 2.38 MB - **Total amount of disk used:** 2.78 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Коми кытшыслӧн ыджытжык тор вӧр увтын куйлӧ, сійӧн и фаунасӧ татӧн аркмӧтӧны вӧрын олісь подаэз. Ассямаӧн лоӧ сія, мый кытшас с..." } ``` #### unshuffled_original_kw - **Size of downloaded dataset files:** 0.01 MB - **Size of the generated dataset:** 0.04 MB - **Total amount of disk used:** 0.05 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼Pray without ceasing🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏..." } ``` #### unshuffled_original_ky - **Size of downloaded dataset files:** 152.64 MB - **Size of the generated dataset:** 630.79 MB - **Total amount of disk used:** 783.43 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Turmush: Бишкек шаардык кеңешинин кезексиз отурумунда мэрге ишенбөөчүлүк көрсөтүү маселеси каралат, - депутат Т.Сагынов\\nБишкек..." } ``` #### unshuffled_original_la - **Size of downloaded dataset files:** 5.46 MB - **Size of the generated dataset:** 27.80 MB - **Total amount of disk used:** 33.26 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Hæ sunt generationes Noë: Noë vir justus atque perfectus fuit in generationibus suis; cum Deo ambulavit.\\nEcce ego adducam aqua..." } ``` #### unshuffled_original_lb - **Size of downloaded dataset files:** 10.73 MB - **Size of the generated dataset:** 30.60 MB - **Total amount of disk used:** 41.32 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Während dem Gaardefestival \\\"Ambiance Jardins\\\" vum 15. bis de 17. Mee huet den SNJ nees zesumme mam Groupe Animateur en Inform..." } ``` #### unshuffled_original_lez - **Size of downloaded dataset files:** 0.83 MB - **Size of the generated dataset:** 3.38 MB - **Total amount of disk used:** 4.20 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Ахцегь хуьр, виридалай ч1ехи лезги хуьрерикая я. Ам Урусатдин виридалай къиблепатавай хуьрерикай я. Ин хуьр...\"..." } ``` #### unshuffled_original_li - **Size of downloaded dataset files:** 0.01 MB - **Size of the generated dataset:** 0.03 MB - **Total amount of disk used:** 0.04 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"'t Good Goedenraad aan de Ezerbaek besjteit oet 'n kesjtièl mèt gesjlote haof en 'n park van 26 hectare. Hie in sjtoon väól beu..." } ``` #### unshuffled_original_lmo - **Size of downloaded dataset files:** 0.10 MB - **Size of the generated dataset:** 0.47 MB - **Total amount of disk used:** 0.58 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Serét (en tortonés: Sregh; en piemontés: Srèj) l'è 'n cümü italià, de la regiù del Piemónt, en Pruvìncia de Alessandria. El g'h..." } ``` #### unshuffled_original_lo - **Size of downloaded dataset files:** 33.92 MB - **Size of the generated dataset:** 182.36 MB - **Total amount of disk used:** 216.28 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"ຜູ້ພິພາກສາ ປະຈຳເຂດ ສຫລ ທ່ານນຶ່ງ ຕັດສິນວ່າ ໂຄງການເກັບກຳຂໍ້ມູນ ທາງໂທລະສັບ ຂອງອົງການ ຄວາມໝັ້ນຄົງແຫ່ງຊາດ ແມ່ນຖືກຕ້ອງ ຕາມກົດໝາຍ.\\nກະ..." } ``` #### unshuffled_original_lrc - **Size of downloaded dataset files:** 0.02 MB - **Size of the generated dataset:** 0.07 MB - **Total amount of disk used:** 0.09 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"آرلینگتون یئ گئل د شأریا ڤولاتچە ڤیرجینیا و یئ گئل د شأریا ڤولات ڤولاتچە یا یأکاگئرئتە ئمریکاە. ئی شأر دویومی کألوٙن شأر د راسا..." } ``` #### unshuffled_original_lt - **Size of downloaded dataset files:** 3.44 GB - **Size of the generated dataset:** 9.45 GB - **Total amount of disk used:** 12.89 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Čir vir vir pavasaris! Čia čia čia… dalinamės labai simpatiška video pamokėle, kurią pristato ab888art galerija.\\nBe galo papra..." } ``` #### unshuffled_original_lv - **Size of downloaded dataset files:** 1.49 GB - **Size of the generated dataset:** 4.27 GB - **Total amount of disk used:** 5.75 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Dekoratīvi sliekšņi MITSUBISHI OUTLANDER 2007, izgatavoti no ovālas formas, pulētas nerūsējošā tērauda caurules...\\ndažādas tūn..." } ``` #### unshuffled_original_mai - **Size of downloaded dataset files:** 0.01 MB - **Size of the generated dataset:** 0.33 MB - **Total amount of disk used:** 0.34 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"१ · २ · ३ · ४ · ५ · ६ · ७ · ८ · ९ · १० · ११ · १२ · १३ · १४ · १५ · १६ · १७ · १८ · १९ · २० · २१ · २२ · २३ · २४ · २५ · २६ · २७ · २..." } ``` #### unshuffled_original_mg - **Size of downloaded dataset files:** 6.22 MB - **Size of the generated dataset:** 21.79 MB - **Total amount of disk used:** 28.01 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Nanamboatra taratasy apetaka sy soso-kevitra ho an'ny olona te-hanatevin-daharana ity fihetsiketsehana ity i Anocrena.\\nNosorat..." } ``` #### unshuffled_original_mhr - **Size of downloaded dataset files:** 1.84 MB - **Size of the generated dataset:** 7.55 MB - **Total amount of disk used:** 9.38 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Акрет жап годым Уганда кундемым Пигмей племена- влак айлен шогеныт. мемнан эран 1 курым гыч Банту племена влакат тиде кундемышк..." } ``` #### unshuffled_original_min - **Size of downloaded dataset files:** 0.01 MB - **Size of the generated dataset:** 0.63 MB - **Total amount of disk used:** 0.64 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ..." } ``` #### unshuffled_original_mk - **Size of downloaded dataset files:** 508.24 MB - **Size of the generated dataset:** 2.20 GB - **Total amount of disk used:** 2.71 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"„Филм плус“ е насловен првиот филмски месечник во Македонија, чиј прв број ќе биде промовиран вечер во „Менада“. Новото македон..." } ``` #### unshuffled_original_ml - **Size of downloaded dataset files:** 938.69 MB - **Size of the generated dataset:** 5.24 GB - **Total amount of disk used:** 6.18 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"സ്ത്രീ പ്രവേശനം സര്‍ക്കാര്‍ പൂര്‍ണമായും അംഗീകരിക്കുന്നുവെന്നും ശബരിമലയുടെ സുരക്ഷയില്‍ ഇടപെടുമെന്നും സര്‍ക്കാര്‍ ഹൈക്കോടതിയില്‍\\..." } ``` #### unshuffled_original_mn - **Size of downloaded dataset files:** 472.36 MB - **Size of the generated dataset:** 2.33 GB - **Total amount of disk used:** 2.81 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Монгол улс, Улаанбаатар хот - 14191 Энхтайваны өргөн чөлөө - 10, Багш хөгжлийн ордон, Багшийн мэргэжил дээшлүүлэх институт\\nБаг..." } ``` #### unshuffled_original_mr - **Size of downloaded dataset files:** 525.31 MB - **Size of the generated dataset:** 2.82 GB - **Total amount of disk used:** 3.34 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Home / motivational marathi story / उद्योजकता (Entrepreneurship) / यांना हे जमलय, तर आपल्याला का नाही जमणार ?\\nयापैकी कोणाचीही ..." } ``` #### unshuffled_original_mrj - **Size of downloaded dataset files:** 0.30 MB - **Size of the generated dataset:** 1.16 MB - **Total amount of disk used:** 1.47 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Лӹпӹвлӓ (латинлӓ Lepidoptera ; алыкмарла лыве-влак) — капшангывлӓ йыхыш пырышы сӱмӓн нӹл шылдыран капшангывлӓ. Цилӓжӹ 180000 тӹ..." } ``` #### unshuffled_original_ms - **Size of downloaded dataset files:** 28.46 MB - **Size of the generated dataset:** 122.33 MB - **Total amount of disk used:** 150.79 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Sanad pertama daripada Zuhair bin Harb daripada ‘Affan daripada Hammad daripada Thabit daripada Anas.\\nSanad kedua daripada ‘Ab..." } ``` #### unshuffled_original_mt - **Size of downloaded dataset files:** 7.53 MB - **Size of the generated dataset:** 24.47 MB - **Total amount of disk used:** 32.00 MB An example of 'train' looks as follows. ``` { "id": 0, "text": "tibgħat il-kawża lura lill-Qorti Ġenerali għall-annullament jew għat-tnaqqis tal-penalità imposta mill-Kummissjoni bid-deċiżjoni inizjali kif emendata bid-deċiżjoni ta’ rettifika;" } ``` #### unshuffled_original_mwl - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Deciplina social i outónoma que angloba atebidades de ouserbaçon, de análeze, de çcriçon, cumparaçon, de sistematizaçon i de sp..." } ``` #### unshuffled_original_my - **Size of downloaded dataset files:** 369.85 MB - **Size of the generated dataset:** 2.02 GB - **Total amount of disk used:** 2.39 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ျမ၀တီ - ရန္ကုန္တိုင္းေဒသႀကီး ေျမာက္ဥကၠလာပႏွင္႕ ဗဟန္းၿမိဳ႔နယ္ မေကြးတိုင္း ေဒသႀကီး ပခုကၠဴၿမိဳ႔နယ္တို႔၌ ျမန္မာ႕တပ္မေတာ္အား ေထာက္ခံ..." } ``` #### unshuffled_original_myv - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"2018 иень умарьковонь 6-це чистэ сась паро куля! Россиянь культурань Министерствась макссь невтемань конёв (прокатной удостовер..." } ``` #### unshuffled_original_mzn - **Size of downloaded dataset files:** 0.18 MB - **Size of the generated dataset:** 0.72 MB - **Total amount of disk used:** 0.90 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"قرآن یا قوران اسلام ِآسمونی کتاب هسته. مسلمونون گانّّه قرآن ره خدا، وحی جه برسنی‌یه، «محمد معجزه» هسته و ثقلین حدیث دله ونه خَو..." } ``` #### unshuffled_original_nah - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.01 MB - **Total amount of disk used:** 0.01 MB An example of 'train' looks as follows. ``` { "id": 0, "text": "In mācuīlpōhualxihuitl VI (inic chicuacē) in mācuīlpōhualli xiuhitl cāhuitl īhuīcpa 501 xihuitl oc 600 xihuitl." } ``` #### unshuffled_original_nap - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.02 MB - **Total amount of disk used:** 0.02 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ò AUDIT í Ç è î ÿ å å 30 ò ÿ ÿ é, õ ñ ì ÿ, ê ã- ò à ì. å â å í ç â à à é ñ è å é ó ó ë. å å å û è å î é è à. à è à AUDIT 1-7 â ..." } ``` #### unshuffled_original_nds - **Size of downloaded dataset files:** 6.74 MB - **Size of the generated dataset:** 18.23 MB - **Total amount of disk used:** 24.99 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Dor kann sik vun nu af an de hele plattdüütsche Welt – vun Niebüll bit New York, vun Helgoland bit Honolulu – drapen. Allens, w..." } ``` #### unshuffled_original_ne - **Size of downloaded dataset files:** 355.29 MB - **Size of the generated dataset:** 1.87 GB - **Total amount of disk used:** 2.22 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"बर्दिबास नगरपालिकाको तेस्रो नगर परिषदबाट पारित आ.व.२०७३।७४ को संशोधित र २०७४।७५ को प्रस्तावित नीति, कार्यक्रम तथा बजेट\\nअार्थिक..." } ``` #### unshuffled_original_new - **Size of downloaded dataset files:** 1.03 MB - **Size of the generated dataset:** 5.77 MB - **Total amount of disk used:** 6.79 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"थ्व शहरयागु अक्षांश ३४.७००१६४ उत्तर व देशान्तर ८६.३७६४६९ पश्चिम खः (34.700164° N 86.376469° W)। थ्व थासे ७२२६७३२ वर्ग मिटर (२.७..." } ``` #### unshuffled_original_nl - **Size of downloaded dataset files:** 29.35 GB - **Size of the generated dataset:** 83.23 GB - **Total amount of disk used:** 112.58 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Op vrijdag 31 augustus wordt het nieuwe studiejaar van de masteropleiding architectuur geopend met een dagexcursie naar Venlo.\\..." } ``` #### unshuffled_original_nn - **Size of downloaded dataset files:** 32.86 MB - **Size of the generated dataset:** 90.84 MB - **Total amount of disk used:** 123.70 MB An example of 'train' looks as follows. ``` { "id": 0, "text": "Planomtale krav til innhald Bakgrunn: Spørsmål frå fleire kommunar om kva ein planomtale/planbeskrivelse bør innehalde Fylkeskommunen og fylkesmannen har i ein del saker reist motsegn på formelt grunnlag" } ``` #### unshuffled_original_no - **Size of downloaded dataset files:** 3.11 GB - **Size of the generated dataset:** 8.65 GB - **Total amount of disk used:** 11.76 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Ytterligere aktører i primærhelsetjenesten og andre NHS-virksomheter ble infisert, inkludert legekontor.Læreren vår er så attra..." } ``` #### unshuffled_original_oc - **Size of downloaded dataset files:** 1.57 MB - **Size of the generated dataset:** 6.12 MB - **Total amount of disk used:** 7.71 MB An example of 'train' looks as follows. ``` { "id": 1, "text": ".рф (rf, còdi punycode: .xn--p1ai)[1] es lo nom de domeni en rus per Russia. Foguèt activat lo 12 de mai de 2010. Lo còdi latin es .ru." } ``` #### unshuffled_original_or - **Size of downloaded dataset files:** 49.84 MB - **Size of the generated dataset:** 260.15 MB - **Total amount of disk used:** 309.99 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ଭୁବନେଶ୍ୱର, ୨୭/୧– (ଓଡ଼ିଆ ପୁଅ) ସିପିଆଇ ଜାତୀୟ ପରିଷଦର ଆହ୍ୱାନକ୍ରମେ ଗତକାଲି ଜାନୁୟାରୀ ୨୬ ସାଧାରଣତନ୍ତ୍ର ଦିବସକୁ ଦେଶ ବ୍ୟାପୀ ସମ୍ବିଧାନ ସୁରକ୍ଷା ..." } ``` #### unshuffled_original_os - **Size of downloaded dataset files:** 3.09 MB - **Size of the generated dataset:** 12.90 MB - **Total amount of disk used:** 15.99 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"1. Лæппу æмæ чызг казрæдзийы зæрдæмæ куы фæцæуынц æмæ, куы сфæнд кæнынц сæ цард баиу кæнын, уæд лæппу бар ракуры чызгæй, цæмæй ..." } ``` #### unshuffled_original_pa - **Size of downloaded dataset files:** 164.21 MB - **Size of the generated dataset:** 801.16 MB - **Total amount of disk used:** 965.37 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ਰਜਿ: ਨੰ: PB/JL-138/2018-20 ਜਿਲਦ 63, ਬਾਨੀ ਸੰਪਾਦਕ (ਸਵ:) ਡਾ: ਸਾਧੂ ਸਿੰਘ ਹਮਦਰਦ ਫ਼ੋਨ : 0181-2455961-62-63, 5032400, ਫੈਕਸ : 2455960, 2..." } ``` #### unshuffled_original_pam - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Áku pu i Anak ning Aláya at ngeni ipákit kó kékayu ngan nûng makanánu lang susúlat détinang kulit a mágkas. Lauan ya ing tarátu..." } ``` #### unshuffled_original_pl - **Size of downloaded dataset files:** 42.88 GB - **Size of the generated dataset:** 117.12 GB - **Total amount of disk used:** 160.01 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"System informatyczny - Załącznik nr 1 do zarządzenia Wójta Gminy Podegrodzie Nr 530/2013 z dnia 27 maja 2013 r\\nSystem informat..." } ``` #### unshuffled_original_pms - **Size of downloaded dataset files:** 0.75 MB - **Size of the generated dataset:** 2.15 MB - **Total amount of disk used:** 2.92 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Louvigné-du-Désert a l'é na comun-a fransèisa ant la region aministrativa dla Brëtagna, ant ël dipartiment d'Ille-et-Vilaine. A..." } ``` #### unshuffled_original_pnb - **Size of downloaded dataset files:** 3.22 MB - **Size of the generated dataset:** 12.04 MB - **Total amount of disk used:** 15.26 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"ایہ فائل Wikimedia Commons توں اے تے دوجیاں ویونتاں تے وی ورتی جاےکدی اے۔ گل بات اس دے فائل گل بات صفہ تے تھلے دتی گئی۔\"..." } ``` #### unshuffled_original_ps - **Size of downloaded dataset files:** 103.66 MB - **Size of the generated dataset:** 379.51 MB - **Total amount of disk used:** 483.17 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Many people usually use the time period ‘business to business (B2B) advertising,’ however most of them do not know precisely wh..." } ``` #### unshuffled_original_pt - **Size of downloaded dataset files:** 47.26 GB - **Size of the generated dataset:** 132.64 GB - **Total amount of disk used:** 179.89 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Você pode estar lendo este texto no sofá, levantar pra pegar uma breja na geladeira, dar uma cagada e sentar novamente, sem int..." } ``` #### unshuffled_original_qu - **Size of downloaded dataset files:** 0.02 MB - **Size of the generated dataset:** 0.08 MB - **Total amount of disk used:** 0.10 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "Warayu wichay (kastilla simipi: Ascensión de Guarayos) nisqaqa Buliwya mama llaqtapi, Santa Krus suyupi, huk llaqtam, Warayu pruwinsyap uma llaqtanmi." } ``` #### unshuffled_original_rm - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.01 MB - **Total amount of disk used:** 0.01 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"practicists agrars / practicistas agraras AFP pon far ina furmaziun da basa scursanida per cuntanscher in attestat federal da q..." } ``` #### unshuffled_original_ro - **Size of downloaded dataset files:** 9.53 GB - **Size of the generated dataset:** 26.87 GB - **Total amount of disk used:** 36.40 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"“În viață, oportunitatea nu este totul. Cine atrage Lumina, cineva bun în umbră. Timpul ne creează.” maestru\\nLyn.Evans: Ce mar..." } ``` #### unshuffled_original_ru - **Size of downloaded dataset files:** 319.76 GB - **Size of the generated dataset:** 1241.63 GB - **Total amount of disk used:** 1561.38 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Доступ к данному профилю для публичного просмотра закрыт администрацией сайта - профиль находится на модерации.\\nРазработчикам ..." } ``` #### unshuffled_original_sa - **Size of downloaded dataset files:** 17.52 MB - **Size of the generated dataset:** 97.06 MB - **Total amount of disk used:** 114.58 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"अनिरुद्धनगरे क्रीडिता रामलीला सम्‍प्रति समाप्‍ता अस्ति । तस्‍य कानिचन् चित्राणि पूर्वमेव प्रकाशितानि सन्ति । द्वौ चलचित्रौ अपि ..." } ``` #### unshuffled_original_sah - **Size of downloaded dataset files:** 9.08 MB - **Size of the generated dataset:** 43.82 MB - **Total amount of disk used:** 52.90 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████..." } ``` #### unshuffled_original_scn - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` { "id": 0, "text": "La gilusìa è nu sintimentu dulurusu ca nasci d'un disideriu di pussessu sclusivu ntê cunfrunti dâ pirsuna amata e dû timuri, dû suspettu o dâ cirtizza dâ sò nfidiltati." } ``` #### unshuffled_original_sd - **Size of downloaded dataset files:** 90.62 MB - **Size of the generated dataset:** 364.25 MB - **Total amount of disk used:** 454.88 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"هر ڪو ڄاڻي ٿو ته جڏهن توهان هڪ وڏي خريد ڪرڻ چاهيون ٿا, توهان پڄي ضروري حڪم ۾ ان جي ڪم ڪرڻ جي هٿ ۾ لاڳاپو ڪيو آهي. جي شيء آهي ته..." } ``` #### unshuffled_original_sh - **Size of downloaded dataset files:** 3.46 MB - **Size of the generated dataset:** 25.84 MB - **Total amount of disk used:** 29.30 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Opština Gornja Radgona se nalazi u sjeveroistočnoj Sloveniji i graniči s susjednom Austriji duž rijeke Mure. Sa tridesetim nase..." } ``` #### unshuffled_original_si - **Size of downloaded dataset files:** 310.93 MB - **Size of the generated dataset:** 1.47 GB - **Total amount of disk used:** 1.78 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"ලාංකීය සිතිවිලි සිංහල බ්ලොග් කියවනය කොත්තු සින්ඩිය ලංකා Blogger හත්මාළුව ලංකා බ්ලොග් කියවනය මාතලන්ගේ සින්ඩිය මොබයිල්lk\\nඅවකාශය ..." } ``` #### unshuffled_original_sk - **Size of downloaded dataset files:** 3.71 GB - **Size of the generated dataset:** 9.81 GB - **Total amount of disk used:** 13.52 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Aktivity | Agentúra podporovaného zamestnávania | vzdelávanie pre klientov, vzdelávanie pre odborníkov, kurzy\\nŠpecializované k..." } ``` #### unshuffled_original_sl - **Size of downloaded dataset files:** 956.20 MB - **Size of the generated dataset:** 2.68 GB - **Total amount of disk used:** 3.63 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Če Creatures, ki je želel, da pridejo na čas, predvsem je povedlo – razlikuje od ljubosumja začel grizenja kolen (ali zadnjica)..." } ``` #### unshuffled_original_so - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.06 MB - **Total amount of disk used:** 0.06 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"тттттттттттттттттттттттттттттттт тттттттттттттттттттттттттттттттт тттттттттттттттттттттттттттттттт ттттттттттттттттуууууууууууу..." } ``` #### unshuffled_original_sq - **Size of downloaded dataset files:** 861.84 MB - **Size of the generated dataset:** 2.44 GB - **Total amount of disk used:** 3.30 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Çfarë do të më pëlqente tek një femër ose çfarë do të më shndërronte në një shpërthim drite? – Albert Vataj\\nTë gjithëve një zo..." } ``` #### unshuffled_original_sr - **Size of downloaded dataset files:** 1.08 GB - **Size of the generated dataset:** 4.13 GB - **Total amount of disk used:** 5.21 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Корисни савети за сваки дан. На сајту су разне категорије, као што су љепота, мода, кување и поправка властитим рукама.\\nШколск..." } ``` #### unshuffled_original_su - **Size of downloaded dataset files:** 0.06 MB - **Size of the generated dataset:** 0.23 MB - **Total amount of disk used:** 0.28 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "Kartu krédit nyaéta \"duit plastik\" anu dikaluarkeun ku bank pikeun alat pambayaran di tempat-tempat nu tangtu samisal jiga di hotél, réstoran, tempat rékréasi jeung sajabana.[1]" } ``` #### unshuffled_original_sv - **Size of downloaded dataset files:** 17.18 GB - **Size of the generated dataset:** 47.00 GB - **Total amount of disk used:** 64.18 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"1783 är ett viktigt årtal i den nya tidens historia. Det året slöts en fred i Paris och därmed blev de 13 brittiska kolonierna ..." } ``` #### unshuffled_original_sw - **Size of downloaded dataset files:** 3.71 MB - **Size of the generated dataset:** 14.07 MB - **Total amount of disk used:** 17.78 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "Miripuko hiyo inakuja mwanzoni mwa Wiki Takatifu kuelekea Pasaka na ikiwa ni wiki chache tu kabla ya Papa Francis kuanza ziara yake katika nchi hiyo yenye idadi kubwa kabisa ya watu katika ulimwengu wa nchi za Kiarabu." } ``` #### unshuffled_original_ta - **Size of downloaded dataset files:** 1.74 GB - **Size of the generated dataset:** 9.93 GB - **Total amount of disk used:** 11.67 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"பொழுது சாய்ந்து வெகு நேரமாகிவிட்டது. கூலி வேலைக்குப் போயிருந்த 'சித்தாள் ' பெண்கள் எல்லோரும் வீடு திரும்பி விட்டார்கள். இன்னும்..." } ``` #### unshuffled_original_te - **Size of downloaded dataset files:** 522.47 MB - **Size of the generated dataset:** 2.61 GB - **Total amount of disk used:** 3.13 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"హర్యానాలో టోల్ దగ్గర సిబ్బంది.. స్థానిక ప్రజలు కొట్టుకున్నారు. కర్నాల్ అనే గ్రామానికి సమీపంలో టోల్ గేట్ ఉంది. అయితే సాధారణంగా స..." } ``` #### unshuffled_original_tg - **Size of downloaded dataset files:** 90.97 MB - **Size of the generated dataset:** 397.43 MB - **Total amount of disk used:** 488.41 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Ҳумайро гуфтааст, мухолифи низом аст, низоме, ки дар Тоҷикистон вуҷуд дорад. Ба ин маънӣ, худро мухолифи давлату ҳукумати Тоҷик..." } ``` #### unshuffled_original_th - **Size of downloaded dataset files:** 7.38 GB - **Size of the generated dataset:** 38.29 GB - **Total amount of disk used:** 45.67 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ฟันที่แลดูขาวสะอาดไม่มีเศษอาหารติดอยู่ เหงือกสีชมพู ไม่เจ็บ หรือมีเลือดออกเวลาแปรงฟันหรือขัดฟัน ไม่มีปัญหาเรื่องกลิ่นปาก ทำให้ก..." } ``` #### unshuffled_original_tk - **Size of downloaded dataset files:** 2.96 MB - **Size of the generated dataset:** 10.66 MB - **Total amount of disk used:** 13.62 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Türkmenistanyň Prezidenti agyr atletika boýunça dünýä çempionatyna taýýarlyk işleriniň barşy bilen tanyşdy\\nHalallykdan kemal t..." } ``` #### unshuffled_original_tl - **Size of downloaded dataset files:** 204.89 MB - **Size of the generated dataset:** 606.30 MB - **Total amount of disk used:** 811.19 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"“Gusto ko manawagan sa mga Unit Head ng Chanel 2 Salve. Kasi napapansin ko iyon mga alaga ko ang taping halos once a week lang,..." } ``` #### unshuffled_original_tr - **Size of downloaded dataset files:** 21.96 GB - **Size of the generated dataset:** 63.58 GB - **Total amount of disk used:** 85.54 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Son yıllarda görülen ay tutulmalarına göre daha etkili olacağı söylenen Kanlı veya Kırmızı Ay Tutulmasına saatler kaldı. Bu akş..." } ``` #### unshuffled_original_tt - **Size of downloaded dataset files:** 151.06 MB - **Size of the generated dataset:** 703.42 MB - **Total amount of disk used:** 854.47 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"\\\"Иремнең вафатына 40 көн узгач, Алмаз да безнең өйгә кереп үлде\\\". Арчада 35 яшьлек ир өстенә кондызлар ега башлаган агач төшк..." } ``` #### unshuffled_original_tyv - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.01 MB - **Total amount of disk used:** 0.01 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Экии, хүндүлуг аалчылар болгаш тыва дылдың деткикчилери! Тыва дылдың болгаш чогаалдың ховар бир башкызынга, Менги Ооржакка, ажы..." } ``` #### unshuffled_original_ug - **Size of downloaded dataset files:** 27.92 MB - **Size of the generated dataset:** 127.42 MB - **Total amount of disk used:** 155.35 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"زاڭ-ءتۇزىم | عىلىم-تەحنيكا | ءتىل-ادەبيەت | تۇرمىس | دەنە تاربيە | ساياحات-ورتا | سۋرەتتى حابار | سىر سۇحبات | ارناۋلى تاقىرىپ ..." } ``` #### unshuffled_original_uk - **Size of downloaded dataset files:** 14.42 GB - **Size of the generated dataset:** 56.44 GB - **Total amount of disk used:** 70.86 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Про надання роз'яснення (щодо форми письмового зобов'язання громадян про зворотне ввезення/вивезення товарів), Державна митна с..." } ``` #### unshuffled_original_ur - **Size of downloaded dataset files:** 712.61 MB - **Size of the generated dataset:** 2.80 GB - **Total amount of disk used:** 3.51 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"آئیے اہم اسلامی کتب کو یونیکوڈ میں انٹرنیٹ پر پیش کرنے کے لئے مل جل کر آن لائن ٹائپنگ کریں۔ محدث ٹائپنگ پراجیکٹ کے ذریعے آپ روز..." } ``` #### unshuffled_original_uz - **Size of downloaded dataset files:** 5.78 MB - **Size of the generated dataset:** 21.46 MB - **Total amount of disk used:** 27.24 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "Qurama tog'lari tizmasining Toshkentdan 154 km uzoqlikdagi Toshkent-Ush yo'li yeqasidaxushmanzara tabiat qo'ynida joylashgan maydoni 30 ga.\nBolalarni sog'lomlashtirish oromgohi Bo'stonliq tumani Oqtosh muntaqasining soy-salqin gushasida joylashgan." } ``` #### unshuffled_original_vec - **Size of downloaded dataset files:** 0.01 MB - **Size of the generated dataset:** 0.02 MB - **Total amount of disk used:** 0.03 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Par ogni pónto, ła derivada ła xe ła pendensa de ła reta tangente a ła curva de ła funsion f. Ła reta de cołor róso l'è senpre ..." } ``` #### unshuffled_original_vi - **Size of downloaded dataset files:** 21.50 GB - **Size of the generated dataset:** 72.23 GB - **Total amount of disk used:** 93.73 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Canh chua cá bông lau không chỉ là món ăn giải nhiệt, thanh mát ngày hè mà còn là món siêu bổ dưỡng, rất tốt cho người gầy ốm. ..." } ``` #### unshuffled_original_vo - **Size of downloaded dataset files:** 0.30 MB - **Size of the generated dataset:** 2.12 MB - **Total amount of disk used:** 2.42 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "Sarniguet binon zif in ziläk: Hautes-Pyrénées, in topäd: Midi-Pyrénées, in Fransän. Sarniguet topon videtü 43°19’ 7’’ N e lunetü 0°5’ 19’’ L." } ``` #### unshuffled_original_wa - **Size of downloaded dataset files:** 0.09 MB - **Size of the generated dataset:** 0.29 MB - **Total amount of disk used:** 0.38 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "Cisse pådje ci n' est co k' on djermon, dj' ô bén k' el pådje est djusse sibåtcheye, eyet co trop tene; et s' divreut ele ecråxhî ene miete." } ``` #### unshuffled_original_war - **Size of downloaded dataset files:** 0.64 MB - **Size of the generated dataset:** 2.68 MB - **Total amount of disk used:** 3.32 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "An Honce amo in usa ka baryo ngan munisipalidad ha distrito han Rožňava ha rehiyon han Košice ha nasod han Slovakia.\nAn Rumegies amo in usa ka komyun ha departamento han Nord ngan ha rehiyon han Nord-Pas-de-Calais ha nasod han Fransya." } ``` #### unshuffled_original_wuu - **Size of downloaded dataset files:** 0.01 MB - **Size of the generated dataset:** 0.12 MB - **Total amount of disk used:** 0.13 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"伊春元旦天气 伊春腊八天气 伊春春节天气 伊春情人节天气 伊春元宵节天气 伊春愚人节天气 伊春清明节天气 伊春劳动节天气 伊春母亲节天气 伊春端午节天气 伊春七夕节天气 伊春教师节天气 伊春中秋节天气 伊春国庆节天气 伊春重阳节天气 伊春万圣节天气 伊春..." } ``` #### unshuffled_original_xal - **Size of downloaded dataset files:** 0.03 MB - **Size of the generated dataset:** 0.12 MB - **Total amount of disk used:** 0.15 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Арнгудин Орн гисн Европд бәәдг һазр. 2007 җилин тooһaр эн орн нутгт 3,600,523 әмтн бәәдг билә. Арнгудин Орнин хотл балһсна нерн..." } ``` #### unshuffled_original_xmf - **Size of downloaded dataset files:** 1.05 MB - **Size of the generated dataset:** 6.12 MB - **Total amount of disk used:** 7.17 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"მოჩამილი ტექსტი წჷმორინელი რე Creative Commons Attribution-ShareAlike ლიცენზიათ; შილებე გეძინელი პირობეფიშ არსებუა. კილიშკილიშა..." } ``` #### unshuffled_original_yi - **Size of downloaded dataset files:** 33.33 MB - **Size of the generated dataset:** 147.60 MB - **Total amount of disk used:** 180.94 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ממשותדיק - חבֿרה, איך אַרבעט איצט אױף אַ זשורנאַל. טאָמער איר האָט עפּעס צוצוגעבן זאָלט איר שיקן מיר אַן אָנזאָג. ס'װעט הײסן \\\"..." } ``` #### unshuffled_original_yo - **Size of downloaded dataset files:** 0.01 MB - **Size of the generated dataset:** 0.06 MB - **Total amount of disk used:** 0.06 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Copyright © 2018 BBC. BBC kò mọ̀ nípa àwọn ohun tí ó wà ní àwọn ojú òpó tí ó wà ní ìta. Ọwọ́ tí a fi mú ìbáṣepọ̀ ti ìta.\"..." } ``` #### unshuffled_original_yue - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"我 灌 我 灌 我 灌 灌 灌 我 灌 我 灌 我 灌 灌 灌 我 灌 我 灌 我 灌 灌 灌 我 灌 我 灌 我 灌 灌 灌 我 灌 我 灌 我 灌 灌 灌 我 灌 我 灌 我 灌 灌 灌 你還不爆 我累了 投降輸一半可以嗎\"..." } ``` #### unshuffled_original_zh - **Size of downloaded dataset files:** 206.00 GB - **Size of the generated dataset:** 545.61 GB - **Total amount of disk used:** 751.61 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"中国铝灰网 中国有色金属矿产网 中国黄莲网 中国水轮发电机网 中国抽油泵网 中国数控雕刻机网 中国不锈钢抛光网 中国磨具加工网 中国压铸铝网 中国耐水腻子网 中国手机摄像头网 中国粗粮网 中国车门锁网 中国钛粉网 中国轮圈网\\n天天中奖彩票图 天天中彩票..." } ``` </details> ### Data Fields The data fields are the same among all configs. - `id`: a `int64` feature. - `text`: a `string` feature. ### Data Splits <details> <summary>Click to expand the number of samples per configuration</summary> | Language | Language code | Name original | Train original | Words original | Size original | Name deduplicated | Train deduplicated | Words deduplicated | Size deduplicated | | ----------------- | ------------- | ----------------------- | -------------- | --------------- | ------------- | --------------------------- | ------------------ | ------------------ | ----------------- | | Afrikaans | af | unshuffled_original_af | 201117 | 43,482,801 | 241M | unshuffled_deduplicated_af | 130640 | 29,533,437 | 163M | | Albanian | sq | unshuffled_original_sq | 672077 | 374,196,110 | 2.3G | unshuffled_deduplicated_sq | 461598 | 186,856,699 | 1.2G | | Alemannic | als | unshuffled_original_als | 7324 | 841,750 | 5.0M | unshuffled_deduplicated_als | 4518 | 459,001 | 2.8M | | Amharic | am | unshuffled_original_am | 83663 | 28,301,601 | 360M | unshuffled_deduplicated_am | 43102 | 16,086,628 | 206M | | Arabic | ar | unshuffled_original_ar | 16365602 | 8,117,162,828 | 82G | unshuffled_deduplicated_ar | 9006977 | 3,171,221,354 | 32G | | Aragonese | an | unshuffled_original_an | 2449 | 52,896 | 1.3M | unshuffled_deduplicated_an | 2025 | 45,669 | 801K | | Armenian | hy | unshuffled_original_hy | 659430 | 273,919,388 | 3.7G | unshuffled_deduplicated_hy | 396093 | 110,196,043 | 1.5G | | Assamese | as | unshuffled_original_as | 14985 | 6,956,663 | 113M | unshuffled_deduplicated_as | 9212 | 4,366,570 | 71M | | Asturian | ast | unshuffled_original_ast | 6999 | 381,005 | 2.4M | unshuffled_deduplicated_ast | 5343 | 325,237 | 2.0M | | Avaric | av | unshuffled_original_av | 456 | 24,720 | 409K | unshuffled_deduplicated_av | 360 | 19,478 | 324K | | Azerbaijani | az | unshuffled_original_az | 912330 | 322,641,710 | 2.8G | unshuffled_deduplicated_az | 626796 | 167,742,296 | 1.5G | | Bashkir | ba | unshuffled_original_ba | 42551 | 9,796,764 | 128M | unshuffled_deduplicated_ba | 27050 | 6,922,589 | 90M | | Basque | eu | unshuffled_original_eu | 506883 | 120,456,652 | 848M | unshuffled_deduplicated_eu | 256513 | 45,359,710 | 342M | | Bavarian | bar | unshuffled_original_bar | 4 | 399 | 503 | unshuffled_deduplicated_bar | 4 | 399 | 503 | | Belarusian | be | unshuffled_original_be | 586031 | 144,579,630 | 1.8G | unshuffled_deduplicated_be | 307405 | 83,499,037 | 1.1G | | Bengali | bn | unshuffled_original_bn | 1675515 | 623,575,733 | 11G | unshuffled_deduplicated_bn | 1114481 | 363,766,143 | 5.8G | | Bihari | bh | unshuffled_original_bh | 336 | 8,848 | 110K | unshuffled_deduplicated_bh | 82 | 2,875 | 34K | | Bishnupriya | bpy | unshuffled_original_bpy | 6046 | 198,286 | 4.1M | unshuffled_deduplicated_bpy | 1770 | 96,940 | 1.7M | | Bosnian | bs | unshuffled_original_bs | 2143 | 106,448 | 447K | unshuffled_deduplicated_bs | 702 | 20,485 | 116K | | Breton | br | unshuffled_original_br | 37085 | 5,013,241 | 29M | unshuffled_deduplicated_br | 14724 | 2,890,384 | 16M | | Bulgarian | bg | unshuffled_original_bg | 5869686 | 2,947,648,106 | 32G | unshuffled_deduplicated_bg | 3398679 | 1,268,114,977 | 14G | | Burmese | my | unshuffled_original_my | 232329 | 56,111,184 | 1.9G | unshuffled_deduplicated_my | 136639 | 30,102,173 | 1.1G | | Catalan | ca | unshuffled_original_ca | 4390754 | 1,360,212,450 | 8.0G | unshuffled_deduplicated_ca | 2458067 | 729,333,440 | 4.3G | | Cebuano | ceb | unshuffled_original_ceb | 56248 | 6,603,567 | 39M | unshuffled_deduplicated_ceb | 26145 | 3,675,024 | 24M | | Central Bikol | bcl | unshuffled_original_bcl | 1 | 312 | 885 | unshuffled_deduplicated_bcl | 1 | 312 | 885 | | Central Khmer | km | unshuffled_original_km | 159363 | 20,690,610 | 1.1G | unshuffled_deduplicated_km | 108346 | 10,082,245 | 581M | | Central Kurdish | ckb | unshuffled_original_ckb | 103639 | 48,478,334 | 487M | unshuffled_deduplicated_ckb | 68210 | 18,726,721 | 226M | | Chavacano | cbk | unshuffled_original_cbk | 1 | 130 | 520 | unshuffled_deduplicated_cbk | 1 | 130 | 520 | | Chechen | ce | unshuffled_original_ce | 4042 | 711,051 | 8.3M | unshuffled_deduplicated_ce | 2984 | 568,146 | 6.7M | | Chinese | zh | unshuffled_original_zh | 60137667 | 14,986,424,850 | 508G | unshuffled_deduplicated_zh | 41708901 | 6,350,215,113 | 249G | | Chuvash | cv | unshuffled_original_cv | 20281 | 3,041,614 | 39M | unshuffled_deduplicated_cv | 10130 | 2,054,810 | 26M | | Cornish | kw | unshuffled_original_kw | 203 | 8,329 | 44K | unshuffled_deduplicated_kw | 68 | 2,704 | 14K | | Croatian | hr | unshuffled_original_hr | 582219 | 34,232,765 | 226M | unshuffled_deduplicated_hr | 321484 | 16,727,640 | 110M | | Czech | cs | unshuffled_original_cs | 21001388 | 7,715,977,441 | 53G | unshuffled_deduplicated_cs | 12308039 | 3,540,997,509 | 24G | | Danish | da | unshuffled_original_da | 7664010 | 2,637,463,889 | 16G | unshuffled_deduplicated_da | 4771098 | 1,620,091,317 | 9.5G | | Dhivehi | dv | unshuffled_original_dv | 21018 | 7,559,472 | 126M | unshuffled_deduplicated_dv | 17024 | 4,726,660 | 79M | | Dimli | diq | unshuffled_original_diq | 1 | 19 | 146 | unshuffled_deduplicated_diq | 1 | 19 | 146 | | Dutch | nl | unshuffled_original_nl | 34682142 | 13,020,136,373 | 78G | unshuffled_deduplicated_nl | 20812149 | 6,598,786,137 | 39G | | Eastern Mari | mhr | unshuffled_original_mhr | 3212 | 565,992 | 7.2M | unshuffled_deduplicated_mhr | 2515 | 469,297 | 6.0M | | Egyptian Arabic | arz | unshuffled_original_arz | 158113 | 7,305,151 | 66M | unshuffled_deduplicated_arz | 79928 | 3,659,419 | 33M | | Emilian-Romagnol | eml | unshuffled_original_eml | 84 | 6,376 | 25K | unshuffled_deduplicated_eml | 80 | 6,121 | 24K | | English | en | unshuffled_original_en | 455994980 | 418,187,793,408 | 2.3T | unshuffled_deduplicated_en | 304230423 | 215,841,256,971 | 1.2T | | Erzya | myv | unshuffled_original_myv | 6 | 90 | 1.4K | unshuffled_deduplicated_myv | 5 | 78 | 1.2K | | Esperanto | eo | unshuffled_original_eo | 121171 | 48,486,161 | 299M | unshuffled_deduplicated_eo | 84752 | 37,324,446 | 228M | | Estonian | et | unshuffled_original_et | 2093621 | 643,163,730 | 4.8G | unshuffled_deduplicated_et | 1172041 | 309,931,463 | 2.3G | | Finnish | fi | unshuffled_original_fi | 8557453 | 3,196,666,419 | 27G | unshuffled_deduplicated_fi | 5326443 | 1,597,855,468 | 13G | | French | fr | unshuffled_original_fr | 96742378 | 46,896,036,417 | 282G | unshuffled_deduplicated_fr | 59448891 | 23,206,776,649 | 138G | | Galician | gl | unshuffled_original_gl | 544388 | 102,011,291 | 620M | unshuffled_deduplicated_gl | 284320 | 63,600,602 | 384M | | Georgian | ka | unshuffled_original_ka | 563916 | 171,950,621 | 3.6G | unshuffled_deduplicated_ka | 372158 | 91,569,739 | 1.9G | | German | de | unshuffled_original_de | 104913504 | 44,878,908,446 | 308G | unshuffled_deduplicated_de | 62398034 | 21,529,164,172 | 145G | | Goan Konkani | gom | unshuffled_original_gom | 640 | 124,277 | 2.2M | unshuffled_deduplicated_gom | 484 | 102,306 | 1.8M | | Guarani | gn | unshuffled_original_gn | 106 | 7,382 | 36K | unshuffled_deduplicated_gn | 68 | 4,680 | 24K | | Gujarati | gu | unshuffled_original_gu | 240691 | 72,045,701 | 1.1G | unshuffled_deduplicated_gu | 169834 | 50,023,432 | 722M | | Haitian | ht | unshuffled_original_ht | 13 | 1,014 | 3.9K | unshuffled_deduplicated_ht | 9 | 832 | 3.3K | | Hebrew | he | unshuffled_original_he | 3808397 | 2,067,753,528 | 20G | unshuffled_deduplicated_he | 2375030 | 1,032,018,056 | 9.8G | | Hindi | hi | unshuffled_original_hi | 3264660 | 1,372,234,782 | 17G | unshuffled_deduplicated_hi | 1909387 | 745,774,934 | 8.9G | | Hungarian | hu | unshuffled_original_hu | 11197780 | 5,163,936,345 | 40G | unshuffled_deduplicated_hu | 6582908 | 2,339,127,555 | 18G | | Icelandic | is | unshuffled_original_is | 625673 | 219,900,094 | 1.5G | unshuffled_deduplicated_is | 389515 | 129,818,331 | 846M | | Ido | io | unshuffled_original_io | 694 | 25,702 | 147K | unshuffled_deduplicated_io | 617 | 22,773 | 130K | | Iloko | ilo | unshuffled_original_ilo | 2638 | 142,942 | 874K | unshuffled_deduplicated_ilo | 1578 | 105,564 | 636K | | Indonesian | id | unshuffled_original_id | 16236463 | 4,574,692,265 | 30G | unshuffled_deduplicated_id | 9948521 | 2,394,957,629 | 16G | | Interlingua | ia | unshuffled_original_ia | 1040 | 180,231 | 662K | unshuffled_deduplicated_ia | 529 | 100,019 | 360K | | Interlingue | ie | unshuffled_original_ie | 101 | 5,352 | 24K | unshuffled_deduplicated_ie | 11 | 602 | 1.6K | | Irish | ga | unshuffled_original_ga | 83223 | 14,483,593 | 88M | unshuffled_deduplicated_ga | 46493 | 10,017,303 | 60M | | Italian | it | unshuffled_original_it | 46981781 | 22,248,707,341 | 137G | unshuffled_deduplicated_it | 28522082 | 11,250,012,896 | 69G | | Japanese | ja | unshuffled_original_ja | 62721527 | 4,962,979,182 | 216G | unshuffled_deduplicated_ja | 39496439 | 1,123,067,063 | 106G | | Javanese | jv | unshuffled_original_jv | 1445 | 104,896 | 659K | unshuffled_deduplicated_jv | 1163 | 86,654 | 583K | | Kalmyk | xal | unshuffled_original_xal | 39 | 10,277 | 113K | unshuffled_deduplicated_xal | 36 | 10,155 | 112K | | Kannada | kn | unshuffled_original_kn | 350363 | 81,186,863 | 1.7G | unshuffled_deduplicated_kn | 251064 | 49,343,462 | 1.1G | | Karachay-Balkar | krc | unshuffled_original_krc | 1581 | 185,436 | 2.6M | unshuffled_deduplicated_krc | 1377 | 166,496 | 2.3M | | Kazakh | kk | unshuffled_original_kk | 524591 | 191,126,469 | 2.7G | unshuffled_deduplicated_kk | 338073 | 108,388,743 | 1.5G | | Kirghiz | ky | unshuffled_original_ky | 146993 | 44,194,823 | 600M | unshuffled_deduplicated_ky | 86561 | 28,982,620 | 388M | | Komi | kv | unshuffled_original_kv | 1549 | 201,404 | 2.3M | unshuffled_deduplicated_kv | 924 | 95,243 | 1.2M | | Korean | ko | unshuffled_original_ko | 7345075 | 2,368,765,142 | 24G | unshuffled_deduplicated_ko | 3675420 | 1,120,375,149 | 12G | | Kurdish | ku | unshuffled_original_ku | 46535 | 15,561,003 | 94M | unshuffled_deduplicated_ku | 29054 | 9,946,440 | 60M | | Lao | lo | unshuffled_original_lo | 52910 | 4,133,311 | 174M | unshuffled_deduplicated_lo | 32652 | 2,583,342 | 114M | | Latin | la | unshuffled_original_la | 94588 | 4,122,201 | 26M | unshuffled_deduplicated_la | 18808 | 1,328,038 | 8.3M | | Latvian | lv | unshuffled_original_lv | 1593820 | 520,761,977 | 4.0G | unshuffled_deduplicated_lv | 843195 | 236,428,905 | 1.8G | | Lezghian | lez | unshuffled_original_lez | 1485 | 247,646 | 3.3M | unshuffled_deduplicated_lez | 1381 | 224,871 | 3.0M | | Limburgan | li | unshuffled_original_li | 137 | 4,730 | 29K | unshuffled_deduplicated_li | 118 | 4,283 | 27K | | Lithuanian | lt | unshuffled_original_lt | 2977757 | 1,159,661,742 | 8.8G | unshuffled_deduplicated_lt | 1737411 | 516,183,525 | 3.9G | | Lojban | jbo | unshuffled_original_jbo | 832 | 154,330 | 736K | unshuffled_deduplicated_jbo | 617 | 141,973 | 678K | | Lombard | lmo | unshuffled_original_lmo | 1401 | 75,229 | 443K | unshuffled_deduplicated_lmo | 1374 | 73,665 | 433K | | Low German | nds | unshuffled_original_nds | 18174 | 2,906,347 | 18M | unshuffled_deduplicated_nds | 8714 | 2,146,417 | 13M | | Lower Sorbian | dsb | unshuffled_original_dsb | 65 | 1,787 | 13K | unshuffled_deduplicated_dsb | 37 | 966 | 7.1K | | Luxembourgish | lb | unshuffled_original_lb | 34807 | 4,403,577 | 29M | unshuffled_deduplicated_lb | 21735 | 3,087,650 | 21M | | Macedonian | mk | unshuffled_original_mk | 437871 | 189,289,873 | 2.1G | unshuffled_deduplicated_mk | 299457 | 102,849,595 | 1.2G | | Maithili | mai | unshuffled_original_mai | 123 | 69,161 | 317K | unshuffled_deduplicated_mai | 25 | 874 | 11K | | Malagasy | mg | unshuffled_original_mg | 17957 | 3,068,360 | 21M | unshuffled_deduplicated_mg | 13343 | 1,872,044 | 13M | | Malay | ms | unshuffled_original_ms | 534016 | 16,696,882 | 111M | unshuffled_deduplicated_ms | 183443 | 6,045,753 | 42M | | Malayalam | ml | unshuffled_original_ml | 603937 | 189,534,472 | 4.9G | unshuffled_deduplicated_ml | 453904 | 95,892,551 | 2.5G | | Maltese | mt | unshuffled_original_mt | 26598 | 2,995,654 | 24M | unshuffled_deduplicated_mt | 16383 | 2,163,358 | 17M | | Marathi | mr | unshuffled_original_mr | 326804 | 162,609,404 | 2.7G | unshuffled_deduplicated_mr | 212556 | 82,130,803 | 1.4G | | Mazanderani | mzn | unshuffled_original_mzn | 1055 | 73,870 | 691K | unshuffled_deduplicated_mzn | 917 | 64,481 | 602K | | Minangkabau | min | unshuffled_original_min | 220 | 5,682 | 608K | unshuffled_deduplicated_min | 166 | 4,825 | 310K | | Mingrelian | xmf | unshuffled_original_xmf | 3783 | 299,098 | 5.8M | unshuffled_deduplicated_xmf | 2418 | 228,629 | 4.4M | | Mirandese | mwl | unshuffled_original_mwl | 8 | 171 | 1.2K | unshuffled_deduplicated_mwl | 7 | 152 | 1.1K | | Modern Greek | el | unshuffled_original_el | 10425596 | 5,479,180,137 | 62G | unshuffled_deduplicated_el | 6521169 | 2,412,419,435 | 27G | | Mongolian | mn | unshuffled_original_mn | 395605 | 181,307,167 | 2.2G | unshuffled_deduplicated_mn | 197878 | 68,362,013 | 838M | | Nahuatl languages | nah | unshuffled_original_nah | 61 | 1,234 | 12K | unshuffled_deduplicated_nah | 58 | 1,193 | 11K | | Neapolitan | nap | unshuffled_original_nap | 73 | 5,282 | 17K | unshuffled_deduplicated_nap | 55 | 4,147 | 13K | | Nepali | ne | unshuffled_original_ne | 299938 | 107,448,208 | 1.8G | unshuffled_deduplicated_ne | 219334 | 71,628,317 | 1.2G | | Newari | new | unshuffled_original_new | 4696 | 564,697 | 5.5M | unshuffled_deduplicated_new | 2126 | 288,995 | 4.1M | | Northern Frisian | frr | unshuffled_original_frr | 7 | 1,516 | 4.4K | unshuffled_deduplicated_frr | 7 | 1,516 | 4.4K | | Northern Luri | lrc | unshuffled_original_lrc | 88 | 8,022 | 76K | unshuffled_deduplicated_lrc | 72 | 6,740 | 63K | | Norwegian | no | unshuffled_original_no | 5546211 | 1,344,326,388 | 8.0G | unshuffled_deduplicated_no | 3229940 | 804,894,377 | 4.7G | | Norwegian Nynorsk | nn | unshuffled_original_nn | 185884 | 14,764,980 | 85M | unshuffled_deduplicated_nn | 109118 | 9,435,139 | 54M | | Occitan | oc | unshuffled_original_oc | 10709 | 750,301 | 5.8M | unshuffled_deduplicated_oc | 6485 | 512,678 | 3.7M | | Oriya | or | unshuffled_original_or | 59463 | 14,938,567 | 248M | unshuffled_deduplicated_or | 44230 | 11,321,740 | 188M | | Ossetian | os | unshuffled_original_os | 5213 | 1,031,268 | 13M | unshuffled_deduplicated_os | 2559 | 878,765 | 11M | | Pampanga | pam | unshuffled_original_pam | 3 | 130 | 760 | unshuffled_deduplicated_pam | 1 | 52 | 304 | | Panjabi | pa | unshuffled_original_pa | 127467 | 61,847,806 | 763M | unshuffled_deduplicated_pa | 87235 | 37,555,835 | 460M | | Persian | fa | unshuffled_original_fa | 13704702 | 9,096,554,121 | 79G | unshuffled_deduplicated_fa | 8203495 | 4,363,505,319 | 38G | | Piemontese | pms | unshuffled_original_pms | 3225 | 362,013 | 2.1M | unshuffled_deduplicated_pms | 2859 | 337,246 | 1.9M | | Polish | pl | unshuffled_original_pl | 35440972 | 15,277,255,137 | 109G | unshuffled_deduplicated_pl | 20682611 | 6,708,709,674 | 47G | | Portuguese | pt | unshuffled_original_pt | 42114520 | 20,641,903,898 | 124G | unshuffled_deduplicated_pt | 26920397 | 10,751,156,918 | 64G | | Pushto | ps | unshuffled_original_ps | 98216 | 46,559,441 | 361M | unshuffled_deduplicated_ps | 67921 | 31,347,348 | 242M | | Quechua | qu | unshuffled_original_qu | 452 | 10,186 | 78K | unshuffled_deduplicated_qu | 411 | 8,691 | 67K | | Romanian | ro | unshuffled_original_ro | 9387265 | 3,984,317,058 | 25G | unshuffled_deduplicated_ro | 5044757 | 1,741,794,069 | 11G | | Romansh | rm | unshuffled_original_rm | 41 | 1,093 | 7.4K | unshuffled_deduplicated_rm | 34 | 960 | 6.5K | | Russia Buriat | bxr | unshuffled_original_bxr | 42 | 963 | 13K | unshuffled_deduplicated_bxr | 36 | 809 | 11K | | Russian | ru | unshuffled_original_ru | 161836003 | 92,522,407,837 | 1.2T | unshuffled_deduplicated_ru | 115954598 | 46,692,691,520 | 568G | | Sanskrit | sa | unshuffled_original_sa | 14291 | 4,331,569 | 93M | unshuffled_deduplicated_sa | 7121 | 1,713,930 | 37M | | Scottish Gaelic | gd | unshuffled_original_gd | 5799 | 310,689 | 1.9M | unshuffled_deduplicated_gd | 3883 | 207,110 | 1.3M | | Serbian | sr | unshuffled_original_sr | 1013619 | 364,395,411 | 3.9G | unshuffled_deduplicated_sr | 645747 | 207,561,168 | 2.2G | | Serbo-Croatian | sh | unshuffled_original_sh | 36700 | 5,292,184 | 25M | unshuffled_deduplicated_sh | 17610 | 1,040,573 | 5.8M | | Sicilian | scn | unshuffled_original_scn | 21 | 554 | 3.3K | unshuffled_deduplicated_scn | 17 | 468 | 2.8K | | Sindhi | sd | unshuffled_original_sd | 44280 | 43,530,158 | 347M | unshuffled_deduplicated_sd | 33925 | 33,028,015 | 263M | | Sinhala | si | unshuffled_original_si | 203082 | 93,053,465 | 1.4G | unshuffled_deduplicated_si | 120684 | 50,864,857 | 802M | | Slovak | sk | unshuffled_original_sk | 5492194 | 1,322,247,763 | 9.1G | unshuffled_deduplicated_sk | 2820821 | 656,346,179 | 4.5G | | Slovenian | sl | unshuffled_original_sl | 1746604 | 387,399,700 | 2.5G | unshuffled_deduplicated_sl | 886223 | 193,926,684 | 1.3G | | Somali | so | unshuffled_original_so | 156 | 1,202 | 61K | unshuffled_deduplicated_so | 42 | 472 | 16K | | South Azerbaijani | azb | unshuffled_original_azb | 15446 | 2,175,054 | 27M | unshuffled_deduplicated_azb | 9985 | 1,528,709 | 19M | | Spanish | es | unshuffled_original_es | 88199221 | 47,545,122,279 | 278G | unshuffled_deduplicated_es | 56326016 | 25,928,290,729 | 149G | | Sundanese | su | unshuffled_original_su | 805 | 30,321 | 211K | unshuffled_deduplicated_su | 511 | 20,278 | 141K | | Swahili | sw | unshuffled_original_sw | 41986 | 2,211,927 | 13M | unshuffled_deduplicated_sw | 24803 | 1,376,963 | 8.1M | | Swedish | sv | unshuffled_original_sv | 17395625 | 7,155,994,312 | 44G | unshuffled_deduplicated_sv | 11014487 | 4,106,120,608 | 25G | | Tagalog | tl | unshuffled_original_tl | 458206 | 98,949,299 | 573M | unshuffled_deduplicated_tl | 294132 | 70,121,601 | 407M | | Tajik | tg | unshuffled_original_tg | 89002 | 31,758,142 | 379M | unshuffled_deduplicated_tg | 56259 | 21,029,893 | 249M | | Tamil | ta | unshuffled_original_ta | 1263280 | 420,537,132 | 9.3G | unshuffled_deduplicated_ta | 833101 | 226,013,330 | 5.1G | | Tatar | tt | unshuffled_original_tt | 135923 | 51,034,893 | 670M | unshuffled_deduplicated_tt | 82738 | 23,825,695 | 305M | | Telugu | te | unshuffled_original_te | 475703 | 123,711,517 | 2.5G | unshuffled_deduplicated_te | 312644 | 79,094,167 | 1.6G | | Thai | th | unshuffled_original_th | 6064129 | 951,743,087 | 36G | unshuffled_deduplicated_th | 3749826 | 368,965,202 | 16G | | Tibetan | bo | unshuffled_original_bo | 26795 | 1,483,589 | 187M | unshuffled_deduplicated_bo | 15762 | 936,556 | 138M | | Turkish | tr | unshuffled_original_tr | 18535253 | 7,577,388,700 | 60G | unshuffled_deduplicated_tr | 11596446 | 3,365,734,289 | 27G | | Turkmen | tk | unshuffled_original_tk | 6456 | 1,113,869 | 11M | unshuffled_deduplicated_tk | 4694 | 752,326 | 6.8M | | Tuvinian | tyv | unshuffled_original_tyv | 34 | 759 | 12K | unshuffled_deduplicated_tyv | 24 | 540 | 7.9K | | Uighur | ug | unshuffled_original_ug | 22255 | 8,657,141 | 122M | unshuffled_deduplicated_ug | 15503 | 5,852,225 | 83M | | Ukrainian | uk | unshuffled_original_uk | 12973467 | 4,204,381,276 | 53G | unshuffled_deduplicated_uk | 7782375 | 2,252,380,351 | 28G | | Upper Sorbian | hsb | unshuffled_original_hsb | 7959 | 545,351 | 4.2M | unshuffled_deduplicated_hsb | 3084 | 236,867 | 1.8M | | Urdu | ur | unshuffled_original_ur | 638596 | 331,817,982 | 2.7G | unshuffled_deduplicated_ur | 428674 | 218,030,228 | 1.7G | | Uzbek | uz | unshuffled_original_uz | 27537 | 2,450,256 | 21M | unshuffled_deduplicated_uz | 15074 | 1,381,644 | 12M | | Venetian | vec | unshuffled_original_vec | 73 | 3,492 | 18K | unshuffled_deduplicated_vec | 64 | 3,199 | 17K | | Vietnamese | vi | unshuffled_original_vi | 14898250 | 12,036,845,359 | 68G | unshuffled_deduplicated_vi | 9897709 | 5,577,159,843 | 32G | | Volapük | vo | unshuffled_original_vo | 3366 | 321,121 | 2.0M | unshuffled_deduplicated_vo | 3317 | 318,568 | 2.0M | | Walloon | wa | unshuffled_original_wa | 1001 | 50,720 | 273K | unshuffled_deduplicated_wa | 677 | 37,543 | 203K | | Waray | war | unshuffled_original_war | 9760 | 397,315 | 2.5M | unshuffled_deduplicated_war | 9161 | 336,311 | 2.2M | | Welsh | cy | unshuffled_original_cy | 157698 | 37,422,441 | 213M | unshuffled_deduplicated_cy | 98225 | 23,574,673 | 133M | | Western Frisian | fy | unshuffled_original_fy | 33053 | 5,691,077 | 35M | unshuffled_deduplicated_fy | 20661 | 4,223,816 | 26M | | Western Mari | mrj | unshuffled_original_mrj | 757 | 93,338 | 1.2M | unshuffled_deduplicated_mrj | 669 | 87,780 | 1.1M | | Western Panjabi | pnb | unshuffled_original_pnb | 4599 | 1,426,986 | 12M | unshuffled_deduplicated_pnb | 3463 | 1,111,112 | 9.0M | | Wu Chinese | wuu | unshuffled_original_wuu | 214 | 11,189 | 109K | unshuffled_deduplicated_wuu | 64 | 4,333 | 32K | | Yakut | sah | unshuffled_original_sah | 22301 | 2,547,623 | 42M | unshuffled_deduplicated_sah | 8555 | 1,789,174 | 26M | | Yiddish | yi | unshuffled_original_yi | 59364 | 13,834,320 | 141M | unshuffled_deduplicated_yi | 32919 | 8,212,970 | 84M | | Yoruba | yo | unshuffled_original_yo | 214 | 8,906 | 55K | unshuffled_deduplicated_yo | 49 | 3,518 | 27K | | Yue Chinese | yue | unshuffled_original_yue | 11 | 186 | 3.7K | unshuffled_deduplicated_yue | 7 | 128 | 2.2K | </details> ## Dataset Creation ### Curation Rationale OSCAR was constructed new pipeline derived from the [fastText's one](https://github.com/facebookresearch/fastText), called [_goclassy_](https://github.com/pjox/goclassy). Goclassy reuses the [fastText linear classifier](https://fasttext.cc) and the pre-trained fastText model for language recognition, but it completely rewrites and parallelises their pipeline in an asynchronous manner. The order of operations is more or less the same as in the fastText pre-processing pipeline but instead of clustering multiple operations into a single blocking process, a worker is launched for each operation but bounding the number of possible parallel operations at a given time by the number of available threads instead of the number of CPUs. Goclassy is implemented in the [Go programming language](https://golang.org/) so it lets the [Go runtime](https://golang.org/src/runtime/mprof.go) handle the scheduling of the processes. Thus the goclassy's pipeline one does not have to wait for a whole WET file to download, decompress and classify in order to start downloading and processing the next one, a new file will start downloading and processing as soon as the scheduler is able to allocate a new process. Filtering and cleaning processes at line level are done before feeding each line to the classifier. Lines shorter than 100 UTF-8 characters and lines containing invalid UTF-8 characters are discarted and are not classified. After all files are proccesed the deduplicated versions are constructed and everything is then splitted in shards and compressed. ### Source Data #### Initial Data Collection and Normalization [Common Crawl](https://commoncrawl.org/) is a non-profit foundation which produces and maintains an open repository of web crawled data that is both accessible and analysable. Common Crawl's complete web archive consists of petabytes of data collected over 8 years of web crawling. The repository contains raw web page HTML data (WARC files), metdata extracts (WAT files) and plain text extracts (WET files). The organisation's crawlers has always respected [nofollow](http://microformats.org/wiki/rel-nofollow) and [robots.txt](https://www.robotstxt.org/) policies. Each monthly Common Crawl snapshot is in itself a massive multilingual corpus, where every single file contains data coming from multiple web pages written in a large variety of languages and covering all possible types of topics. To construct OSCAR the WET files of Common Crawl were used. These contain the extracted plain texts from the websites mostly converted to UTF-8, as well as headers containing the metatada of each crawled document. Each WET file comes compressed in gzip format and is stored on Amazon Web Services. In the case of OSCAR, the **November 2018** snapshot was used. It surpasses 20TB of uncompressed data and contains more than 50 thousand plain text files where each file consists of the plain text from multiple websites along its metadata header. #### Who are the source language producers? The data comes from multiple web pages in a large variety of languages. ### Annotations The dataset does not contain any additional annotations. #### Annotation process N/A #### Who are the annotators? N/A ### Personal and Sensitive Information Being constructed from Common Crawl, Personal and sensitive information might be present. This **must** be considered before training deep learning models with OSCAR, specially in the case of text-generation models. ## Considerations for Using the Data ### Social Impact of Dataset OSCAR is intended to bring more data to a wide variety of lanuages, the aim of the corpus is to make large amounts of data available to lower resource languages in order to facilitate the pre-training of state-of-the-art language modeling architectures. ### Discussion of Biases OSCAR is not properly filtered yet and this can be reflected on the models trained with it. Care is advised specially concerning biases of the resulting models. ### Other Known Limitations The [fastText linear classifier](https://fasttext.cc) is limed both in performance and the variety of languages it can recognize, so the quality of some OSCAR sub-corpora might be lower than expected, specially for the lowest-resource langiuages. Some audits have already been done by [third parties](https://arxiv.org/abs/2010.14571). ## Additional Information ### Dataset Curators The corpus was put together by [Pedro J. Ortiz](https://pjortiz.eu/), [Benoît Sagot](http://pauillac.inria.fr/~sagot/), and [Laurent Romary](https://cv.archives-ouvertes.fr/laurentromary), during work done at [Inria](https://www.inria.fr/en), particularly at the [ALMAnaCH team](https://team.inria.fr/almanach/). ### Licensing Information These data are released under this licensing scheme We do not own any of the text from which these data has been extracted. We license the actual packaging of these data under the Creative Commons CC0 license ("no rights reserved") http://creativecommons.org/publicdomain/zero/1.0/ To the extent possible under law, Inria has waived all copyright and related or neighboring rights to OSCAR This work is published from: France. Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please: * Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted. * Clearly identify the copyrighted work claimed to be infringed. * Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material. We will comply to legitimate requests by removing the affected sources from the next release of the corpus. ### Citation Information ``` @inproceedings{ortiz-suarez-etal-2020-monolingual, title = "A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages", author = "Ortiz Su{'a}rez, Pedro Javier and Romary, Laurent and Sagot, Benoit", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.acl-main.156", pages = "1703--1714", abstract = "We use the multilingual OSCAR corpus, extracted from Common Crawl via language classification, filtering and cleaning, to train monolingual contextualized word embeddings (ELMo) for five mid-resource languages. We then compare the performance of OSCAR-based and Wikipedia-based ELMo embeddings for these languages on the part-of-speech tagging and parsing tasks. We show that, despite the noise in the Common-Crawl-based OSCAR data, embeddings trained on OSCAR perform much better than monolingual embeddings trained on Wikipedia. They actually equal or improve the current state of the art in tagging and parsing for all five languages. In particular, they also improve over multilingual Wikipedia-based contextual embeddings (multilingual BERT), which almost always constitutes the previous state of the art, thereby showing that the benefit of a larger, more diverse corpus surpasses the cross-lingual benefit of multilingual embedding architectures.", } @inproceedings{OrtizSuarezSagotRomary2019, author = {Pedro Javier {Ortiz Su{'a}rez} and Benoit Sagot and Laurent Romary}, title = {Asynchronous pipelines for processing huge corpora on medium to low resource infrastructures}, series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-7) 2019. Cardiff, 22nd July 2019}, editor = {Piotr Bański and Adrien Barbaresi and Hanno Biber and Evelyn Breiteneder and Simon Clematide and Marc Kupietz and Harald L{"u}ngen and Caroline Iliadi}, publisher = {Leibniz-Institut f{"u}r Deutsche Sprache}, address = {Mannheim}, doi = {10.14618/ids-pub-9021}, url = {http://nbn-resolving.de/urn:nbn:de:bsz:mh39-90215}, pages = {9 -- 16}, year = {2019}, abstract = {Common Crawl is a considerably large, heterogeneous multilingual corpus comprised of crawled documents from the internet, surpassing 20TB of data and distributed as a set of more than 50 thousand plain text files where each contains many documents written in a wide variety of languages. Even though each document has a metadata block associated to it, this data lacks any information about the language in which each document is written, making it extremely difficult to use Common Crawl for monolingual applications. We propose a general, highly parallel, multithreaded pipeline to clean and classify Common Crawl by language; we specifically design it so that it runs efficiently on medium to low resource infrastructures where I/O speeds are the main constraint. We develop the pipeline so that it can be easily reapplied to any kind of heterogeneous corpus and so that it can be parameterised to a wide range of infrastructures. We also distribute a 6.3TB version of Common Crawl, filtered, classified by language, shuffled at line level in order to avoid copyright issues, and ready to be used for NLP applications.}, language = {en} } ``` ### Contributions Thanks to [@pjox](https://github.com/pjox) and [@lhoestq](https://github.com/lhoestq) for adding this dataset.

oscar

2022-03-02T23:29:22+00:00

{"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["af", "als", "am", "an", "ar", "arz", "as", "ast", "av", "az", "azb", "ba", "bar", "bcl", "be", "bg", "bh", "bn", "bo", "bpy", "br", "bs", "bxr", "ca", "cbk", "ce", "ceb", "ckb", "cs", "cv", "cy", "da", "de", "diq", "dsb", "dv", "el", "eml", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "frr", "fy", "ga", "gd", "gl", "gn", "gom", "gu", "he", "hi", "hr", "hsb", "ht", "hu", "hy", "ia", "id", "ie", "ilo", "io", "is", "it", "ja", "jbo", "jv", "ka", "kk", "km", "kn", "ko", "krc", "ku", "kv", "kw", "ky", "la", "lb", "lez", "li", "lmo", "lo", "lrc", "lt", "lv", "mai", "mg", "mhr", "min", "mk", "ml", "mn", "mr", "mrj", "ms", "mt", "mwl", "my", "myv", "mzn", "nah", "nap", "nds", "ne", "new", "nl", "nn", "no", "oc", "or", "os", "pa", "pam", "pl", "pms", "pnb", "ps", "pt", "qu", "rm", "ro", "ru", "sa", "sah", "scn", "sd", "sh", "si", "sk", "sl", "so", "sq", "sr", "su", "sv", "sw", "ta", "te", "tg", "th", "tk", "tl", "tr", "tt", "tyv", "ug", "uk", "ur", "uz", "vec", "vi", "vo", "wa", "war", "wuu", "xal", "xmf", "yi", "yo", "yue", "zh"], "license": ["cc0-1.0"], "multilinguality": ["multilingual"], "size_categories": ["100K<n<1M", "100M<n<1B", "10K<n<100K", "10M<n<100M", "1K<n<10K", "1M<n<10M", "n<1K"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"], "paperswithcode_id": "oscar", "pretty_name": "OSCAR", "config_names": ["unshuffled_deduplicated_af", "unshuffled_deduplicated_als", "unshuffled_deduplicated_am", "unshuffled_deduplicated_an", "unshuffled_deduplicated_ar", "unshuffled_deduplicated_arz", "unshuffled_deduplicated_as", "unshuffled_deduplicated_ast", "unshuffled_deduplicated_av", "unshuffled_deduplicated_az", "unshuffled_deduplicated_azb", "unshuffled_deduplicated_ba", "unshuffled_deduplicated_bar", "unshuffled_deduplicated_bcl", "unshuffled_deduplicated_be", "unshuffled_deduplicated_bg", "unshuffled_deduplicated_bh", "unshuffled_deduplicated_bn", "unshuffled_deduplicated_bo", "unshuffled_deduplicated_bpy", "unshuffled_deduplicated_br", "unshuffled_deduplicated_bs", "unshuffled_deduplicated_bxr", "unshuffled_deduplicated_ca", "unshuffled_deduplicated_cbk", "unshuffled_deduplicated_ce", "unshuffled_deduplicated_ceb", "unshuffled_deduplicated_ckb", "unshuffled_deduplicated_cs", "unshuffled_deduplicated_cv", "unshuffled_deduplicated_cy", "unshuffled_deduplicated_da", "unshuffled_deduplicated_de", "unshuffled_deduplicated_diq", "unshuffled_deduplicated_dsb", "unshuffled_deduplicated_dv", "unshuffled_deduplicated_el", "unshuffled_deduplicated_eml", "unshuffled_deduplicated_en", "unshuffled_deduplicated_eo", "unshuffled_deduplicated_es", "unshuffled_deduplicated_et", "unshuffled_deduplicated_eu", "unshuffled_deduplicated_fa", "unshuffled_deduplicated_fi", "unshuffled_deduplicated_fr", "unshuffled_deduplicated_frr", "unshuffled_deduplicated_fy", "unshuffled_deduplicated_ga", "unshuffled_deduplicated_gd", "unshuffled_deduplicated_gl", "unshuffled_deduplicated_gn", "unshuffled_deduplicated_gom", "unshuffled_deduplicated_gu", "unshuffled_deduplicated_he", "unshuffled_deduplicated_hi", "unshuffled_deduplicated_hr", "unshuffled_deduplicated_hsb", "unshuffled_deduplicated_ht", "unshuffled_deduplicated_hu", "unshuffled_deduplicated_hy", "unshuffled_deduplicated_ia", "unshuffled_deduplicated_id", "unshuffled_deduplicated_ie", "unshuffled_deduplicated_ilo", "unshuffled_deduplicated_io", "unshuffled_deduplicated_is", "unshuffled_deduplicated_it", "unshuffled_deduplicated_ja", "unshuffled_deduplicated_jbo", "unshuffled_deduplicated_jv", "unshuffled_deduplicated_ka", "unshuffled_deduplicated_kk", "unshuffled_deduplicated_km", "unshuffled_deduplicated_kn", "unshuffled_deduplicated_ko", "unshuffled_deduplicated_krc", "unshuffled_deduplicated_ku", "unshuffled_deduplicated_kv", "unshuffled_deduplicated_kw", "unshuffled_deduplicated_ky", "unshuffled_deduplicated_la", "unshuffled_deduplicated_lb", "unshuffled_deduplicated_lez", "unshuffled_deduplicated_li", "unshuffled_deduplicated_lmo", "unshuffled_deduplicated_lo", "unshuffled_deduplicated_lrc", "unshuffled_deduplicated_lt", "unshuffled_deduplicated_lv", "unshuffled_deduplicated_mai", "unshuffled_deduplicated_mg", "unshuffled_deduplicated_mhr", "unshuffled_deduplicated_min", "unshuffled_deduplicated_mk", "unshuffled_deduplicated_ml", "unshuffled_deduplicated_mn", "unshuffled_deduplicated_mr", "unshuffled_deduplicated_mrj", "unshuffled_deduplicated_ms", "unshuffled_deduplicated_mt", "unshuffled_deduplicated_mwl", "unshuffled_deduplicated_my", "unshuffled_deduplicated_myv", "unshuffled_deduplicated_mzn", "unshuffled_deduplicated_nah", "unshuffled_deduplicated_nap", "unshuffled_deduplicated_nds", "unshuffled_deduplicated_ne", "unshuffled_deduplicated_new", "unshuffled_deduplicated_nl", "unshuffled_deduplicated_nn", "unshuffled_deduplicated_no", "unshuffled_deduplicated_oc", "unshuffled_deduplicated_or", "unshuffled_deduplicated_os", "unshuffled_deduplicated_pa", "unshuffled_deduplicated_pam", "unshuffled_deduplicated_pl", "unshuffled_deduplicated_pms", "unshuffled_deduplicated_pnb", "unshuffled_deduplicated_ps", "unshuffled_deduplicated_pt", "unshuffled_deduplicated_qu", "unshuffled_deduplicated_rm", "unshuffled_deduplicated_ro", "unshuffled_deduplicated_ru", "unshuffled_deduplicated_sa", "unshuffled_deduplicated_sah", "unshuffled_deduplicated_scn", "unshuffled_deduplicated_sd", "unshuffled_deduplicated_sh", "unshuffled_deduplicated_si", "unshuffled_deduplicated_sk", "unshuffled_deduplicated_sl", "unshuffled_deduplicated_so", "unshuffled_deduplicated_sq", "unshuffled_deduplicated_sr", "unshuffled_deduplicated_su", "unshuffled_deduplicated_sv", "unshuffled_deduplicated_sw", "unshuffled_deduplicated_ta", "unshuffled_deduplicated_te", "unshuffled_deduplicated_tg", "unshuffled_deduplicated_th", "unshuffled_deduplicated_tk", "unshuffled_deduplicated_tl", "unshuffled_deduplicated_tr", "unshuffled_deduplicated_tt", "unshuffled_deduplicated_tyv", "unshuffled_deduplicated_ug", "unshuffled_deduplicated_uk", "unshuffled_deduplicated_ur", "unshuffled_deduplicated_uz", "unshuffled_deduplicated_vec", "unshuffled_deduplicated_vi", "unshuffled_deduplicated_vo", "unshuffled_deduplicated_wa", "unshuffled_deduplicated_war", "unshuffled_deduplicated_wuu", "unshuffled_deduplicated_xal", "unshuffled_deduplicated_xmf", "unshuffled_deduplicated_yi", "unshuffled_deduplicated_yo", "unshuffled_deduplicated_yue", "unshuffled_deduplicated_zh", "unshuffled_original_af", "unshuffled_original_als", "unshuffled_original_am", "unshuffled_original_an", "unshuffled_original_ar", "unshuffled_original_arz", "unshuffled_original_as", "unshuffled_original_ast", "unshuffled_original_av", "unshuffled_original_az", "unshuffled_original_azb", "unshuffled_original_ba", "unshuffled_original_bar", "unshuffled_original_bcl", 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"unshuffled_original_gom", "unshuffled_original_gu", "unshuffled_original_he", "unshuffled_original_hi", "unshuffled_original_hr", "unshuffled_original_hsb", "unshuffled_original_ht", "unshuffled_original_hu", "unshuffled_original_hy", "unshuffled_original_ia", "unshuffled_original_id", "unshuffled_original_ie", "unshuffled_original_ilo", "unshuffled_original_io", "unshuffled_original_is", "unshuffled_original_it", "unshuffled_original_ja", "unshuffled_original_jbo", "unshuffled_original_jv", "unshuffled_original_ka", "unshuffled_original_kk", "unshuffled_original_km", "unshuffled_original_kn", "unshuffled_original_ko", "unshuffled_original_krc", "unshuffled_original_ku", "unshuffled_original_kv", "unshuffled_original_kw", "unshuffled_original_ky", "unshuffled_original_la", "unshuffled_original_lb", "unshuffled_original_lez", "unshuffled_original_li", "unshuffled_original_lmo", "unshuffled_original_lo", "unshuffled_original_lrc", "unshuffled_original_lt", "unshuffled_original_lv", 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2024-01-18T11:12:28+00:00

TAGS #task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-multilingual #size_categories-100K<n<1M #size_categories-100M<n<1B #size_categories-10K<n<100K #size_categories-10M<n<100M #size_categories-1K<n<10K #size_categories-1M<n<10M #size_categories-n<1K #source_datasets-original #language-Afrikaans #language-Tosk Albanian #language-Amharic #language-Aragonese #language-Arabic #language-Egyptian Arabic #language-Assamese #language-Asturian #language-Avaric #language-Azerbaijani #language-South Azerbaijani #language-Bashkir #language-Bavarian #language-Central Bikol #language-Belarusian #language-Bulgarian #language-bh #language-Bengali #language-Tibetan #language-Bishnupriya #language-Breton #language-Bosnian #language-Russia Buriat #language-Catalan #language-Chavacano #language-Chechen #language-Cebuano #language-Central Kurdish #language-Czech #language-Chuvash #language-Welsh #language-Danish #language-German #language-Dimli (individual language) #language-Lower Sorbian #language-Dhivehi #language-Modern Greek (1453-) #language-Emiliano-Romagnolo #language-English #language-Esperanto #language-Spanish #language-Estonian #language-Basque #language-Persian #language-Finnish #language-French #language-Northern Frisian #language-Western Frisian #language-Irish #language-Scottish Gaelic #language-Galician #language-Guarani #language-Goan Konkani #language-Gujarati #language-Hebrew #language-Hindi #language-Croatian #language-Upper Sorbian #language-Haitian #language-Hungarian #language-Armenian #language-Interlingua (International Auxiliary Language Association) #language-Indonesian #language-Interlingue #language-Iloko #language-Ido #language-Icelandic #language-Italian #language-Japanese #language-Lojban #language-Javanese #language-Georgian #language-Kazakh #language-Khmer #language-Kannada #language-Korean #language-Karachay-Balkar #language-Kurdish #language-Komi #language-Cornish #language-Kirghiz #language-Latin #language-Luxembourgish #language-Lezghian #language-Limburgan #language-Lombard #language-Lao #language-Northern Luri #language-Lithuanian #language-Latvian #language-Maithili #language-Malagasy #language-Eastern Mari #language-Minangkabau #language-Macedonian #language-Malayalam #language-Mongolian #language-Marathi #language-Western Mari #language-Malay (macrolanguage) #language-Maltese #language-Mirandese #language-Burmese #language-Erzya #language-Mazanderani #language-nah #language-Neapolitan #language-Low German #language-Nepali (macrolanguage) #language-Newari #language-Dutch #language-Norwegian Nynorsk #language-Norwegian #language-Occitan (post 1500) #language-Oriya (macrolanguage) #language-Ossetian #language-Panjabi #language-Pampanga #language-Polish #language-Piemontese #language-Western Panjabi #language-Pushto #language-Portuguese #language-Quechua #language-Romansh #language-Romanian #language-Russian #language-Sanskrit #language-Yakut #language-Sicilian #language-Sindhi #language-Serbo-Croatian #language-Sinhala #language-Slovak #language-Slovenian #language-Somali #language-Albanian #language-Serbian #language-Sundanese #language-Swedish #language-Swahili (macrolanguage) #language-Tamil #language-Telugu #language-Tajik #language-Thai #language-Turkmen #language-Tagalog #language-Turkish #language-Tatar #language-Tuvinian #language-Uighur #language-Ukrainian #language-Urdu #language-Uzbek #language-Venetian #language-Vietnamese #language-Volapük #language-Walloon #language-Waray (Philippines) #language-Wu Chinese #language-Kalmyk #language-Mingrelian #language-Yiddish #language-Yoruba #language-Yue Chinese #language-Chinese #license-cc0-1.0 #arxiv-2010.14571 #region-us

Dataset Card for "oscar" ======================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: * Point of Contact: ### Dataset Summary OSCAR or Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture. Data is distributed by language in both original and deduplicated form. The version here is the original OSCAR 2019 release: URL For more recent versions, visit the oscar-corpus organization on the Hub: * OSCAR 22.01 (released in January 2022): oscar-corpus/OSCAR-2201 * OSCAR 21.09 (released in September 2021): oscar-corpus/OSCAR-2109 ### Supported Tasks and Leaderboards OSCAR is mainly inteded to pretrain language models and word represantations. ### Languages All the data is distributed by language, both the original and the deduplicated versions of the data are available. 166 different languages are available. The table in subsection Data Splits Sample Size provides the language code for each subcorpus as well as the number of words (space separated tokens), lines and sizes for both the original and the deduplicated versions of OSCAR. Dataset Structure ----------------- We show detailed information for all the configurations of the dataset. ### Data Instances Click to expand the Data/size information for each language (deduplicated) #### unshuffled\_deduplicated\_af * Size of downloaded dataset files: 65.99 MB * Size of the generated dataset: 172.30 MB * Total amount of disk used: 238.29 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_als * Size of downloaded dataset files: 1.26 MB * Size of the generated dataset: 2.96 MB * Total amount of disk used: 4.22 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_am * Size of downloaded dataset files: 61.35 MB * Size of the generated dataset: 216.15 MB * Total amount of disk used: 277.50 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_an * Size of downloaded dataset files: 0.14 MB * Size of the generated dataset: 0.85 MB * Total amount of disk used: 0.99 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_ar * Size of downloaded dataset files: 9.67 GB * Size of the generated dataset: 33.57 GB * Total amount of disk used: 43.23 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_arz * Size of downloaded dataset files: 10.02 MB * Size of the generated dataset: 35.91 MB * Total amount of disk used: 45.94 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_as * Size of downloaded dataset files: 15.51 MB * Size of the generated dataset: 74.07 MB * Total amount of disk used: 89.58 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_ast * Size of downloaded dataset files: 0.86 MB * Size of the generated dataset: 2.17 MB * Total amount of disk used: 3.03 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_av * Size of downloaded dataset files: 0.07 MB * Size of the generated dataset: 0.34 MB * Total amount of disk used: 0.41 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_az * Size of downloaded dataset files: 521.74 MB * Size of the generated dataset: 1.53 GB * Total amount of disk used: 2.05 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_azb * Size of downloaded dataset files: 5.19 MB * Size of the generated dataset: 20.08 MB * Total amount of disk used: 25.27 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_ba * Size of downloaded dataset files: 25.98 MB * Size of the generated dataset: 93.84 MB * Total amount of disk used: 119.82 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_bar * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 0.00 MB * Total amount of disk used: 0.00 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_bcl * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 0.00 MB * Total amount of disk used: 0.00 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_be * Size of downloaded dataset files: 306.70 MB * Size of the generated dataset: 1.08 GB * Total amount of disk used: 1.39 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_bg * Size of downloaded dataset files: 3.85 GB * Size of the generated dataset: 14.45 GB * Total amount of disk used: 18.30 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_bh * Size of downloaded dataset files: 0.01 MB * Size of the generated dataset: 0.04 MB * Total amount of disk used: 0.04 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_bn * Size of downloaded dataset files: 1.26 GB * Size of the generated dataset: 6.24 GB * Total amount of disk used: 7.50 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_bo * Size of downloaded dataset files: 22.37 MB * Size of the generated dataset: 144.65 MB * Total amount of disk used: 167.02 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_bpy * Size of downloaded dataset files: 0.19 MB * Size of the generated dataset: 1.78 MB * Total amount of disk used: 1.97 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_br * Size of downloaded dataset files: 6.47 MB * Size of the generated dataset: 17.00 MB * Total amount of disk used: 23.47 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_bs * Size of downloaded dataset files: 0.04 MB * Size of the generated dataset: 0.15 MB * Total amount of disk used: 0.18 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_bxr * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 0.01 MB * Total amount of disk used: 0.01 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_ca * Size of downloaded dataset files: 1.73 GB * Size of the generated dataset: 4.57 GB * Total amount of disk used: 6.30 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_cbk * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 0.00 MB * Total amount of disk used: 0.00 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_ce * Size of downloaded dataset files: 1.87 MB * Size of the generated dataset: 7.04 MB * Total amount of disk used: 8.90 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_ceb * Size of downloaded dataset files: 7.12 MB * Size of the generated dataset: 24.83 MB * Total amount of disk used: 31.95 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_ckb * Size of downloaded dataset files: 60.32 MB * Size of the generated dataset: 237.72 MB * Total amount of disk used: 298.05 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_cs * Size of downloaded dataset files: 10.49 GB * Size of the generated dataset: 25.71 GB * Total amount of disk used: 36.20 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_cv * Size of downloaded dataset files: 7.47 MB * Size of the generated dataset: 27.49 MB * Total amount of disk used: 34.95 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_cy * Size of downloaded dataset files: 53.63 MB * Size of the generated dataset: 141.22 MB * Total amount of disk used: 194.86 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_da * Size of downloaded dataset files: 3.82 GB * Size of the generated dataset: 10.24 GB * Total amount of disk used: 14.06 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_de * Size of downloaded dataset files: 60.80 GB * Size of the generated dataset: 156.30 GB * Total amount of disk used: 217.10 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_diq * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 0.00 MB * Total amount of disk used: 0.00 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_dsb * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 0.01 MB * Total amount of disk used: 0.01 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_dv * Size of downloaded dataset files: 16.84 MB * Size of the generated dataset: 82.19 MB * Total amount of disk used: 99.03 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_el * Size of downloaded dataset files: 7.91 GB * Size of the generated dataset: 28.74 GB * Total amount of disk used: 36.65 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_eml * Size of downloaded dataset files: 0.01 MB * Size of the generated dataset: 0.02 MB * Total amount of disk used: 0.03 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_en * Size of downloaded dataset files: 496.50 GB * Size of the generated dataset: 1299.75 GB * Total amount of disk used: 1796.24 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_eo * Size of downloaded dataset files: 92.86 MB * Size of the generated dataset: 240.12 MB * Total amount of disk used: 332.99 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_es * Size of downloaded dataset files: 60.46 GB * Size of the generated dataset: 160.86 GB * Total amount of disk used: 221.32 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_et * Size of downloaded dataset files: 966.79 MB * Size of the generated dataset: 2.45 GB * Total amount of disk used: 3.41 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_eu * Size of downloaded dataset files: 134.68 MB * Size of the generated dataset: 363.93 MB * Total amount of disk used: 498.61 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_fa * Size of downloaded dataset files: 10.46 GB * Size of the generated dataset: 40.06 GB * Total amount of disk used: 50.52 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_fi * Size of downloaded dataset files: 5.38 GB * Size of the generated dataset: 13.99 GB * Total amount of disk used: 19.37 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_fr * Size of downloaded dataset files: 55.46 GB * Size of the generated dataset: 148.28 GB * Total amount of disk used: 203.75 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_frr * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 0.00 MB * Total amount of disk used: 0.00 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_fy * Size of downloaded dataset files: 10.27 MB * Size of the generated dataset: 26.73 MB * Total amount of disk used: 37.00 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_ga * Size of downloaded dataset files: 22.22 MB * Size of the generated dataset: 63.86 MB * Total amount of disk used: 86.08 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_gd * Size of downloaded dataset files: 0.42 MB * Size of the generated dataset: 1.36 MB * Total amount of disk used: 1.78 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_gl * Size of downloaded dataset files: 155.85 MB * Size of the generated dataset: 408.34 MB * Total amount of disk used: 564.19 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_gn * Size of downloaded dataset files: 0.01 MB * Size of the generated dataset: 0.02 MB * Total amount of disk used: 0.03 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_gom * Size of downloaded dataset files: 0.38 MB * Size of the generated dataset: 1.87 MB * Total amount of disk used: 2.24 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_gu * Size of downloaded dataset files: 162.97 MB * Size of the generated dataset: 759.34 MB * Total amount of disk used: 922.32 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_he * Size of downloaded dataset files: 3.04 GB * Size of the generated dataset: 10.47 GB * Total amount of disk used: 13.51 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_hi * Size of downloaded dataset files: 2.01 GB * Size of the generated dataset: 9.57 GB * Total amount of disk used: 11.58 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_hr * Size of downloaded dataset files: 46.74 MB * Size of the generated dataset: 121.50 MB * Total amount of disk used: 168.23 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_hsb * Size of downloaded dataset files: 0.72 MB * Size of the generated dataset: 1.89 MB * Total amount of disk used: 2.61 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_ht * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 0.00 MB * Total amount of disk used: 0.00 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_hu * Size of downloaded dataset files: 7.37 GB * Size of the generated dataset: 19.09 GB * Total amount of disk used: 26.46 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_hy * Size of downloaded dataset files: 393.62 MB * Size of the generated dataset: 1.56 GB * Total amount of disk used: 1.96 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_ia * Size of downloaded dataset files: 0.05 MB * Size of the generated dataset: 0.38 MB * Total amount of disk used: 0.43 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_id * Size of downloaded dataset files: 6.00 GB * Size of the generated dataset: 17.05 GB * Total amount of disk used: 23.05 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_ie * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 0.00 MB * Total amount of disk used: 0.00 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_ilo * Size of downloaded dataset files: 0.23 MB * Size of the generated dataset: 0.68 MB * Total amount of disk used: 0.91 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_io * Size of downloaded dataset files: 0.04 MB * Size of the generated dataset: 0.14 MB * Total amount of disk used: 0.19 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_is * Size of downloaded dataset files: 332.87 MB * Size of the generated dataset: 894.28 MB * Total amount of disk used: 1.23 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_it * Size of downloaded dataset files: 27.93 GB * Size of the generated dataset: 74.09 GB * Total amount of disk used: 102.03 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_ja * Size of downloaded dataset files: 40.80 GB * Size of the generated dataset: 113.63 GB * Total amount of disk used: 154.44 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_jbo * Size of downloaded dataset files: 0.20 MB * Size of the generated dataset: 0.70 MB * Total amount of disk used: 0.91 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_jv * Size of downloaded dataset files: 0.21 MB * Size of the generated dataset: 0.62 MB * Total amount of disk used: 0.82 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_ka * Size of downloaded dataset files: 377.23 MB * Size of the generated dataset: 1.99 GB * Total amount of disk used: 2.36 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_kk * Size of downloaded dataset files: 389.12 MB * Size of the generated dataset: 1.59 GB * Total amount of disk used: 1.97 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_km * Size of downloaded dataset files: 114.48 MB * Size of the generated dataset: 610.61 MB * Total amount of disk used: 725.09 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_kn * Size of downloaded dataset files: 215.52 MB * Size of the generated dataset: 1.08 GB * Total amount of disk used: 1.30 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_ko * Size of downloaded dataset files: 4.46 GB * Size of the generated dataset: 12.00 GB * Total amount of disk used: 16.47 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_krc * Size of downloaded dataset files: 0.62 MB * Size of the generated dataset: 2.41 MB * Total amount of disk used: 3.03 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_ku * Size of downloaded dataset files: 23.34 MB * Size of the generated dataset: 63.09 MB * Total amount of disk used: 86.43 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_kv * Size of downloaded dataset files: 0.33 MB * Size of the generated dataset: 1.21 MB * Total amount of disk used: 1.54 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_kw * Size of downloaded dataset files: 0.01 MB * Size of the generated dataset: 0.02 MB * Total amount of disk used: 0.02 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_ky * Size of downloaded dataset files: 106.22 MB * Size of the generated dataset: 408.40 MB * Total amount of disk used: 514.61 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_la * Size of downloaded dataset files: 3.42 MB * Size of the generated dataset: 9.79 MB * Total amount of disk used: 13.22 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_lb * Size of downloaded dataset files: 8.30 MB * Size of the generated dataset: 21.42 MB * Total amount of disk used: 29.72 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_lez * Size of downloaded dataset files: 0.77 MB * Size of the generated dataset: 3.08 MB * Total amount of disk used: 3.84 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_li * Size of downloaded dataset files: 0.01 MB * Size of the generated dataset: 0.03 MB * Total amount of disk used: 0.04 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_lmo * Size of downloaded dataset files: 0.10 MB * Size of the generated dataset: 0.46 MB * Total amount of disk used: 0.57 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_lo * Size of downloaded dataset files: 23.63 MB * Size of the generated dataset: 119.29 MB * Total amount of disk used: 142.92 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_lrc * Size of downloaded dataset files: 0.02 MB * Size of the generated dataset: 0.06 MB * Total amount of disk used: 0.08 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_lt * Size of downloaded dataset files: 1.65 GB * Size of the generated dataset: 4.20 GB * Total amount of disk used: 5.86 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_lv * Size of downloaded dataset files: 710.45 MB * Size of the generated dataset: 1.91 GB * Total amount of disk used: 2.62 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_mai * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 0.01 MB * Total amount of disk used: 0.01 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_mg * Size of downloaded dataset files: 4.30 MB * Size of the generated dataset: 13.59 MB * Total amount of disk used: 17.89 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_mhr * Size of downloaded dataset files: 1.63 MB * Size of the generated dataset: 6.26 MB * Total amount of disk used: 7.89 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_min * Size of downloaded dataset files: 0.01 MB * Size of the generated dataset: 0.31 MB * Total amount of disk used: 0.33 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_mk * Size of downloaded dataset files: 303.12 MB * Size of the generated dataset: 1.19 GB * Total amount of disk used: 1.49 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_ml * Size of downloaded dataset files: 496.80 MB * Size of the generated dataset: 2.69 GB * Total amount of disk used: 3.18 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_mn * Size of downloaded dataset files: 219.52 MB * Size of the generated dataset: 883.46 MB * Total amount of disk used: 1.10 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_mr * Size of downloaded dataset files: 299.68 MB * Size of the generated dataset: 1.49 GB * Total amount of disk used: 1.79 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_mrj * Size of downloaded dataset files: 0.29 MB * Size of the generated dataset: 1.10 MB * Total amount of disk used: 1.38 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_ms * Size of downloaded dataset files: 16.39 MB * Size of the generated dataset: 49.45 MB * Total amount of disk used: 65.85 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_mt * Size of downloaded dataset files: 5.90 MB * Size of the generated dataset: 17.68 MB * Total amount of disk used: 23.58 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_mwl * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 0.00 MB * Total amount of disk used: 0.00 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_my * Size of downloaded dataset files: 207.14 MB * Size of the generated dataset: 1.11 GB * Total amount of disk used: 1.32 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_myv * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 0.00 MB * Total amount of disk used: 0.00 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_mzn * Size of downloaded dataset files: 0.16 MB * Size of the generated dataset: 0.63 MB * Total amount of disk used: 0.79 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_nah * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 0.01 MB * Total amount of disk used: 0.01 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_nap * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 0.01 MB * Total amount of disk used: 0.02 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_nds * Size of downloaded dataset files: 5.27 MB * Size of the generated dataset: 13.48 MB * Total amount of disk used: 18.76 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_ne * Size of downloaded dataset files: 240.63 MB * Size of the generated dataset: 1.24 GB * Total amount of disk used: 1.48 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_new * Size of downloaded dataset files: 0.83 MB * Size of the generated dataset: 4.26 MB * Total amount of disk used: 5.09 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_nl * Size of downloaded dataset files: 15.73 GB * Size of the generated dataset: 41.91 GB * Total amount of disk used: 57.65 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_nn * Size of downloaded dataset files: 23.58 MB * Size of the generated dataset: 58.32 MB * Total amount of disk used: 81.90 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_no * Size of downloaded dataset files: 1.96 GB * Size of the generated dataset: 5.11 GB * Total amount of disk used: 7.07 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_oc * Size of downloaded dataset files: 1.34 MB * Size of the generated dataset: 4.00 MB * Total amount of disk used: 5.34 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_or * Size of downloaded dataset files: 38.72 MB * Size of the generated dataset: 197.63 MB * Total amount of disk used: 236.36 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_os * Size of downloaded dataset files: 2.83 MB * Size of the generated dataset: 11.00 MB * Total amount of disk used: 13.83 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_pa * Size of downloaded dataset files: 102.39 MB * Size of the generated dataset: 483.04 MB * Total amount of disk used: 585.42 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_pam * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 0.00 MB * Total amount of disk used: 0.00 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_pl * Size of downloaded dataset files: 20.19 GB * Size of the generated dataset: 50.59 GB * Total amount of disk used: 70.78 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_pms * Size of downloaded dataset files: 0.71 MB * Size of the generated dataset: 2.00 MB * Total amount of disk used: 2.72 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_pnb * Size of downloaded dataset files: 2.58 MB * Size of the generated dataset: 9.44 MB * Total amount of disk used: 12.02 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_ps * Size of downloaded dataset files: 71.83 MB * Size of the generated dataset: 254.79 MB * Total amount of disk used: 326.61 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_pt * Size of downloaded dataset files: 26.00 GB * Size of the generated dataset: 68.37 GB * Total amount of disk used: 94.37 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_qu * Size of downloaded dataset files: 0.02 MB * Size of the generated dataset: 0.07 MB * Total amount of disk used: 0.09 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_rm * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 0.01 MB * Total amount of disk used: 0.01 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_ro * Size of downloaded dataset files: 4.48 GB * Size of the generated dataset: 11.66 GB * Total amount of disk used: 16.14 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_ru * Size of downloaded dataset files: 166.68 GB * Size of the generated dataset: 611.70 GB * Total amount of disk used: 778.38 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_sa * Size of downloaded dataset files: 7.27 MB * Size of the generated dataset: 38.33 MB * Total amount of disk used: 45.60 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_sah * Size of downloaded dataset files: 7.01 MB * Size of the generated dataset: 27.46 MB * Total amount of disk used: 34.49 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_scn * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 0.00 MB * Total amount of disk used: 0.00 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_sd * Size of downloaded dataset files: 74.17 MB * Size of the generated dataset: 275.48 MB * Total amount of disk used: 349.66 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_sh * Size of downloaded dataset files: 1.45 MB * Size of the generated dataset: 6.44 MB * Total amount of disk used: 7.87 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_si * Size of downloaded dataset files: 175.62 MB * Size of the generated dataset: 842.57 MB * Total amount of disk used: 1.02 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_sk * Size of downloaded dataset files: 1.96 GB * Size of the generated dataset: 4.80 GB * Total amount of disk used: 6.76 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_sl * Size of downloaded dataset files: 523.22 MB * Size of the generated dataset: 1.32 GB * Total amount of disk used: 1.85 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_so * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 0.02 MB * Total amount of disk used: 0.02 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_sq * Size of downloaded dataset files: 445.36 MB * Size of the generated dataset: 1.21 GB * Total amount of disk used: 1.66 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_sr * Size of downloaded dataset files: 665.03 MB * Size of the generated dataset: 2.36 GB * Total amount of disk used: 3.03 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_su * Size of downloaded dataset files: 0.05 MB * Size of the generated dataset: 0.16 MB * Total amount of disk used: 0.21 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_sv * Size of downloaded dataset files: 10.19 GB * Size of the generated dataset: 26.33 GB * Total amount of disk used: 36.51 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_sw * Size of downloaded dataset files: 2.95 MB * Size of the generated dataset: 8.98 MB * Total amount of disk used: 11.92 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_ta * Size of downloaded dataset files: 971.12 MB * Size of the generated dataset: 5.48 GB * Total amount of disk used: 6.45 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_te * Size of downloaded dataset files: 342.43 MB * Size of the generated dataset: 1.70 GB * Total amount of disk used: 2.04 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_tg * Size of downloaded dataset files: 62.90 MB * Size of the generated dataset: 261.68 MB * Total amount of disk used: 324.60 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_th * Size of downloaded dataset files: 3.54 GB * Size of the generated dataset: 17.11 GB * Total amount of disk used: 20.65 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_tk * Size of downloaded dataset files: 2.22 MB * Size of the generated dataset: 7.12 MB * Total amount of disk used: 9.34 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_tl * Size of downloaded dataset files: 151.34 MB * Size of the generated dataset: 431.69 MB * Total amount of disk used: 583.04 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_tr * Size of downloaded dataset files: 10.39 GB * Size of the generated dataset: 28.47 GB * Total amount of disk used: 38.86 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_tt * Size of downloaded dataset files: 85.89 MB * Size of the generated dataset: 321.37 MB * Total amount of disk used: 407.26 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_tyv * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 0.01 MB * Total amount of disk used: 0.01 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_ug * Size of downloaded dataset files: 20.53 MB * Size of the generated dataset: 86.44 MB * Total amount of disk used: 106.97 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_uk * Size of downloaded dataset files: 8.04 GB * Size of the generated dataset: 29.86 GB * Total amount of disk used: 37.90 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_ur * Size of downloaded dataset files: 483.59 MB * Size of the generated dataset: 1.82 GB * Total amount of disk used: 2.31 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_uz * Size of downloaded dataset files: 4.30 MB * Size of the generated dataset: 12.00 MB * Total amount of disk used: 16.29 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_vec * Size of downloaded dataset files: 0.01 MB * Size of the generated dataset: 0.02 MB * Total amount of disk used: 0.02 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_vi * Size of downloaded dataset files: 10.71 GB * Size of the generated dataset: 33.60 GB * Total amount of disk used: 44.31 GB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_vo * Size of downloaded dataset files: 0.30 MB * Size of the generated dataset: 2.10 MB * Total amount of disk used: 2.40 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_wa * Size of downloaded dataset files: 0.08 MB * Size of the generated dataset: 0.22 MB * Total amount of disk used: 0.29 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_war * Size of downloaded dataset files: 0.55 MB * Size of the generated dataset: 2.36 MB * Total amount of disk used: 2.90 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_wuu * Size of downloaded dataset files: 0.01 MB * Size of the generated dataset: 0.03 MB * Total amount of disk used: 0.04 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_xal * Size of downloaded dataset files: 0.03 MB * Size of the generated dataset: 0.12 MB * Total amount of disk used: 0.15 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_xmf * Size of downloaded dataset files: 0.94 MB * Size of the generated dataset: 4.63 MB * Total amount of disk used: 5.58 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_yi * Size of downloaded dataset files: 22.20 MB * Size of the generated dataset: 88.29 MB * Total amount of disk used: 110.49 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_yo * Size of downloaded dataset files: 0.01 MB * Size of the generated dataset: 0.03 MB * Total amount of disk used: 0.04 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_yue * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 0.00 MB * Total amount of disk used: 0.00 MB An example of 'train' looks as follows. #### unshuffled\_deduplicated\_zh * Size of downloaded dataset files: 99.98 GB * Size of the generated dataset: 267.88 GB * Total amount of disk used: 367.86 GB An example of 'train' looks as follows. Click to expand the Data/size information for each language (original) #### unshuffled\_original\_af * Size of downloaded dataset files: 85.79 MB * Size of the generated dataset: 254.08 MB * Total amount of disk used: 339.87 MB An example of 'train' looks as follows. #### unshuffled\_original\_als * Size of downloaded dataset files: 1.49 MB * Size of the generated dataset: 5.30 MB * Total amount of disk used: 6.78 MB An example of 'train' looks as follows. #### unshuffled\_original\_am * Size of downloaded dataset files: 102.79 MB * Size of the generated dataset: 378.06 MB * Total amount of disk used: 480.85 MB An example of 'train' looks as follows. #### unshuffled\_original\_an * Size of downloaded dataset files: 0.15 MB * Size of the generated dataset: 1.33 MB * Total amount of disk used: 1.48 MB An example of 'train' looks as follows. #### unshuffled\_original\_ar * Size of downloaded dataset files: 22.23 GB * Size of the generated dataset: 87.94 GB * Total amount of disk used: 110.17 GB An example of 'train' looks as follows. #### unshuffled\_original\_arz * Size of downloaded dataset files: 15.90 MB * Size of the generated dataset: 70.13 MB * Total amount of disk used: 86.03 MB An example of 'train' looks as follows. #### unshuffled\_original\_as * Size of downloaded dataset files: 21.43 MB * Size of the generated dataset: 117.73 MB * Total amount of disk used: 139.17 MB An example of 'train' looks as follows. #### unshuffled\_original\_ast * Size of downloaded dataset files: 0.92 MB * Size of the generated dataset: 2.54 MB * Total amount of disk used: 3.46 MB An example of 'train' looks as follows. #### unshuffled\_original\_av * Size of downloaded dataset files: 0.08 MB * Size of the generated dataset: 0.42 MB * Total amount of disk used: 0.50 MB An example of 'train' looks as follows. #### unshuffled\_original\_az * Size of downloaded dataset files: 927.76 MB * Size of the generated dataset: 2.96 GB * Total amount of disk used: 3.89 GB An example of 'train' looks as follows. #### unshuffled\_original\_azb * Size of downloaded dataset files: 6.64 MB * Size of the generated dataset: 28.47 MB * Total amount of disk used: 35.11 MB An example of 'train' looks as follows. #### unshuffled\_original\_ba * Size of downloaded dataset files: 33.22 MB * Size of the generated dataset: 133.70 MB * Total amount of disk used: 166.92 MB An example of 'train' looks as follows. #### unshuffled\_original\_bar * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 0.00 MB * Total amount of disk used: 0.00 MB An example of 'train' looks as follows. #### unshuffled\_original\_bcl * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 0.00 MB * Total amount of disk used: 0.00 MB An example of 'train' looks as follows. #### unshuffled\_original\_be * Size of downloaded dataset files: 498.29 MB * Size of the generated dataset: 1.88 GB * Total amount of disk used: 2.38 GB An example of 'train' looks as follows. #### unshuffled\_original\_bg * Size of downloaded dataset files: 8.34 GB * Size of the generated dataset: 33.75 GB * Total amount of disk used: 42.09 GB An example of 'train' looks as follows. #### unshuffled\_original\_bh * Size of downloaded dataset files: 0.01 MB * Size of the generated dataset: 0.12 MB * Total amount of disk used: 0.13 MB An example of 'train' looks as follows. #### unshuffled\_original\_bn * Size of downloaded dataset files: 2.14 GB * Size of the generated dataset: 10.77 GB * Total amount of disk used: 12.91 GB An example of 'train' looks as follows. #### unshuffled\_original\_bo * Size of downloaded dataset files: 28.94 MB * Size of the generated dataset: 195.40 MB * Total amount of disk used: 224.34 MB An example of 'train' looks as follows. #### unshuffled\_original\_bpy * Size of downloaded dataset files: 0.34 MB * Size of the generated dataset: 4.35 MB * Total amount of disk used: 4.69 MB An example of 'train' looks as follows. #### unshuffled\_original\_br * Size of downloaded dataset files: 9.18 MB * Size of the generated dataset: 30.20 MB * Total amount of disk used: 39.38 MB An example of 'train' looks as follows. #### unshuffled\_original\_bs * Size of downloaded dataset files: 0.05 MB * Size of the generated dataset: 0.48 MB * Total amount of disk used: 0.53 MB An example of 'train' looks as follows. #### unshuffled\_original\_bxr * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 0.01 MB * Total amount of disk used: 0.02 MB An example of 'train' looks as follows. #### unshuffled\_original\_ca * Size of downloaded dataset files: 3.10 GB * Size of the generated dataset: 8.62 GB * Total amount of disk used: 11.73 GB An example of 'train' looks as follows. #### unshuffled\_original\_cbk * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 0.00 MB * Total amount of disk used: 0.00 MB An example of 'train' looks as follows. #### unshuffled\_original\_ce * Size of downloaded dataset files: 2.09 MB * Size of the generated dataset: 8.73 MB * Total amount of disk used: 10.82 MB An example of 'train' looks as follows. #### unshuffled\_original\_ceb * Size of downloaded dataset files: 11.07 MB * Size of the generated dataset: 40.97 MB * Total amount of disk used: 52.03 MB An example of 'train' looks as follows. #### unshuffled\_original\_ckb * Size of downloaded dataset files: 111.88 MB * Size of the generated dataset: 510.97 MB * Total amount of disk used: 622.85 MB An example of 'train' looks as follows. #### unshuffled\_original\_cs * Size of downloaded dataset files: 21.72 GB * Size of the generated dataset: 57.08 GB * Total amount of disk used: 78.80 GB An example of 'train' looks as follows. #### unshuffled\_original\_cv * Size of downloaded dataset files: 9.40 MB * Size of the generated dataset: 41.05 MB * Total amount of disk used: 50.45 MB An example of 'train' looks as follows. #### unshuffled\_original\_cy * Size of downloaded dataset files: 81.74 MB * Size of the generated dataset: 224.93 MB * Total amount of disk used: 306.67 MB An example of 'train' looks as follows. #### unshuffled\_original\_da * Size of downloaded dataset files: 6.00 GB * Size of the generated dataset: 16.76 GB * Total amount of disk used: 22.76 GB An example of 'train' looks as follows. #### unshuffled\_original\_de * Size of downloaded dataset files: 119.51 GB * Size of the generated dataset: 331.22 GB * Total amount of disk used: 450.73 GB An example of 'train' looks as follows. #### unshuffled\_original\_diq * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 0.00 MB * Total amount of disk used: 0.00 MB An example of 'train' looks as follows. #### unshuffled\_original\_dsb * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 0.01 MB * Total amount of disk used: 0.02 MB An example of 'train' looks as follows. #### unshuffled\_original\_dv * Size of downloaded dataset files: 24.91 MB * Size of the generated dataset: 131.63 MB * Total amount of disk used: 156.54 MB An example of 'train' looks as follows. #### unshuffled\_original\_el * Size of downloaded dataset files: 17.31 GB * Size of the generated dataset: 66.27 GB * Total amount of disk used: 83.58 GB An example of 'train' looks as follows. #### unshuffled\_original\_eml * Size of downloaded dataset files: 0.01 MB * Size of the generated dataset: 0.02 MB * Total amount of disk used: 0.03 MB An example of 'train' looks as follows. #### unshuffled\_original\_en * Size of downloaded dataset files: 903.83 GB * Size of the generated dataset: 2525.44 GB * Total amount of disk used: 3429.27 GB An example of 'train' looks as follows. #### unshuffled\_original\_eo * Size of downloaded dataset files: 117.07 MB * Size of the generated dataset: 314.18 MB * Total amount of disk used: 431.27 MB An example of 'train' looks as follows. #### unshuffled\_original\_es * Size of downloaded dataset files: 106.04 GB * Size of the generated dataset: 298.49 GB * Total amount of disk used: 404.53 GB An example of 'train' looks as follows. #### unshuffled\_original\_et * Size of downloaded dataset files: 1.88 GB * Size of the generated dataset: 5.17 GB * Total amount of disk used: 7.06 GB An example of 'train' looks as follows. #### unshuffled\_original\_eu * Size of downloaded dataset files: 248.19 MB * Size of the generated dataset: 894.83 MB * Total amount of disk used: 1.14 GB An example of 'train' looks as follows. #### unshuffled\_original\_fa * Size of downloaded dataset files: 20.96 GB * Size of the generated dataset: 84.21 GB * Total amount of disk used: 105.17 GB An example of 'train' looks as follows. #### unshuffled\_original\_fi * Size of downloaded dataset files: 9.97 GB * Size of the generated dataset: 28.57 GB * Total amount of disk used: 38.54 GB An example of 'train' looks as follows. #### unshuffled\_original\_fr * Size of downloaded dataset files: 105.32 GB * Size of the generated dataset: 303.19 GB * Total amount of disk used: 408.51 GB An example of 'train' looks as follows. #### unshuffled\_original\_frr * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 0.00 MB * Total amount of disk used: 0.00 MB An example of 'train' looks as follows. #### unshuffled\_original\_fy * Size of downloaded dataset files: 12.40 MB * Size of the generated dataset: 36.24 MB * Total amount of disk used: 48.64 MB An example of 'train' looks as follows. #### unshuffled\_original\_ga * Size of downloaded dataset files: 29.27 MB * Size of the generated dataset: 92.37 MB * Total amount of disk used: 121.63 MB An example of 'train' looks as follows. #### unshuffled\_original\_gd * Size of downloaded dataset files: 0.52 MB * Size of the generated dataset: 2.02 MB * Total amount of disk used: 2.55 MB An example of 'train' looks as follows. #### unshuffled\_original\_gl * Size of downloaded dataset files: 235.38 MB * Size of the generated dataset: 656.48 MB * Total amount of disk used: 891.87 MB An example of 'train' looks as follows. #### unshuffled\_original\_gn * Size of downloaded dataset files: 0.01 MB * Size of the generated dataset: 0.04 MB * Total amount of disk used: 0.05 MB An example of 'train' looks as follows. #### unshuffled\_original\_gom * Size of downloaded dataset files: 0.44 MB * Size of the generated dataset: 2.25 MB * Total amount of disk used: 2.71 MB An example of 'train' looks as follows. #### unshuffled\_original\_gu * Size of downloaded dataset files: 232.02 MB * Size of the generated dataset: 1.09 GB * Total amount of disk used: 1.33 GB An example of 'train' looks as follows. #### unshuffled\_original\_he * Size of downloaded dataset files: 5.66 GB * Size of the generated dataset: 21.11 GB * Total amount of disk used: 26.77 GB An example of 'train' looks as follows. #### unshuffled\_original\_hi * Size of downloaded dataset files: 3.66 GB * Size of the generated dataset: 17.93 GB * Total amount of disk used: 21.59 GB An example of 'train' looks as follows. #### unshuffled\_original\_hr * Size of downloaded dataset files: 79.42 MB * Size of the generated dataset: 243.83 MB * Total amount of disk used: 323.24 MB An example of 'train' looks as follows. #### unshuffled\_original\_hsb * Size of downloaded dataset files: 1.39 MB * Size of the generated dataset: 4.49 MB * Total amount of disk used: 5.87 MB An example of 'train' looks as follows. #### unshuffled\_original\_ht * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 0.00 MB * Total amount of disk used: 0.00 MB An example of 'train' looks as follows. #### unshuffled\_original\_hu * Size of downloaded dataset files: 15.69 GB * Size of the generated dataset: 43.07 GB * Total amount of disk used: 58.77 GB An example of 'train' looks as follows. #### unshuffled\_original\_hy * Size of downloaded dataset files: 897.36 MB * Size of the generated dataset: 3.94 GB * Total amount of disk used: 4.84 GB An example of 'train' looks as follows. #### unshuffled\_original\_ia * Size of downloaded dataset files: 0.08 MB * Size of the generated dataset: 0.69 MB * Total amount of disk used: 0.78 MB An example of 'train' looks as follows. #### unshuffled\_original\_id * Size of downloaded dataset files: 10.60 GB * Size of the generated dataset: 32.32 GB * Total amount of disk used: 42.91 GB An example of 'train' looks as follows. #### unshuffled\_original\_ie * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 0.02 MB * Total amount of disk used: 0.02 MB An example of 'train' looks as follows. #### unshuffled\_original\_ilo * Size of downloaded dataset files: 0.27 MB * Size of the generated dataset: 0.92 MB * Total amount of disk used: 1.20 MB An example of 'train' looks as follows. #### unshuffled\_original\_io * Size of downloaded dataset files: 0.04 MB * Size of the generated dataset: 0.16 MB * Total amount of disk used: 0.20 MB An example of 'train' looks as follows. #### unshuffled\_original\_is * Size of downloaded dataset files: 533.03 MB * Size of the generated dataset: 1.52 GB * Total amount of disk used: 2.06 GB An example of 'train' looks as follows. #### unshuffled\_original\_it * Size of downloaded dataset files: 52.16 GB * Size of the generated dataset: 147.38 GB * Total amount of disk used: 199.54 GB An example of 'train' looks as follows. #### unshuffled\_original\_ja * Size of downloaded dataset files: 79.56 GB * Size of the generated dataset: 232.22 GB * Total amount of disk used: 311.78 GB An example of 'train' looks as follows. #### unshuffled\_original\_jbo * Size of downloaded dataset files: 0.21 MB * Size of the generated dataset: 0.77 MB * Total amount of disk used: 0.98 MB An example of 'train' looks as follows. #### unshuffled\_original\_jv * Size of downloaded dataset files: 0.22 MB * Size of the generated dataset: 0.69 MB * Total amount of disk used: 0.91 MB An example of 'train' looks as follows. #### unshuffled\_original\_ka * Size of downloaded dataset files: 680.74 MB * Size of the generated dataset: 3.77 GB * Total amount of disk used: 4.45 GB An example of 'train' looks as follows. #### unshuffled\_original\_kk * Size of downloaded dataset files: 615.06 MB * Size of the generated dataset: 2.83 GB * Total amount of disk used: 3.45 GB An example of 'train' looks as follows. #### unshuffled\_original\_km * Size of downloaded dataset files: 193.28 MB * Size of the generated dataset: 1.10 GB * Total amount of disk used: 1.30 GB An example of 'train' looks as follows. #### unshuffled\_original\_kn * Size of downloaded dataset files: 342.15 MB * Size of the generated dataset: 1.76 GB * Total amount of disk used: 2.11 GB An example of 'train' looks as follows. #### unshuffled\_original\_ko * Size of downloaded dataset files: 8.81 GB * Size of the generated dataset: 25.29 GB * Total amount of disk used: 34.10 GB An example of 'train' looks as follows. #### unshuffled\_original\_krc * Size of downloaded dataset files: 0.66 MB * Size of the generated dataset: 2.68 MB * Total amount of disk used: 3.34 MB An example of 'train' looks as follows. #### unshuffled\_original\_ku * Size of downloaded dataset files: 33.38 MB * Size of the generated dataset: 99.06 MB * Total amount of disk used: 132.44 MB An example of 'train' looks as follows. #### unshuffled\_original\_kv * Size of downloaded dataset files: 0.40 MB * Size of the generated dataset: 2.38 MB * Total amount of disk used: 2.78 MB An example of 'train' looks as follows. #### unshuffled\_original\_kw * Size of downloaded dataset files: 0.01 MB * Size of the generated dataset: 0.04 MB * Total amount of disk used: 0.05 MB An example of 'train' looks as follows. #### unshuffled\_original\_ky * Size of downloaded dataset files: 152.64 MB * Size of the generated dataset: 630.79 MB * Total amount of disk used: 783.43 MB An example of 'train' looks as follows. #### unshuffled\_original\_la * Size of downloaded dataset files: 5.46 MB * Size of the generated dataset: 27.80 MB * Total amount of disk used: 33.26 MB An example of 'train' looks as follows. #### unshuffled\_original\_lb * Size of downloaded dataset files: 10.73 MB * Size of the generated dataset: 30.60 MB * Total amount of disk used: 41.32 MB An example of 'train' looks as follows. #### unshuffled\_original\_lez * Size of downloaded dataset files: 0.83 MB * Size of the generated dataset: 3.38 MB * Total amount of disk used: 4.20 MB An example of 'train' looks as follows. #### unshuffled\_original\_li * Size of downloaded dataset files: 0.01 MB * Size of the generated dataset: 0.03 MB * Total amount of disk used: 0.04 MB An example of 'train' looks as follows. #### unshuffled\_original\_lmo * Size of downloaded dataset files: 0.10 MB * Size of the generated dataset: 0.47 MB * Total amount of disk used: 0.58 MB An example of 'train' looks as follows. #### unshuffled\_original\_lo * Size of downloaded dataset files: 33.92 MB * Size of the generated dataset: 182.36 MB * Total amount of disk used: 216.28 MB An example of 'train' looks as follows. #### unshuffled\_original\_lrc * Size of downloaded dataset files: 0.02 MB * Size of the generated dataset: 0.07 MB * Total amount of disk used: 0.09 MB An example of 'train' looks as follows. #### unshuffled\_original\_lt * Size of downloaded dataset files: 3.44 GB * Size of the generated dataset: 9.45 GB * Total amount of disk used: 12.89 GB An example of 'train' looks as follows. #### unshuffled\_original\_lv * Size of downloaded dataset files: 1.49 GB * Size of the generated dataset: 4.27 GB * Total amount of disk used: 5.75 GB An example of 'train' looks as follows. #### unshuffled\_original\_mai * Size of downloaded dataset files: 0.01 MB * Size of the generated dataset: 0.33 MB * Total amount of disk used: 0.34 MB An example of 'train' looks as follows. #### unshuffled\_original\_mg * Size of downloaded dataset files: 6.22 MB * Size of the generated dataset: 21.79 MB * Total amount of disk used: 28.01 MB An example of 'train' looks as follows. #### unshuffled\_original\_mhr * Size of downloaded dataset files: 1.84 MB * Size of the generated dataset: 7.55 MB * Total amount of disk used: 9.38 MB An example of 'train' looks as follows. #### unshuffled\_original\_min * Size of downloaded dataset files: 0.01 MB * Size of the generated dataset: 0.63 MB * Total amount of disk used: 0.64 MB An example of 'train' looks as follows. #### unshuffled\_original\_mk * Size of downloaded dataset files: 508.24 MB * Size of the generated dataset: 2.20 GB * Total amount of disk used: 2.71 GB An example of 'train' looks as follows. #### unshuffled\_original\_ml * Size of downloaded dataset files: 938.69 MB * Size of the generated dataset: 5.24 GB * Total amount of disk used: 6.18 GB An example of 'train' looks as follows. #### unshuffled\_original\_mn * Size of downloaded dataset files: 472.36 MB * Size of the generated dataset: 2.33 GB * Total amount of disk used: 2.81 GB An example of 'train' looks as follows. #### unshuffled\_original\_mr * Size of downloaded dataset files: 525.31 MB * Size of the generated dataset: 2.82 GB * Total amount of disk used: 3.34 GB An example of 'train' looks as follows. #### unshuffled\_original\_mrj * Size of downloaded dataset files: 0.30 MB * Size of the generated dataset: 1.16 MB * Total amount of disk used: 1.47 MB An example of 'train' looks as follows. #### unshuffled\_original\_ms * Size of downloaded dataset files: 28.46 MB * Size of the generated dataset: 122.33 MB * Total amount of disk used: 150.79 MB An example of 'train' looks as follows. #### unshuffled\_original\_mt * Size of downloaded dataset files: 7.53 MB * Size of the generated dataset: 24.47 MB * Total amount of disk used: 32.00 MB An example of 'train' looks as follows. #### unshuffled\_original\_mwl * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 0.00 MB * Total amount of disk used: 0.00 MB An example of 'train' looks as follows. #### unshuffled\_original\_my * Size of downloaded dataset files: 369.85 MB * Size of the generated dataset: 2.02 GB * Total amount of disk used: 2.39 GB An example of 'train' looks as follows. #### unshuffled\_original\_myv * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 0.00 MB * Total amount of disk used: 0.00 MB An example of 'train' looks as follows. #### unshuffled\_original\_mzn * Size of downloaded dataset files: 0.18 MB * Size of the generated dataset: 0.72 MB * Total amount of disk used: 0.90 MB An example of 'train' looks as follows. #### unshuffled\_original\_nah * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 0.01 MB * Total amount of disk used: 0.01 MB An example of 'train' looks as follows. #### unshuffled\_original\_nap * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 0.02 MB * Total amount of disk used: 0.02 MB An example of 'train' looks as follows. #### unshuffled\_original\_nds * Size of downloaded dataset files: 6.74 MB * Size of the generated dataset: 18.23 MB * Total amount of disk used: 24.99 MB An example of 'train' looks as follows. #### unshuffled\_original\_ne * Size of downloaded dataset files: 355.29 MB * Size of the generated dataset: 1.87 GB * Total amount of disk used: 2.22 GB An example of 'train' looks as follows. #### unshuffled\_original\_new * Size of downloaded dataset files: 1.03 MB * Size of the generated dataset: 5.77 MB * Total amount of disk used: 6.79 MB An example of 'train' looks as follows. #### unshuffled\_original\_nl * Size of downloaded dataset files: 29.35 GB * Size of the generated dataset: 83.23 GB * Total amount of disk used: 112.58 GB An example of 'train' looks as follows. #### unshuffled\_original\_nn * Size of downloaded dataset files: 32.86 MB * Size of the generated dataset: 90.84 MB * Total amount of disk used: 123.70 MB An example of 'train' looks as follows. #### unshuffled\_original\_no * Size of downloaded dataset files: 3.11 GB * Size of the generated dataset: 8.65 GB * Total amount of disk used: 11.76 GB An example of 'train' looks as follows. #### unshuffled\_original\_oc * Size of downloaded dataset files: 1.57 MB * Size of the generated dataset: 6.12 MB * Total amount of disk used: 7.71 MB An example of 'train' looks as follows. #### unshuffled\_original\_or * Size of downloaded dataset files: 49.84 MB * Size of the generated dataset: 260.15 MB * Total amount of disk used: 309.99 MB An example of 'train' looks as follows. #### unshuffled\_original\_os * Size of downloaded dataset files: 3.09 MB * Size of the generated dataset: 12.90 MB * Total amount of disk used: 15.99 MB An example of 'train' looks as follows. #### unshuffled\_original\_pa * Size of downloaded dataset files: 164.21 MB * Size of the generated dataset: 801.16 MB * Total amount of disk used: 965.37 MB An example of 'train' looks as follows. #### unshuffled\_original\_pam * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 0.00 MB * Total amount of disk used: 0.00 MB An example of 'train' looks as follows. #### unshuffled\_original\_pl * Size of downloaded dataset files: 42.88 GB * Size of the generated dataset: 117.12 GB * Total amount of disk used: 160.01 GB An example of 'train' looks as follows. #### unshuffled\_original\_pms * Size of downloaded dataset files: 0.75 MB * Size of the generated dataset: 2.15 MB * Total amount of disk used: 2.92 MB An example of 'train' looks as follows. #### unshuffled\_original\_pnb * Size of downloaded dataset files: 3.22 MB * Size of the generated dataset: 12.04 MB * Total amount of disk used: 15.26 MB An example of 'train' looks as follows. #### unshuffled\_original\_ps * Size of downloaded dataset files: 103.66 MB * Size of the generated dataset: 379.51 MB * Total amount of disk used: 483.17 MB An example of 'train' looks as follows. #### unshuffled\_original\_pt * Size of downloaded dataset files: 47.26 GB * Size of the generated dataset: 132.64 GB * Total amount of disk used: 179.89 GB An example of 'train' looks as follows. #### unshuffled\_original\_qu * Size of downloaded dataset files: 0.02 MB * Size of the generated dataset: 0.08 MB * Total amount of disk used: 0.10 MB An example of 'train' looks as follows. #### unshuffled\_original\_rm * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 0.01 MB * Total amount of disk used: 0.01 MB An example of 'train' looks as follows. #### unshuffled\_original\_ro * Size of downloaded dataset files: 9.53 GB * Size of the generated dataset: 26.87 GB * Total amount of disk used: 36.40 GB An example of 'train' looks as follows. #### unshuffled\_original\_ru * Size of downloaded dataset files: 319.76 GB * Size of the generated dataset: 1241.63 GB * Total amount of disk used: 1561.38 GB An example of 'train' looks as follows. #### unshuffled\_original\_sa * Size of downloaded dataset files: 17.52 MB * Size of the generated dataset: 97.06 MB * Total amount of disk used: 114.58 MB An example of 'train' looks as follows. #### unshuffled\_original\_sah * Size of downloaded dataset files: 9.08 MB * Size of the generated dataset: 43.82 MB * Total amount of disk used: 52.90 MB An example of 'train' looks as follows. #### unshuffled\_original\_scn * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 0.00 MB * Total amount of disk used: 0.00 MB An example of 'train' looks as follows. #### unshuffled\_original\_sd * Size of downloaded dataset files: 90.62 MB * Size of the generated dataset: 364.25 MB * Total amount of disk used: 454.88 MB An example of 'train' looks as follows. #### unshuffled\_original\_sh * Size of downloaded dataset files: 3.46 MB * Size of the generated dataset: 25.84 MB * Total amount of disk used: 29.30 MB An example of 'train' looks as follows. #### unshuffled\_original\_si * Size of downloaded dataset files: 310.93 MB * Size of the generated dataset: 1.47 GB * Total amount of disk used: 1.78 GB An example of 'train' looks as follows. #### unshuffled\_original\_sk * Size of downloaded dataset files: 3.71 GB * Size of the generated dataset: 9.81 GB * Total amount of disk used: 13.52 GB An example of 'train' looks as follows. #### unshuffled\_original\_sl * Size of downloaded dataset files: 956.20 MB * Size of the generated dataset: 2.68 GB * Total amount of disk used: 3.63 GB An example of 'train' looks as follows. #### unshuffled\_original\_so * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 0.06 MB * Total amount of disk used: 0.06 MB An example of 'train' looks as follows. #### unshuffled\_original\_sq * Size of downloaded dataset files: 861.84 MB * Size of the generated dataset: 2.44 GB * Total amount of disk used: 3.30 GB An example of 'train' looks as follows. #### unshuffled\_original\_sr * Size of downloaded dataset files: 1.08 GB * Size of the generated dataset: 4.13 GB * Total amount of disk used: 5.21 GB An example of 'train' looks as follows. #### unshuffled\_original\_su * Size of downloaded dataset files: 0.06 MB * Size of the generated dataset: 0.23 MB * Total amount of disk used: 0.28 MB An example of 'train' looks as follows. #### unshuffled\_original\_sv * Size of downloaded dataset files: 17.18 GB * Size of the generated dataset: 47.00 GB * Total amount of disk used: 64.18 GB An example of 'train' looks as follows. #### unshuffled\_original\_sw * Size of downloaded dataset files: 3.71 MB * Size of the generated dataset: 14.07 MB * Total amount of disk used: 17.78 MB An example of 'train' looks as follows. #### unshuffled\_original\_ta * Size of downloaded dataset files: 1.74 GB * Size of the generated dataset: 9.93 GB * Total amount of disk used: 11.67 GB An example of 'train' looks as follows. #### unshuffled\_original\_te * Size of downloaded dataset files: 522.47 MB * Size of the generated dataset: 2.61 GB * Total amount of disk used: 3.13 GB An example of 'train' looks as follows. #### unshuffled\_original\_tg * Size of downloaded dataset files: 90.97 MB * Size of the generated dataset: 397.43 MB * Total amount of disk used: 488.41 MB An example of 'train' looks as follows. #### unshuffled\_original\_th * Size of downloaded dataset files: 7.38 GB * Size of the generated dataset: 38.29 GB * Total amount of disk used: 45.67 GB An example of 'train' looks as follows. #### unshuffled\_original\_tk * Size of downloaded dataset files: 2.96 MB * Size of the generated dataset: 10.66 MB * Total amount of disk used: 13.62 MB An example of 'train' looks as follows. #### unshuffled\_original\_tl * Size of downloaded dataset files: 204.89 MB * Size of the generated dataset: 606.30 MB * Total amount of disk used: 811.19 MB An example of 'train' looks as follows. #### unshuffled\_original\_tr * Size of downloaded dataset files: 21.96 GB * Size of the generated dataset: 63.58 GB * Total amount of disk used: 85.54 GB An example of 'train' looks as follows. #### unshuffled\_original\_tt * Size of downloaded dataset files: 151.06 MB * Size of the generated dataset: 703.42 MB * Total amount of disk used: 854.47 MB An example of 'train' looks as follows. #### unshuffled\_original\_tyv * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 0.01 MB * Total amount of disk used: 0.01 MB An example of 'train' looks as follows. #### unshuffled\_original\_ug * Size of downloaded dataset files: 27.92 MB * Size of the generated dataset: 127.42 MB * Total amount of disk used: 155.35 MB An example of 'train' looks as follows. #### unshuffled\_original\_uk * Size of downloaded dataset files: 14.42 GB * Size of the generated dataset: 56.44 GB * Total amount of disk used: 70.86 GB An example of 'train' looks as follows. #### unshuffled\_original\_ur * Size of downloaded dataset files: 712.61 MB * Size of the generated dataset: 2.80 GB * Total amount of disk used: 3.51 GB An example of 'train' looks as follows. #### unshuffled\_original\_uz * Size of downloaded dataset files: 5.78 MB * Size of the generated dataset: 21.46 MB * Total amount of disk used: 27.24 MB An example of 'train' looks as follows. #### unshuffled\_original\_vec * Size of downloaded dataset files: 0.01 MB * Size of the generated dataset: 0.02 MB * Total amount of disk used: 0.03 MB An example of 'train' looks as follows. #### unshuffled\_original\_vi * Size of downloaded dataset files: 21.50 GB * Size of the generated dataset: 72.23 GB * Total amount of disk used: 93.73 GB An example of 'train' looks as follows. #### unshuffled\_original\_vo * Size of downloaded dataset files: 0.30 MB * Size of the generated dataset: 2.12 MB * Total amount of disk used: 2.42 MB An example of 'train' looks as follows. #### unshuffled\_original\_wa * Size of downloaded dataset files: 0.09 MB * Size of the generated dataset: 0.29 MB * Total amount of disk used: 0.38 MB An example of 'train' looks as follows. #### unshuffled\_original\_war * Size of downloaded dataset files: 0.64 MB * Size of the generated dataset: 2.68 MB * Total amount of disk used: 3.32 MB An example of 'train' looks as follows. #### unshuffled\_original\_wuu * Size of downloaded dataset files: 0.01 MB * Size of the generated dataset: 0.12 MB * Total amount of disk used: 0.13 MB An example of 'train' looks as follows. #### unshuffled\_original\_xal * Size of downloaded dataset files: 0.03 MB * Size of the generated dataset: 0.12 MB * Total amount of disk used: 0.15 MB An example of 'train' looks as follows. #### unshuffled\_original\_xmf * Size of downloaded dataset files: 1.05 MB * Size of the generated dataset: 6.12 MB * Total amount of disk used: 7.17 MB An example of 'train' looks as follows. #### unshuffled\_original\_yi * Size of downloaded dataset files: 33.33 MB * Size of the generated dataset: 147.60 MB * Total amount of disk used: 180.94 MB An example of 'train' looks as follows. #### unshuffled\_original\_yo * Size of downloaded dataset files: 0.01 MB * Size of the generated dataset: 0.06 MB * Total amount of disk used: 0.06 MB An example of 'train' looks as follows. #### unshuffled\_original\_yue * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 0.00 MB * Total amount of disk used: 0.00 MB An example of 'train' looks as follows. #### unshuffled\_original\_zh * Size of downloaded dataset files: 206.00 GB * Size of the generated dataset: 545.61 GB * Total amount of disk used: 751.61 GB An example of 'train' looks as follows. ### Data Fields The data fields are the same among all configs. * 'id': a 'int64' feature. * 'text': a 'string' feature. ### Data Splits Click to expand the number of samples per configuration Dataset Creation ---------------- ### Curation Rationale OSCAR was constructed new pipeline derived from the fastText's one, called *goclassy*. Goclassy reuses the fastText linear classifier and the pre-trained fastText model for language recognition, but it completely rewrites and parallelises their pipeline in an asynchronous manner. The order of operations is more or less the same as in the fastText pre-processing pipeline but instead of clustering multiple operations into a single blocking process, a worker is launched for each operation but bounding the number of possible parallel operations at a given time by the number of available threads instead of the number of CPUs. Goclassy is implemented in the Go programming language so it lets the Go runtime handle the scheduling of the processes. Thus the goclassy's pipeline one does not have to wait for a whole WET file to download, decompress and classify in order to start downloading and processing the next one, a new file will start downloading and processing as soon as the scheduler is able to allocate a new process. Filtering and cleaning processes at line level are done before feeding each line to the classifier. Lines shorter than 100 UTF-8 characters and lines containing invalid UTF-8 characters are discarted and are not classified. After all files are proccesed the deduplicated versions are constructed and everything is then splitted in shards and compressed. ### Source Data #### Initial Data Collection and Normalization Common Crawl is a non-profit foundation which produces and maintains an open repository of web crawled data that is both accessible and analysable. Common Crawl's complete web archive consists of petabytes of data collected over 8 years of web crawling. The repository contains raw web page HTML data (WARC files), metdata extracts (WAT files) and plain text extracts (WET files). The organisation's crawlers has always respected nofollow and URL policies. Each monthly Common Crawl snapshot is in itself a massive multilingual corpus, where every single file contains data coming from multiple web pages written in a large variety of languages and covering all possible types of topics. To construct OSCAR the WET files of Common Crawl were used. These contain the extracted plain texts from the websites mostly converted to UTF-8, as well as headers containing the metatada of each crawled document. Each WET file comes compressed in gzip format and is stored on Amazon Web Services. In the case of OSCAR, the November 2018 snapshot was used. It surpasses 20TB of uncompressed data and contains more than 50 thousand plain text files where each file consists of the plain text from multiple websites along its metadata header. #### Who are the source language producers? The data comes from multiple web pages in a large variety of languages. ### Annotations The dataset does not contain any additional annotations. #### Annotation process N/A #### Who are the annotators? N/A ### Personal and Sensitive Information Being constructed from Common Crawl, Personal and sensitive information might be present. This must be considered before training deep learning models with OSCAR, specially in the case of text-generation models. Considerations for Using the Data --------------------------------- ### Social Impact of Dataset OSCAR is intended to bring more data to a wide variety of lanuages, the aim of the corpus is to make large amounts of data available to lower resource languages in order to facilitate the pre-training of state-of-the-art language modeling architectures. ### Discussion of Biases OSCAR is not properly filtered yet and this can be reflected on the models trained with it. Care is advised specially concerning biases of the resulting models. ### Other Known Limitations The fastText linear classifier is limed both in performance and the variety of languages it can recognize, so the quality of some OSCAR sub-corpora might be lower than expected, specially for the lowest-resource langiuages. Some audits have already been done by third parties. Additional Information ---------------------- ### Dataset Curators The corpus was put together by Pedro J. Ortiz, Benoît Sagot, and Laurent Romary, during work done at Inria, particularly at the ALMAnaCH team. ### Licensing Information ``` These data are released under this licensing scheme We do not own any of the text from which these data has been extracted. We license the actual packaging of these data under the Creative Commons CC0 license ("no rights reserved") URL To the extent possible under law, Inria has waived all copyright and related or neighboring rights to OSCAR This work is published from: France. Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please: * Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted. * Clearly identify the copyrighted work claimed to be infringed. * Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material. We will comply to legitimate requests by removing the affected sources from the next release of the corpus. ``` ### Contributions Thanks to @pjox and @lhoestq for adding this dataset.

6a7692c2e68f087bf6910d45f99a843d4249ee66

# Dataset Card for "para_crawl" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://paracrawl.eu/releases.html](https://paracrawl.eu/releases.html) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 10.36 GB - **Size of the generated dataset:** 32.90 GB - **Total amount of disk used:** 43.26 GB ### Dataset Summary Web-Scale Parallel Corpora for Official European Languages. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### enbg - **Size of downloaded dataset files:** 103.75 MB - **Size of the generated dataset:** 356.54 MB - **Total amount of disk used:** 460.27 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "translation": "{\"bg\": \". “A felirat faragott karnis a bejárat fölött, templom épült 14 Július 1643, A földesúr és felesége Jeremiás Murguleţ, C..." } ``` #### encs - **Size of downloaded dataset files:** 196.41 MB - **Size of the generated dataset:** 638.07 MB - **Total amount of disk used:** 834.48 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "translation": "{\"cs\": \". “A felirat faragott karnis a bejárat fölött, templom épült 14 Július 1643, A földesúr és felesége Jeremiás Murguleţ, C..." } ``` #### enda - **Size of downloaded dataset files:** 182.81 MB - **Size of the generated dataset:** 598.62 MB - **Total amount of disk used:** 781.43 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "translation": "{\"da\": \". “A felirat faragott karnis a bejárat fölött, templom épült 14 Július 1643, A földesúr és felesége Jeremiás Murguleţ, C..." } ``` #### ende - **Size of downloaded dataset files:** 1.31 GB - **Size of the generated dataset:** 4.00 GB - **Total amount of disk used:** 5.30 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "translation": "{\"de\": \". “A felirat faragott karnis a bejárat fölött, templom épült 14 Július 1643, A földesúr és felesége Jeremiás Murguleţ, C..." } ``` #### enel - **Size of downloaded dataset files:** 193.56 MB - **Size of the generated dataset:** 688.07 MB - **Total amount of disk used:** 881.62 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "translation": "{\"el\": \". “A felirat faragott karnis a bejárat fölött, templom épült 14 Július 1643, A földesúr és felesége Jeremiás Murguleţ, C..." } ``` ### Data Fields The data fields are the same among all splits. #### enbg - `translation`: a multilingual `string` variable, with possible languages including `en`, `bg`. #### encs - `translation`: a multilingual `string` variable, with possible languages including `en`, `cs`. #### enda - `translation`: a multilingual `string` variable, with possible languages including `en`, `da`. #### ende - `translation`: a multilingual `string` variable, with possible languages including `en`, `de`. #### enel - `translation`: a multilingual `string` variable, with possible languages including `en`, `el`. ### Data Splits | name | train | |------|---------:| | enbg | 1039885 | | encs | 2981949 | | enda | 2414895 | | ende | 16264448 | | enel | 1985233 | ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [Creative Commons CC0 license ("no rights reserved")](https://creativecommons.org/share-your-work/public-domain/cc0/). ### Citation Information ``` @inproceedings{banon-etal-2020-paracrawl, title = "{P}ara{C}rawl: Web-Scale Acquisition of Parallel Corpora", author = "Ba{\~n}{\'o}n, Marta and Chen, Pinzhen and Haddow, Barry and Heafield, Kenneth and Hoang, Hieu and Espl{\`a}-Gomis, Miquel and Forcada, Mikel L. and Kamran, Amir and Kirefu, Faheem and Koehn, Philipp and Ortiz Rojas, Sergio and Pla Sempere, Leopoldo and Ram{\'\i}rez-S{\'a}nchez, Gema and Sarr{\'\i}as, Elsa and Strelec, Marek and Thompson, Brian and Waites, William and Wiggins, Dion and Zaragoza, Jaume", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.acl-main.417", doi = "10.18653/v1/2020.acl-main.417", pages = "4555--4567", abstract = "We report on methods to create the largest publicly available parallel corpora by crawling the web, using open source software. We empirically compare alternative methods and publish benchmark data sets for sentence alignment and sentence pair filtering. We also describe the parallel corpora released and evaluate their quality and their usefulness to create machine translation systems.", } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset.

para_crawl

2022-03-02T23:29:22+00:00

{"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["bg", "cs", "da", "de", "el", "en", "es", "et", "fi", "fr", "ga", "hr", "hu", "it", "lt", "lv", "mt", "nl", "pl", "pt", "ro", "sk", "sl", "sv"], "license": ["cc0-1.0"], "multilinguality": ["translation"], "size_categories": ["10M<n<100M"], "source_datasets": ["original"], "task_categories": ["translation"], "task_ids": [], "paperswithcode_id": "paracrawl", "pretty_name": "ParaCrawl", "dataset_info": [{"config_name": "enbg", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["en", "bg"]}}}], "splits": [{"name": "train", "num_bytes": 356532771, "num_examples": 1039885}], "download_size": 103743335, "dataset_size": 356532771}, {"config_name": "encs", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["en", "cs"]}}}], "splits": [{"name": "train", "num_bytes": 638068353, "num_examples": 2981949}], "download_size": 196410022, "dataset_size": 638068353}, {"config_name": "enda", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["en", "da"]}}}], "splits": [{"name": "train", "num_bytes": 598624306, "num_examples": 2414895}], "download_size": 182804827, "dataset_size": 598624306}, {"config_name": "ende", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["en", "de"]}}}], "splits": [{"name": "train", "num_bytes": 3997191986, "num_examples": 16264448}], "download_size": 1307754745, "dataset_size": 3997191986}, {"config_name": "enel", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["en", "el"]}}}], "splits": [{"name": "train", "num_bytes": 688069020, "num_examples": 1985233}], "download_size": 193553374, "dataset_size": 688069020}, {"config_name": "enes", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["en", "es"]}}}], "splits": [{"name": "train", "num_bytes": 6209466040, "num_examples": 21987267}], "download_size": 1953839527, "dataset_size": 6209466040}, {"config_name": "enet", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["en", "et"]}}}], "splits": [{"name": "train", "num_bytes": 201408919, "num_examples": 853422}], "download_size": 70158650, "dataset_size": 201408919}, {"config_name": "enfi", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["en", "fi"]}}}], "splits": [{"name": "train", "num_bytes": 524624150, "num_examples": 2156069}], "download_size": 159209242, "dataset_size": 524624150}, {"config_name": "enfr", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["en", "fr"]}}}], "splits": [{"name": "train", "num_bytes": 9015440258, "num_examples": 31374161}], "download_size": 2827554088, "dataset_size": 9015440258}, {"config_name": "enga", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["en", "ga"]}}}], "splits": [{"name": "train", "num_bytes": 104523278, "num_examples": 357399}], "download_size": 29394367, "dataset_size": 104523278}, {"config_name": "enhr", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["en", "hr"]}}}], "splits": [{"name": "train", "num_bytes": 247646552, "num_examples": 1002053}], "download_size": 84904103, "dataset_size": 247646552}, {"config_name": "enhu", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["en", "hu"]}}}], "splits": [{"name": "train", "num_bytes": 403168065, "num_examples": 1901342}], "download_size": 119784765, "dataset_size": 403168065}, {"config_name": "enit", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["en", "it"]}}}], "splits": [{"name": "train", "num_bytes": 3340542050, "num_examples": 12162239}], "download_size": 1066720197, "dataset_size": 3340542050}, {"config_name": "enlt", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["en", "lt"]}}}], "splits": [{"name": "train", "num_bytes": 197053694, "num_examples": 844643}], "download_size": 66358392, "dataset_size": 197053694}, {"config_name": "enlv", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["en", "lv"]}}}], "splits": [{"name": "train", "num_bytes": 142409870, "num_examples": 553060}], "download_size": 47368967, "dataset_size": 142409870}, {"config_name": "enmt", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["en", "mt"]}}}], "splits": [{"name": "train", "num_bytes": 52786023, "num_examples": 195502}], "download_size": 19028352, "dataset_size": 52786023}, {"config_name": "ennl", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["en", "nl"]}}}], "splits": [{"name": "train", "num_bytes": 1384042007, "num_examples": 5659268}], "download_size": 420090979, "dataset_size": 1384042007}, {"config_name": "enpl", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["en", "pl"]}}}], "splits": [{"name": "train", "num_bytes": 854786500, "num_examples": 3503276}], "download_size": 270427885, "dataset_size": 854786500}, {"config_name": "enpt", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["en", "pt"]}}}], "splits": [{"name": "train", "num_bytes": 2031891156, "num_examples": 8141940}], "download_size": 638184462, "dataset_size": 2031891156}, {"config_name": "enro", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["en", "ro"]}}}], "splits": [{"name": "train", "num_bytes": 518359240, "num_examples": 1952043}], "download_size": 160684751, "dataset_size": 518359240}, {"config_name": "ensk", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["en", "sk"]}}}], "splits": [{"name": "train", "num_bytes": 337704729, "num_examples": 1591831}], "download_size": 101307152, "dataset_size": 337704729}, {"config_name": "ensl", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["en", "sl"]}}}], "splits": [{"name": "train", "num_bytes": 182399034, "num_examples": 660161}], "download_size": 65037465, "dataset_size": 182399034}, {"config_name": "ensv", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["en", "sv"]}}}], "splits": [{"name": "train", "num_bytes": 875576366, "num_examples": 3476729}], "download_size": 275528370, "dataset_size": 875576366}]}

2024-01-18T11:12:30+00:00

TAGS #task_categories-translation #annotations_creators-no-annotation #language_creators-found #multilinguality-translation #size_categories-10M<n<100M #source_datasets-original #language-Bulgarian #language-Czech #language-Danish #language-German #language-Modern Greek (1453-) #language-English #language-Spanish #language-Estonian #language-Finnish #language-French #language-Irish #language-Croatian #language-Hungarian #language-Italian #language-Lithuanian #language-Latvian #language-Maltese #language-Dutch #language-Polish #language-Portuguese #language-Romanian #language-Slovak #language-Slovenian #language-Swedish #license-cc0-1.0 #region-us

Dataset Card for "para\_crawl" ============================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: * Point of Contact: * Size of downloaded dataset files: 10.36 GB * Size of the generated dataset: 32.90 GB * Total amount of disk used: 43.26 GB ### Dataset Summary Web-Scale Parallel Corpora for Official European Languages. ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### enbg * Size of downloaded dataset files: 103.75 MB * Size of the generated dataset: 356.54 MB * Total amount of disk used: 460.27 MB An example of 'train' looks as follows. #### encs * Size of downloaded dataset files: 196.41 MB * Size of the generated dataset: 638.07 MB * Total amount of disk used: 834.48 MB An example of 'train' looks as follows. #### enda * Size of downloaded dataset files: 182.81 MB * Size of the generated dataset: 598.62 MB * Total amount of disk used: 781.43 MB An example of 'train' looks as follows. #### ende * Size of downloaded dataset files: 1.31 GB * Size of the generated dataset: 4.00 GB * Total amount of disk used: 5.30 GB An example of 'train' looks as follows. #### enel * Size of downloaded dataset files: 193.56 MB * Size of the generated dataset: 688.07 MB * Total amount of disk used: 881.62 MB An example of 'train' looks as follows. ### Data Fields The data fields are the same among all splits. #### enbg * 'translation': a multilingual 'string' variable, with possible languages including 'en', 'bg'. #### encs * 'translation': a multilingual 'string' variable, with possible languages including 'en', 'cs'. #### enda * 'translation': a multilingual 'string' variable, with possible languages including 'en', 'da'. #### ende * 'translation': a multilingual 'string' variable, with possible languages including 'en', 'de'. #### enel * 'translation': a multilingual 'string' variable, with possible languages including 'en', 'el'. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information Creative Commons CC0 license ("no rights reserved"). ### Contributions Thanks to @thomwolf, @lewtun, @patrickvonplaten, @mariamabarham for adding this dataset.

1a3f8b448219bc67adacf724966fc62719711207

# Dataset Card for ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts](https://figshare.com/articles/ParaPat_The_Multi-Million_Sentences_Parallel_Corpus_of_Patents_Abstracts/12627632) - **Repository:** [ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts](https://github.com/soares-f/parapat) - **Paper:** [ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts](https://www.aclweb.org/anthology/2020.lrec-1.465/) - **Point of Contact:** [Felipe Soares](fs@felipesoares.net) ### Dataset Summary ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts This dataset contains the developed parallel corpus from the open access Google Patents dataset in 74 language pairs, comprising more than 68 million sentences and 800 million tokens. Sentences were automatically aligned using the Hunalign algorithm for the largest 22 language pairs, while the others were abstract (i.e. paragraph) aligned. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The dataset contains samples in cs, de, el, en, es, fr, hu, ja, ko, pt, ro, ru, sk, uk, zh, hu ## Dataset Structure ### Data Instances They are of 2 types depending on the dataset: First type { "translation":{ "en":"A method for converting a series of m-bit information words to a modulated signal is described.", "es":"Se describe un método para convertir una serie de palabras de informacion de bits m a una señal modulada." } } Second type { "family_id":10944407, "index":844, "translation":{ "el":"αφές ο οποίος παρασκευάζεται με χαρμάνι ελληνικού καφέ είτε σε συσκευή καφέ εσπρέσο είτε σε συσκευή γαλλικού καφέ (φίλτρου) είτε κατά τον παραδοσιακό τρόπο του ελληνικού καφέ και διυλίζεται, κτυπιέται στη συνέχεια με πάγο σε χειροκίνητο ή ηλεκτρικόμίξερ ώστε να παγώσει ομοιόμορφα και να αποκτήσει πλούσιο αφρό και σερβίρεται σε ποτήρι. ΰ", "en":"offee prepared using the mix for Greek coffee either in an espresso - type coffee making machine, or in a filter coffee making machine or in the traditional way for preparing Greek coffee and is then filtered , shaken with ice manually or with an electric mixer so that it freezes homogeneously, obtains a rich froth and is served in a glass." } } ### Data Fields **index:** position in the corpus **family id:** for each abstract, such that researchers can use that information for other text mining purposes. **translation:** distionary containing source and target sentence for that example ### Data Splits No official train/val/test splits given. Parallel corpora aligned into sentence level |Language Pair|# Sentences|# Unique Tokens| |--------|-----|------| |EN/ZH|4.9M|155.8M| |EN/JA|6.1M|189.6M| |EN/FR|12.2M|455M| |EN/KO|2.3M|91.4M| |EN/DE|2.2M|81.7M| |EN/RU|4.3M|107.3M| |DE/FR|1.2M|38.8M| |FR/JA|0.3M|9.9M| |EN/ES|0.6M|24.6M| Parallel corpora aligned into abstract level |Language Pair|# Abstracts| |--------|-----| |FR/KO|120,607| |EN/UK|89,227| |RU/UK|85,963| |CS/EN|78,978| |EN/RO|48,789| |EN/HU|42,629| |ES/FR|32,553| |EN/SK|23,410| |EN/PT|23,122| |BG/EN|16,177| |FR/RU|10,889| ## Dataset Creation ### Curation Rationale The availability of parallel corpora is required by current Statistical and Neural Machine Translation systems (SMT and NMT). Acquiring a high-quality parallel corpus that is large enough to train MT systems, particularly NMT ones, is not a trivial task due to the need for correct alignment and, in many cases, human curation. In this context, the automated creation of parallel corpora from freely available resources is extremely important in Natural Language Pro- cessing (NLP). ### Source Data #### Initial Data Collection and Normalization Google makes patents data available under the Google Cloud Public Datasets. BigQuery is a Google service that supports the efficient storage and querying of massive datasets which are usually a challenging task for usual SQL databases. For instance, filtering the September 2019 release of the dataset, which contains more than 119 million rows, can take less than 1 minute for text fields. The on-demand billing for BigQuery is based on the amount of data processed by each query run, thus for a single query that performs a full-scan, the cost can be over USD 15.00, since the cost per TB is currently USD 5.00. #### Who are the source language producers? BigQuery is a Google service that supports the efficient storage and querying of massive datasets which are usually a challenging task for usual SQL databases. ### Annotations #### Annotation process The following steps describe the process of producing patent aligned abstracts: 1. Load the nth individual file 2. Remove rows where the number of abstracts with more than one language is less than 2 for a given family id. The family id attribute is used to group patents that refers to the same invention. By removing these rows, we remove abstracts that are available only in one language. 3. From the resulting set, create all possible parallel abstracts from the available languages. For instance, an abstract may be available in English, French and German, thus, the possible language pairs are English/French, English/German, and French/German. 4. Store the parallel patents into an SQL database for easier future handling and sampling. #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Funded by Google Tensorflow Research Cloud. ### Licensing Information CC BY 4.0 ### Citation Information ``` @inproceedings{soares-etal-2020-parapat, title = "{P}ara{P}at: The Multi-Million Sentences Parallel Corpus of Patents Abstracts", author = "Soares, Felipe and Stevenson, Mark and Bartolome, Diego and Zaretskaya, Anna", booktitle = "Proceedings of The 12th Language Resources and Evaluation Conference", month = may, year = "2020", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://www.aclweb.org/anthology/2020.lrec-1.465", pages = "3769--3774", language = "English", ISBN = "979-10-95546-34-4", } ``` [DOI](https://doi.org/10.6084/m9.figshare.12627632) ### Contributions Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik) for adding this dataset.

para_pat

2022-03-02T23:29:22+00:00

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2024-01-18T11:12:32+00:00

TAGS #task_categories-text-generation #task_categories-fill-mask #task_categories-translation #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-machine-generated #language_creators-expert-generated #multilinguality-translation #size_categories-10K<n<100K #source_datasets-original #language-Czech #language-German #language-Modern Greek (1453-) #language-English #language-Spanish #language-French #language-Hungarian #language-Japanese #language-Korean #language-Portuguese #language-Romanian #language-Russian #language-Slovak #language-Ukrainian #language-Chinese #license-cc-by-4.0 #region-us

Dataset Card for ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts ========================================================================================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts * Repository: ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts * Paper: ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts * Point of Contact: Felipe Soares ### Dataset Summary ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts This dataset contains the developed parallel corpus from the open access Google Patents dataset in 74 language pairs, comprising more than 68 million sentences and 800 million tokens. Sentences were automatically aligned using the Hunalign algorithm for the largest 22 language pairs, while the others were abstract (i.e. paragraph) aligned. ### Supported Tasks and Leaderboards ### Languages The dataset contains samples in cs, de, el, en, es, fr, hu, ja, ko, pt, ro, ru, sk, uk, zh, hu Dataset Structure ----------------- ### Data Instances They are of 2 types depending on the dataset: First type { "translation":{ "en":"A method for converting a series of m-bit information words to a modulated signal is described.", "es":"Se describe un método para convertir una serie de palabras de informacion de bits m a una señal modulada." } } Second type { "family\_id":10944407, "index":844, "translation":{ "el":"αφές ο οποίος παρασκευάζεται με χαρμάνι ελληνικού καφέ είτε σε συσκευή καφέ εσπρέσο είτε σε συσκευή γαλλικού καφέ (φίλτρου) είτε κατά τον παραδοσιακό τρόπο του ελληνικού καφέ και διυλίζεται, κτυπιέται στη συνέχεια με πάγο σε χειροκίνητο ή ηλεκτρικόμίξερ ώστε να παγώσει ομοιόμορφα και να αποκτήσει πλούσιο αφρό και σερβίρεται σε ποτήρι. ΰ", "en":"offee prepared using the mix for Greek coffee either in an espresso - type coffee making machine, or in a filter coffee making machine or in the traditional way for preparing Greek coffee and is then filtered , shaken with ice manually or with an electric mixer so that it freezes homogeneously, obtains a rich froth and is served in a glass." } } ### Data Fields index: position in the corpus family id: for each abstract, such that researchers can use that information for other text mining purposes. translation: distionary containing source and target sentence for that example ### Data Splits No official train/val/test splits given. Parallel corpora aligned into sentence level Language Pair: EN/ZH, # Sentences: 4.9M, # Unique Tokens: 155.8M Language Pair: EN/JA, # Sentences: 6.1M, # Unique Tokens: 189.6M Language Pair: EN/FR, # Sentences: 12.2M, # Unique Tokens: 455M Language Pair: EN/KO, # Sentences: 2.3M, # Unique Tokens: 91.4M Language Pair: EN/DE, # Sentences: 2.2M, # Unique Tokens: 81.7M Language Pair: EN/RU, # Sentences: 4.3M, # Unique Tokens: 107.3M Language Pair: DE/FR, # Sentences: 1.2M, # Unique Tokens: 38.8M Language Pair: FR/JA, # Sentences: 0.3M, # Unique Tokens: 9.9M Language Pair: EN/ES, # Sentences: 0.6M, # Unique Tokens: 24.6M Parallel corpora aligned into abstract level Dataset Creation ---------------- ### Curation Rationale The availability of parallel corpora is required by current Statistical and Neural Machine Translation systems (SMT and NMT). Acquiring a high-quality parallel corpus that is large enough to train MT systems, particularly NMT ones, is not a trivial task due to the need for correct alignment and, in many cases, human curation. In this context, the automated creation of parallel corpora from freely available resources is extremely important in Natural Language Pro- cessing (NLP). ### Source Data #### Initial Data Collection and Normalization Google makes patents data available under the Google Cloud Public Datasets. BigQuery is a Google service that supports the efficient storage and querying of massive datasets which are usually a challenging task for usual SQL databases. For instance, filtering the September 2019 release of the dataset, which contains more than 119 million rows, can take less than 1 minute for text fields. The on-demand billing for BigQuery is based on the amount of data processed by each query run, thus for a single query that performs a full-scan, the cost can be over USD 15.00, since the cost per TB is currently USD 5.00. #### Who are the source language producers? BigQuery is a Google service that supports the efficient storage and querying of massive datasets which are usually a challenging task for usual SQL databases. ### Annotations #### Annotation process The following steps describe the process of producing patent aligned abstracts: 1. Load the nth individual file 2. Remove rows where the number of abstracts with more than one language is less than 2 for a given family id. The family id attribute is used to group patents that refers to the same invention. By removing these rows, we remove abstracts that are available only in one language. 3. From the resulting set, create all possible parallel abstracts from the available languages. For instance, an abstract may be available in English, French and German, thus, the possible language pairs are English/French, English/German, and French/German. 4. Store the parallel patents into an SQL database for easier future handling and sampling. #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators Funded by Google Tensorflow Research Cloud. ### Licensing Information CC BY 4.0 DOI ### Contributions Thanks to @bhavitvyamalik for adding this dataset.

1ecef505b04ed347ccc330b3e0b22face243409b

# Dataset Card for PersiNLU (Reading Comprehension) ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Github](https://github.com/persiannlp/parsinlu/) - **Repository:** [Github](https://github.com/persiannlp/parsinlu/) - **Paper:** [Arxiv](https://arxiv.org/abs/2012.06154) - **Leaderboard:** - **Point of Contact:** [email](d.khashabi@gmail.com) ### Dataset Summary A Persian reading comprehenion task (generating an answer, given a question and a context paragraph). The questions are mined using Google auto-complete, their answers and the corresponding evidence documents are manually annotated by native speakers. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The text dataset is in Persian (`fa`). ## Dataset Structure ### Data Instances Here is an example from the dataset: ``` { 'question': 'پیامبر در چه سالی به پیامبری رسید؟', 'url': 'https://fa.wikipedia.org/wiki/%D9%85%D8%AD%D9%85%D8%AF', 'passage': 'محمد که از روش زندگی مردم مکه ناخشنود بود، گهگاه در غار حرا در یکی از کوه\u200cهای اطراف آن دیار به تفکر و عبادت می\u200cپرداخت. به باور مسلمانان، محمد در همین مکان و در حدود ۴۰ سالگی از طرف خدا به پیامبری برگزیده، و وحی بر او فروفرستاده شد. در نظر آنان، دعوت محمد همانند دعوت دیگر پیامبرانِ کیش یکتاپرستی مبنی بر این بود که خداوند (الله) یکتاست و تسلیم شدن برابر خدا راه رسیدن به اوست.', 'answers': [ {'answer_start': 160, 'answer_text': 'حدود ۴۰ سالگی'} ] } ``` ### Data Fields - `question`: the question, mined using Google auto-complete. - `passage`: the passage that contains the answer. - `url`: the url from which the passage was mined. - `answers`: a list of answers, containing the string and the index of the answer with the fields `answer_start` and `answer_text`. Note that in the test set, some `answer_start` values are missing and replaced with `-1` ### Data Splits The train/test split contains 600/575 samples. ## Dataset Creation ### Curation Rationale The question were collected via Google auto-complete. The answers were annotated by native speakers. For more details, check [the corresponding draft](https://arxiv.org/abs/2012.06154). ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information CC BY-NC-SA 4.0 License ### Citation Information ```bibtex @article{huggingface:dataset, title = {ParsiNLU: A Suite of Language Understanding Challenges for Persian}, authors = {Khashabi, Daniel and Cohan, Arman and Shakeri, Siamak and Hosseini, Pedram and Pezeshkpour, Pouya and Alikhani, Malihe and Aminnaseri, Moin and Bitaab, Marzieh and Brahman, Faeze and Ghazarian, Sarik and others}, year={2020} journal = {arXiv e-prints}, eprint = {2012.06154}, } ``` ### Contributions Thanks to [@danyaljj](https://github.com/danyaljj) for adding this dataset.

parsinlu_reading_comprehension

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["fa"], "license": ["cc-by-nc-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["extended|wikipedia|google"], "task_categories": ["question-answering"], "task_ids": ["extractive-qa"], "pretty_name": "PersiNLU (Reading Comprehension)", "dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "answer_text", "dtype": "string"}]}], "config_name": "parsinlu-repo", "splits": [{"name": "train", "num_bytes": 747679, "num_examples": 600}, {"name": "test", "num_bytes": 674711, "num_examples": 570}, {"name": "validation", "num_bytes": 163161, "num_examples": 125}], "download_size": 4105495, "dataset_size": 1585527}}

2023-08-16T16:04:40+00:00

TAGS #task_categories-question-answering #task_ids-extractive-qa #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-extended|wikipedia|google #language-Persian #license-cc-by-nc-sa-4.0 #arxiv-2012.06154 #region-us

# Dataset Card for PersiNLU (Reading Comprehension) ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: Github - Repository: Github - Paper: Arxiv - Leaderboard: - Point of Contact: email ### Dataset Summary A Persian reading comprehenion task (generating an answer, given a question and a context paragraph). The questions are mined using Google auto-complete, their answers and the corresponding evidence documents are manually annotated by native speakers. ### Supported Tasks and Leaderboards ### Languages The text dataset is in Persian ('fa'). ## Dataset Structure ### Data Instances Here is an example from the dataset: ### Data Fields - 'question': the question, mined using Google auto-complete. - 'passage': the passage that contains the answer. - 'url': the url from which the passage was mined. - 'answers': a list of answers, containing the string and the index of the answer with the fields 'answer_start' and 'answer_text'. Note that in the test set, some 'answer_start' values are missing and replaced with '-1' ### Data Splits The train/test split contains 600/575 samples. ## Dataset Creation ### Curation Rationale The question were collected via Google auto-complete. The answers were annotated by native speakers. For more details, check the corresponding draft. ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators ### Licensing Information CC BY-NC-SA 4.0 License ### Contributions Thanks to @danyaljj for adding this dataset.

32899b23deb8c7c46c4068fda79760919859d26a

# Dataset Card for PASS ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [PASS homepage](https://www.robots.ox.ac.uk/~vgg/research/pass/) - **Repository:** [PASS repository](https://github.com/yukimasano/PASS) - **Paper:** [PASS: An ImageNet replacement for self-supervised pretraining without humans](https://arxiv.org/abs/2109.13228) - **Leaderboard:** [Pretrained models with scores](https://github.com/yukimasano/PASS#pretrained-models) - **Point of Contact:** [Yuki M. Asano](mailto:yukiATMARKrobots.ox.ac.uk) ### Dataset Summary PASS is a large-scale image dataset, containing 1.4 million images, that does not include any humans and which can be used for high-quality pretraining while significantly reducing privacy concerns. ### Supported Tasks and Leaderboards From the paper: > **Has the dataset been used for any tasks already?** In the paper we show and benchmark the intended use of this dataset as a pretraining dataset. For this the dataset is used an unlabelled image collection on which visual features are learned and then transferred to downstream tasks. We show that with this dataset it is possible to learn competitive visual features, without any humans in the pretraining dataset and with complete license information. > **Is there a repository that links to any or all papers or systems that use the dataset?** We will be listing these at the repository. > **What (other) tasks could the dataset be used for?** We believe this dataset might allow researchers and practitioners to further evaluate the differences that pretraining datasets can have on the learned features. Furthermore, since the meta-data is available for the images, it is possible to investigate the effect of image resolution on self-supervised learning methods, a domain largely underresearched thus far, as the current de-facto standard, ImageNet, only comes in one size. > **Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses?** Given that this dataset is a subset of a dataset that randomly samples images from flickr, the image distribution is biased towards European and American creators. As in the main papers discussion, this can lead to non-generalizeable features, or even biased features as the images taken in other countries might be more likely to further reflect and propagate stereotypes [84], though in our case these do not refer to sterotypes about humans. > **Are there tasks for which the dataset should not be used?** This dataset is meant for research purposes only. The dataset should also not be used for, e.g. connecting images and usernames, as this might risk de-anonymising the dataset in the long term. The usernames are solely provided for attribution. ### Languages English. ## Dataset Structure ### Data Instances A data point comprises an image and its meta-data: ``` { 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x375 at 0x7FFAD48E35F8>, 'creator_username': 'NTShieldsy', 'hash': 'e1662344ffa8c231d198c367c692cc', 'gps_latitude': 21.206675, 'gps_longitude': 39.166558, 'date_taken': datetime.datetime(2012, 8, 9, 18, 0, 20) } ``` ### Data Fields - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`. - `creator_username`: The photographer. - `hash`: The hash, as computed from YFCC-100M. - `gps_latitude`: Latitude of image if existent, otherwise None. - `gps_longitude`: Longitude of image if existent, otherwise None. - `date_taken`: Datetime of image if existent, otherwise None. ### Data Splits All the data is contained in the training set. The training set has 1,439,588 instances as this implementation corresponds to the most recent release (v3) from the [version history](https://github.com/yukimasano/PASS/blob/main/version_history.txt). From the paper: > **Are there recommended data splits (e.g., training, development/validation, testing)?** As outlined in the intended usecases, this dataset is meant for pretraining representations. As such, the models derived from training on this dataset need to be evaluated on different datasets, so called down-stream tasks. Thus the recommended split is to use all samples for training. ## Dataset Creation ### Curation Rationale From the paper: > **For what purpose was the dataset created?** Neural networks pretrained on large image collections have been shown to transfer well to other visual tasks where there is little labelled data, i.e. transferring a model works better than starting with a randomly initialized network every time for a new task, as many visual features can be repurposed. This dataset has as its goal to provide a safer large-scale dataset for such pretraining of visual features. In particular, this dataset does not contain any humans or human parts and does not contain any labels. The first point is important, as the current standard for pretraining, ImageNet and its face-blurred version only provide pseudo-anonymity and furthermore do not provide correct licences to the creators. The second point is relevant as pretraining is moving towards the self-supervised paradigm, where labels are not required. Yet most methods are developed on the highly curated ImageNet dataset, yielding potentially non-generalizeable research. ### Source Data #### Initial Data Collection and Normalization From the paper: * **Collection process**: > **How was the data associated with each instance acquired?** The data was collected from the publicly available dataset YFCC-100M which is hosted on the AWS public datasets platform. We have used the meta-data, namely the copyright information to filter only images with the CC-BY licence and have downloaded these using the aws command line interface, allowing for quick and stable downloading. In addition, all files were subsequently scanned for viruses using Sophos SAVScan virus detection utility, v.5.74.0. > **What mechanisms or procedures were used to collect the data (e.g., hardware apparatus or sensor, manual human curation, software program, software API)?** Our dataset is a subset of the YFCC-100M dataset. The YFCC-100M dataset itself was created by effectively randomly selecting publicly available images from flickr, resulting in approximately 98M images. > **Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set?** The dataset is a sample of a larger set—all possible digital photographs. As outlined in Section 3 we start from an existing dataset, YFCC-100M, and stratify the images (removing images with people and personal information, removing images with harmful content, removing images with unsuitable licenses, each user contributes at most 80 images to the dataset). This leaves 1.6M images, out of which we take a random sample of 1.28M images to replicate the size of the ImageNet dataset. While this dataset can thus be extended, this is the set that we have verified to not contain humans, human parts and disturbing content. > **Over what timeframe was the data collected?** The images underlying the dataset were downloaded between March and June 2021 from the AWS public datasets’ S3 bucket, following the download code provided in the repo. However the images contained were originally and taken anywhere from 2000 to 2015, with the majority being shot between 2010-2014. * **Preprocessing/cleaning/labeling**: > **Was any preprocessing/cleaning/labeling of the data done (e.g., discretization or bucketing,tokenization, part-of-speech tagging, SIFT feature extraction, removal of instances, processing of missing values)?** After the download of approx. 17M images, the corrupted, or single-color images were removed from the dataset prior to the generation of the dataset(s) used in the paper. The images were not further preprocessed or edited. > **Was the “raw” data saved in addition to the preprocessed/cleaned/labeled data (e.g., to support unanticipated future uses)?** Yes. The creators of the dataset maintain a copy of the 17M original images with the CC-BY licence of YFCC100M that sits at the start of our dataset creation pipeline. Is the software used to preprocess/clean/label the instances available? We have only used basic Python primitives for this. For the annotations we have used VIA [27, 28]. #### Who are the source language producers? From the paper: > **Who was involved in the data collection process (e.g., students, crowdworkers, contractors) and how were they compensated (e.g., how much were crowdworkers paid)?** As described, the data was collected automatically by simply downloading images from a publicly hosted S3 bucket. The human verification was done using a professional data annotation company that pays 150% of the local minimum wage. ### Annotations #### Annotation process This dataset doesn't contain annotations. #### Who are the annotators? This dataset doesn't contain annotations. ### Personal and Sensitive Information From the paper: > **Does the dataset contain data that might be considered confidential (e.g., data that is protected by legal privilege or by doctor-patient confidentiality, data that includes the content of individuals’ non-public communications)?** No. > **Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety?** No. Besides checking for human presence in the images, the annotators were also given the choice of flagging images for disturbing content, which once flagged was removed. > **Does the dataset relate to people? If not, you may skip the remaining questions in this section.** No. > **Does the dataset identify any subpopulations (e.g., by age, gender)?** NA > **Is it possible to identify individuals (i.e., one or more natural persons), either directly or indirectly (i.e., in combination with other data) from the dataset?** NA > **Does the dataset contain data that might be considered sensitive in any way (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history)?** NA > **Were any ethical review processes conducted (e.g., by an institutional review board)?** No ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases From the paper: > **Is your dataset free of biases?** No. There are many kinds of biases that can either be quantified, e.g. geo-location (most images originate from the US and Europe) or camera-model (most images are taken with professional DSLR cameras not easily affordable), there are likely many more biases that this dataset does contain. The only thing that this dataset does not contain are humans and parts of humans, as far as our validation procedure is accurate. ### Other Known Limitations From the paper: > **Can you guarantee compliance to GDPR?** No, we cannot comment on legal issues. ## Additional Information ### Dataset Curators YM. Asano, C. Rupprecht, A. Zisserman and A. Vedaldi. From the paper: > **Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)?** The dataset has been constructed by the research group “Visual Geometry Group” at the University of Oxford at the Engineering Science Department. ### Licensing Information The PASS dataset is available to download for commercial/research purposes under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). A complete version of the license can be found [here](https://www.robots.ox.ac.uk/~vgg/research/pass/license_pass.txt). The whole dataset only contains CC-BY licensed images with full attribution information. ### Citation Information ```bibtex @Article{asano21pass, author = "Yuki M. Asano and Christian Rupprecht and Andrew Zisserman and Andrea Vedaldi", title = "PASS: An ImageNet replacement for self-supervised pretraining without humans", journal = "NeurIPS Track on Datasets and Benchmarks", year = "2021" } ``` ### Contributions Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset.

pass

2022-03-02T23:29:22+00:00

{"annotations_creators": ["no-annotation"], "language_creators": ["machine-generated", "expert-generated"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["extended|yffc100M"], "task_categories": ["other"], "task_ids": [], "paperswithcode_id": "pass", "pretty_name": "Pictures without humAns for Self-Supervision", "tags": ["image-self-supervised pretraining"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "creator_username", "dtype": "string"}, {"name": "hash", "dtype": "string"}, {"name": "gps_latitude", "dtype": "float32"}, {"name": "gps_longitude", "dtype": "float32"}, {"name": "date_taken", "dtype": "timestamp[us]"}], "splits": [{"name": "train", "num_bytes": 178563446100, "num_examples": 1439588}], "download_size": 179640190811, "dataset_size": 178563446100}}

2024-01-18T11:12:34+00:00

TAGS #task_categories-other #annotations_creators-no-annotation #language_creators-machine-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-extended|yffc100M #language-English #license-cc-by-4.0 #image-self-supervised pretraining #arxiv-2109.13228 #region-us

# Dataset Card for PASS ## Table of Contents - Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: PASS homepage - Repository: PASS repository - Paper: PASS: An ImageNet replacement for self-supervised pretraining without humans - Leaderboard: Pretrained models with scores - Point of Contact: Yuki M. Asano ### Dataset Summary PASS is a large-scale image dataset, containing 1.4 million images, that does not include any humans and which can be used for high-quality pretraining while significantly reducing privacy concerns. ### Supported Tasks and Leaderboards From the paper: > Has the dataset been used for any tasks already? In the paper we show and benchmark the intended use of this dataset as a pretraining dataset. For this the dataset is used an unlabelled image collection on which visual features are learned and then transferred to downstream tasks. We show that with this dataset it is possible to learn competitive visual features, without any humans in the pretraining dataset and with complete license information. > Is there a repository that links to any or all papers or systems that use the dataset? We will be listing these at the repository. > What (other) tasks could the dataset be used for? We believe this dataset might allow researchers and practitioners to further evaluate the differences that pretraining datasets can have on the learned features. Furthermore, since the meta-data is available for the images, it is possible to investigate the effect of image resolution on self-supervised learning methods, a domain largely underresearched thus far, as the current de-facto standard, ImageNet, only comes in one size. > Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses? Given that this dataset is a subset of a dataset that randomly samples images from flickr, the image distribution is biased towards European and American creators. As in the main papers discussion, this can lead to non-generalizeable features, or even biased features as the images taken in other countries might be more likely to further reflect and propagate stereotypes [84], though in our case these do not refer to sterotypes about humans. > Are there tasks for which the dataset should not be used? This dataset is meant for research purposes only. The dataset should also not be used for, e.g. connecting images and usernames, as this might risk de-anonymising the dataset in the long term. The usernames are solely provided for attribution. ### Languages English. ## Dataset Structure ### Data Instances A data point comprises an image and its meta-data: ### Data Fields - 'image': A 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'. - 'creator_username': The photographer. - 'hash': The hash, as computed from YFCC-100M. - 'gps_latitude': Latitude of image if existent, otherwise None. - 'gps_longitude': Longitude of image if existent, otherwise None. - 'date_taken': Datetime of image if existent, otherwise None. ### Data Splits All the data is contained in the training set. The training set has 1,439,588 instances as this implementation corresponds to the most recent release (v3) from the version history. From the paper: > Are there recommended data splits (e.g., training, development/validation, testing)? As outlined in the intended usecases, this dataset is meant for pretraining representations. As such, the models derived from training on this dataset need to be evaluated on different datasets, so called down-stream tasks. Thus the recommended split is to use all samples for training. ## Dataset Creation ### Curation Rationale From the paper: > For what purpose was the dataset created? Neural networks pretrained on large image collections have been shown to transfer well to other visual tasks where there is little labelled data, i.e. transferring a model works better than starting with a randomly initialized network every time for a new task, as many visual features can be repurposed. This dataset has as its goal to provide a safer large-scale dataset for such pretraining of visual features. In particular, this dataset does not contain any humans or human parts and does not contain any labels. The first point is important, as the current standard for pretraining, ImageNet and its face-blurred version only provide pseudo-anonymity and furthermore do not provide correct licences to the creators. The second point is relevant as pretraining is moving towards the self-supervised paradigm, where labels are not required. Yet most methods are developed on the highly curated ImageNet dataset, yielding potentially non-generalizeable research. ### Source Data #### Initial Data Collection and Normalization From the paper: * Collection process: > How was the data associated with each instance acquired? The data was collected from the publicly available dataset YFCC-100M which is hosted on the AWS public datasets platform. We have used the meta-data, namely the copyright information to filter only images with the CC-BY licence and have downloaded these using the aws command line interface, allowing for quick and stable downloading. In addition, all files were subsequently scanned for viruses using Sophos SAVScan virus detection utility, v.5.74.0. > What mechanisms or procedures were used to collect the data (e.g., hardware apparatus or sensor, manual human curation, software program, software API)? Our dataset is a subset of the YFCC-100M dataset. The YFCC-100M dataset itself was created by effectively randomly selecting publicly available images from flickr, resulting in approximately 98M images. > Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set? The dataset is a sample of a larger set—all possible digital photographs. As outlined in Section 3 we start from an existing dataset, YFCC-100M, and stratify the images (removing images with people and personal information, removing images with harmful content, removing images with unsuitable licenses, each user contributes at most 80 images to the dataset). This leaves 1.6M images, out of which we take a random sample of 1.28M images to replicate the size of the ImageNet dataset. While this dataset can thus be extended, this is the set that we have verified to not contain humans, human parts and disturbing content. > Over what timeframe was the data collected? The images underlying the dataset were downloaded between March and June 2021 from the AWS public datasets’ S3 bucket, following the download code provided in the repo. However the images contained were originally and taken anywhere from 2000 to 2015, with the majority being shot between 2010-2014. * Preprocessing/cleaning/labeling: > Was any preprocessing/cleaning/labeling of the data done (e.g., discretization or bucketing,tokenization, part-of-speech tagging, SIFT feature extraction, removal of instances, processing of missing values)? After the download of approx. 17M images, the corrupted, or single-color images were removed from the dataset prior to the generation of the dataset(s) used in the paper. The images were not further preprocessed or edited. > Was the “raw” data saved in addition to the preprocessed/cleaned/labeled data (e.g., to support unanticipated future uses)? Yes. The creators of the dataset maintain a copy of the 17M original images with the CC-BY licence of YFCC100M that sits at the start of our dataset creation pipeline. Is the software used to preprocess/clean/label the instances available? We have only used basic Python primitives for this. For the annotations we have used VIA [27, 28]. #### Who are the source language producers? From the paper: > Who was involved in the data collection process (e.g., students, crowdworkers, contractors) and how were they compensated (e.g., how much were crowdworkers paid)? As described, the data was collected automatically by simply downloading images from a publicly hosted S3 bucket. The human verification was done using a professional data annotation company that pays 150% of the local minimum wage. ### Annotations #### Annotation process This dataset doesn't contain annotations. #### Who are the annotators? This dataset doesn't contain annotations. ### Personal and Sensitive Information From the paper: > Does the dataset contain data that might be considered confidential (e.g., data that is protected by legal privilege or by doctor-patient confidentiality, data that includes the content of individuals’ non-public communications)? No. > Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety? No. Besides checking for human presence in the images, the annotators were also given the choice of flagging images for disturbing content, which once flagged was removed. > Does the dataset relate to people? If not, you may skip the remaining questions in this section. No. > Does the dataset identify any subpopulations (e.g., by age, gender)? NA > Is it possible to identify individuals (i.e., one or more natural persons), either directly or indirectly (i.e., in combination with other data) from the dataset? NA > Does the dataset contain data that might be considered sensitive in any way (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history)? NA > Were any ethical review processes conducted (e.g., by an institutional review board)? No ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases From the paper: > Is your dataset free of biases? No. There are many kinds of biases that can either be quantified, e.g. geo-location (most images originate from the US and Europe) or camera-model (most images are taken with professional DSLR cameras not easily affordable), there are likely many more biases that this dataset does contain. The only thing that this dataset does not contain are humans and parts of humans, as far as our validation procedure is accurate. ### Other Known Limitations From the paper: > Can you guarantee compliance to GDPR? No, we cannot comment on legal issues. ## Additional Information ### Dataset Curators YM. Asano, C. Rupprecht, A. Zisserman and A. Vedaldi. From the paper: > Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)? The dataset has been constructed by the research group “Visual Geometry Group” at the University of Oxford at the Engineering Science Department. ### Licensing Information The PASS dataset is available to download for commercial/research purposes under a Creative Commons Attribution 4.0 International License. A complete version of the license can be found here. The whole dataset only contains CC-BY licensed images with full attribution information. ### Contributions Thanks to @mariosasko for adding this dataset.

4cd8187c404bda33cb1f62b49b001115862acf37

# Dataset Card for PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [PAWS-X](https://github.com/google-research-datasets/paws/tree/master/pawsx) - **Repository:** [PAWS-X](https://github.com/google-research-datasets/paws/tree/master/pawsx) - **Paper:** [PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification](https://arxiv.org/abs/1908.11828) - **Point of Contact:** [Yinfei Yang](yinfeiy@google.com) ### Dataset Summary This dataset contains 23,659 **human** translated PAWS evaluation pairs and 296,406 **machine** translated training pairs in six typologically distinct languages: French, Spanish, German, Chinese, Japanese, and Korean. All translated pairs are sourced from examples in [PAWS-Wiki](https://github.com/google-research-datasets/paws#paws-wiki). For further details, see the accompanying paper: [PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification](https://arxiv.org/abs/1908.11828) ### Supported Tasks and Leaderboards It has been majorly used for paraphrase identification for English and other 6 languages namely French, Spanish, German, Chinese, Japanese, and Korean ### Languages The dataset is in English, French, Spanish, German, Chinese, Japanese, and Korean ## Dataset Structure ### Data Instances For en: ``` id : 1 sentence1 : In Paris , in October 1560 , he secretly met the English ambassador , Nicolas Throckmorton , asking him for a passport to return to England through Scotland . sentence2 : In October 1560 , he secretly met with the English ambassador , Nicolas Throckmorton , in Paris , and asked him for a passport to return to Scotland through England . label : 0 ``` For fr: ``` id : 1 sentence1 : À Paris, en octobre 1560, il rencontra secrètement l'ambassadeur d'Angleterre, Nicolas Throckmorton, lui demandant un passeport pour retourner en Angleterre en passant par l'Écosse. sentence2 : En octobre 1560, il rencontra secrètement l'ambassadeur d'Angleterre, Nicolas Throckmorton, à Paris, et lui demanda un passeport pour retourner en Écosse par l'Angleterre. label : 0 ``` ### Data Fields All files are in tsv format with four columns: Column Name | Data :---------- | :-------------------------------------------------------- id | An ID that matches the ID of the source pair in PAWS-Wiki sentence1 | The first sentence sentence2 | The second sentence label | Label for each pair The source text of each translation can be retrieved by looking up the ID in the corresponding file in PAWS-Wiki. ### Data Splits The numbers of examples for each of the seven languages are shown below: Language | Train | Dev | Test :------- | ------: | -----: | -----: en | 49,401 | 2,000 | 2,000 fr | 49,401 | 2,000 | 2,000 es | 49,401 | 2,000 | 2,000 de | 49,401 | 2,000 | 2,000 zh | 49,401 | 2,000 | 2,000 ja | 49,401 | 2,000 | 2,000 ko | 49,401 | 2,000 | 2,000 > **Caveat**: please note that the dev and test sets of PAWS-X are both sourced > from the dev set of PAWS-Wiki. As a consequence, the same `sentence 1` may > appear in both the dev and test sets. Nevertheless our data split guarantees > that there is no overlap on sentence pairs (`sentence 1` + `sentence 2`) > between dev and test. ## Dataset Creation ### Curation Rationale Most existing work on adversarial data generation focuses on English. For example, PAWS (Paraphrase Adversaries from Word Scrambling) (Zhang et al., 2019) consists of challenging English paraphrase identification pairs from Wikipedia and Quora. They remedy this gap with PAWS-X, a new dataset of 23,659 human translated PAWS evaluation pairs in six typologically distinct languages: French, Spanish, German, Chinese, Japanese, and Korean. They provide baseline numbers for three models with different capacity to capture non-local context and sentence structure, and using different multilingual training and evaluation regimes. Multilingual BERT (Devlin et al., 2019) fine-tuned on PAWS English plus machine-translated data performs the best, with a range of 83.1-90.8 accuracy across the non-English languages and an average accuracy gain of 23% over the next best model. PAWS-X shows the effectiveness of deep, multilingual pre-training while also leaving considerable headroom as a new challenge to drive multilingual research that better captures structure and contextual information. ### Source Data PAWS (Paraphrase Adversaries from Word Scrambling) #### Initial Data Collection and Normalization All translated pairs are sourced from examples in [PAWS-Wiki](https://github.com/google-research-datasets/paws#paws-wiki) #### Who are the source language producers? This dataset contains 23,659 human translated PAWS evaluation pairs and 296,406 machine translated training pairs in six typologically distinct languages: French, Spanish, German, Chinese, Japanese, and Korean. ### Annotations #### Annotation process If applicable, describe the annotation process and any tools used, or state otherwise. Describe the amount of data annotated, if not all. Describe or reference annotation guidelines provided to the annotators. If available, provide interannotator statistics. Describe any annotation validation processes. #### Who are the annotators? The paper mentions the translate team, especially Mengmeng Niu, for the help with the annotations. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators List the people involved in collecting the dataset and their affiliation(s). If funding information is known, include it here. ### Licensing Information The dataset may be freely used for any purpose, although acknowledgement of Google LLC ("Google") as the data source would be appreciated. The dataset is provided "AS IS" without any warranty, express or implied. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset. ### Citation Information ``` @InProceedings{pawsx2019emnlp, title = {{PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification}}, author = {Yang, Yinfei and Zhang, Yuan and Tar, Chris and Baldridge, Jason}, booktitle = {Proc. of EMNLP}, year = {2019} } ``` ### Contributions Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik), [@gowtham1997](https://github.com/gowtham1997) for adding this dataset.

paws-x

2022-03-02T23:29:22+00:00

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2024-01-04T16:17:17+00:00

TAGS #task_categories-text-classification #task_ids-semantic-similarity-classification #task_ids-semantic-similarity-scoring #task_ids-text-scoring #task_ids-multi-input-text-classification #annotations_creators-expert-generated #annotations_creators-machine-generated #language_creators-expert-generated #language_creators-machine-generated #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-extended|other-paws #language-German #language-English #language-Spanish #language-French #language-Japanese #language-Korean #language-Chinese #license-other #paraphrase-identification #arxiv-1908.11828 #region-us

Dataset Card for PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification ========================================================================================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: PAWS-X * Repository: PAWS-X * Paper: PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification * Point of Contact: Yinfei Yang ### Dataset Summary This dataset contains 23,659 human translated PAWS evaluation pairs and 296,406 machine translated training pairs in six typologically distinct languages: French, Spanish, German, Chinese, Japanese, and Korean. All translated pairs are sourced from examples in PAWS-Wiki. For further details, see the accompanying paper: PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification ### Supported Tasks and Leaderboards It has been majorly used for paraphrase identification for English and other 6 languages namely French, Spanish, German, Chinese, Japanese, and Korean ### Languages The dataset is in English, French, Spanish, German, Chinese, Japanese, and Korean Dataset Structure ----------------- ### Data Instances For en: For fr: ### Data Fields All files are in tsv format with four columns: The source text of each translation can be retrieved by looking up the ID in the corresponding file in PAWS-Wiki. ### Data Splits The numbers of examples for each of the seven languages are shown below: > > Caveat: please note that the dev and test sets of PAWS-X are both sourced > from the dev set of PAWS-Wiki. As a consequence, the same 'sentence 1' may > appear in both the dev and test sets. Nevertheless our data split guarantees > that there is no overlap on sentence pairs ('sentence 1' + 'sentence 2') > between dev and test. > > > Dataset Creation ---------------- ### Curation Rationale Most existing work on adversarial data generation focuses on English. For example, PAWS (Paraphrase Adversaries from Word Scrambling) (Zhang et al., 2019) consists of challenging English paraphrase identification pairs from Wikipedia and Quora. They remedy this gap with PAWS-X, a new dataset of 23,659 human translated PAWS evaluation pairs in six typologically distinct languages: French, Spanish, German, Chinese, Japanese, and Korean. They provide baseline numbers for three models with different capacity to capture non-local context and sentence structure, and using different multilingual training and evaluation regimes. Multilingual BERT (Devlin et al., 2019) fine-tuned on PAWS English plus machine-translated data performs the best, with a range of 83.1-90.8 accuracy across the non-English languages and an average accuracy gain of 23% over the next best model. PAWS-X shows the effectiveness of deep, multilingual pre-training while also leaving considerable headroom as a new challenge to drive multilingual research that better captures structure and contextual information. ### Source Data PAWS (Paraphrase Adversaries from Word Scrambling) #### Initial Data Collection and Normalization All translated pairs are sourced from examples in PAWS-Wiki #### Who are the source language producers? This dataset contains 23,659 human translated PAWS evaluation pairs and 296,406 machine translated training pairs in six typologically distinct languages: French, Spanish, German, Chinese, Japanese, and Korean. ### Annotations #### Annotation process If applicable, describe the annotation process and any tools used, or state otherwise. Describe the amount of data annotated, if not all. Describe or reference annotation guidelines provided to the annotators. If available, provide interannotator statistics. Describe any annotation validation processes. #### Who are the annotators? The paper mentions the translate team, especially Mengmeng Niu, for the help with the annotations. ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators List the people involved in collecting the dataset and their affiliation(s). If funding information is known, include it here. ### Licensing Information The dataset may be freely used for any purpose, although acknowledgement of Google LLC ("Google") as the data source would be appreciated. The dataset is provided "AS IS" without any warranty, express or implied. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset. ### Contributions Thanks to @bhavitvyamalik, @gowtham1997 for adding this dataset.

161ece9501cf0a11f3e48bd356eaa82de46d6a09

# Dataset Card for PAWS: Paraphrase Adversaries from Word Scrambling ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [PAWS](https://github.com/google-research-datasets/paws) - **Repository:** [PAWS](https://github.com/google-research-datasets/paws) - **Paper:** [PAWS: Paraphrase Adversaries from Word Scrambling](https://arxiv.org/abs/1904.01130) - **Point of Contact:** [Yuan Zhang](zhangyua@google.com) ### Dataset Summary PAWS: Paraphrase Adversaries from Word Scrambling This dataset contains 108,463 human-labeled and 656k noisily labeled pairs that feature the importance of modeling structure, context, and word order information for the problem of paraphrase identification. The dataset has two subsets, one based on Wikipedia and the other one based on the Quora Question Pairs (QQP) dataset. For further details, see the accompanying paper: PAWS: Paraphrase Adversaries from Word Scrambling (https://arxiv.org/abs/1904.01130) PAWS-QQP is not available due to license of QQP. It must be reconstructed by downloading the original data and then running our scripts to produce the data and attach the labels. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The text in the dataset is in English. ## Dataset Structure ### Data Instances Below are two examples from the dataset: | | Sentence 1 | Sentence 2 | Label | | :-- | :---------------------------- | :---------------------------- | :---- | | (1) | Although interchangeable, the body pieces on the 2 cars are not similar. | Although similar, the body parts are not interchangeable on the 2 cars. | 0 | | (2) | Katz was born in Sweden in 1947 and moved to New York City at the age of 1. | Katz was born in 1947 in Sweden and moved to New York at the age of one. | 1 | The first pair has different semantic meaning while the second pair is a paraphrase. State-of-the-art models trained on existing datasets have dismal performance on PAWS (<40% accuracy); however, including PAWS training data for these models improves their accuracy to 85% while maintaining performance on existing datasets such as the [Quora Question Pairs](https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs). ### Data Fields This corpus contains pairs generated from Wikipedia pages, and can be downloaded here: * **PAWS-Wiki Labeled (Final)**: containing pairs that are generated from both word swapping and back translation methods. All pairs have human judgements on both paraphrasing and fluency and they are split into Train/Dev/Test sections. * **PAWS-Wiki Labeled (Swap-only)**: containing pairs that have no back translation counterparts and therefore they are not included in the first set. Nevertheless, they are high-quality pairs with human judgements on both paraphrasing and fluency, and they can be included as an auxiliary training set. * **PAWS-Wiki Unlabeled (Final)**: Pairs in this set have noisy labels without human judgments and can also be used as an auxiliary training set. They are generated from both word swapping and back translation methods. All files are in the tsv format with four columns: Column Name | Data :------------ | :-------------------------- id | A unique id for each pair sentence1 | The first sentence sentence2 | The second sentence (noisy_)label | (Noisy) label for each pair Each label has two possible values: `0` indicates the pair has different meaning, while `1` indicates the pair is a paraphrase. ### Data Splits The number of examples and the proportion of paraphrase (Yes%) pairs are shown below: Data | Train | Dev | Test | Yes% :------------------ | ------: | -----: | ----: | ----: Labeled (Final) | 49,401 | 8,000 | 8,000 | 44.2% Labeled (Swap-only) | 30,397 | -- | -- | 9.6% Unlabeled (Final) | 645,652 | 10,000 | -- | 50.0% ## Dataset Creation ### Curation Rationale Existing paraphrase identification datasets lack sentence pairs that have high lexical overlap without being paraphrases. Models trained on such data fail to distinguish pairs like *flights from New York to Florida* and *flights from Florida to New York*. ### Source Data #### Initial Data Collection and Normalization Their automatic generation method is based on two ideas. The first swaps words to generate a sentence pair with the same BOW, controlled by a language model. The second uses back translation to generate paraphrases with high BOW overlap but different word order. These two strategies generate high-quality, diverse PAWS pairs, balanced evenly between paraphrases and non-paraphrases. #### Who are the source language producers? Mentioned above. ### Annotations #### Annotation process Sentence pairs are presented to five annotators, each of which gives a binary judgment as to whether they are paraphrases or not. They chose binary judgments to make dataset have the same label schema as the QQP corpus. Overall, human agreement is high on both Quora (92.0%) and Wikipedia (94.7%) and each label only takes about 24 seconds. As such, answers are usually straight-forward to human raters. #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators List the people involved in collecting the dataset and their affiliation(s). If funding information is known, include it here. ### Licensing Information The dataset may be freely used for any purpose, although acknowledgement of Google LLC ("Google") as the data source would be appreciated. The dataset is provided "AS IS" without any warranty, express or implied. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset. ### Citation Information ``` @InProceedings{paws2019naacl, title = {{PAWS: Paraphrase Adversaries from Word Scrambling}}, author = {Zhang, Yuan and Baldridge, Jason and He, Luheng}, booktitle = {Proc. of NAACL}, year = {2019} } ``` ### Contributions Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik) for adding this dataset.

paws

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated", "machine-generated"], "language_creators": ["machine-generated"], "language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M", "10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["semantic-similarity-classification", "semantic-similarity-scoring", "text-scoring", "multi-input-text-classification"], "paperswithcode_id": "paws", "pretty_name": "PAWS: Paraphrase Adversaries from Word Scrambling", "config_names": ["labeled_final", "labeled_swap", "unlabeled_final"], "tags": ["paraphrase-identification"], "dataset_info": [{"config_name": "labeled_final", "features": [{"name": "id", "dtype": "int32"}, {"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}], "splits": [{"name": "train", "num_bytes": 12239938, "num_examples": 49401}, {"name": "test", "num_bytes": 1987794, "num_examples": 8000}, {"name": "validation", "num_bytes": 1975862, "num_examples": 8000}], "download_size": 10899391, "dataset_size": 16203594}, {"config_name": "labeled_swap", "features": [{"name": "id", "dtype": "int32"}, {"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}], "splits": [{"name": "train", "num_bytes": 7963619, "num_examples": 30397}], "download_size": 5741756, "dataset_size": 7963619}, {"config_name": "unlabeled_final", "features": [{"name": "id", "dtype": "int32"}, {"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}], "splits": [{"name": "train", "num_bytes": 157806476, "num_examples": 645652}, {"name": "validation", "num_bytes": 2442165, "num_examples": 10000}], "download_size": 112644285, "dataset_size": 160248641}], "configs": [{"config_name": "labeled_final", "data_files": [{"split": "train", "path": "labeled_final/train-*"}, {"split": "test", "path": "labeled_final/test-*"}, {"split": "validation", "path": "labeled_final/validation-*"}]}, {"config_name": "labeled_swap", "data_files": [{"split": "train", "path": "labeled_swap/train-*"}]}, {"config_name": "unlabeled_final", "data_files": [{"split": "train", "path": "unlabeled_final/train-*"}, {"split": "validation", "path": "unlabeled_final/validation-*"}]}]}

2024-01-04T16:14:11+00:00

TAGS #task_categories-text-classification #task_ids-semantic-similarity-classification #task_ids-semantic-similarity-scoring #task_ids-text-scoring #task_ids-multi-input-text-classification #annotations_creators-expert-generated #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-100K<n<1M #size_categories-10K<n<100K #source_datasets-original #language-English #license-other #paraphrase-identification #arxiv-1904.01130 #region-us

Dataset Card for PAWS: Paraphrase Adversaries from Word Scrambling ================================================================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: PAWS * Repository: PAWS * Paper: PAWS: Paraphrase Adversaries from Word Scrambling * Point of Contact: Yuan Zhang ### Dataset Summary PAWS: Paraphrase Adversaries from Word Scrambling This dataset contains 108,463 human-labeled and 656k noisily labeled pairs that feature the importance of modeling structure, context, and word order information for the problem of paraphrase identification. The dataset has two subsets, one based on Wikipedia and the other one based on the Quora Question Pairs (QQP) dataset. For further details, see the accompanying paper: PAWS: Paraphrase Adversaries from Word Scrambling (URL PAWS-QQP is not available due to license of QQP. It must be reconstructed by downloading the original data and then running our scripts to produce the data and attach the labels. ### Supported Tasks and Leaderboards ### Languages The text in the dataset is in English. Dataset Structure ----------------- ### Data Instances Below are two examples from the dataset: The first pair has different semantic meaning while the second pair is a paraphrase. State-of-the-art models trained on existing datasets have dismal performance on PAWS (<40% accuracy); however, including PAWS training data for these models improves their accuracy to 85% while maintaining performance on existing datasets such as the Quora Question Pairs. ### Data Fields This corpus contains pairs generated from Wikipedia pages, and can be downloaded here: * PAWS-Wiki Labeled (Final): containing pairs that are generated from both word swapping and back translation methods. All pairs have human judgements on both paraphrasing and fluency and they are split into Train/Dev/Test sections. * PAWS-Wiki Labeled (Swap-only): containing pairs that have no back translation counterparts and therefore they are not included in the first set. Nevertheless, they are high-quality pairs with human judgements on both paraphrasing and fluency, and they can be included as an auxiliary training set. * PAWS-Wiki Unlabeled (Final): Pairs in this set have noisy labels without human judgments and can also be used as an auxiliary training set. They are generated from both word swapping and back translation methods. All files are in the tsv format with four columns: Each label has two possible values: '0' indicates the pair has different meaning, while '1' indicates the pair is a paraphrase. ### Data Splits The number of examples and the proportion of paraphrase (Yes%) pairs are shown below: Dataset Creation ---------------- ### Curation Rationale Existing paraphrase identification datasets lack sentence pairs that have high lexical overlap without being paraphrases. Models trained on such data fail to distinguish pairs like *flights from New York to Florida* and *flights from Florida to New York*. ### Source Data #### Initial Data Collection and Normalization Their automatic generation method is based on two ideas. The first swaps words to generate a sentence pair with the same BOW, controlled by a language model. The second uses back translation to generate paraphrases with high BOW overlap but different word order. These two strategies generate high-quality, diverse PAWS pairs, balanced evenly between paraphrases and non-paraphrases. #### Who are the source language producers? Mentioned above. ### Annotations #### Annotation process Sentence pairs are presented to five annotators, each of which gives a binary judgment as to whether they are paraphrases or not. They chose binary judgments to make dataset have the same label schema as the QQP corpus. Overall, human agreement is high on both Quora (92.0%) and Wikipedia (94.7%) and each label only takes about 24 seconds. As such, answers are usually straight-forward to human raters. #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators List the people involved in collecting the dataset and their affiliation(s). If funding information is known, include it here. ### Licensing Information The dataset may be freely used for any purpose, although acknowledgement of Google LLC ("Google") as the data source would be appreciated. The dataset is provided "AS IS" without any warranty, express or implied. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset. ### Contributions Thanks to @bhavitvyamalik for adding this dataset.

eec562f0714d4af1394c6f44822a6f20413db49a

# Dataset Card for PEC ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** [PEC repository](https://github.com/zhongpeixiang/PEC) - **Paper:** [Towards Persona-Based Empathetic Conversational Models](https://www.aclweb.org/anthology/2020.emnlp-main.531/) - **Point of Contact:** [Peixiang Zhong](mailto:zhongpeixiang@gmail.com) ### Dataset Summary The PEC dataset is an English-language dataset of open-domain conversations gathered from two subreddits on Reddit, i.e., happy and offmychest. PEC has around 350K persona-based empathetic conversations. Each utterance is associated with a speaker, and each speaker has a persona of multiple persona sentences. The conversations in PEC are more empathetic than casual conversations. The conversations in the happy domain are mostly positive, whereas the conversations in the offmychest domain are mostly negative. ### Supported Tasks and Leaderboards - `dialogue-modeling`, `utterance-retrieval`: this dataset can be used to train a generative or retrieval-based conversational model. ### Languages English ## Dataset Structure ### Data Instances A typical data example comprises a list of context utterances, a list of context speakers, a response to the context, the response speaker and the persona of the response speaker. An example from PEC looks as follows: ``` {'context': ['found out this morning i got a job promotion ! ! !'], 'context_speakers': ['HeWentToJared91'], 'personas': [ "i ca n't stand working in the ugli .", 'i ’ve always liked my eyes except for the fact that they ca n’t shoot lasers', 'i feel really bad about myself as a person right now , and i could really use a hand .', 'i drank a coffee , and it just made me feel even more exhausted .', 'i want a natsuki t shirt', "i 've dealt with depression in the past .", 'i love red dead 2'], 'response': "you look like a nice person ! we 're proud of you , and i bet you earned that promotion !", 'response_speaker': 'tylock'} ``` ### Data Fields - `context`: a list of strings, each string denotes a context utterance. - `context_speakers`: a list of strings, each string denotes a speaker. - `response`: a string denoting the response to the `context`. - `response_speaker`: a string denoting the speaker of `response`. - `personas`: a list of strings, each string denotes a persona sentence of `response_speaker`. ### Data Splits The data is split into a training, validation and test set for each of the three domains. Note that the *all* domain is the concatenation of the *happy* and *offmychest* domains. | domain | train | validation | test | |------------|-------:|-----------:|------:| | happy | 157195 | 19829 | 22730 | | offmychest | 123968 | 16004 | 15324 | | all | 281163 | 35833 | 38054 | ## Dataset Creation ### Curation Rationale PEC was built to provide a testbed for machines to learn persona-based empathetic responding. In our empirical analysis, we found that different personas have different styles of empathetic responding. This dataset can also be used to investigate the link between persona and empathy in human conversations. According to our human assessment, the conversations on the happy and offmychest subreddits are significantly more empathetic than casual conversations. ### Source Data #### Initial Data Collection and Normalization The data was obtained via the [pushshift API](https://pushshift.io/using-bigquery-with-reddit-data/) via Google BigQuery. #### Who are the source language producers? The language producers are users of the [r/happy](https://www.reddit.com/r/happy/), and [r/offmychest](https://www.reddit.com/r/offmychest/) subreddits between 2012 and 2020. No further demographic information was available from the data source. ### Annotations #### Annotation process The dataset does not contain any additional annotations. #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information The dataset includes the speaker IDs of users on *happy* and *offmychest* subreddits. ## Considerations for Using the Data ### Social Impact of Dataset The purpose of this dataset is to help develop more personalised and empathetic conversational systems, which is an important milestone towards truly human-like conversational agents. ### Discussion of Biases [More Information Needed] ### Other Known Limitations A small portion of the dataset has the issues of sexism, hate, and harassment. The persona sentences are noisy. ## Additional Information ### Dataset Curators The dataset was initially created by Peixiang Zhong, Chen Zhang, Hao Wang, Yong Liu, and Chunyan Miao, jointly done at Nanyang Technological University and Alibaba Group. ### Licensing Information The licensing status of the dataset hinges on the legal status of the [Pushshift.io](https://files.pushshift.io/reddit/) data which is unclear. ### Citation Information ``` @inproceedings{zhong-etal-2020-towards, title = "Towards Persona-Based Empathetic Conversational Models", author = "Zhong, Peixiang and Zhang, Chen and Wang, Hao and Liu, Yong and Miao, Chunyan", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", year = "2020", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.emnlp-main.531", pages = "6556--6566" } ``` ### Contributions Thanks to [@zhongpeixiang](https://github.com/zhongpeixiang) for adding this dataset.

pec

2022-03-02T23:29:22+00:00

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2024-01-18T11:12:41+00:00

TAGS #task_categories-text-generation #task_categories-fill-mask #task_categories-text-retrieval #task_ids-dialogue-modeling #task_ids-utterance-retrieval #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-gpl-3.0 #region-us

Dataset Card for PEC ==================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Repository: PEC repository * Paper: Towards Persona-Based Empathetic Conversational Models * Point of Contact: Peixiang Zhong ### Dataset Summary The PEC dataset is an English-language dataset of open-domain conversations gathered from two subreddits on Reddit, i.e., happy and offmychest. PEC has around 350K persona-based empathetic conversations. Each utterance is associated with a speaker, and each speaker has a persona of multiple persona sentences. The conversations in PEC are more empathetic than casual conversations. The conversations in the happy domain are mostly positive, whereas the conversations in the offmychest domain are mostly negative. ### Supported Tasks and Leaderboards * 'dialogue-modeling', 'utterance-retrieval': this dataset can be used to train a generative or retrieval-based conversational model. ### Languages English Dataset Structure ----------------- ### Data Instances A typical data example comprises a list of context utterances, a list of context speakers, a response to the context, the response speaker and the persona of the response speaker. An example from PEC looks as follows: ### Data Fields * 'context': a list of strings, each string denotes a context utterance. * 'context\_speakers': a list of strings, each string denotes a speaker. * 'response': a string denoting the response to the 'context'. * 'response\_speaker': a string denoting the speaker of 'response'. * 'personas': a list of strings, each string denotes a persona sentence of 'response\_speaker'. ### Data Splits The data is split into a training, validation and test set for each of the three domains. Note that the *all* domain is the concatenation of the *happy* and *offmychest* domains. Dataset Creation ---------------- ### Curation Rationale PEC was built to provide a testbed for machines to learn persona-based empathetic responding. In our empirical analysis, we found that different personas have different styles of empathetic responding. This dataset can also be used to investigate the link between persona and empathy in human conversations. According to our human assessment, the conversations on the happy and offmychest subreddits are significantly more empathetic than casual conversations. ### Source Data #### Initial Data Collection and Normalization The data was obtained via the pushshift API via Google BigQuery. #### Who are the source language producers? The language producers are users of the r/happy, and r/offmychest subreddits between 2012 and 2020. No further demographic information was available from the data source. ### Annotations #### Annotation process The dataset does not contain any additional annotations. #### Who are the annotators? ### Personal and Sensitive Information The dataset includes the speaker IDs of users on *happy* and *offmychest* subreddits. Considerations for Using the Data --------------------------------- ### Social Impact of Dataset The purpose of this dataset is to help develop more personalised and empathetic conversational systems, which is an important milestone towards truly human-like conversational agents. ### Discussion of Biases ### Other Known Limitations A small portion of the dataset has the issues of sexism, hate, and harassment. The persona sentences are noisy. Additional Information ---------------------- ### Dataset Curators The dataset was initially created by Peixiang Zhong, Chen Zhang, Hao Wang, Yong Liu, and Chunyan Miao, jointly done at Nanyang Technological University and Alibaba Group. ### Licensing Information The licensing status of the dataset hinges on the legal status of the URL data which is unclear. ### Contributions Thanks to @zhongpeixiang for adding this dataset.

53e19322c88bb01c1b0c6a61bde68bc2b1c3028e

# Dataset Card for peer_read ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://arxiv.org/abs/1804.09635 - **Repository:** https://github.com/allenai/PeerRead - **Paper:** https://arxiv.org/pdf/1804.09635.pdf - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary PearRead is a dataset of scientific peer reviews available to help researchers study this important artifact. The dataset consists of over 14K paper drafts and the corresponding accept/reject decisions in top-tier venues including ACL, NIPS and ICLR, as well as over 10K textual peer reviews written by experts for a subset of the papers. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages en-English ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields #### parsed_pdfs - `name`: `string` Filename in the dataset - `metadata`: `dict` Paper metadata - `source`: `string` Paper source - `authors`: `list<string>` List of paper authors - `title`: `string` Paper title - `sections`: `list<dict>` List of section heading and corresponding description - `heading`: `string` Section heading - `text`: `string` Section description - `references`: `string` List of references - `title`: `string` Title of reference paper - `author`: `list<string>` List of reference paper authors - `venue`: `string` Reference venue - `citeRegEx`: `string` Reference citeRegEx - `shortCiteRegEx`: `string` Reference shortCiteRegEx - `year`: `int` Reference publish year - `referenceMentions`: `list<string>` List of reference mentions - `referenceID`: `int` Reference mention ID - `context`: `string` Reference mention context - `startOffset`: `int` Reference startOffset - `endOffset`: `int` Reference endOffset - `year`: `int` Paper publish year - `abstractText`: `string` Paper abstract - `creator`: `string` Paper creator #### reviews - `id`: `int` Review ID - `conference`: `string` Conference name - `comments`: `string` Review comments - `subjects`: `string` Review subjects - `version`: `string` Review version - `date_of_submission`: `string` Submission date - `title`: `string` Paper title - `authors`: `list<string>` List of paper authors - `accepted`: `bool` Paper accepted flag - `abstract`: `string` Paper abstract - `histories`: `list<string>` Paper details with link - `reviews`: `dict` Paper reviews - `date`: `string` Date of review - `title`: `string` Paper title - `other_keys`: `string` Reviewer other details - `originality`: `string` Originality score - `comments`: `string` Reviewer comments - `is_meta_review`: `bool` Review type flag - `recommendation`: `string` Reviewer recommendation - `replicability`: `string` Replicability score - `presentation_format`: `string` Presentation type - `clarity`: `string` Clarity score - `meaningful_comparison`: `string` Meaningful comparison score - `substance`: `string` Substance score - `reviewer_confidence`: `string` Reviewer confidence score - `soundness_correctness`: `string` Soundness correctness score - `appropriateness`: `string` Appropriateness score - `impact`: `string` Impact score ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Dongyeop Kang, Waleed Ammar, Bhavana Dalvi Mishra, Madeleine van Zuylen, Sebastian Kohlmeier, Eduard Hovy, Roy Schwartz ### Licensing Information [More Information Needed] ### Citation Information @inproceedings{kang18naacl, title = {A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications}, author = {Dongyeop Kang and Waleed Ammar and Bhavana Dalvi and Madeleine van Zuylen and Sebastian Kohlmeier and Eduard Hovy and Roy Schwartz}, booktitle = {Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL)}, address = {New Orleans, USA}, month = {June}, url = {https://arxiv.org/abs/1804.09635}, year = {2018} } ### Contributions Thanks to [@vinaykudari](https://github.com/vinaykudari) for adding this dataset.

allenai/peer_read

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": [], "paperswithcode_id": "peerread", "pretty_name": "PeerRead", "tags": ["acceptability-classification"], "dataset_info": [{"config_name": "parsed_pdfs", "features": [{"name": "name", "dtype": "string"}, {"name": "metadata", "struct": [{"name": "source", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "authors", "sequence": "string"}, {"name": "emails", "sequence": "string"}, {"name": "sections", "sequence": [{"name": "heading", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "references", "sequence": [{"name": "title", "dtype": "string"}, {"name": "author", "sequence": "string"}, {"name": "venue", "dtype": "string"}, {"name": "citeRegEx", "dtype": "string"}, {"name": "shortCiteRegEx", "dtype": "string"}, {"name": "year", "dtype": "int32"}]}, {"name": "referenceMentions", "sequence": [{"name": "referenceID", "dtype": "int32"}, {"name": "context", "dtype": "string"}, {"name": "startOffset", "dtype": "int32"}, {"name": "endOffset", "dtype": "int32"}]}, {"name": "year", "dtype": "int32"}, {"name": "abstractText", "dtype": "string"}, {"name": "creator", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 571263679, "num_examples": 11090}, {"name": "test", "num_bytes": 34284777, "num_examples": 637}, {"name": "validation", "num_bytes": 32488519, "num_examples": 637}], "download_size": 1246688292, "dataset_size": 638036975}, {"config_name": "reviews", "features": [{"name": "id", "dtype": "string"}, {"name": "conference", "dtype": "string"}, {"name": "comments", "dtype": "string"}, {"name": "subjects", "dtype": "string"}, {"name": "version", "dtype": "string"}, {"name": "date_of_submission", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "authors", "sequence": "string"}, {"name": "accepted", "dtype": "bool"}, {"name": "abstract", "dtype": "string"}, {"name": "histories", "sequence": {"sequence": "string"}}, {"name": "reviews", "sequence": [{"name": "date", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "other_keys", "dtype": "string"}, {"name": "originality", "dtype": "string"}, {"name": "comments", "dtype": "string"}, {"name": "is_meta_review", "dtype": "bool"}, {"name": "is_annotated", "dtype": "bool"}, {"name": "recommendation", "dtype": "string"}, {"name": "replicability", "dtype": "string"}, {"name": "presentation_format", "dtype": "string"}, {"name": "clarity", "dtype": "string"}, {"name": "meaningful_comparison", "dtype": "string"}, {"name": "substance", "dtype": "string"}, {"name": "reviewer_confidence", "dtype": "string"}, {"name": "soundness_correctness", "dtype": "string"}, {"name": "appropriateness", "dtype": "string"}, {"name": "impact", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 15234922, "num_examples": 11090}, {"name": "test", "num_bytes": 878906, "num_examples": 637}, {"name": "validation", "num_bytes": 864799, "num_examples": 637}], "download_size": 1246688292, "dataset_size": 16978627}]}

2022-11-18T21:37:46+00:00

TAGS #task_categories-text-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-unknown #acceptability-classification #arxiv-1804.09635 #region-us

# Dataset Card for peer_read ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: URL - Paper: URL - Leaderboard: - Point of Contact: ### Dataset Summary PearRead is a dataset of scientific peer reviews available to help researchers study this important artifact. The dataset consists of over 14K paper drafts and the corresponding accept/reject decisions in top-tier venues including ACL, NIPS and ICLR, as well as over 10K textual peer reviews written by experts for a subset of the papers. ### Supported Tasks and Leaderboards ### Languages en-English ## Dataset Structure ### Data Instances ### Data Fields #### parsed_pdfs - 'name': 'string' Filename in the dataset - 'metadata': 'dict' Paper metadata - 'source': 'string' Paper source - 'authors': 'list<string>' List of paper authors - 'title': 'string' Paper title - 'sections': 'list<dict>' List of section heading and corresponding description - 'heading': 'string' Section heading - 'text': 'string' Section description - 'references': 'string' List of references - 'title': 'string' Title of reference paper - 'author': 'list<string>' List of reference paper authors - 'venue': 'string' Reference venue - 'citeRegEx': 'string' Reference citeRegEx - 'shortCiteRegEx': 'string' Reference shortCiteRegEx - 'year': 'int' Reference publish year - 'referenceMentions': 'list<string>' List of reference mentions - 'referenceID': 'int' Reference mention ID - 'context': 'string' Reference mention context - 'startOffset': 'int' Reference startOffset - 'endOffset': 'int' Reference endOffset - 'year': 'int' Paper publish year - 'abstractText': 'string' Paper abstract - 'creator': 'string' Paper creator #### reviews - 'id': 'int' Review ID - 'conference': 'string' Conference name - 'comments': 'string' Review comments - 'subjects': 'string' Review subjects - 'version': 'string' Review version - 'date_of_submission': 'string' Submission date - 'title': 'string' Paper title - 'authors': 'list<string>' List of paper authors - 'accepted': 'bool' Paper accepted flag - 'abstract': 'string' Paper abstract - 'histories': 'list<string>' Paper details with link - 'reviews': 'dict' Paper reviews - 'date': 'string' Date of review - 'title': 'string' Paper title - 'other_keys': 'string' Reviewer other details - 'originality': 'string' Originality score - 'comments': 'string' Reviewer comments - 'is_meta_review': 'bool' Review type flag - 'recommendation': 'string' Reviewer recommendation - 'replicability': 'string' Replicability score - 'presentation_format': 'string' Presentation type - 'clarity': 'string' Clarity score - 'meaningful_comparison': 'string' Meaningful comparison score - 'substance': 'string' Substance score - 'reviewer_confidence': 'string' Reviewer confidence score - 'soundness_correctness': 'string' Soundness correctness score - 'appropriateness': 'string' Appropriateness score - 'impact': 'string' Impact score ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators Dongyeop Kang, Waleed Ammar, Bhavana Dalvi Mishra, Madeleine van Zuylen, Sebastian Kohlmeier, Eduard Hovy, Roy Schwartz ### Licensing Information @inproceedings{kang18naacl, title = {A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications}, author = {Dongyeop Kang and Waleed Ammar and Bhavana Dalvi and Madeleine van Zuylen and Sebastian Kohlmeier and Eduard Hovy and Roy Schwartz}, booktitle = {Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL)}, address = {New Orleans, USA}, month = {June}, url = {URL year = {2018} } ### Contributions Thanks to @vinaykudari for adding this dataset.

4f5e2db0399cfb18256eb8c987dd7b78b92340ee

# Dataset Card for People's Daily NER ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Github](https://github.com/OYE93/Chinese-NLP-Corpus/tree/master/NER/People's%20Daily) - **Repository:** [Github](https://github.com/OYE93/Chinese-NLP-Corpus/) - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information No citation available for this dataset. ### Contributions Thanks to [@JetRunner](https://github.com/JetRunner) for adding this dataset.

peoples_daily_ner

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["zh"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "People's Daily NER", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}], "config_name": "peoples_daily_ner", "splits": [{"name": "train", "num_bytes": 14972456, "num_examples": 20865}, {"name": "validation", "num_bytes": 1676741, "num_examples": 2319}, {"name": "test", "num_bytes": 3346975, "num_examples": 4637}], "download_size": 8385672, "dataset_size": 19996172}}

2024-01-18T11:12:44+00:00

TAGS #task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Chinese #license-unknown #region-us

# Dataset Card for People's Daily NER ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: Github - Repository: Github - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information No citation available for this dataset. ### Contributions Thanks to @JetRunner for adding this dataset.

fcc40c46cbbc5032a29f7e947e8a67ed756dd756

# Dataset Card for PerSenT ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [PerSenT](https://stonybrooknlp.github.io/PerSenT/) - **Repository:** [https://github.com/MHDBST/PerSenT](https://github.com/MHDBST/PerSenT) - **Paper:** [arXiv](https://arxiv.org/abs/2011.06128) - **Leaderboard:** NA - **Point of Contact:** [Mohaddeseh Bastan](mbastan@cs.stonybrook.edu) ### Dataset Summary PerSenT is a crowd-sourced dataset that captures the sentiment of an author towards the main entity in a news article. This dataset contains annotations for 5.3k documents and 38k paragraphs covering 3.2k unique entities. For each article, annotators judge what the author’s sentiment is towards the main (target) entity of the article. The annotations also include similar judgments on paragraphs within the article. ### Supported Tasks and Leaderboards Sentiment Classification: Each document consists of multiple paragraphs. Each paragraph is labeled separately (Positive, Neutral, Negative) and the author’s sentiment towards the whole document is included as a document-level label. ### Languages English ## Dataset Structure ### Data Instances ```json {'DOCUMENT': "Germany's Landesbank Baden Wuertemberg won EU approval Tuesday for a state bailout after it promised to shrink its balance sheet by 40 percent and refocus on lending to companies.\n The bank was several state-owned German institutions to run into trouble last year after it ran up more huge losses from investing in high-risk proprietary trading and capital market activities -- a business the EU has now told it to shun.\n Seven current and former managers of the bank are also being investigated by German authorities for risking or damaging the bank's capital by carrying out or failing to block investments in high-risk deals worth hundreds of millions from 2006.\n The European Commission said its Tuesday approval for the state rescue of the bank and its new restructuring plan would allow it become a viable business again -- and that the cutbacks would help limit the unfair advantage over rivals that the bank would get from the state aid.\n Stuttgart-based LBBW earlier this year received a capital injection of (EURO)5 billion from the bank's shareholders all of them public authorities or state-owned including the state of Baden-Wuerttemberg the region's savings bank association and the city of Stuttgart.", 'DOCUMENT_INDEX': 1, 'MASKED_DOCUMENT': "[TGT] won EU approval Tuesday for a state bailout after it promised to shrink its balance sheet by 40 percent and refocus on lending to companies.\n [TGT] was several state-owned German institutions to run into trouble last year after [TGT] ran up more huge losses from investing in high-risk proprietary trading and capital market activities -- a business the EU has now told it to shun.\n Seven current and former managers of [TGT] are also being investigated by German authorities for risking or damaging [TGT]'s capital by carrying out or failing to block investments in high-risk deals worth hundreds of millions from 2006.\n The European Commission said its Tuesday approval for the state rescue of [TGT] and its new restructuring plan would allow it become a viable business again -- and that the cutbacks would help limit the unfair advantage over rivals that [TGT] would get from the state aid.\n Stuttgart-based LBBW earlier this year received a capital injection of (EURO)5 billion from [TGT]'s shareholders all of them public authorities or state-owned including the state of Baden-Wuerttemberg the region's savings bank association and the city of Stuttgart.", 'Paragraph0': 2, 'Paragraph1': 0, 'Paragraph10': -1, 'Paragraph11': -1, 'Paragraph12': -1, 'Paragraph13': -1, 'Paragraph14': -1, 'Paragraph15': -1, 'Paragraph2': 0, 'Paragraph3': 1, 'Paragraph4': 1, 'Paragraph5': -1, 'Paragraph6': -1, 'Paragraph7': -1, 'Paragraph8': -1, 'Paragraph9': -1, 'TARGET_ENTITY': 'Landesbank Baden Wuertemberg', 'TITLE': 'German bank LBBW wins EU bailout approval', 'TRUE_SENTIMENT': 0} ``` ### Data Fields - DOCUMENT_INDEX: ID of the document per original dataset - TITLE: Title of the article - DOCUMENT: Text of the article - MASKED_DOCUMENT: Text of the article with the target entity masked with `[TGT]` token - TARGET_ENTITY: The entity that the author is expressing opinion about - TRUE_SENTIMENT: Label for entire article - Paragraph{0..15}: Label for each paragraph in the article **Note**: Labels are one of `[Negative, Neutral, Positive]`. Missing labels were replaced with `-1`. ### Data Splits To split the dataset, entities were split into 4 mutually exclusive sets. Due to the nature of news collections, some entities tend to dominate the collection. In the collection, there were four entities which were the main entity in nearly 800 articles. To avoid these entities from dominating the train or test splits, these were moved them to a separate test collection. The remaining was split into a training, dev, and test sets at random. Thus the collection includes one standard test set consisting of articles drawn at random (Test Standard), while the other is a test set which contains multiple articles about a small number of popular entities (Test Frequent). ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization Articles were selected from 3 sources: 1. MPQA (Deng and Wiebe, 2015; Wiebe et al., 2005): This dataset contains news articles manually annotated for opinions, beliefs, emotions, sentiments, speculations, etc. It also has target annotations which are entities and event anchored to the heads of noun or verb phrases. All decisions on this dataset are made on sentence-level and over short spans. 2. KBP Challenge (Ellis et al., 2014): This resource contains TAC 2014 KBP English sentiment slot filling challenge dataset. This is a document-level sentiment filling dataset. In this task, given an entity and a sentiment (positive/negative) from the document, the goal is to find entities toward which the original entity holds the given sentimental view. We selected documents from this resource which have been used in the following similar work in sentiment analysis task (Choi et al., 2016). 3. Media Rank (Ye and Skiena, 2019): This dataset ranks about 50k news sources along different aspects. It is also used for classifying political ideology of news articles (Kulkarni et al., 2018). Pre-processing steps: - First we find all the person entities in each article, using Stanford NER (Name Entity Resolution) tagger (Finkel et al., 2005) and all mentions of them using co-reference resolution (Clark and Manning, 2016; Co, 2017). - We removed articles which are not likely to have a main entity of focus. We used a simple heuristic of removing articles in which the most frequent person entity is mentioned only three times or less (even when counting co-referent mentions). - For the articles that remain we deemed the most frequent entity to be the main entity of the article. We also filtered out extremely long and extremely short articles to keep the articles which have at least 3 paragraphs and at most 16 paragraphs. Documents are randomly separated into train, dev, and two test sets. We ensure that each entity appears in only one of the sets. Our goal here is to avoid easy to learn biases over entities. To avoid the most frequent entities from dominating the training or the test sets, we remove articles that covered the most frequent entities and use them as a separate test set (referred to as frequent test set) in addition to the randomly drawn standard test set. ### Annotations #### Annotation process We obtained document and paragraph level annotations with the help of Amazon Mechanical Turk workers. The workers first verified if the target entity we provide is indeed the main entity in the document. Then, they rated each paragraph in a document that contained a direct mention or a reference to the target entity. Last, they rated the sentiment towards the entity based on the entire document. In both cases, the workers made assessments about the authors view based on what they said about the target entity. For both paragraph and document level sentiment, the workers chose from five rating categories: Negative, Slightly Negative, Neutral, Slightly Positive, or Positive. We then combine the fine-grained annotations to obtain three coarse-grained classes Negative, Neutral, or Positive. #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data [More Information Needed] ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/) ### Citation Information ``` @inproceedings{bastan2020authors, title={Author's Sentiment Prediction}, author={Mohaddeseh Bastan and Mahnaz Koupaee and Youngseo Son and Richard Sicoli and Niranjan Balasubramanian}, year={2020}, eprint={2011.06128}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@jeromeku](https://github.com/jeromeku) for adding this dataset.

per_sent

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["extended|other-MPQA-KBP Challenge-MediaRank"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "paperswithcode_id": "persent", "pretty_name": "PerSenT", "dataset_info": {"features": [{"name": "DOCUMENT_INDEX", "dtype": "int64"}, {"name": "TITLE", "dtype": "string"}, {"name": "TARGET_ENTITY", "dtype": "string"}, {"name": "DOCUMENT", "dtype": "string"}, {"name": "MASKED_DOCUMENT", "dtype": "string"}, {"name": "TRUE_SENTIMENT", "dtype": {"class_label": {"names": {"0": "Negative", "1": "Neutral", "2": "Positive"}}}}, {"name": "Paragraph0", "dtype": {"class_label": {"names": {"0": "Negative", "1": "Neutral", "2": "Positive"}}}}, {"name": "Paragraph1", "dtype": {"class_label": {"names": {"0": "Negative", "1": "Neutral", "2": "Positive"}}}}, {"name": "Paragraph2", "dtype": {"class_label": {"names": {"0": "Negative", "1": "Neutral", "2": "Positive"}}}}, {"name": "Paragraph3", "dtype": {"class_label": {"names": {"0": "Negative", "1": "Neutral", "2": "Positive"}}}}, {"name": "Paragraph4", "dtype": {"class_label": {"names": {"0": "Negative", "1": "Neutral", "2": "Positive"}}}}, {"name": "Paragraph5", "dtype": {"class_label": {"names": {"0": "Negative", "1": "Neutral", "2": "Positive"}}}}, {"name": "Paragraph6", "dtype": {"class_label": {"names": {"0": "Negative", "1": "Neutral", "2": "Positive"}}}}, {"name": "Paragraph7", "dtype": {"class_label": {"names": {"0": "Negative", "1": "Neutral", "2": "Positive"}}}}, {"name": "Paragraph8", "dtype": {"class_label": {"names": {"0": "Negative", "1": "Neutral", "2": "Positive"}}}}, {"name": "Paragraph9", "dtype": {"class_label": {"names": {"0": "Negative", "1": "Neutral", "2": "Positive"}}}}, {"name": "Paragraph10", "dtype": {"class_label": {"names": {"0": "Negative", "1": "Neutral", "2": "Positive"}}}}, {"name": "Paragraph11", "dtype": {"class_label": {"names": {"0": "Negative", "1": "Neutral", "2": "Positive"}}}}, {"name": "Paragraph12", "dtype": {"class_label": {"names": {"0": "Negative", "1": "Neutral", "2": "Positive"}}}}, {"name": "Paragraph13", "dtype": {"class_label": {"names": {"0": "Negative", "1": "Neutral", "2": "Positive"}}}}, {"name": "Paragraph14", "dtype": {"class_label": {"names": {"0": "Negative", "1": "Neutral", "2": "Positive"}}}}, {"name": "Paragraph15", "dtype": {"class_label": {"names": {"0": "Negative", "1": "Neutral", "2": "Positive"}}}}], "splits": [{"name": "train", "num_bytes": 14595163, "num_examples": 3355}, {"name": "test_random", "num_bytes": 2629500, "num_examples": 579}, {"name": "test_fixed", "num_bytes": 3881800, "num_examples": 827}, {"name": "validation", "num_bytes": 2322922, "num_examples": 578}], "download_size": 23117196, "dataset_size": 23429385}}

2024-01-18T11:12:45+00:00

TAGS #task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-extended|other-MPQA-KBP Challenge-MediaRank #language-English #license-unknown #arxiv-2011.06128 #region-us

# Dataset Card for PerSenT ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: PerSenT - Repository: URL - Paper: arXiv - Leaderboard: NA - Point of Contact: Mohaddeseh Bastan ### Dataset Summary PerSenT is a crowd-sourced dataset that captures the sentiment of an author towards the main entity in a news article. This dataset contains annotations for 5.3k documents and 38k paragraphs covering 3.2k unique entities. For each article, annotators judge what the author’s sentiment is towards the main (target) entity of the article. The annotations also include similar judgments on paragraphs within the article. ### Supported Tasks and Leaderboards Sentiment Classification: Each document consists of multiple paragraphs. Each paragraph is labeled separately (Positive, Neutral, Negative) and the author’s sentiment towards the whole document is included as a document-level label. ### Languages English ## Dataset Structure ### Data Instances ### Data Fields - DOCUMENT_INDEX: ID of the document per original dataset - TITLE: Title of the article - DOCUMENT: Text of the article - MASKED_DOCUMENT: Text of the article with the target entity masked with '[TGT]' token - TARGET_ENTITY: The entity that the author is expressing opinion about - TRUE_SENTIMENT: Label for entire article - Paragraph{0..15}: Label for each paragraph in the article Note: Labels are one of '[Negative, Neutral, Positive]'. Missing labels were replaced with '-1'. ### Data Splits To split the dataset, entities were split into 4 mutually exclusive sets. Due to the nature of news collections, some entities tend to dominate the collection. In the collection, there were four entities which were the main entity in nearly 800 articles. To avoid these entities from dominating the train or test splits, these were moved them to a separate test collection. The remaining was split into a training, dev, and test sets at random. Thus the collection includes one standard test set consisting of articles drawn at random (Test Standard), while the other is a test set which contains multiple articles about a small number of popular entities (Test Frequent). ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization Articles were selected from 3 sources: 1. MPQA (Deng and Wiebe, 2015; Wiebe et al., 2005): This dataset contains news articles manually annotated for opinions, beliefs, emotions, sentiments, speculations, etc. It also has target annotations which are entities and event anchored to the heads of noun or verb phrases. All decisions on this dataset are made on sentence-level and over short spans. 2. KBP Challenge (Ellis et al., 2014): This resource contains TAC 2014 KBP English sentiment slot filling challenge dataset. This is a document-level sentiment filling dataset. In this task, given an entity and a sentiment (positive/negative) from the document, the goal is to find entities toward which the original entity holds the given sentimental view. We selected documents from this resource which have been used in the following similar work in sentiment analysis task (Choi et al., 2016). 3. Media Rank (Ye and Skiena, 2019): This dataset ranks about 50k news sources along different aspects. It is also used for classifying political ideology of news articles (Kulkarni et al., 2018). Pre-processing steps: - First we find all the person entities in each article, using Stanford NER (Name Entity Resolution) tagger (Finkel et al., 2005) and all mentions of them using co-reference resolution (Clark and Manning, 2016; Co, 2017). - We removed articles which are not likely to have a main entity of focus. We used a simple heuristic of removing articles in which the most frequent person entity is mentioned only three times or less (even when counting co-referent mentions). - For the articles that remain we deemed the most frequent entity to be the main entity of the article. We also filtered out extremely long and extremely short articles to keep the articles which have at least 3 paragraphs and at most 16 paragraphs. Documents are randomly separated into train, dev, and two test sets. We ensure that each entity appears in only one of the sets. Our goal here is to avoid easy to learn biases over entities. To avoid the most frequent entities from dominating the training or the test sets, we remove articles that covered the most frequent entities and use them as a separate test set (referred to as frequent test set) in addition to the randomly drawn standard test set. ### Annotations #### Annotation process We obtained document and paragraph level annotations with the help of Amazon Mechanical Turk workers. The workers first verified if the target entity we provide is indeed the main entity in the document. Then, they rated each paragraph in a document that contained a direct mention or a reference to the target entity. Last, they rated the sentiment towards the entity based on the entire document. In both cases, the workers made assessments about the authors view based on what they said about the target entity. For both paragraph and document level sentiment, the workers chose from five rating categories: Negative, Slightly Negative, Neutral, Slightly Positive, or Positive. We then combine the fine-grained annotations to obtain three coarse-grained classes Negative, Neutral, or Positive. #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information Creative Commons Attribution 4.0 International License ### Contributions Thanks to @jeromeku for adding this dataset.

60ca359e5bd33b3ac5d0cdac8e572b6b7a853a7b

# Dataset Card for [Persian NER] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Github](https://github.com/HaniehP/PersianNER) - **Repository:** [Github](https://github.com/HaniehP/PersianNER) - **Paper:** [Aclweb](https://www.aclweb.org/anthology/C16-1319) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The dataset includes 7,682 Persian sentences, split into 250,015 tokens and their NER labels. It is available in 3 folds to be used in turn as training and test sets. The NER tags are in IOB format. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances ### Data Fields - `id`: id of the sample - `tokens`: the tokens of the example text - `ner_tags`: the NER tags of each token The NER tags correspond to this list: ``` "O", "I-event", "I-fac", "I-loc", "I-org", "I-pers", "I-pro", "B-event", "B-fac", "B-loc", "B-org", "B-pers", "B-pro" ``` ### Data Splits Training and test splits ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? Hanieh Poostchi, Ehsan Zare Borzeshi, Mohammad Abdous, Massimo Piccardi ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? Hanieh Poostchi, Ehsan Zare Borzeshi, Mohammad Abdous, Massimo Piccardi ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information Dataset is published for academic use only ### Dataset Curators [More Information Needed] ### Licensing Information Creative Commons Attribution 4.0 International License. ### Citation Information @inproceedings{poostchi-etal-2016-personer, title = "{P}erso{NER}: {P}ersian Named-Entity Recognition", author = "Poostchi, Hanieh and Zare Borzeshi, Ehsan and Abdous, Mohammad and Piccardi, Massimo", booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers", month = dec, year = "2016", address = "Osaka, Japan", publisher = "The COLING 2016 Organizing Committee", url = "https://www.aclweb.org/anthology/C16-1319", pages = "3381--3389", abstract = "Named-Entity Recognition (NER) is still a challenging task for languages with low digital resources. The main difficulties arise from the scarcity of annotated corpora and the consequent problematic training of an effective NER pipeline. To abridge this gap, in this paper we target the Persian language that is spoken by a population of over a hundred million people world-wide. We first present and provide ArmanPerosNERCorpus, the first manually-annotated Persian NER corpus. Then, we introduce PersoNER, an NER pipeline for Persian that leverages a word embedding and a sequential max-margin classifier. The experimental results show that the proposed approach is capable of achieving interesting MUC7 and CoNNL scores while outperforming two alternatives based on a CRF and a recurrent neural network.", } ### Contributions Thanks to [@KMFODA](https://github.com/KMFODA) for adding this dataset.

persian_ner

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["fa"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "Persian NER", "dataset_info": [{"config_name": "fold1", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "I-event", "2": "I-fac", "3": "I-loc", "4": "I-org", "5": "I-pers", "6": "I-pro", "7": "B-event", "8": "B-fac", "9": "B-loc", "10": "B-org", "11": "B-pers", "12": "B-pro"}}}}], "splits": [{"name": "train", "num_bytes": 3362102, "num_examples": 5121}, {"name": "test", "num_bytes": 1646481, "num_examples": 2560}], "download_size": 1931170, "dataset_size": 5008583}, {"config_name": "fold2", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "I-event", "2": "I-fac", "3": "I-loc", "4": "I-org", "5": "I-pers", "6": "I-pro", "7": "B-event", "8": "B-fac", "9": "B-loc", "10": "B-org", "11": "B-pers", "12": "B-pro"}}}}], "splits": [{"name": "train", "num_bytes": 3344561, "num_examples": 5120}, {"name": "test", "num_bytes": 1664022, "num_examples": 2561}], "download_size": 1931170, "dataset_size": 5008583}, {"config_name": "fold3", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "I-event", "2": "I-fac", "3": "I-loc", "4": "I-org", "5": "I-pers", "6": "I-pro", "7": "B-event", "8": "B-fac", "9": "B-loc", "10": "B-org", "11": "B-pers", "12": "B-pro"}}}}], "splits": [{"name": "train", "num_bytes": 3310491, "num_examples": 5121}, {"name": "test", "num_bytes": 1698092, "num_examples": 2560}], "download_size": 1931170, "dataset_size": 5008583}]}

2024-01-18T11:12:48+00:00

TAGS #task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Persian #license-cc-by-4.0 #region-us

# Dataset Card for [Persian NER] ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: Github - Repository: Github - Paper: Aclweb - Leaderboard: - Point of Contact: ### Dataset Summary The dataset includes 7,682 Persian sentences, split into 250,015 tokens and their NER labels. It is available in 3 folds to be used in turn as training and test sets. The NER tags are in IOB format. ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields - 'id': id of the sample - 'tokens': the tokens of the example text - 'ner_tags': the NER tags of each token The NER tags correspond to this list: ### Data Splits Training and test splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? Hanieh Poostchi, Ehsan Zare Borzeshi, Mohammad Abdous, Massimo Piccardi ### Annotations #### Annotation process #### Who are the annotators? Hanieh Poostchi, Ehsan Zare Borzeshi, Mohammad Abdous, Massimo Piccardi ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information Dataset is published for academic use only ### Dataset Curators ### Licensing Information Creative Commons Attribution 4.0 International License. @inproceedings{poostchi-etal-2016-personer, title = "{P}erso{NER}: {P}ersian Named-Entity Recognition", author = "Poostchi, Hanieh and Zare Borzeshi, Ehsan and Abdous, Mohammad and Piccardi, Massimo", booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers", month = dec, year = "2016", address = "Osaka, Japan", publisher = "The COLING 2016 Organizing Committee", url = "URL pages = "3381--3389", abstract = "Named-Entity Recognition (NER) is still a challenging task for languages with low digital resources. The main difficulties arise from the scarcity of annotated corpora and the consequent problematic training of an effective NER pipeline. To abridge this gap, in this paper we target the Persian language that is spoken by a population of over a hundred million people world-wide. We first present and provide ArmanPerosNERCorpus, the first manually-annotated Persian NER corpus. Then, we introduce PersoNER, an NER pipeline for Persian that leverages a word embedding and a sequential max-margin classifier. The experimental results show that the proposed approach is capable of achieving interesting MUC7 and CoNNL scores while outperforming two alternatives based on a CRF and a recurrent neural network.", } ### Contributions Thanks to @KMFODA for adding this dataset.

4d28bd77e66947ad3835cf78ed7aaeb4dd87ad8b

# Dataset Card for "pg19" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/deepmind/pg19](https://github.com/deepmind/pg19) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [Compressive Transformers for Long-Range Sequence Modelling](https://arxiv.org/abs/1911.05507) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 11.74 GB - **Size of the generated dataset:** 11.51 GB - **Total amount of disk used:** 23.25 GB ### Dataset Summary This repository contains the PG-19 language modeling benchmark. It includes a set of books extracted from the Project Gutenberg books library, that were published before 1919. It also contains metadata of book titles and publication dates. PG-19 is over double the size of the Billion Word benchmark and contains documents that are 20X longer, on average, than the WikiText long-range language modelling benchmark. Books are partitioned into a train, validation, and test set. Book metadata is stored in metadata.csv which contains (book_id, short_book_title, publication_date). Unlike prior benchmarks, we do not constrain the vocabulary size --- i.e. mapping rare words to an UNK token --- but instead release the data as an open-vocabulary benchmark. The only processing of the text that has been applied is the removal of boilerplate license text, and the mapping of offensive discriminatory words as specified by Ofcom to placeholder tokens. Users are free to model the data at the character-level, subword-level, or via any mechanism that can model an arbitrary string of text. To compare models we propose to continue measuring the word-level perplexity, by calculating the total likelihood of the dataset (via any chosen subword vocabulary or character-based scheme) divided by the number of tokens --- specified below in the dataset statistics table. One could use this dataset for benchmarking long-range language models, or use it to pre-train for other natural language processing tasks which require long-range reasoning, such as LAMBADA or NarrativeQA. We would not recommend using this dataset to train a general-purpose language model, e.g. for applications to a production-system dialogue agent, due to the dated linguistic style of old texts and the inherent biases present in historical writing. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 11.74 GB - **Size of the generated dataset:** 11.51 GB - **Total amount of disk used:** 23.25 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "publication_date": 1907, "short_book_title": "La Fiammetta by Giovanni Boccaccio", "text": "\"\\n\\n\\n\\nProduced by Ted Garvin, Dave Morgan and PG Distributed Proofreaders\\n\\n\\n\\n\\nLA FIAMMETTA\\n\\nBY\\n\\nGIOVANNI BOCCACCIO\\n...", "url": "http://www.gutenberg.org/ebooks/10006" } ``` ### Data Fields The data fields are the same among all splits. #### default - `short_book_title`: a `string` feature. - `publication_date`: a `int32` feature. - `url`: a `string` feature. - `text`: a `string` feature. ### Data Splits | name |train|validation|test| |-------|----:|---------:|---:| |default|28602| 50| 100| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information The dataset is licensed under [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0.html). ### Citation Information ``` @article{raecompressive2019, author = {Rae, Jack W and Potapenko, Anna and Jayakumar, Siddhant M and Hillier, Chloe and Lillicrap, Timothy P}, title = {Compressive Transformers for Long-Range Sequence Modelling}, journal = {arXiv preprint}, url = {https://arxiv.org/abs/1911.05507}, year = {2019}, } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@lucidrains](https://github.com/lucidrains), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

pg19

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-generation"], "task_ids": ["language-modeling"], "paperswithcode_id": "pg-19", "pretty_name": "PG-19", "dataset_info": {"features": [{"name": "short_book_title", "dtype": "string"}, {"name": "publication_date", "dtype": "int32"}, {"name": "url", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 11453688452, "num_examples": 28602}, {"name": "validation", "num_bytes": 17402295, "num_examples": 50}, {"name": "test", "num_bytes": 40482852, "num_examples": 100}], "download_size": 11740397875, "dataset_size": 11511573599}}

2024-01-18T11:12:51+00:00

TAGS #task_categories-text-generation #task_ids-language-modeling #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-apache-2.0 #arxiv-1911.05507 #region-us

Dataset Card for "pg19" ======================= Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: Compressive Transformers for Long-Range Sequence Modelling * Point of Contact: * Size of downloaded dataset files: 11.74 GB * Size of the generated dataset: 11.51 GB * Total amount of disk used: 23.25 GB ### Dataset Summary This repository contains the PG-19 language modeling benchmark. It includes a set of books extracted from the Project Gutenberg books library, that were published before 1919. It also contains metadata of book titles and publication dates. PG-19 is over double the size of the Billion Word benchmark and contains documents that are 20X longer, on average, than the WikiText long-range language modelling benchmark. Books are partitioned into a train, validation, and test set. Book metadata is stored in URL which contains (book\_id, short\_book\_title, publication\_date). Unlike prior benchmarks, we do not constrain the vocabulary size --- i.e. mapping rare words to an UNK token --- but instead release the data as an open-vocabulary benchmark. The only processing of the text that has been applied is the removal of boilerplate license text, and the mapping of offensive discriminatory words as specified by Ofcom to placeholder tokens. Users are free to model the data at the character-level, subword-level, or via any mechanism that can model an arbitrary string of text. To compare models we propose to continue measuring the word-level perplexity, by calculating the total likelihood of the dataset (via any chosen subword vocabulary or character-based scheme) divided by the number of tokens --- specified below in the dataset statistics table. One could use this dataset for benchmarking long-range language models, or use it to pre-train for other natural language processing tasks which require long-range reasoning, such as LAMBADA or NarrativeQA. We would not recommend using this dataset to train a general-purpose language model, e.g. for applications to a production-system dialogue agent, due to the dated linguistic style of old texts and the inherent biases present in historical writing. ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### default * Size of downloaded dataset files: 11.74 GB * Size of the generated dataset: 11.51 GB * Total amount of disk used: 23.25 GB An example of 'train' looks as follows. ### Data Fields The data fields are the same among all splits. #### default * 'short\_book\_title': a 'string' feature. * 'publication\_date': a 'int32' feature. * 'url': a 'string' feature. * 'text': a 'string' feature. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information The dataset is licensed under Apache License, Version 2.0. ### Contributions Thanks to @thomwolf, @lewtun, @lucidrains, @lhoestq for adding this dataset.

56e5d2449ef50b39df68ffac3f20b88fc1e25650

# Dataset Card for php ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://opus.nlpl.eu/PHP.php - **Repository:** None - **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf - **Leaderboard:** [More Information Needed] - **Point of Contact:** [More Information Needed] ### Dataset Summary To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs. You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/PHP.php E.g. `dataset = load_dataset("php", lang1="it", lang2="pl")` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances Here are some examples of questions and facts: ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.

php

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["cs", "de", "en", "es", "fi", "fr", "he", "hu", "it", "ja", "ko", "nl", "pl", "pt", "ro", "ru", "sk", "sl", "sv", "tr", "tw", "zh"], "license": ["unknown"], "multilinguality": ["multilingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["translation"], "task_ids": [], "pretty_name": "php", "language_bcp47": ["pt-BR", "zh-TW"], "dataset_info": [{"config_name": "fi-nl", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["fi", "nl"]}}}], "splits": [{"name": "train", "num_bytes": 1197502, "num_examples": 27870}], "download_size": 43228, "dataset_size": 1197502}, {"config_name": "it-ro", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["it", "ro"]}}}], "splits": [{"name": "train", "num_bytes": 1422966, "num_examples": 28507}], "download_size": 108885, "dataset_size": 1422966}, {"config_name": "nl-sv", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["nl", "sv"]}}}], "splits": [{"name": "train", "num_bytes": 1298041, "num_examples": 28079}], "download_size": 58495, "dataset_size": 1298041}, {"config_name": "en-it", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["en", "it"]}}}], "splits": [{"name": "train", "num_bytes": 2758463, "num_examples": 35538}], "download_size": 478646, "dataset_size": 2758463}, {"config_name": "en-fr", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["en", "fr"]}}}], "splits": [{"name": "train", "num_bytes": 4288513, "num_examples": 42222}], "download_size": 905396, "dataset_size": 4288513}]}

2024-01-18T11:12:57+00:00

TAGS #task_categories-translation #annotations_creators-found #language_creators-found #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #language-Czech #language-German #language-English #language-Spanish #language-Finnish #language-French #language-Hebrew #language-Hungarian #language-Italian #language-Japanese #language-Korean #language-Dutch #language-Polish #language-Portuguese #language-Romanian #language-Russian #language-Slovak #language-Slovenian #language-Swedish #language-Turkish #language-Twi #language-Chinese #license-unknown #region-us

# Dataset Card for php ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: None - Paper: URL - Leaderboard: - Point of Contact: ### Dataset Summary To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs. You can find the valid pairs in Homepage section of Dataset Description: URL E.g. 'dataset = load_dataset("php", lang1="it", lang2="pl")' ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances Here are some examples of questions and facts: ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @abhishekkrthakur for adding this dataset.

f7293b384902c44f6919d10851de0ea1b8eaf2f3

# Dataset Card for Piaf ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://piaf.etalab.studio](https://piaf.etalab.studio) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 1.31 MB - **Size of the generated dataset:** 3.18 MB - **Total amount of disk used:** 4.49 MB ### Dataset Summary Piaf is a reading comprehension dataset. This version, published in February 2020, contains 3835 questions on French Wikipedia. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### plain_text - **Size of downloaded dataset files:** 1.31 MB - **Size of the generated dataset:** 3.18 MB - **Total amount of disk used:** 4.49 MB An example of 'train' looks as follows. ``` { "answers": { "answer_start": [0], "text": ["Voici"] }, "context": "Voici le contexte du premier paragraphe du deuxième article.", "id": "p140295460356960", "question": "Suis-je la troisième question ?", "title": "Jakob Böhme" } ``` ### Data Fields The data fields are the same among all splits. #### plain_text - `id`: a `string` feature. - `title`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `answer_start`: a `int32` feature. ### Data Splits | name | train | |------------|------:| | plain_text | 3835 | ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{keraron-EtAl:2020:LREC, author = {Keraron, Rachel and Lancrenon, Guillaume and Bras, Mathilde and Allary, Frédéric and Moyse, Gilles and Scialom, Thomas and Soriano-Morales, Edmundo-Pavel and Staiano, Jacopo}, title = {Project PIAF: Building a Native French Question-Answering Dataset}, booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference}, month = {May}, year = {2020}, address = {Marseille, France}, publisher = {European Language Resources Association}, pages = {5483--5492}, abstract = {Motivated by the lack of data for non-English languages, in particular for the evaluation of downstream tasks such as Question Answering, we present a participatory effort to collect a native French Question Answering Dataset. Furthermore, we describe and publicly release the annotation tool developed for our collection effort, along with the data obtained and preliminary baselines.}, url = {https://www.aclweb.org/anthology/2020.lrec-1.673} } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf), [@albertvillanova](https://github.com/albertvillanova), [@RachelKer](https://github.com/RachelKer) for adding this dataset.

AgentPublic/piaf

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["fr"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["extractive-qa", "open-domain-qa"], "pretty_name": "Piaf", "language_bcp47": ["fr-FR"], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}], "config_name": "plain_text", "splits": [{"name": "train", "num_bytes": 3332905, "num_examples": 3835}], "download_size": 1370384, "dataset_size": 3332905}}

2022-11-03T16:31:15+00:00

TAGS #task_categories-question-answering #task_ids-extractive-qa #task_ids-open-domain-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-French #license-mit #region-us

Dataset Card for Piaf ===================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: * Point of Contact: * Size of downloaded dataset files: 1.31 MB * Size of the generated dataset: 3.18 MB * Total amount of disk used: 4.49 MB ### Dataset Summary Piaf is a reading comprehension dataset. This version, published in February 2020, contains 3835 questions on French Wikipedia. ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### plain\_text * Size of downloaded dataset files: 1.31 MB * Size of the generated dataset: 3.18 MB * Total amount of disk used: 4.49 MB An example of 'train' looks as follows. ### Data Fields The data fields are the same among all splits. #### plain\_text * 'id': a 'string' feature. * 'title': a 'string' feature. * 'context': a 'string' feature. * 'question': a 'string' feature. * 'answers': a dictionary feature containing: + 'text': a 'string' feature. + 'answer\_start': a 'int32' feature. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @lewtun, @lhoestq, @thomwolf, @albertvillanova, @RachelKer for adding this dataset.

1fc7006fd3407a8b2e3ab6520849e4a3554c2bbf

# Dataset Card for CVIT PIB ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://preon.iiit.ac.in/~jerin/bhasha/ - **Paper:** https://arxiv.org/abs/2008.04860 - **Point of Contact:** [Mailing List](cvit-bhasha@googlegroups.com) ### Dataset Summary This dataset is the large scale sentence aligned corpus in 11 Indian languages, viz. CVIT-PIB corpus that is the largest multilingual corpus available for Indian languages. ### Supported Tasks and Leaderboards - Machine Translation ### Languages Parallel data for following languages [en, bn, gu, hi, ml, mr, pa, or, ta, te, ur] are covered. ## Dataset Structure ### Data Instances An example for the "gu-pa" language pair: ``` { 'translation': { 'gu': 'એવો નિર્ણય લેવાયો હતો કે ખંતપૂર્વકની કામગીરી હાથ ધરવા, કાયદેસર અને ટેકનિકલ મૂલ્યાંકન કરવા, વેન્ચર કેપિટલ ઇન્વેસ્ટમેન્ટ સમિતિની બેઠક યોજવા વગેરે એઆઇએફને કરવામાં આવેલ પ્રતિબદ્ધતાના 0.50 ટકા સુધી અને બાકીની રકમ એફએફએસને પૂર્ણ કરવામાં આવશે.', 'pa': 'ਇਹ ਵੀ ਫੈਸਲਾ ਕੀਤਾ ਗਿਆ ਕਿ ਐੱਫਆਈਆਈ ਅਤੇ ਬਕਾਏ ਲਈ ਕੀਤੀਆਂ ਗਈਆਂ ਵਚਨਬੱਧਤਾਵਾਂ ਦੇ 0.50 % ਦੀ ਸੀਮਾ ਤੱਕ ਐੱਫਈਐੱਸ ਨੂੰ ਮਿਲਿਆ ਜਾਏਗਾ, ਇਸ ਨਾਲ ਉੱਦਮ ਪੂੰਜੀ ਨਿਵੇਸ਼ ਕਮੇਟੀ ਦੀ ਬੈਠਕ ਦਾ ਆਯੋਜਨ ਉਚਿਤ ਸਾਵਧਾਨੀ, ਕਾਨੂੰਨੀ ਅਤੇ ਤਕਨੀਕੀ ਮੁੱਲਾਂਕਣ ਲਈ ਸੰਚਾਲਨ ਖਰਚ ਆਦਿ ਦੀ ਪੂਰਤੀ ਹੋਵੇਗੀ।' } } ``` ### Data Fields - `translation`: Translation field containing the parallel text for the pair of languages. ### Data Splits The dataset is in a single "train" split. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [Creative Commons Attribution-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-sa/4.0/) license. ### Citation Information ``` @inproceedings{siripragada-etal-2020-multilingual, title = "A Multilingual Parallel Corpora Collection Effort for {I}ndian Languages", author = "Siripragada, Shashank and Philip, Jerin and Namboodiri, Vinay P. and Jawahar, C V", booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference", month = may, year = "2020", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2020.lrec-1.462", pages = "3743--3751", language = "English", ISBN = "979-10-95546-34-4", } @article{2020, title={Revisiting Low Resource Status of Indian Languages in Machine Translation}, url={http://dx.doi.org/10.1145/3430984.3431026}, DOI={10.1145/3430984.3431026}, journal={8th ACM IKDD CODS and 26th COMAD}, publisher={ACM}, author={Philip, Jerin and Siripragada, Shashank and Namboodiri, Vinay P. and Jawahar, C. V.}, year={2020}, month={Dec} } ``` ### Contributions Thanks to [@vasudevgupta7](https://github.com/vasudevgupta7) for adding this dataset, and [@albertvillanova](https://github.com/albertvillanova) for updating its version.

pib

2022-03-02T23:29:22+00:00

{"annotations_creators": ["no-annotation"], "language_creators": ["other"], "language": ["bn", "en", "gu", "hi", "ml", "mr", "or", "pa", "ta", "te", "ur"], "license": ["cc-by-4.0"], "multilinguality": ["translation"], "size_categories": ["100K<n<1M", "10K<n<100K"], "source_datasets": ["original"], "task_categories": ["translation", "text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"], "pretty_name": "CVIT PIB", "config_names": ["bn-en", "bn-gu", "bn-hi", "bn-ml", "bn-mr", "bn-or", "bn-pa", "bn-ta", "bn-te", "bn-ur", "en-gu", "en-hi", "en-ml", "en-mr", "en-or", "en-pa", "en-ta", "en-te", "en-ur", "gu-hi", "gu-ml", "gu-mr", "gu-or", "gu-pa", "gu-ta", "gu-te", "gu-ur", "hi-ml", "hi-mr", "hi-or", "hi-pa", "hi-ta", "hi-te", "hi-ur", "ml-mr", "ml-or", "ml-pa", "ml-ta", "ml-te", "ml-ur", "mr-or", "mr-pa", "mr-ta", "mr-te", "mr-ur", "or-pa", "or-ta", "or-te", "or-ur", "pa-ta", "pa-te", "pa-ur", "ta-te", "ta-ur", "te-ur"], "dataset_info": [{"config_name": "or-ur", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["or", "ur"]}}}], "splits": [{"name": "train", "num_bytes": 27790211, "num_examples": 43766}], "download_size": 393352875, "dataset_size": 27790211}, {"config_name": "ml-or", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["ml", "or"]}}}], "splits": [{"name": "train", "num_bytes": 16011549, "num_examples": 19413}], "download_size": 393352875, "dataset_size": 16011549}, {"config_name": "bn-ta", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["bn", "ta"]}}}], "splits": [{"name": "train", "num_bytes": 28706668, "num_examples": 33005}], "download_size": 393352875, "dataset_size": 28706668}, {"config_name": "gu-mr", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["gu", "mr"]}}}], "splits": [{"name": "train", "num_bytes": 24253770, "num_examples": 30766}], "download_size": 393352875, "dataset_size": 24253770}, {"config_name": "hi-or", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["hi", "or"]}}}], "splits": [{"name": "train", "num_bytes": 45086618, "num_examples": 61070}], "download_size": 393352875, "dataset_size": 45086618}, {"config_name": "en-or", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["en", "or"]}}}], "splits": [{"name": "train", "num_bytes": 51258494, "num_examples": 98230}], "download_size": 393352875, "dataset_size": 51258494}, {"config_name": "mr-ur", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["mr", "ur"]}}}], "splits": [{"name": "train", "num_bytes": 34053295, "num_examples": 49691}], "download_size": 393352875, "dataset_size": 34053295}, {"config_name": "en-ta", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["en", "ta"]}}}], "splits": [{"name": "train", "num_bytes": 74931542, "num_examples": 118759}], "download_size": 393352875, "dataset_size": 74931542}, {"config_name": "hi-ta", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["hi", "ta"]}}}], "splits": [{"name": "train", "num_bytes": 57628429, "num_examples": 64945}], "download_size": 393352875, "dataset_size": 57628429}, {"config_name": "bn-en", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["bn", "en"]}}}], "splits": [{"name": "train", "num_bytes": 53291968, "num_examples": 93560}], "download_size": 393352875, "dataset_size": 53291968}, {"config_name": "bn-or", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["bn", "or"]}}}], "splits": [{"name": "train", "num_bytes": 19819136, "num_examples": 26456}], "download_size": 393352875, "dataset_size": 19819136}, {"config_name": "ml-ta", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["ml", "ta"]}}}], "splits": [{"name": "train", "num_bytes": 21685938, "num_examples": 23609}], "download_size": 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2024-01-18T11:12:59+00:00

TAGS #task_categories-translation #task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-other #multilinguality-translation #size_categories-100K<n<1M #size_categories-10K<n<100K #source_datasets-original #language-Bengali #language-English #language-Gujarati #language-Hindi #language-Malayalam #language-Marathi #language-Oriya (macrolanguage) #language-Panjabi #language-Tamil #language-Telugu #language-Urdu #license-cc-by-4.0 #arxiv-2008.04860 #region-us

# Dataset Card for CVIT PIB ## Table of Contents - Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Paper: URL - Point of Contact: Mailing List ### Dataset Summary This dataset is the large scale sentence aligned corpus in 11 Indian languages, viz. CVIT-PIB corpus that is the largest multilingual corpus available for Indian languages. ### Supported Tasks and Leaderboards - Machine Translation ### Languages Parallel data for following languages [en, bn, gu, hi, ml, mr, pa, or, ta, te, ur] are covered. ## Dataset Structure ### Data Instances An example for the "gu-pa" language pair: ### Data Fields - 'translation': Translation field containing the parallel text for the pair of languages. ### Data Splits The dataset is in a single "train" split. ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information Creative Commons Attribution-ShareAlike 4.0 International license. ### Contributions Thanks to @vasudevgupta7 for adding this dataset, and @albertvillanova for updating its version.

2e8ac2dffd59bac8c3c6714948f4c551a0848bb0

# Dataset Card for "Physical Interaction: Question Answering" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [PIQA homepage](https://yonatanbisk.com/piqa/) - **Paper:** [PIQA: Reasoning about Physical Commonsense in Natural Language](https://arxiv.org/abs/1911.11641) - **Leaderboard:** [Official leaderboard](https://yonatanbisk.com/piqa/) *Note that there is a [2nd leaderboard](https://leaderboard.allenai.org/physicaliqa) featuring a different (blind) test set with 3,446 examples as part of the Machine Commonsense DARPA project.* - **Point of Contact:** [Yonatan Bisk](https://yonatanbisk.com/piqa/) ### Dataset Summary *To apply eyeshadow without a brush, should I use a cotton swab or a toothpick?* Questions requiring this kind of physical commonsense pose a challenge to state-of-the-art natural language understanding systems. The PIQA dataset introduces the task of physical commonsense reasoning and a corresponding benchmark dataset Physical Interaction: Question Answering or PIQA. Physical commonsense knowledge is a major challenge on the road to true AI-completeness, including robots that interact with the world and understand natural language. PIQA focuses on everyday situations with a preference for atypical solutions. The dataset is inspired by instructables.com, which provides users with instructions on how to build, craft, bake, or manipulate objects using everyday materials. ### Supported Tasks and Leaderboards The underlying task is formualted as multiple choice question answering: given a question `q` and two possible solutions `s1`, `s2`, a model or a human must choose the most appropriate solution, of which exactly one is correct. ### Languages The text in the dataset is in English. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances An example looks like this: ``` { "goal": "How do I ready a guinea pig cage for it's new occupants?", "sol1": "Provide the guinea pig with a cage full of a few inches of bedding made of ripped paper strips, you will also need to supply it with a water bottle and a food dish.", "sol2": "Provide the guinea pig with a cage full of a few inches of bedding made of ripped jeans material, you will also need to supply it with a water bottle and a food dish.", "label": 0, } ``` Note that the test set contains no labels. Predictions need to be submitted to the leaderboard. ### Data Fields List and describe the fields present in the dataset. Mention their data type, and whether they are used as input or output in any of the tasks the dataset currently supports. If the data has span indices, describe their attributes, such as whether they are at the character level or word level, whether they are contiguous or not, etc. If the datasets contains example IDs, state whether they have an inherent meaning, such as a mapping to other datasets or pointing to relationships between data points. - `goal`: the question which requires physical commonsense to be answered correctly - `sol1`: the first solution - `sol2`: the second solution - `label`: the correct solution. `0` refers to `sol1` and `1` refers to `sol2` ### Data Splits The dataset contains 16,000 examples for training, 2,000 for development and 3,000 for testing. ## Dataset Creation ### Curation Rationale The goal of the dataset is to construct a resource that requires concrete physical reasoning. ### Source Data The authors provide a prompt to the annotators derived from instructables.com. The instructables website is a crowdsourced collection of instruc- tions for doing everything from cooking to car repair. In most cases, users provide images or videos detailing each step and a list of tools that will be required. Most goals are simultaneously rare and unsurprising. While an annotator is unlikely to have built a UV-Flourescent steampunk lamp or made a backpack out of duct tape, it is not surprising that someone interested in home crafting would create these, nor will the tools and materials be unfamiliar to the average person. Using these examples as the seed for their annotation, helps remind annotators about the less prototypical uses of everyday objects. Second, and equally important, is that instructions build on one another. This means that any QA pair inspired by an instructable is more likely to explicitly state assumptions about what preconditions need to be met to start the task and what postconditions define success. Annotators were asked to glance at the instructions of an instructable and pull out or have it inspire them to construct two component tasks. They would then articulate the goal (often centered on atypical materials) and how to achieve it. In addition, annotaters were asked to provide a permutation to their own solution which makes it invalid (the negative solution), often subtly. #### Initial Data Collection and Normalization During validation, examples with low agreement were removed from the data. The dataset is further cleaned to remove stylistic artifacts and trivial examples from the data, which have been shown to artificially inflate model performance on previous NLI benchmarks.using the AFLite algorithm introduced in ([Sakaguchi et al. 2020](https://arxiv.org/abs/1907.10641); [Sap et al. 2019](https://arxiv.org/abs/1904.09728)) which is an improvement on adversarial filtering ([Zellers et al, 2018](https://arxiv.org/abs/1808.05326)). #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process Annotations are by construction obtained when crowdsourcers complete the prompt. #### Who are the annotators? Paid crowdsourcers ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Unknown ### Citation Information ``` @inproceedings{Bisk2020, author = {Yonatan Bisk and Rowan Zellers and Ronan Le Bras and Jianfeng Gao and Yejin Choi}, title = {PIQA: Reasoning about Physical Commonsense in Natural Language}, booktitle = {Thirty-Fourth AAAI Conference on Artificial Intelligence}, year = {2020}, } ``` ### Contributions Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset.

piqa

2022-03-02T23:29:22+00:00

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2024-01-18T11:13:02+00:00

TAGS #task_categories-question-answering #task_ids-multiple-choice-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-unknown #arxiv-1911.11641 #arxiv-1907.10641 #arxiv-1904.09728 #arxiv-1808.05326 #region-us

# Dataset Card for "Physical Interaction: Question Answering" ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: PIQA homepage - Paper: PIQA: Reasoning about Physical Commonsense in Natural Language - Leaderboard: Official leaderboard *Note that there is a 2nd leaderboard featuring a different (blind) test set with 3,446 examples as part of the Machine Commonsense DARPA project.* - Point of Contact: Yonatan Bisk ### Dataset Summary *To apply eyeshadow without a brush, should I use a cotton swab or a toothpick?* Questions requiring this kind of physical commonsense pose a challenge to state-of-the-art natural language understanding systems. The PIQA dataset introduces the task of physical commonsense reasoning and a corresponding benchmark dataset Physical Interaction: Question Answering or PIQA. Physical commonsense knowledge is a major challenge on the road to true AI-completeness, including robots that interact with the world and understand natural language. PIQA focuses on everyday situations with a preference for atypical solutions. The dataset is inspired by URL, which provides users with instructions on how to build, craft, bake, or manipulate objects using everyday materials. ### Supported Tasks and Leaderboards The underlying task is formualted as multiple choice question answering: given a question 'q' and two possible solutions 's1', 's2', a model or a human must choose the most appropriate solution, of which exactly one is correct. ### Languages The text in the dataset is in English. The associated BCP-47 code is 'en'. ## Dataset Structure ### Data Instances An example looks like this: Note that the test set contains no labels. Predictions need to be submitted to the leaderboard. ### Data Fields List and describe the fields present in the dataset. Mention their data type, and whether they are used as input or output in any of the tasks the dataset currently supports. If the data has span indices, describe their attributes, such as whether they are at the character level or word level, whether they are contiguous or not, etc. If the datasets contains example IDs, state whether they have an inherent meaning, such as a mapping to other datasets or pointing to relationships between data points. - 'goal': the question which requires physical commonsense to be answered correctly - 'sol1': the first solution - 'sol2': the second solution - 'label': the correct solution. '0' refers to 'sol1' and '1' refers to 'sol2' ### Data Splits The dataset contains 16,000 examples for training, 2,000 for development and 3,000 for testing. ## Dataset Creation ### Curation Rationale The goal of the dataset is to construct a resource that requires concrete physical reasoning. ### Source Data The authors provide a prompt to the annotators derived from URL. The instructables website is a crowdsourced collection of instruc- tions for doing everything from cooking to car repair. In most cases, users provide images or videos detailing each step and a list of tools that will be required. Most goals are simultaneously rare and unsurprising. While an annotator is unlikely to have built a UV-Flourescent steampunk lamp or made a backpack out of duct tape, it is not surprising that someone interested in home crafting would create these, nor will the tools and materials be unfamiliar to the average person. Using these examples as the seed for their annotation, helps remind annotators about the less prototypical uses of everyday objects. Second, and equally important, is that instructions build on one another. This means that any QA pair inspired by an instructable is more likely to explicitly state assumptions about what preconditions need to be met to start the task and what postconditions define success. Annotators were asked to glance at the instructions of an instructable and pull out or have it inspire them to construct two component tasks. They would then articulate the goal (often centered on atypical materials) and how to achieve it. In addition, annotaters were asked to provide a permutation to their own solution which makes it invalid (the negative solution), often subtly. #### Initial Data Collection and Normalization During validation, examples with low agreement were removed from the data. The dataset is further cleaned to remove stylistic artifacts and trivial examples from the data, which have been shown to artificially inflate model performance on previous NLI URL the AFLite algorithm introduced in (Sakaguchi et al. 2020; Sap et al. 2019) which is an improvement on adversarial filtering (Zellers et al, 2018). #### Who are the source language producers? ### Annotations #### Annotation process Annotations are by construction obtained when crowdsourcers complete the prompt. #### Who are the annotators? Paid crowdsourcers ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information Unknown ### Contributions Thanks to @VictorSanh for adding this dataset.

d023c3f4133f08aed2ce57d28469275a93c9166c

# Dataset Card for Persian News Summary (pn_summary) ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** https://github.com/hooshvare/pn-summary/ - **Paper:** https://arxiv.org/abs/2012.11204 - **Leaderboard:** [More Information Needed] - **Point of Contact:** [Mehrdad Farahani](mailto:m3hrdadfphi@gmail.com) ### Dataset Summary A well-structured summarization dataset for the Persian language consists of 93,207 records. It is prepared for Abstractive/Extractive tasks (like cnn_dailymail for English). It can also be used in other scopes like Text Generation, Title Generation, and News Category Classification. It is imperative to consider that the newlines were replaced with the `[n]` symbol. Please interpret them into normal newlines (for ex. `t.replace("[n]", "\n")`) and then use them for your purposes. ### Supported Tasks and Leaderboards The dataset is prepared for Abstractive/Extractive summarization tasks (like cnn_dailymail for English). It can also be used in other scopes like Text Generation, Title Generation, and News Category Classification. ### Languages The dataset covers Persian mostly and somewhere a combination with English. ## Dataset Structure ### Data Instances A record consists of 8 features: ```python record = ['id','title', 'article', 'summary', 'category', 'categories', 'network', 'link'] ``` In the following, you can see an example of `pn_summmary`. ```json { "article": "به گزارش شانا، علی کاردر امروز (۲۷ دی ماه) در مراسم تودیع محسن قمصری، مدیر سابق امور بین الملل شرکت ملی نفت ایران و معارفه سعید خوشرو، مدیر جدید امور بین الملل این شرکت، گفت: مدیریت امور بین\u200eالملل به عنوان یکی از تاثیرگذارترین مدیریت\u200cهای شرکت ملی نفت ایران در دوران تحریم\u200cهای ظالمانه غرب علیه کشورمان بسیار هوشمندانه عمل کرد و ما توانستیم به خوبی از عهده تحریم\u200cها برآییم. [n] وی افزود: مجموعه امور بین الملل در همه دوران\u200cها با سختی\u200cها و مشکلات بسیاری مواجه بوده است، به ویژه در دوره اخیر به دلیل مسائل پیرامون تحریم وظیفه سنگینی بر عهده داشت که با تدبیر مدیریت خوب این مجموعه سربلند از آن بیرون آمد. [n] کاردر با قدردانی از زحمات محسن قمصری، به سلامت مدیریت امور بین الملل این شرکت اشاره کرد و افزود: محوریت کار مدیریت اموربین الملل سلامت مالی بوده است. [n] وی بر ضرورت نهادینه سازی جوانگرایی در مدیریت شرکت ملی نفت ایران تاکید کرد و گفت: مدیریت امور بین الملل در پرورش نیروهای زبده و کارآزموده آنچنان قوی عملکرده است که برای انتخاب مدیر جدید مشکلی وجود نداشت. [n] کاردر، حرفه\u200eای\u200eگری و کار استاندارد را از ویژگی\u200cهای مدیران این مدیریت برشمرد و گفت: نگاه جامع، خلاقیت و نوآوری و بکارگیری نیروهای جوان باید همچنان مد نظر مدیریت جدید امور بین الملل شرکت ملی نفت ایران باشد.", "categories": "نفت", "category": 5, "id": "738e296491f8b24c5aa63e9829fd249fb4428a66", "link": "https://www.shana.ir/news/275284/%D9%85%D8%AF%DB%8C%D8%B1%DB%8C%D8%AA-%D9%81%D8%B1%D9%88%D8%B4-%D9%86%D9%81%D8%AA-%D8%AF%D8%B1-%D8%AF%D9%88%D8%B1%D8%A7%D9%86-%D8%AA%D8%AD%D8%B1%DB%8C%D9%85-%D9%87%D9%88%D8%B4%D9%85%D9%86%D8%AF%D8%A7%D9%86%D9%87-%D8%B9%D9%85%D9%84-%DA%A9%D8%B1%D8%AF", "network": 2, "summary": "مدیرعامل شرکت ملی نفت، عملکرد مدیریت امور بین\u200eالملل این شرکت را در دوران تحریم بسیار هوشمندانه خواند و گفت: امور بین الملل در دوران پس از تحریم\u200eها نیز می\u200cتواند نقش بزرگی در تسریع روند توسعه داشته باشد.", "title": "مدیریت فروش نفت در دوران تحریم هوشمندانه عمل کرد" } ``` ### Data Fields - `id (string)`: ID of the news. - `title (string)`: The title of the news. - `article (string)`: The article of the news. - `summary (string)`: The summary of the news. - `category (int)`: The category of news in English (index of categories), including `Economy`, `Roads-Urban`, `Banking-Insurance`, `Agriculture`, `International`, `Oil-Energy`, `Industry`, `Transportation`, `Science-Technology`, `Local`, `Sports`, `Politics`, `Art-Culture`, `Society`, `Health`, `Research`, `Education-University`, `Tourism`. - `categories (string)`: The category and sub-category of the news in Persian. - `network (int)`: The news agency name (index of news agencies), including `Tahlilbazaar`, `Imna`, `Shana`, `Mehr`, `Irna`, `Khabaronline`. - `link (string)`: The link of the news. The category in English includes 18 different article categories from economy to tourism. ```bash Economy, Roads-Urban, Banking-Insurance, Agriculture, International, Oil-Energy, Industry, Transportation, Science-Technology, Local, Sports, Politics, Art-Culture, Society, Health, Research, Education-University, Tourism ``` ### Data Splits Training (82,022 records, 8 features), validation (5,592 records, 8 features), and test split (5,593 records and 8 features). ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? The dataset comprises numerous articles of various categories that have been crawled from six news agency websites (Tahlilbazaar, Imna, Shana, Mehr, Irna, and Khabaronline). ### Annotations #### Annotation process Each record (article) includes the long original text as well as a human-generated summary. The total number of cleaned articles is 93,207 (from 200,000 crawled articles). #### Who are the annotators? The dataset was organized by [Mehrdad Farahani](https://github.com/m3hrdadfi), [Mohammad Gharachorloo](https://github.com/baarsaam) and [Mohammad Manthouri](https://github.com/mmanthouri) for this paper [Leveraging ParsBERT and Pretrained mT5 for Persian Abstractive Text Summarization](https://arxiv.org/abs/2012.11204) ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators This dataset was curated by [Mehrdad Farahani](https://github.com/m3hrdadfi), [Mohammad Gharachorloo](https://github.com/baarsaam) and [Mohammad Manthouri](https://github.com/mmanthouri). ### Licensing Information This dataset is licensed under MIT License. ### Citation Information ```bibtex @article{pnSummary, title={Leveraging ParsBERT and Pretrained mT5 for Persian Abstractive Text Summarization}, author={Mehrdad Farahani, Mohammad Gharachorloo, Mohammad Manthouri}, year={2020}, eprint={2012.11204}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@m3hrdadfi](https://github.com/m3hrdadfi) for adding this dataset.

pn_summary

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["fa"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["summarization", "text-classification"], "task_ids": ["news-articles-summarization", "news-articles-headline-generation", "text-simplification", "topic-classification"], "paperswithcode_id": "pn-summary", "pretty_name": "Persian News Summary (PnSummary)", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "article", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "category", "dtype": {"class_label": {"names": {"0": "Economy", "1": "Roads-Urban", "2": "Banking-Insurance", "3": "Agriculture", "4": "International", "5": "Oil-Energy", "6": "Industry", "7": "Transportation", "8": "Science-Technology", "9": "Local", "10": "Sports", "11": "Politics", "12": "Art-Culture", "13": "Society", "14": "Health", "15": "Research", "16": "Education-University", "17": "Tourism"}}}}, {"name": "categories", "dtype": "string"}, {"name": "network", "dtype": {"class_label": {"names": {"0": "Tahlilbazaar", "1": "Imna", "2": "Shana", "3": "Mehr", "4": "Irna", "5": "Khabaronline"}}}}, {"name": "link", "dtype": "string"}], "config_name": "1.0.0", "splits": [{"name": "train", "num_bytes": 309436493, "num_examples": 82022}, {"name": "validation", "num_bytes": 21311817, "num_examples": 5592}, {"name": "test", "num_bytes": 20936820, "num_examples": 5593}], "download_size": 89591141, "dataset_size": 351685130}}

2024-01-18T11:13:04+00:00

TAGS #task_categories-summarization #task_categories-text-classification #task_ids-news-articles-summarization #task_ids-news-articles-headline-generation #task_ids-text-simplification #task_ids-topic-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Persian #license-mit #arxiv-2012.11204 #region-us

# Dataset Card for Persian News Summary (pn_summary) ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Repository: URL - Paper: URL - Leaderboard: - Point of Contact: Mehrdad Farahani ### Dataset Summary A well-structured summarization dataset for the Persian language consists of 93,207 records. It is prepared for Abstractive/Extractive tasks (like cnn_dailymail for English). It can also be used in other scopes like Text Generation, Title Generation, and News Category Classification. It is imperative to consider that the newlines were replaced with the '[n]' symbol. Please interpret them into normal newlines (for ex. 't.replace("[n]", "\n")') and then use them for your purposes. ### Supported Tasks and Leaderboards The dataset is prepared for Abstractive/Extractive summarization tasks (like cnn_dailymail for English). It can also be used in other scopes like Text Generation, Title Generation, and News Category Classification. ### Languages The dataset covers Persian mostly and somewhere a combination with English. ## Dataset Structure ### Data Instances A record consists of 8 features: In the following, you can see an example of 'pn_summmary'. ### Data Fields - 'id (string)': ID of the news. - 'title (string)': The title of the news. - 'article (string)': The article of the news. - 'summary (string)': The summary of the news. - 'category (int)': The category of news in English (index of categories), including 'Economy', 'Roads-Urban', 'Banking-Insurance', 'Agriculture', 'International', 'Oil-Energy', 'Industry', 'Transportation', 'Science-Technology', 'Local', 'Sports', 'Politics', 'Art-Culture', 'Society', 'Health', 'Research', 'Education-University', 'Tourism'. - 'categories (string)': The category and sub-category of the news in Persian. - 'network (int)': The news agency name (index of news agencies), including 'Tahlilbazaar', 'Imna', 'Shana', 'Mehr', 'Irna', 'Khabaronline'. - 'link (string)': The link of the news. The category in English includes 18 different article categories from economy to tourism. ### Data Splits Training (82,022 records, 8 features), validation (5,592 records, 8 features), and test split (5,593 records and 8 features). ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? The dataset comprises numerous articles of various categories that have been crawled from six news agency websites (Tahlilbazaar, Imna, Shana, Mehr, Irna, and Khabaronline). ### Annotations #### Annotation process Each record (article) includes the long original text as well as a human-generated summary. The total number of cleaned articles is 93,207 (from 200,000 crawled articles). #### Who are the annotators? The dataset was organized by Mehrdad Farahani, Mohammad Gharachorloo and Mohammad Manthouri for this paper Leveraging ParsBERT and Pretrained mT5 for Persian Abstractive Text Summarization ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators This dataset was curated by Mehrdad Farahani, Mohammad Gharachorloo and Mohammad Manthouri. ### Licensing Information This dataset is licensed under MIT License. ### Contributions Thanks to @m3hrdadfi for adding this dataset.

329d529d875a00c47ec71954a1a96ae167584770

# Dataset Card for Gutenberg Poem Dataset ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** N/A - **Repository:** [GitHub](https://github.com/google-research-datasets/poem-sentiment) - **Paper:** [Investigating Societal Biases in a Poetry Composition System](https://arxiv.org/abs/2011.02686) - **Leaderboard:** N/A - **Point of Contact:** - ### Dataset Summary Poem Sentiment is a sentiment dataset of poem verses from Project Gutenberg. This dataset can be used for tasks such as sentiment classification or style transfer for poems. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The text in the dataset is in English (`en`). ## Dataset Structure ### Data Instances Example of one instance in the dataset. ```{'id': 0, 'label': 2, 'verse_text': 'with pale blue berries. in these peaceful shades--'}``` ### Data Fields - `id`: index of the example - `verse_text`: The text of the poem verse - `label`: The sentiment label. Here - 0 = negative - 1 = positive - 2 = no impact - 3 = mixed (both negative and positive) > Note: The original dataset uses different label indices (negative = -1, no impact = 0, positive = 1) ### Data Splits The dataset is split into a `train`, `validation`, and `test` split with the following sizes: | | train | validation | test | |--------------------|------:|-----------:|-----:| | Number of examples | 892 | 105 | 104 | [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information This work is licensed under a Creative Commons Attribution 4.0 International License ### Citation Information ``` @misc{sheng2020investigating, title={Investigating Societal Biases in a Poetry Composition System}, author={Emily Sheng and David Uthus}, year={2020}, eprint={2011.02686}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.

poem_sentiment

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "paperswithcode_id": "gutenberg-poem-dataset", "pretty_name": "Gutenberg Poem Dataset", "dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "verse_text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "negative", "1": "positive", "2": "no_impact"}}}}], "splits": [{"name": "train", "num_bytes": 48555, "num_examples": 892}, {"name": "validation", "num_bytes": 5788, "num_examples": 105}, {"name": "test", "num_bytes": 5588, "num_examples": 104}], "download_size": 49870, "dataset_size": 59931}, "train-eval-index": [{"config": "default", "task": "text-classification", "task_id": "multi_class_classification", "splits": {"train_split": "train", "eval_split": "test"}, "col_mapping": {"verse_text": "text", "label": "target"}, "metrics": [{"type": "accuracy", "name": "Accuracy"}, {"type": "f1", "name": "F1 macro", "args": {"average": "macro"}}, {"type": "f1", "name": "F1 micro", "args": {"average": "micro"}}, {"type": "f1", "name": "F1 weighted", "args": {"average": "weighted"}}, {"type": "precision", "name": "Precision macro", "args": {"average": "macro"}}, {"type": "precision", "name": "Precision micro", "args": {"average": "micro"}}, {"type": "precision", "name": "Precision weighted", "args": {"average": "weighted"}}, {"type": "recall", "name": "Recall macro", "args": {"average": "macro"}}, {"type": "recall", "name": "Recall micro", "args": {"average": "micro"}}, {"type": "recall", "name": "Recall weighted", "args": {"average": "weighted"}}]}]}

2024-01-18T11:13:06+00:00

TAGS #task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc-by-4.0 #arxiv-2011.02686 #region-us

Dataset Card for Gutenberg Poem Dataset ======================================= Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: N/A * Repository: GitHub * Paper: Investigating Societal Biases in a Poetry Composition System * Leaderboard: N/A * Point of Contact: - ### Dataset Summary Poem Sentiment is a sentiment dataset of poem verses from Project Gutenberg. This dataset can be used for tasks such as sentiment classification or style transfer for poems. ### Supported Tasks and Leaderboards ### Languages The text in the dataset is in English ('en'). Dataset Structure ----------------- ### Data Instances Example of one instance in the dataset. ### Data Fields * 'id': index of the example * 'verse\_text': The text of the poem verse * 'label': The sentiment label. Here + 0 = negative + 1 = positive + 2 = no impact + 3 = mixed (both negative and positive) > > Note: The original dataset uses different label indices (negative = -1, no impact = 0, positive = 1) > > > ### Data Splits The dataset is split into a 'train', 'validation', and 'test' split with the following sizes: Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information This work is licensed under a Creative Commons Attribution 4.0 International License ### Contributions Thanks to @patil-suraj for adding this dataset.

ecd39c467892f767733c9368ccd2148c2094f472

# Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://clarin-pl.eu/dspace/handle/11321/710 - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The PolEmo2.0 is a set of online reviews from medicine and hotels domains. The task is to predict the sentiment of a review. There are two separate test sets, to allow for in-domain (medicine and hotels) as well as out-of-domain (products and university) validation. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Polish ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - sentence: string, the review - target: sentiment of the sentence class The same tag system is used in plWordNet Emo for lexical units: [+m] (strong positive), [+s] (weak positive), [-m] (strong negative), [-s] (weak negative), [amb] (ambiguous) and [0] (neutral). Note that the test set doesn't have targets so -1 is used instead ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information CC BY-NC-SA 4.0 ### Citation Information [More Information Needed] ### Contributions Thanks to [@abecadel](https://github.com/abecadel) for adding this dataset.

polemo2

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["other"], "language": ["pl"], "license": ["bsd-3-clause"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "pretty_name": "polemo2", "dataset_info": [{"config_name": "in", "features": [{"name": "sentence", "dtype": "string"}, {"name": "target", "dtype": {"class_label": {"names": {"0": "__label__meta_amb", "1": "__label__meta_minus_m", "2": "__label__meta_plus_m", "3": "__label__meta_zero"}}}}], "splits": [{"name": "train", "num_bytes": 4810215, "num_examples": 5783}, {"name": "test", "num_bytes": 582052, "num_examples": 722}, {"name": "validation", "num_bytes": 593530, "num_examples": 723}], "download_size": 2350339, "dataset_size": 5985797}, {"config_name": "out", "features": [{"name": "sentence", "dtype": "string"}, {"name": "target", "dtype": {"class_label": {"names": {"0": "__label__meta_amb", "1": "__label__meta_minus_m", "2": "__label__meta_plus_m", "3": "__label__meta_zero"}}}}], "splits": [{"name": "train", "num_bytes": 4810215, "num_examples": 5783}, {"name": "test", "num_bytes": 309790, "num_examples": 494}, {"name": "validation", "num_bytes": 310977, "num_examples": 494}], "download_size": 2139891, "dataset_size": 5430982}]}

2024-01-18T11:13:10+00:00

TAGS #task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-expert-generated #language_creators-other #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Polish #license-bsd-3-clause #region-us

# Dataset Card for [Dataset Name] ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary The PolEmo2.0 is a set of online reviews from medicine and hotels domains. The task is to predict the sentiment of a review. There are two separate test sets, to allow for in-domain (medicine and hotels) as well as out-of-domain (products and university) validation. ### Supported Tasks and Leaderboards ### Languages Polish ## Dataset Structure ### Data Instances ### Data Fields - sentence: string, the review - target: sentiment of the sentence class The same tag system is used in plWordNet Emo for lexical units: [+m] (strong positive), [+s] (weak positive), [-m] (strong negative), [-s] (weak negative), [amb] (ambiguous) and [0] (neutral). Note that the test set doesn't have targets so -1 is used instead ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators ### Licensing Information CC BY-NC-SA 4.0 ### Contributions Thanks to @abecadel for adding this dataset.

b40df4e1692cea9b66e2e717fd993315b309967e

# Dataset Card for Poleval 2019 cyberbullying ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://2019.poleval.pl/index.php/tasks/task6 - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary Task 6-1: Harmful vs non-harmful In this task, the participants are to distinguish between normal/non-harmful tweets (class: 0) and tweets that contain any kind of harmful information (class: 1). This includes cyberbullying, hate speech and related phenomena. The data for the task is available now and can be downloaded from the link provided below. Task 6-2: Type of harmfulness In this task, the participants shall distinguish between three classes of tweets: 0 (non-harmful), 1 (cyberbullying), 2 (hate-speech). There are various definitions of both cyberbullying and hate-speech, some of them even putting those two phenomena in the same group. The specific conditions on which we based our annotations for both cyberbullying and hate-speech, which have been worked out during ten years of research will be summarized in an introductory paper for the task, however, the main and definitive condition to distinguish the two is whether the harmful action is addressed towards a private person(s) (cyberbullying), or a public person/entity/large group (hate-speech). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Polish ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - text: the provided tweet - label: for task 6-1 the label can be 0 (non-harmful) or 1 (harmful) for task 6-2 the label can be 0 (non-harmful), 1 (cyberbullying) or 2 (hate-speech) ### Data Splits Train and Test ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @proceedings{ogr:kob:19:poleval, editor = {Maciej Ogrodniczuk and Łukasz Kobyliński}, title = {{Proceedings of the PolEval 2019 Workshop}}, year = {2019}, address = {Warsaw, Poland}, publisher = {Institute of Computer Science, Polish Academy of Sciences}, url = {http://2019.poleval.pl/files/poleval2019.pdf}, isbn = "978-83-63159-28-3"} } ``` ### Contributions Thanks to [@czabo](https://github.com/czabo) for adding this dataset.

poleval2019_cyberbullying

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["pl"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["intent-classification"], "pretty_name": "Poleval 2019 cyberbullying", "dataset_info": [{"config_name": "task01", "features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}], "splits": [{"name": "train", "num_bytes": 1104322, "num_examples": 10041}, {"name": "test", "num_bytes": 109681, "num_examples": 1000}], "download_size": 410001, "dataset_size": 1214003}, {"config_name": "task02", "features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "0", "1": "1", "2": "2"}}}}], "splits": [{"name": "train", "num_bytes": 1104322, "num_examples": 10041}, {"name": "test", "num_bytes": 109681, "num_examples": 1000}], "download_size": 410147, "dataset_size": 1214003}]}

2024-01-18T11:13:15+00:00

TAGS #task_categories-text-classification #task_ids-intent-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Polish #license-unknown #region-us

# Dataset Card for Poleval 2019 cyberbullying ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary Task 6-1: Harmful vs non-harmful In this task, the participants are to distinguish between normal/non-harmful tweets (class: 0) and tweets that contain any kind of harmful information (class: 1). This includes cyberbullying, hate speech and related phenomena. The data for the task is available now and can be downloaded from the link provided below. Task 6-2: Type of harmfulness In this task, the participants shall distinguish between three classes of tweets: 0 (non-harmful), 1 (cyberbullying), 2 (hate-speech). There are various definitions of both cyberbullying and hate-speech, some of them even putting those two phenomena in the same group. The specific conditions on which we based our annotations for both cyberbullying and hate-speech, which have been worked out during ten years of research will be summarized in an introductory paper for the task, however, the main and definitive condition to distinguish the two is whether the harmful action is addressed towards a private person(s) (cyberbullying), or a public person/entity/large group (hate-speech). ### Supported Tasks and Leaderboards ### Languages Polish ## Dataset Structure ### Data Instances ### Data Fields - text: the provided tweet - label: for task 6-1 the label can be 0 (non-harmful) or 1 (harmful) for task 6-2 the label can be 0 (non-harmful), 1 (cyberbullying) or 2 (hate-speech) ### Data Splits Train and Test ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @czabo for adding this dataset.

94f575cbc7ba1bc13753eadf956280f7375086a2

# Dataset Card for poleval2019_mt ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** PolEval-2019 competition. http://2019.poleval.pl/ - **Repository:** Links available [in this page](http://2019.poleval.pl/index.php/tasks/task4) - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary PolEval is a SemEval-inspired evaluation campaign for natural language processing tools for Polish. Submitted solutions compete against one another within certain tasks selected by organizers, using available data and are evaluated according to pre-established procedures. One of the tasks in PolEval-2019 was Machine Translation (Task-4). The task is to train as good as possible machine translation system, using any technology,with limited textual resources. The competition will be done for 2 language pairs, more popular English-Polish (into Polish direction) and pair that can be called low resourced Russian-Polish (in both directions). Here, Polish-English is also made available to allow for training in both directions. However, the test data is ONLY available for English-Polish ### Supported Tasks and Leaderboards Supports Machine Translation between Russian to Polish and English to Polish (and vice versa). ### Languages - Polish (pl) - Russian (ru) - English (en) ## Dataset Structure ### Data Instances As the training data set, a set of bi-lingual corpora aligned at the sentence level has been prepared. The corpora are saved in UTF-8 encoding as plain text, one language per file. ### Data Fields One example of the translation is as below: ``` { 'translation': {'ru': 'не содержала в себе моделей. Модели это сравнительно новое явление. ', 'pl': 'nie miała w sobie modeli. Modele to względnie nowa dziedzina. Tak więc, jeśli '} } ``` ### Data Splits The dataset is divided into two splits. All the headlines are scraped from news websites on the internet. | | train | validation | test | |-------|-------:|-----------:|-----:| | ru-pl | 20001 | 3001 | 2969 | | pl-ru | 20001 | 3001 | 2969 | | en-pl | 129255 | 1000 | 9845 | ## Dataset Creation ### Curation Rationale This data was curated as a task for the PolEval-2019. The task is to train as good as possible machine translation system, using any technology, with limited textual resources. The competition will be done for 2 language pairs, more popular English-Polish (into Polish direction) and pair that can be called low resourced Russian-Polish (in both directions). PolEval is a SemEval-inspired evaluation campaign for natural language processing tools for Polish. Submitted tools compete against one another within certain tasks selected by organizers, using available data and are evaluated according to pre-established procedures. PolEval 2019-related papers were presented at AI & NLP Workshop Day (Warsaw, May 31, 2019). The links for the top performing models on various tasks (including the Task-4: Machine Translation) is present in [this](http://2019.poleval.pl/index.php/publication) link ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? The organization details of PolEval is present in this [link](http://2019.poleval.pl/index.php/organizers) ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @proceedings{ogr:kob:19:poleval, editor = {Maciej Ogrodniczuk and Łukasz Kobyliński}, title = {{Proceedings of the PolEval 2019 Workshop}}, year = {2019}, address = {Warsaw, Poland}, publisher = {Institute of Computer Science, Polish Academy of Sciences}, url = {http://2019.poleval.pl/files/poleval2019.pdf}, isbn = "978-83-63159-28-3"} } ``` ### Contributions Thanks to [@vrindaprabhu](https://github.com/vrindaprabhu) for adding this dataset.

poleval2019_mt

2022-03-02T23:29:22+00:00

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2024-01-18T11:13:18+00:00

TAGS #task_categories-translation #annotations_creators-no-annotation #language_creators-expert-generated #language_creators-found #multilinguality-translation #size_categories-10K<n<100K #source_datasets-original #language-English #language-Polish #language-Russian #license-unknown #region-us

Dataset Card for poleval2019\_mt ================================ Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: PolEval-2019 competition. URL * Repository: Links available in this page * Paper: * Leaderboard: * Point of Contact: ### Dataset Summary PolEval is a SemEval-inspired evaluation campaign for natural language processing tools for Polish. Submitted solutions compete against one another within certain tasks selected by organizers, using available data and are evaluated according to pre-established procedures. One of the tasks in PolEval-2019 was Machine Translation (Task-4). The task is to train as good as possible machine translation system, using any technology,with limited textual resources. The competition will be done for 2 language pairs, more popular English-Polish (into Polish direction) and pair that can be called low resourced Russian-Polish (in both directions). Here, Polish-English is also made available to allow for training in both directions. However, the test data is ONLY available for English-Polish ### Supported Tasks and Leaderboards Supports Machine Translation between Russian to Polish and English to Polish (and vice versa). ### Languages * Polish (pl) * Russian (ru) * English (en) Dataset Structure ----------------- ### Data Instances As the training data set, a set of bi-lingual corpora aligned at the sentence level has been prepared. The corpora are saved in UTF-8 encoding as plain text, one language per file. ### Data Fields One example of the translation is as below: ### Data Splits The dataset is divided into two splits. All the headlines are scraped from news websites on the internet. Dataset Creation ---------------- ### Curation Rationale This data was curated as a task for the PolEval-2019. The task is to train as good as possible machine translation system, using any technology, with limited textual resources. The competition will be done for 2 language pairs, more popular English-Polish (into Polish direction) and pair that can be called low resourced Russian-Polish (in both directions). PolEval is a SemEval-inspired evaluation campaign for natural language processing tools for Polish. Submitted tools compete against one another within certain tasks selected by organizers, using available data and are evaluated according to pre-established procedures. PolEval 2019-related papers were presented at AI & NLP Workshop Day (Warsaw, May 31, 2019). The links for the top performing models on various tasks (including the Task-4: Machine Translation) is present in this link ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? The organization details of PolEval is present in this link ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @vrindaprabhu for adding this dataset.

2309a67b9eaf63526a6784ae7024d5bbaf396867

# Dataset Card for Polish Summaries Corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://zil.ipipan.waw.pl/PolishSummariesCorpus - **Repository:** http://zil.ipipan.waw.pl/PolishSummariesCorpus - **Paper:** http://nlp.ipipan.waw.pl/Bib/ogro:kop:14:lrec.pdf - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Mateusz Kopeć](http://zil.ipipan.waw.pl/MateuszKopec) ### Dataset Summary The Corpus contains a large number of manual summaries of news articles, with many independently created summaries for a single text. Such approach is supposed to overcome the annotator bias, which is often described as a problem during the evaluation of the summarization algorithms against a single gold standard. ### Supported Tasks and Leaderboards [Needs More Information] ### Languages Polish ## Dataset Structure ### Data Instances See below an example from the dataset. Detailed descriptions of the fields are provided in the following section. ``` {'authors': 'Krystyna Forowicz', 'body': "ROZMOWA\n\nProf. Krzysztof Ernst, kierownik Zakładu Optyki Instytutu Fizyki Doświadczalnej Uniwersytetu Warszawskiego\n\nLidarowe oczy\n\nRYS. MAREK KONECKI\n\nJutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar? \n\nPROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym.\n\nCzy to kosztowne urządzenie będzie służyło tylko naukowcom?\n\nTego typu lidar jest rzeczywiście drogi, kosztuje około miliona marek niemieckich. Jest to najnowsza generacja tego typu lidarów. DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, korzyść naukową - rozwijamy badania nad tym urządzeniem, staramy się m.in. rozszerzyć jego zastosowanie także na inne substancje występujące w atmosferze. I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska. Nad lidarem pracują specjaliści od laserów i od komputerów. Współpracujemy z doskonałym laboratorium prof. Ludgera Wöste z Freie Universitat Berlin rozwijającym m.in. problematykę lidarową. Pakiet software'u wzbogacamy o nowe algorytmy, które potrafią lepiej i dokładniej rozszyfrowywać sygnał lidarowy, a w konsekwencji skażenia. Żeby przetworzyć tzw. sygnał lidarowy, czyli to co wraca po rozproszeniu światła do układu, i otrzymać rozsądne dane dotyczące rozkładu koncentracji - trzeba dokonać skomplikowanych operacji. \n\nBadania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej, dzięki której ten lidar u nas zaistniał i dla której w ramach naszych zobowiązań wykonujemy pomiary zanieczyszczeń nad naszą wspólną granicą. Zasadniczy koszt jego budowy pokryła uzyskana od Fundacji dotacja. Część pieniędzy przekazał też Narodowy Fundusz Ochrony Środowiska i Gospodarki Wodnej oraz Komitet Badań Naukowych.\n\nCzy wszystkie zanieczyszczenia będzie można wykryć za pomocą lidaru?\n\nNie ma takiego jednostkowego urządzenia, które by wykrywało i mierzyło wszystkie szkodliwe gazy w atmosferze łącznie z dostarczeniem informacji o ich rozkładzie. Ale np. obecnie prowadzimy badania mające na celu rozszerzenie możliwości lidaru o taką substancję jak fosgen. Tym szkodliwym gazem może być skażone powietrze w miastach, w których zlokalizowane są zakłady chemiczne, np. w Bydgoszczy pewne ilości fosgenu emitują Zakłady Chemiczne Organika- Zachem. \n\nLidar typu DIAL jest oparty na pomiarze absorbcji różnicowej, czyli muszą być zastosowane dwie wiązki laserowe o dwóch różnych długościach fali, z których jedna jest absorbowana, a druga nie jest absorbowana przez substancję, którą chcemy wykryć. Cząsteczki, które wykrywamy mają pasma absorbcji w bliskim nadfiolecie. Możemy np. badać zawartość ozonu w troposferze. Okazuje się bowiem, że o ile brak tego gazu w wysokich warstwach atmosfery powoduje groźny efekt cieplarniany, to jego nadmiar tuż nad Ziemią jest szkodliwy. Groźne są też substancje gazowe, jak np. tlenki azotu, będące następstwem spalin samochodowych. A samochodów przybywa.\n\nCzy stać nas będzie na prowadzenie pomiarów ozonu w miastach? \n\nKoszt jednego dnia kampanii pomiarowej firmy zachodnie szacują na kilka tysięcy DM. Potrzebne są pieniądze na utrzymanie lidaru, na prowadzenie badań. Nasze przedsięwzięcie nie ma charakteru komercyjnego. Koszt pomiarów będzie znacznie niższy. Chcemy np. mierzyć w Warszawie rozkłady koncentracji tlenków azotu, ich ewolucję czasową nad różnymi arteriami miasta. Chcielibyśmy rozwinąć tutaj współpracę z państwowymi i wojewódzkimi służbami ochrony środowiska. Tego typu badania były prowadzone np. w Lyonie. Okazało się, że najwięcej tlenków azotu występuje niekoniecznie tam gdzie są one produkowane, to znaczy nie przy najruchliwszych ulicach, jeśli są one dobrze wentylowane a gromadzą się one w małych uliczkach. Przede wszystkim jednak do końca tego roku zamierzamy zakończyć pomiary skażeń atmosferycznych nad granicą polsko-niemiecką. Koncentrujemy się głównie na Czarnym Trójkącie - obszarze u zbiegu trzech granic: Polski, Niemiec i Czech, do niedawna uważanym za najbardziej zdegradowany region w Europie. Prowadziliśmy pomiary w samym Turowie, gdzie elektrownia Turoszowska jest głównym źródłem emisji. W planie mamy Bogatynię, zagłębie miedziowe. \n\nW Czarnym Trójkącie istnieje wiele stacjonarnych stacji monitoringowych.\n\nNasz lidar ma większe możliwości niż stacje monitoringowe. Mierzy zanieczyszczenia nie tylko lokalnie, ale też ich rozkład w przestrzeni, z wysoką rozdzielczością przestrzenną i na odległość kilku kilometrów. Możemy zatem śledzić ewolucję rozprzestrzeniania się tych zanieczyszczeń, ich kierunek i zmiany spowodowane m.in. warunkami atmosferycznymi. Wyniki naszych pomiarów porównujemy z danymi uzyskanymi ze stacji monitoringowych. \n\nJak wypadł Czarny Trójkąt?\n\nKiedy występowaliśmy o finansowanie tego projektu do Fundacji Współpracy Polsko-Niemieckiej zanieczyszczenie powietrza w Czarnym Trójkącie było dużo większe niż obecnie i wszystko wskazuje na to, że będzie dalej spadać. Obecnie stężenie dwutlenku siarki jest na granicy naszych możliwości pomiarowych. Dla regionu Turoszowskiego to dobra wiadomość i dla stosunków polsko-niemieckich też.\n\nTypów lidarów jest wiele \n\nTen lidar pracuje w obszarze bliskiego nadfioletu i promieniowania widzialnego, które jest wynikiem wykorzystania drugiej lub trzeciej harmonicznej lasera szafirowego, pracującego na granicy czerwieni i podczerwieni. DIAL jest tym typem lidara, który dzisiaj ma zdecydowanie największe wzięcie w ochronie środowiska. Z lidarów korzysta meteorologia. W Stanach Zjednoczonych lidary umieszcza się na satelitach (program NASA). Określają na przestrzeni kilkudziesięciu kilometrów rozkłady temperatury, wilgotności, ciśnienia, a także prędkości wiatru. Wykrywają pojawianie się huraganów, a nawet mogą określać rozmiary oka tajfunu.\n\nIle takich urządzeń jest w Europie?\n\n- W Europie takich lidarów jak nasz jest zaledwie kilka. Większość z nich mierzy ozon, dwutlenek siarki i tlenek azotu. Wykrywanie toluenu i benzenu jest oryginalnym rozwiązaniem. Długość fali dla benzenu jest już na skraju możliwości widmowych. Nasz lidar typu DIAL jest najnowocześniejszym w Polsce. Ponadto jest lidarem ruchomym, zainstalowanym na samochodzie. Ale historia lidarów w naszym kraju jest dłuższa i zaczęła się na początku lat 60. Pierwsze próby prowadzone były w stacji geofizycznej PAN w Belsku, niedługo po skonstruowaniu pierwszego w świecie lasera rubinowego. Potem powstał lidar stacjonarny, również typu DIAL, w Gdańsku, a w Krakowie sodary - urządzenia oparte na falach akustycznych, wygodne np. do pomiarów szybkości wiatru. Lidar umieszczony na samochodzie i zbudowany w latach 80 na Politechnice Poznańskiej w perspektywie miał być lidarem typu DIAL.\n\nFizycy dotychczas nie zajmowali się ochroną środowiska?\n\nTaka specjalizacja powstala na Wydziale Fizyki UW dwa lata temu. Gwarancją sukcesu naszego programu dydaktyczno-badawczego jest udział w nim zakładów należących do Instytutu Fizyki Doświadczalnej UW, Pracowni Przetwarzania Informacji (zdjęć satelitarnych) Instytutu Geofizyki i, co bardzo ważne, współpraca z Freie Universität Berlin. Mamy również na UW Międzywydziałowe Studia Ochrony Środowiska i studentom przekazujemy informacje o lidarze i fizycznych metodach badania środowiska. Nasze działania dydaktyczne bardzo efektywnie wspiera NFOŚ.\n\nRozmawiała Krystyna Forowicz", 'date': '1997-04-21', 'id': '199704210011', 'section': 'Nauka i Technika', 'summaries': {'author': ['I', 'I', 'I', 'C', 'C', 'C', 'K', 'K', 'K', 'G', 'G', 'G', 'J', 'J', 'J'], 'body': ['Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar?PROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym.Czy to kosztowne urządzenie będzie służyło tylko naukowcom? Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, naukową - rozwijamy badania nad tym urządzeniem. I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska. Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej, dzięki której ten lidar u nas zaistniał i dla której w ramach naszych zobowiązań wykonujemy pomiary zanieczyszczeń nad naszą wspólną granicą.', 'Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar?PROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym.Czy to kosztowne urządzenie będzie służyło tylko naukowcom? Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, naukową - rozwijamy badania nad tym urządzeniem. I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska. Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej, dzięki której ten lidar u nas zaistniał i dla której w ramach naszych zobowiązań wykonujemy pomiary zanieczyszczeń nad naszą wspólną granicą. Czy wszystkie zanieczyszczenia będzie można wykryć za pomocą lidaru?Nie ma takiego jednostkowego urządzenia, które by wykrywało i mierzyło wszystkie szkodliwe gazy w atmosferze łącznie z dostarczeniem informacji o ich rozkładzie. Możemy np. badać zawartość ozonu w troposferze. W Europie takich lidarów jak nasz jest zaledwie kilka. Większość z nich mierzy ozon, dwutlenek siarki i tlenek azotu. Fizycy dotychczas nie zajmowali się ochroną środowiska?Taka specjalizacja powstala na Wydziale Fizyki UW dwa lata temu. Gwarancją sukcesu naszego programu dydaktyczno-badawczego jest udział w nim zakładów należących do Instytutu Fizyki Doświadczalnej UW, Pracowni Przetwarzania Informacji Instytutu Geofizyki i współpraca z Freie Universität Berlin.', 'Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar?PROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym. Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej, dzięki której ten lidar u nas zaistniał.', 'Jutro odbędzie sie pokaz nowego polskiego lidara typu DIAL. lidar Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną, naukową I dydaktyczną. Żeby przetworzyć sygnał lidarowy, czyli to co wraca po rozproszeniu światła do układu, i otrzymać dane dotyczące rozkładu koncentracji - trzeba dokonać skomplikowanych operacji. muszą być zastosowane dwie wiązki laserowe o dwóch różnych długościach fali, z których jedna jest absorbowana, a druga nie jest absorbowana przez substancję, którą chcemy wykryć.', 'Jutro odbędzie sie pokaz nowego polskiego lidara typu DIAL. lidar Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym. Jest to najnowsza generacja tego typu lidarów. DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, korzyść naukową - rozwijamy badania nad tym urządzeniem. I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska. Żeby przetworzyć tzw. sygnał lidarowy, czyli to co wraca po rozproszeniu światła do układu, i otrzymać rozsądne dane dotyczące rozkładu koncentracji - trzeba dokonać skomplikowanych operacji. Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej, dzięki której ten lidar u nas zaistniał i dla której w ramach naszych zobowiązań wykonujemy pomiary zanieczyszczeń nad naszą wspólną granicą. Zasadniczy koszt jego budowy pokryła uzyskana od Fundacji dotacja. Część pieniędzy przekazał też Narodowy Fundusz Ochrony Środowiska i Gospodarki Wodnej oraz Komitet Badań Naukowych. Lidar typu DIAL jest oparty na pomiarze absorbcji różnicowej, czyli muszą być zastosowane dwie wiązki laserowe o dwóch różnych długościach fali, z których jedna jest absorbowana, a druga nie jest absorbowana przez substancję, którą chcemy wykryć.', 'Jutro odbędzie sie pokaz nowego polskiego lidara typu DIAL. lidar Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną, naukową I dydaktyczną.', 'Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar? \nPROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym. DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, korzyść naukową - rozwijamy badania nad tym urządzeniem I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.Nasz lidar ma większe możliwości niż stacje monitoringowe. Mierzy zanieczyszczenia nie tylko lokalnie, ale też ich rozkład w przestrzeni, z wysoką rozdzielczością przestrzenną i na odległość kilku kilometrów.', 'Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar? \nPROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym.Tego typu lidar jest drogi, kosztuje około miliona marek niemieckich. DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, korzyść naukową - rozwijamy badania nad tym urządzeniem, staramy się m.in. rozszerzyć jego zastosowanie także na inne substancje występujące w atmosferze. I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.Lidar typu DIAL jest oparty na pomiarze absorbcji różnicowej, czyli muszą być zastosowane dwie wiązki laserowe o dwóch różnych długościach fali, z których jedna jest absorbowana, a druga nie jest absorbowana przez substancję, którą chcemy wykryć. Cząsteczki, które wykrywamy mają pasma absorbcji w bliskim nadfiolecie.Nasz lidar ma większe możliwości niż stacje monitoringowe. Mierzy zanieczyszczenia nie tylko lokalnie, ale też ich rozkład w przestrzeni, z wysoką rozdzielczością przestrzenną i na odległość kilku kilometrów. Możemy zatem śledzić ewolucję rozprzestrzeniania się tych zanieczyszczeń, ich kierunek i zmiany spowodowane m.in. warunkami atmosferycznymi. Wyniki naszych pomiarów porównujemy z danymi uzyskanymi ze stacji monitoringowych.', 'Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar? \nPROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, korzyść naukową i dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.', 'Jutro odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar? \n\nPROF. KRZYSZTOF ERNST: urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.\nto najnowsza generacja tego typu lidarów. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. korzyść mamy potrójną: użyteczną, przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, naukową - rozwijamy badania nad urządzeniem I dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.\nNasze przedsięwzięcie nie ma charakteru komercyjnego. Chcemy np. mierzyć w Warszawie rozkłady koncentracji tlenków azotu. Koncentrujemy się głównie na Czarnym Trójkącie - obszarze u zbiegu granic: Polski, Niemiec i Czech, do niedawna uważanym za najbardziej zdegradowany region w Europie.', 'Jutro odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar? \n\nPROF. KRZYSZTOF ERNST: urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.\n\nto kosztowne urządzenie będzie służyło tylko naukowcom?\n\nlidar jest rzeczywiście drogi. to najnowsza generacja tego typu lidarów. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. korzyść mamy potrójną: użyteczną, przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, naukową - rozwijamy badania nad tym urządzeniem I dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.\n\nCzy wszystkie zanieczyszczenia będzie można wykryć za pomocą lidaru?\n\nNie ma takiego jednostkowego urządzenia, które by wykrywało i mierzyło wszystkie szkodliwe gazy w atmosferze. Ale prowadzimy badania mające na celu rozszerzenie możliwości lidaru o taką substancję jak fosgen.\n\nstać nas będzie na prowadzenie pomiarów ozonu w miastach? \n\nNasze przedsięwzięcie nie ma charakteru komercyjnego. Chcemy np. mierzyć w Warszawie rozkłady koncentracji tlenków azotu, ich ewolucję czasową nad różnymi arteriami miasta. Koncentrujemy się głównie na Czarnym Trójkącie - obszarze u zbiegu granic: Polski, Niemiec i Czech, do niedawna uważanym za najbardziej zdegradowany region w Europie. zanieczyszczenie było dużo większe niż obecnie i wszystko wskazuje na to, że będzie dalej spadać.\nDIAL dzisiaj ma zdecydowanie największe wzięcie w ochronie środowiska. \n\nFizycy dotychczas nie zajmowali się ochroną środowiska?\n\nTaka specjalizacja powstala na Wydziale Fizyki UW dwa lata temu.', 'Co to jest lidar? \n\nPROF. KRZYSZTOF ERNST: urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.\nto najnowsza generacja tego typu lidarów. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. korzyść mamy potrójną: użyteczną, wykonujemy pomiary skażeń atmosferycznych, naukową - rozwijamy badania nad urządzeniem I dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.', 'Co to jest lidar? \nPROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. staramy się rozszerzyć jego zastosowanie na inne substancje występujące w atmosferze. Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej. zamierzamy zakończyć pomiary skażeń atmosferycznych nad granicą polsko-niemiecką. Nasz lidar ma większe możliwości niż stacje monitoringowe. Możemy śledzić ewolucję rozprzestrzeniania się zanieczyszczeń, ich kierunek i zmiany. Gwarancją sukcesu naszego programu dydaktyczno-badawczego jest udział w nim zakładów należących do Instytutu Fizyki Doświadczalnej UW, Pracowni Przetwarzania Informacji Instytutu Geofizyki i współpraca z Freie Universität Berlin.', "Co to jest lidar? \nPROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. DIAL - lidar absorbcji różnicowej potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. staramy się rozszerzyć jego zastosowanie także na inne substancje występujące w atmosferze. Pakiet software'u wzbogacamy o nowe algorytmy, które potrafią dokładniej rozszyfrowywać sygnał lidarowy, a w konsekwencji skażenia. Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej. \n\nChcemy mierzyć w Warszawie rozkłady koncentracji tlenków azotu, ich ewolucję czasową nad różnymi arteriami miasta. zamierzamy zakończyć pomiary skażeń atmosferycznych nad granicą polsko-niemiecką. Nasz lidar ma większe możliwości niż stacje monitoringowe. Możemy śledzić ewolucję rozprzestrzeniania się zanieczyszczeń, ich kierunek i zmiany spowodowane m.in. warunkami atmosferycznymi. \n\nDIAL jest tym typem lidara, który dzisiaj ma największe wzięcie w ochronie środowiska. Z lidarów korzysta meteorologia. W Europie takich lidarów jak nasz jest zaledwie kilka. Nasz lidar jest najnowocześniejszym w Polsce. Ponadto jest lidarem ruchomym, zainstalowanym na samochodzie. \n\nFizycy dotychczas nie zajmowali się ochroną środowiska?\nTaka specjalizacja powstala na Wydziale Fizyki UW dwa lata temu. Gwarancją sukcesu naszego programu dydaktyczno-badawczego jest udział w nim zakładów należących do Instytutu Fizyki Doświadczalnej UW, Pracowni Przetwarzania Informacji Instytutu Geofizyki i współpraca z Freie Universität Berlin.", 'Co to jest lidar? \nPROF. KRZYSZTOF ERNST: to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. zamierzamy zakończyć pomiary skażeń atmosferycznych nad granicą polsko-niemiecką. Nasz lidar ma większe możliwości niż stacje monitoringowe. Możemy śledzić ewolucję rozprzestrzeniania się zanieczyszczeń, ich kierunek i zmiany.'], 'ratio': [10, 20, 5, 10, 20, 5, 10, 20, 5, 10, 20, 5, 10, 20, 5], 'spans': [{'end': [244, 396, 457, 867, 922, 1022, 1103, 1877], 'span_text': ['Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar?', 'PROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym.', 'Czy to kosztowne urządzenie będzie służyło tylko naukowcom?', 'Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych,', 'naukową - rozwijamy badania nad tym urządzeniem', '.', 'I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.', 'Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej, dzięki której ten lidar u nas zaistniał i dla której w ramach naszych zobowiązań wykonujemy pomiary zanieczyszczeń nad naszą wspólną granicą.'], 'start': [153, 247, 398, 760, 875, 1020, 1023, 1631]}, {'end': [244, 396, 457, 867, 922, 1022, 1103, 1878, 2132, 2296, 2969, 6225, 6985, 7047, 7282, 7326, 7383], 'span_text': ['Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar?', 'PROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym.', 'Czy to kosztowne urządzenie będzie służyło tylko naukowcom?', 'Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych,', 'naukową - rozwijamy badania nad tym urządzeniem', '.', 'I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.', 'Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej, dzięki której ten lidar u nas zaistniał i dla której w ramach naszych zobowiązań wykonujemy pomiary zanieczyszczeń nad naszą wspólną granicą.', 'Czy wszystkie zanieczyszczenia będzie można wykryć za pomocą lidaru?', 'Nie ma takiego jednostkowego urządzenia, które by wykrywało i mierzyło wszystkie szkodliwe gazy w atmosferze łącznie z dostarczeniem informacji o ich rozkładzie.', 'Możemy np. badać zawartość ozonu w troposferze.', 'W Europie takich lidarów jak nasz jest zaledwie kilka. Większość z nich mierzy ozon, dwutlenek siarki i tlenek azotu.', '', 'Fizycy dotychczas nie zajmowali się ochroną środowiska?', 'Taka specjalizacja powstala na Wydziale Fizyki UW dwa lata temu. Gwarancją sukcesu naszego programu dydaktyczno-badawczego jest udział w nim zakładów należących do Instytutu Fizyki Doświadczalnej UW, Pracowni Przetwarzania Informacji', 'Instytutu Geofizyki i', 'współpraca z Freie Universität Berlin.'], 'start': [153, 247, 398, 760, 875, 1020, 1023, 1631, 2064, 2134, 2921, 6108, 6984, 6992, 7049, 7304, 7344]}, {'end': [244, 396, 1103, 1774, 1877], 'span_text': ['Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar?', 'PROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym.', '', 'Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej, dzięki której ten lidar u nas zaistniał', '.'], 'start': [153, 247, 1102, 1631, 1876]}, {'end': [159, 227, 243, 360, 804, 882, 1025, 1044, 1103, 1454, 1540, 1629, 2848], 'span_text': ['Jutro', 'odbędzie sie pokaz nowego polskiego lidara typu DIAL.', 'lidar', 'Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.', 'DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną,', 'naukową', 'I', 'dydaktyczną', '.', 'Żeby przetworzyć', 'sygnał lidarowy, czyli to co wraca po rozproszeniu światła do układu, i otrzymać', 'dane dotyczące rozkładu koncentracji - trzeba dokonać skomplikowanych operacji.', 'muszą być zastosowane dwie wiązki laserowe o dwóch różnych długościach fali, z których jedna jest absorbowana, a druga nie jest absorbowana przez substancję, którą chcemy wykryć.'], 'start': [153, 173, 238, 270, 591, 875, 1022, 1033, 1101, 1437, 1459, 1549, 2670]}, {'end': [159, 227, 243, 396, 922, 1103, 1629, 2062, 2582, 2848], 'span_text': ['Jutro', 'odbędzie sie pokaz nowego polskiego lidara typu DIAL.', 'lidar', 'Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym.', 'Jest to najnowsza generacja tego typu lidarów. DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, korzyść naukową - rozwijamy badania nad tym urządzeniem', '. I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.', 'Żeby przetworzyć tzw. sygnał lidarowy, czyli to co wraca po rozproszeniu światła do układu, i otrzymać rozsądne dane dotyczące rozkładu koncentracji - trzeba dokonać skomplikowanych operacji.', 'Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej, dzięki której ten lidar u nas zaistniał i dla której w ramach naszych zobowiązań wykonujemy pomiary zanieczyszczeń nad naszą wspólną granicą. Zasadniczy koszt jego budowy pokryła uzyskana od Fundacji dotacja. Część pieniędzy przekazał też Narodowy Fundusz Ochrony Środowiska i Gospodarki Wodnej oraz Komitet Badań Naukowych.', '', 'Lidar typu DIAL jest oparty na pomiarze absorbcji różnicowej, czyli muszą być zastosowane dwie wiązki laserowe o dwóch różnych długościach fali, z których jedna jest absorbowana, a druga nie jest absorbowana przez substancję, którą chcemy wykryć.'], 'start': [153, 173, 238, 270, 542, 1020, 1437, 1631, 2581, 2602]}, {'end': [159, 227, 243, 360, 804, 882, 1025, 1044, 1102], 'span_text': ['Jutro', 'odbędzie sie pokaz nowego polskiego lidara typu DIAL.', 'lidar', 'Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.', 'DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną,', 'naukową', 'I', 'dydaktyczną', '.'], 'start': [153, 173, 238, 270, 591, 875, 1022, 1033, 1101]}, {'end': [246, 396, 922, 1102, 4763], 'span_text': ['Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar?', 'PROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym.', 'DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, korzyść naukową - rozwijamy badania nad tym urządzeniem', 'I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.', 'Nasz lidar ma większe możliwości niż stacje monitoringowe. Mierzy zanieczyszczenia nie tylko lokalnie, ale też ich rozkład w przestrzeni, z wysoką rozdzielczością przestrzenną i na odległość kilku kilometrów.'], 'start': [153, 247, 590, 1022, 4555]}, {'end': [246, 396, 480, 542, 1021, 1102, 2920, 4989], 'span_text': ['Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar?', 'PROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym.', 'Tego typu lidar jest', 'drogi, kosztuje około miliona marek niemieckich.', 'DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, korzyść naukową - rozwijamy badania nad tym urządzeniem, staramy się m.in. rozszerzyć jego zastosowanie także na inne substancje występujące w atmosferze.', 'I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.', 'Lidar typu DIAL jest oparty na pomiarze absorbcji różnicowej, czyli muszą być zastosowane dwie wiązki laserowe o dwóch różnych długościach fali, z których jedna jest absorbowana, a druga nie jest absorbowana przez substancję, którą chcemy wykryć. Cząsteczki, które wykrywamy mają pasma absorbcji w bliskim nadfiolecie.', 'Nasz lidar ma większe możliwości niż stacje monitoringowe. Mierzy zanieczyszczenia nie tylko lokalnie, ale też ich rozkład w przestrzeni, z wysoką rozdzielczością przestrzenną i na odległość kilku kilometrów. Możemy zatem śledzić ewolucję rozprzestrzeniania się tych zanieczyszczeń, ich kierunek i zmiany spowodowane m.in. warunkami atmosferycznymi. Wyniki naszych pomiarów porównujemy z danymi uzyskanymi ze stacji monitoringowych.'], 'start': [153, 247, 459, 493, 590, 1022, 2602, 4555]}, {'end': [246, 360, 626, 883, 920, 1102], 'span_text': ['Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar?', 'PROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.', '', 'Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, korzyść naukową', 'i', 'dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.'], 'start': [153, 247, 625, 760, 919, 1032]}, {'end': [158, 262, 271, 359, 397, 590, 761, 803, 867, 907, 922, 1025, 1102, 3311, 3516, 3595, 3623, 3675, 4226, 4332], 'span_text': ['Jutro', 'odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar? \n\nPROF. KRZYSZTOF', 'ERNST:', 'urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.', '', 'to najnowsza generacja tego typu lidarów.', 'Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen.', 'korzyść mamy potrójną: użyteczną,', 'przy jego pomocy wykonujemy pomiary skażeń atmosferycznych,', 'naukową - rozwijamy badania nad', 'urządzeniem', 'I', 'dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.', '', 'Nasze przedsięwzięcie nie ma charakteru komercyjnego.', 'Chcemy np. mierzyć w Warszawie rozkłady', 'koncentracji tlenków azotu', '.', 'Koncentrujemy się głównie na Czarnym Trójkącie - obszarze u zbiegu', 'granic: Polski, Niemiec i Czech, do niedawna uważanym za najbardziej zdegradowany region w Europie.'], 'start': [153, 172, 263, 279, 396, 548, 699, 769, 806, 875, 911, 1022, 1033, 3310, 3462, 3556, 3596, 3674, 4158, 4233]}, {'end': [158, 262, 271, 359, 398, 459, 498, 543, 590, 761, 803, 867, 922, 1025, 1102, 2242, 2300, 2406, 3247, 3311, 3516, 3595, 3675, 4226, 4333, 5130, 5241, 5439, 5661, 5756, 7113], 'span_text': ['Jutro', 'odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar? \n\nPROF. KRZYSZTOF', 'ERNST:', 'urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.', '', 'to kosztowne urządzenie będzie służyło tylko naukowcom?', 'lidar jest rzeczywiście drogi', '.', 'to najnowsza generacja tego typu lidarów.', 'Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen.', 'korzyść mamy potrójną: użyteczną,', 'przy jego pomocy wykonujemy pomiary skażeń atmosferycznych,', 'naukową - rozwijamy badania nad tym urządzeniem', 'I', 'dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.', 'Czy wszystkie zanieczyszczenia będzie można wykryć za pomocą lidaru?\n\nNie ma takiego jednostkowego urządzenia, które by wykrywało i mierzyło wszystkie szkodliwe gazy w atmosferze', '. Ale', 'prowadzimy badania mające na celu rozszerzenie możliwości lidaru o taką substancję jak fosgen.', '', 'stać nas będzie na prowadzenie pomiarów ozonu w miastach?', 'Nasze przedsięwzięcie nie ma charakteru komercyjnego.', 'Chcemy np. mierzyć w Warszawie rozkłady', 'koncentracji tlenków azotu, ich ewolucję czasową nad różnymi arteriami miasta.', 'Koncentrujemy się głównie na Czarnym Trójkącie - obszarze u zbiegu', 'granic: Polski, Niemiec i Czech, do niedawna uważanym za najbardziej zdegradowany region w Europie.', 'zanieczyszczenie', 'było dużo większe niż obecnie i wszystko wskazuje na to, że będzie dalej spadać.', '', 'DIAL', 'dzisiaj ma zdecydowanie największe wzięcie w ochronie środowiska.', 'Fizycy dotychczas nie zajmowali się ochroną środowiska?\n\nTaka specjalizacja powstala na Wydziale Fizyki UW dwa lata temu.'], 'start': [153, 172, 263, 279, 396, 402, 469, 541, 548, 699, 769, 806, 875, 1022, 1033, 2062, 2294, 2312, 3245, 3251, 3462, 3556, 3596, 4158, 4233, 5114, 5160, 5438, 5656, 5690, 6990]}, {'end': [262, 271, 359, 397, 590, 761, 803, 807, 867, 907, 922, 1025, 1102], 'span_text': ['Co to jest lidar? \n\nPROF. KRZYSZTOF', 'ERNST:', 'urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.', '', 'to najnowsza generacja tego typu lidarów.', 'Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen.', 'korzyść mamy potrójną: użyteczną,', '', 'wykonujemy pomiary skażeń atmosferycznych,', 'naukową - rozwijamy badania nad', 'urządzeniem', 'I', 'dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.'], 'start': [227, 263, 279, 396, 548, 699, 769, 806, 824, 875, 911, 1022, 1033]}, {'end': [245, 360, 761, 936, 971, 1022, 1733, 1878, 4159, 4614, 4772, 4818, 4860, 4906, 7283, 7326, 7383], 'span_text': ['Co to jest lidar?', 'PROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.', 'Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen.', 'staramy się', 'rozszerzyć jego zastosowanie', 'na inne substancje występujące w atmosferze.', 'Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej', '.', 'zamierzamy zakończyć pomiary skażeń atmosferycznych nad granicą polsko-niemiecką.', 'Nasz lidar ma większe możliwości niż stacje monitoringowe.', 'Możemy', 'śledzić ewolucję rozprzestrzeniania się', 'zanieczyszczeń, ich kierunek i zmiany', '.', 'Gwarancją sukcesu naszego programu dydaktyczno-badawczego jest udział w nim zakładów należących do Instytutu Fizyki Doświadczalnej UW, Pracowni Przetwarzania Informacji', 'Instytutu Geofizyki i', 'współpraca z Freie Universität Berlin.'], 'start': [227, 246, 699, 924, 942, 977, 1631, 1876, 4076, 4555, 4765, 4778, 4823, 4904, 7114, 7305, 7344]}, {'end': [245, 360, 625, 761, 936, 1022, 1311, 1357, 1436, 1733, 1878, 3247, 3311, 3563, 3676, 4159, 4614, 4772, 4818, 4906, 5410, 5439, 5701, 5789, 6163, 6364, 6472, 7048, 7283, 7326, 7383], 'span_text': ['Co to jest lidar?', 'PROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.', 'DIAL - lidar absorbcji różnicowej', 'potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen.', 'staramy się', 'rozszerzyć jego zastosowanie także na inne substancje występujące w atmosferze.', "Pakiet software'u", 'wzbogacamy o nowe algorytmy, które potrafią', 'dokładniej rozszyfrowywać sygnał lidarowy, a w konsekwencji skażenia.', 'Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej', '.', '', '', 'Chcemy', 'mierzyć w Warszawie rozkłady koncentracji tlenków azotu, ich ewolucję czasową nad różnymi arteriami miasta.', 'zamierzamy zakończyć pomiary skażeń atmosferycznych nad granicą polsko-niemiecką.', 'Nasz lidar ma większe możliwości niż stacje monitoringowe.', 'Możemy', 'śledzić ewolucję rozprzestrzeniania się', 'zanieczyszczeń, ich kierunek i zmiany spowodowane m.in. warunkami atmosferycznymi.', '', '', 'DIAL jest tym typem lidara, który dzisiaj ma', 'największe wzięcie w ochronie środowiska. Z lidarów korzysta meteorologia.', 'W Europie takich lidarów jak nasz jest zaledwie kilka.', 'Nasz lidar', 'jest najnowocześniejszym w Polsce. Ponadto jest lidarem ruchomym, zainstalowanym na samochodzie.', 'Fizycy dotychczas nie zajmowali się ochroną środowiska?', 'Taka specjalizacja powstala na Wydziale Fizyki UW dwa lata temu. Gwarancją sukcesu naszego programu dydaktyczno-badawczego jest udział w nim zakładów należących do Instytutu Fizyki Doświadczalnej UW, Pracowni Przetwarzania Informacji', 'Instytutu Geofizyki i', 'współpraca z Freie Universität Berlin.'], 'start': [227, 246, 591, 668, 924, 942, 1293, 1313, 1366, 1631, 1876, 3246, 3310, 3556, 3567, 4076, 4555, 4765, 4778, 4823, 5409, 5438, 5656, 5714, 6108, 6353, 6374, 6990, 7049, 7305, 7344]}, {'end': [245, 271, 360, 761, 4159, 4614, 4772, 4818, 4860, 4905], 'span_text': ['Co to jest lidar?', 'PROF. KRZYSZTOF ERNST:', 'to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.', 'Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen.', 'zamierzamy zakończyć pomiary skażeń atmosferycznych nad granicą polsko-niemiecką.', 'Nasz lidar ma większe możliwości niż stacje monitoringowe.', 'Możemy', 'śledzić ewolucję rozprzestrzeniania się', 'zanieczyszczeń, ich kierunek i zmiany', '.'], 'start': [227, 246, 276, 699, 4076, 4555, 4765, 4778, 4823, 4904]}], 'type': ['extract', 'extract', 'extract', 'extract', 'extract', 'extract', 'extract', 'extract', 'extract', 'extract', 'extract', 'extract', 'extract', 'extract', 'extract']}, 'title': 'Lidarowe oczy'} ``` ### Data Fields - `id`: a `string` example identifier - `date`: date of the original article (`string`) - `title`: title of the original article (`string`) - `section`: the section of the newspaper the original article belonged to (`string`) - `authors`: original article authors (`string`) - `body`: original article body (list of `string`s) - `summaries`: a dictionary feature containing summaries of the original article with the following attributes: - `ratio`: ratio of summary - percentage of the original article (list of `int32`s) - `type`: type of summary - extractive (`extract`) or abstractive (`abstract`) (list of `string`s) - `author`: acronym of summary author (list of `string`) - `body`: body of summary (list of `string`) - `spans`: a list containing spans for extractive summaries (empty for abstractive summaries): - `start`: start of span (`int32`) - `end`: end of span (`int32`) - `span_text`: span text (`string`) ### Data Splits Single train split ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information ``` @inproceedings{ ogro:kop:14:lrec, author = "Ogrodniczuk, Maciej and Kopeć, Mateusz", pdf = "http://nlp.ipipan.waw.pl/Bib/ogro:kop:14:lrec.pdf", title = "The {P}olish {S}ummaries {C}orpus", pages = "3712--3715", crossref = "lrec:14" } @proceedings{ lrec:14, editor = "Calzolari, Nicoletta and Choukri, Khalid and Declerck, Thierry and Loftsson, Hrafn and Maegaard, Bente and Mariani, Joseph and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios", isbn = "978-2-9517408-8-4", title = "Proceedings of the Ninth International {C}onference on {L}anguage {R}esources and {E}valuation, {LREC}~2014", url = "http://www.lrec-conf.org/proceedings/lrec2014/index.html", booktitle = "Proceedings of the Ninth International {C}onference on {L}anguage {R}esources and {E}valuation, {LREC}~2014", address = "Reykjavík, Iceland", key = "LREC", year = "2014", organization = "European Language Resources Association (ELRA)" } ``` ### Contributions Thanks to [@kldarek](https://github.com/kldarek) for adding this dataset.

polsum

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["pl"], "license": ["cc-by-3.0"], "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "source_datasets": ["original"], "task_categories": ["summarization"], "task_ids": ["news-articles-summarization"], "pretty_name": "Polish Summaries Corpus", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "section", "dtype": "string"}, {"name": "authors", "dtype": "string"}, {"name": "body", "dtype": "string"}, {"name": "summaries", "sequence": [{"name": "ratio", "dtype": "int32"}, {"name": "type", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "body", "dtype": "string"}, {"name": "spans", "sequence": [{"name": "start", "dtype": "int32"}, {"name": "end", "dtype": "int32"}, {"name": "span_text", "dtype": "string"}]}]}], "splits": [{"name": "train", "num_bytes": 34787575, "num_examples": 569}], "download_size": 6082812, "dataset_size": 34787575}}

2024-01-18T11:13:22+00:00

TAGS #task_categories-summarization #task_ids-news-articles-summarization #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-Polish #license-cc-by-3.0 #region-us

# Dataset Card for Polish Summaries Corpus ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: URL - Paper: URL - Leaderboard: - Point of Contact: Mateusz Kopeć ### Dataset Summary The Corpus contains a large number of manual summaries of news articles, with many independently created summaries for a single text. Such approach is supposed to overcome the annotator bias, which is often described as a problem during the evaluation of the summarization algorithms against a single gold standard. ### Supported Tasks and Leaderboards ### Languages Polish ## Dataset Structure ### Data Instances See below an example from the dataset. Detailed descriptions of the fields are provided in the following section. ### Data Fields - 'id': a 'string' example identifier - 'date': date of the original article ('string') - 'title': title of the original article ('string') - 'section': the section of the newspaper the original article belonged to ('string') - 'authors': original article authors ('string') - 'body': original article body (list of 'string's) - 'summaries': a dictionary feature containing summaries of the original article with the following attributes: - 'ratio': ratio of summary - percentage of the original article (list of 'int32's) - 'type': type of summary - extractive ('extract') or abstractive ('abstract') (list of 'string's) - 'author': acronym of summary author (list of 'string') - 'body': body of summary (list of 'string') - 'spans': a list containing spans for extractive summaries (empty for abstractive summaries): - 'start': start of span ('int32') - 'end': end of span ('int32') - 'span_text': span text ('string') ### Data Splits Single train split ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @kldarek for adding this dataset.

1c4ec77a49b25d59198e9c80b652925e50163379

# Dataset Card for Polyglot-NER ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://sites.google.com/site/rmyeid/projects/polylgot-ner](https://sites.google.com/site/rmyeid/projects/polylgot-ner) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 45.39 GB - **Size of the generated dataset:** 12.54 GB - **Total amount of disk used:** 57.93 GB ### Dataset Summary Polyglot-NER A training dataset automatically generated from Wikipedia and Freebase the task of named entity recognition. The dataset contains the basic Wikipedia based training data for 40 languages we have (with coreference resolution) for the task of named entity recognition. The details of the procedure of generating them is outlined in Section 3 of the paper (https://arxiv.org/abs/1410.3791). Each config contains the data corresponding to a different language. For example, "es" includes only spanish examples. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### ar - **Size of downloaded dataset files:** 1.11 GB - **Size of the generated dataset:** 183.55 MB - **Total amount of disk used:** 1.29 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": "2", "lang": "ar", "ner": ["O", "O", "O", "O", "O", "O", "O", "O", "LOC", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "PER", "PER", "PER", "PER", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"], "words": "[\"وفي\", \"مرحلة\", \"موالية\", \"أنشأت\", \"قبيلة\", \"مكناسة\", \"الزناتية\", \"مكناسة\", \"تازة\", \",\", \"وأقام\", \"بها\", \"المرابطون\", \"قلعة\", \"..." } ``` #### bg - **Size of downloaded dataset files:** 1.11 GB - **Size of the generated dataset:** 190.51 MB - **Total amount of disk used:** 1.30 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": "1", "lang": "bg", "ner": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"], "words": "[\"Дефиниция\", \"Наименованията\", \"\\\"\", \"книжовен\", \"\\\"/\\\"\", \"литературен\", \"\\\"\", \"език\", \"на\", \"български\", \"за\", \"тази\", \"кодифи..." } ``` #### ca - **Size of downloaded dataset files:** 1.11 GB - **Size of the generated dataset:** 143.75 MB - **Total amount of disk used:** 1.25 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": "2", "lang": "ca", "ner": "[\"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O...", "words": "[\"Com\", \"a\", \"compositor\", \"deixà\", \"un\", \"immens\", \"llegat\", \"que\", \"inclou\", \"8\", \"simfonies\", \"(\", \"1822\", \"),\", \"diverses\", ..." } ``` #### combined - **Size of downloaded dataset files:** 1.11 GB - **Size of the generated dataset:** 6.29 GB - **Total amount of disk used:** 7.39 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": "18", "lang": "es", "ner": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"], "words": "[\"Los\", \"cambios\", \"en\", \"la\", \"energía\", \"libre\", \"de\", \"Gibbs\", \"\\\\\", \"Delta\", \"G\", \"nos\", \"dan\", \"una\", \"cuantificación\", \"de..." } ``` #### cs - **Size of downloaded dataset files:** 1.11 GB - **Size of the generated dataset:** 156.79 MB - **Total amount of disk used:** 1.26 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": "3", "lang": "cs", "ner": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"], "words": "[\"Historie\", \"Symfonická\", \"forma\", \"se\", \"rozvinula\", \"se\", \"především\", \"v\", \"období\", \"klasicismu\", \"a\", \"romantismu\", \",\", \"..." } ``` ### Data Fields The data fields are the same among all splits. #### ar - `id`: a `string` feature. - `lang`: a `string` feature. - `words`: a `list` of `string` features. - `ner`: a `list` of `string` features. #### bg - `id`: a `string` feature. - `lang`: a `string` feature. - `words`: a `list` of `string` features. - `ner`: a `list` of `string` features. #### ca - `id`: a `string` feature. - `lang`: a `string` feature. - `words`: a `list` of `string` features. - `ner`: a `list` of `string` features. #### combined - `id`: a `string` feature. - `lang`: a `string` feature. - `words`: a `list` of `string` features. - `ner`: a `list` of `string` features. #### cs - `id`: a `string` feature. - `lang`: a `string` feature. - `words`: a `list` of `string` features. - `ner`: a `list` of `string` features. ### Data Splits | name | train | |----------|---------:| | ar | 339109 | | bg | 559694 | | ca | 372665 | | combined | 21070925 | | cs | 564462 | ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @article{polyglotner, author = {Al-Rfou, Rami and Kulkarni, Vivek and Perozzi, Bryan and Skiena, Steven}, title = {{Polyglot-NER}: Massive Multilingual Named Entity Recognition}, journal = {{Proceedings of the 2015 {SIAM} International Conference on Data Mining, Vancouver, British Columbia, Canada, April 30- May 2, 2015}}, month = {April}, year = {2015}, publisher = {SIAM}, } ``` ### Contributions Thanks to [@joeddav](https://github.com/joeddav) for adding this dataset.

polyglot_ner

2022-03-02T23:29:22+00:00

{"annotations_creators": ["machine-generated"], "language_creators": ["found"], "language": ["ar", "bg", "ca", "cs", "da", "de", "el", "en", "es", "et", "fa", "fi", "fr", "he", "hi", "hr", "hu", "id", "it", "ja", "ko", "lt", "lv", "ms", "nl", "no", "pl", "pt", "ro", "ru", "sk", "sl", "sr", "sv", "th", "tl", "tr", "uk", "vi", "zh"], "license": ["unknown"], "multilinguality": ["multilingual"], "size_categories": ["unknown"], "source_datasets": ["original"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "paperswithcode_id": "polyglot-ner", "pretty_name": "Polyglot-NER", "dataset_info": [{"config_name": "ca", "features": [{"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "words", "sequence": "string"}, {"name": "ner", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 143746026, "num_examples": 372665}], "download_size": 1107018606, "dataset_size": 143746026}, {"config_name": "de", "features": [{"name": 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2024-01-18T11:13:26+00:00

TAGS #task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-machine-generated #language_creators-found #multilinguality-multilingual #size_categories-unknown #source_datasets-original #language-Arabic #language-Bulgarian #language-Catalan #language-Czech #language-Danish #language-German #language-Modern Greek (1453-) #language-English #language-Spanish #language-Estonian #language-Persian #language-Finnish #language-French #language-Hebrew #language-Hindi #language-Croatian #language-Hungarian #language-Indonesian #language-Italian #language-Japanese #language-Korean #language-Lithuanian #language-Latvian #language-Malay (macrolanguage) #language-Dutch #language-Norwegian #language-Polish #language-Portuguese #language-Romanian #language-Russian #language-Slovak #language-Slovenian #language-Serbian #language-Swedish #language-Thai #language-Tagalog #language-Turkish #language-Ukrainian #language-Vietnamese #language-Chinese #license-unknown #arxiv-1410.3791 #region-us

Dataset Card for Polyglot-NER ============================= Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: * Point of Contact: * Size of downloaded dataset files: 45.39 GB * Size of the generated dataset: 12.54 GB * Total amount of disk used: 57.93 GB ### Dataset Summary Polyglot-NER A training dataset automatically generated from Wikipedia and Freebase the task of named entity recognition. The dataset contains the basic Wikipedia based training data for 40 languages we have (with coreference resolution) for the task of named entity recognition. The details of the procedure of generating them is outlined in Section 3 of the paper (URL Each config contains the data corresponding to a different language. For example, "es" includes only spanish examples. ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### ar * Size of downloaded dataset files: 1.11 GB * Size of the generated dataset: 183.55 MB * Total amount of disk used: 1.29 GB An example of 'train' looks as follows. #### bg * Size of downloaded dataset files: 1.11 GB * Size of the generated dataset: 190.51 MB * Total amount of disk used: 1.30 GB An example of 'train' looks as follows. #### ca * Size of downloaded dataset files: 1.11 GB * Size of the generated dataset: 143.75 MB * Total amount of disk used: 1.25 GB An example of 'train' looks as follows. #### combined * Size of downloaded dataset files: 1.11 GB * Size of the generated dataset: 6.29 GB * Total amount of disk used: 7.39 GB An example of 'train' looks as follows. #### cs * Size of downloaded dataset files: 1.11 GB * Size of the generated dataset: 156.79 MB * Total amount of disk used: 1.26 GB An example of 'train' looks as follows. ### Data Fields The data fields are the same among all splits. #### ar * 'id': a 'string' feature. * 'lang': a 'string' feature. * 'words': a 'list' of 'string' features. * 'ner': a 'list' of 'string' features. #### bg * 'id': a 'string' feature. * 'lang': a 'string' feature. * 'words': a 'list' of 'string' features. * 'ner': a 'list' of 'string' features. #### ca * 'id': a 'string' feature. * 'lang': a 'string' feature. * 'words': a 'list' of 'string' features. * 'ner': a 'list' of 'string' features. #### combined * 'id': a 'string' feature. * 'lang': a 'string' feature. * 'words': a 'list' of 'string' features. * 'ner': a 'list' of 'string' features. #### cs * 'id': a 'string' feature. * 'lang': a 'string' feature. * 'words': a 'list' of 'string' features. * 'ner': a 'list' of 'string' features. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @joeddav for adding this dataset.

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# Dataset Card for `prachathai67k` ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/PyThaiNLP/prachathai-67k - **Repository:** https://github.com/PyThaiNLP/prachathai-67k - **Paper:** - **Leaderboard:** - **Point of Contact:** https://github.com/PyThaiNLP/ ### Dataset Summary `prachathai-67k`: News Article Corpus and Multi-label Text Classificdation from Prachathai.com The `prachathai-67k` dataset was scraped from the news site [Prachathai](prachathai.com). We filtered out those articles with less than 500 characters of body text, mostly images and cartoons. It contains 67,889 articles wtih 12 curated tags from August 24, 2004 to November 15, 2018. The dataset was originally scraped by [@lukkiddd](https://github.com/lukkiddd) and cleaned by [@cstorm125](https://github.com/cstorm125). Download the dataset [here](https://www.dropbox.com/s/fsxepdka4l2pr45/prachathai-67k.zip?dl=1). You can also see preliminary exploration in [exploration.ipynb](https://github.com/PyThaiNLP/prachathai-67k/blob/master/exploration.ipynb). This dataset is a part of [pyThaiNLP](https://github.com/PyThaiNLP/) Thai text [classification-benchmarks](https://github.com/PyThaiNLP/classification-benchmarks). For the benchmark, we selected the following tags with substantial volume that resemble **classifying types of articles**: * `การเมือง` - politics * `สิทธิมนุษยชน` - human_rights * `คุณภาพชีวิต` - quality_of_life * `ต่างประเทศ` - international * `สังคม` - social * `สิ่งแวดล้อม` - environment * `เศรษฐกิจ` - economics * `วัฒนธรรม` - culture * `แรงงาน` - labor * `ความมั่นคง` - national_security * `ไอซีที` - ict * `การศึกษา` - education ### Supported Tasks and Leaderboards multi-label text classification, language modeling ### Languages Thai ## Dataset Structure ### Data Instances {'body_text': '17 พ.ย. 2558 Blognone [1] รายงานว่า กลุ่มแฮคเกอร์ Anonymous ประกาศสงครามไซเบอร์กับกลุ่มหัวรุนแรงหลังจากกลุ่ม IS ออกมาประกาศว่าเป็นผู้อยู่เบื้องหลังการโจมตีกรุงปารีสในคืนวันศุกร์ที่ผ่านมา\n\n\nภาพในคลิปใน YouTube โฆษกของกลุ่มแฮคเกอร์สวมหน้ากากที่เป็นสัญลักษณ์ของกลุ่มได้ออกมาอ่านแถลงเป็นภาษาฝรั่งเศส มีใจความว่า จากการโจมตีของกลุ่ม IS ในกรุงปารีส กลุ่ม Anonymous ทั่วโลกจะตามล่ากลุ่ม IS เหมือนที่เคยทำตอนที่มีการโจมตีสำนักพิมพ์ Charlie Hebdo และครั้งนี้จะเป็นปฏิบัติการโจมตีครั้งใหญ่ที่สุดของกลุ่ม Anonymous เลย นอกจากนี้กลุ่ม Anonymous ยังแสดงความเสียใจต่อครอบครัวผู้สูญเสียในเหตุการณ์ครั้งนี้\nกลุ่ม Anonymous เคยประกาศสงครามกับกลุ่ม IS หลังจากการโจมตีสำนักพิมพ์ Charlie Hebdo ที่ฝรั่งเศสเมื่อต้นปีที่ผ่านมา ซึ่งครั้งนั้นกลุ่ม Anonymous อ้างว่าได้ระงับบัญชีผู้ใช้งานที่เกี่ยวข้องกับ IS ไปหลายพันบัญชี (อ่านรายละเอียดเพิ่มเติม จากBlognone ที่\xa0\xa0กลุ่มแฮคเกอร์ Anonymous ประกาศสงครามไซเบอร์ขอกวาดล้างพวก ISIS [2])', 'culture': 0, 'date': '2015-11-17 18:14', 'economics': 0, 'education': 0, 'environment': 0, 'human_rights': 0, 'ict': 1, 'international': 1, 'labor': 0, 'national_security': 0, 'politics': 0, 'quality_of_life': 0, 'social': 0, 'title': 'แฮคเกอร์ Anonymous ลั่นทำสงครามไซเบอร์ครั้งใหญ่สุดกับกลุ่ม IS', 'url': 'https://prachatai.com/print/62490'} {'body_text': 'แถลงการณ์\n\n\xa0\n\nองค์การนักศึกษามหาวิทยาลัยธรรมศาสตร์\n\n\xa0\n\nมหาวิทยาลัยธรรมศาสตร์ก่อตั้งขึ้นภายใต้แนวคิดการให้การศึกษากับประชาชนเพื่อสนับสนุนการปกครองระบอบประชาธิปไตย อีกทั้งยังเป็นสถาบันหนึ่งที่อยู่เคียงข้างประชาชนมาโดยตลอด\n\n\xa0\n\nสถานการณ์สังคมไทยปัจจุบันได้เกิดความขัดแย้งทางการเมือง ทางแนวคิด จนลุกลามเป็นวิกฤตการณ์อันหาทางออกได้ยากยิ่ง องค์กรนักศึกษามหาวิทยาลัยธรรมศาสตร์ขอร้องเรียนและเสนอแนะต่อทุกฝ่าย โดยยึดหลักแนวทางตามรัฐธรรมนูญแห่งราชอาณาจักรไทย พ.ศ. ๒๕๕๐ อันเป็นกฎหมายสูงสุดในการจัดการปกครองรัฐ ที่มีผลบังคับใช้อยู่ในปัจจุบันซึ่งผ่านการประชามติจากปวงชนชาวไทยเมื่อวันที่ ๑๙ สิงหาคม พ.ศ. ๒๕๕๐ แล้วดังต่อนี้\n\n\xa0\n\n๑.การชุมชมโดยสงบและปราศจากอาวุธย่อมได้รับการคุ้มครองตามรัฐธรรมนูญ แต่หากการชุมนุมและเคลื่อนไหวของกลุ่มใดๆ มีการละเมิดสิทธิและเสรีภาพของผู้อื่นหรือก่อให้เกิดความเสียหายต่อชีวิตและทรัพย์สินของบุคคลและส่วนรวมนั้น ไม่สามารถกระทำได้ การใช้ความรุนแรง การกระทำอุกอาจต่างๆ ทั้งต่อบุคคลและทรัพย์สิน การยั่วยุ ปลุกระดมเพื่อหวังผลในการปะทะต่อสู้ จึงควรได้รับการกล่าวโทษ\n\n\xa0\n\nดังนั้นทั้งกลุ่มพันธมิตรประชาชนเพื่อประชาธิปไตย (พธม.) และกลุ่มแนวร่วมประชาธิปไตยไม่เอาเผด็จการแห่งชาติ (นปช.) จึงควรยอมรับกระบวนการตามกฎหมาย และหากถูกกล่าวหาไม่ว่ากรณีใดๆ ก็ควรพิสูจน์ความบริสุทธิ์โดยใช้กระบวนการยุติธรรม และหากจะยังชุมนุมต่อไปก็ยังคงทำได้ภายใต้บทบัญญัติแห่งกฎหมาย\n\n\xa0\n\nองค์กรนักศึกษามหาวิทยาลัยธรรมศาสตร์ จึงร้องขอให้หน่วยงานต่างๆ ที่เกี่ยวข้องดำเนินการตามกระบวนการทางกฎหมายกับการกระทำที่ผิดบทบัญญัติแห่งกฎหมายที่ทุกฝ่ายได้กระทำไป\n\n\xa0\n\n๒.นายสมัคร สุนทรเวช นายกรัฐมนตรี ไม่มีความเหมาะสมในการบริหารราชการแผ่นดินขาดหลักธรรมาภิบาล แต่ทั้งนี้นายสมัคร สุนทรเวช ยังคงยืนยันและกล่าวอ้างความชอบธรรมตามระบอบประชาธิปไตยภายใต้รัฐธรรมนูญ โดยไม่คำนึงถึงกระแสเรียกร้องใดๆ อันส่งผลให้ความขัดแย้งทางสังคมยิ่งบานปลายจนกลายเป็นวิกฤตการณ์เช่นปัจจุบัน ซึ่งก่อให้เกิดความเสียหายต่อประเทศแนวโน้มจะคลี่คลาย\n\n\xa0\n\nองค์การนักศึกษามหาวิทยาลัยธรรมศาสตร์ จึงเห็นว่า ควรใช้สิทธิตามรัฐธรรมนูญแห่งราชอาณาจักรไทย พุทธศักราช ๒๕๕๐ มาตรา ๑๖๔ โดยการเข้าชื่อเพื่อร้องต่อประธานวุฒิสภาเพื่อให้มีมติตามมาตรา ๒๗๔ ให้ถอดถอนนายสมัคร สุนทรเวช ออกจากตำแหน่งนายกรัฐมนตรีตามมาตรา ๒๗๐ ณ ลานโพ มหาวิทยาลัยธรรมศาสตร์ ท่าพระจันทร์ อาคารเรียนรวมสังคมศาสตร์ อาคารปิยชาติ และตึกกิจกรรมนักศึกษา มหาวิทยาลัยธรรมศาสตร์ ศูนย์รังสิต\n\n\xa0\n\n\xa0\n\nด้วยความสมานฉันท์\n\nองค์การนักศึกษามหาวิทยาลัยธรรมศาสตร์', 'culture': 0, 'date': '2008-09-06 03:36', 'economics': 0, 'education': 0, 'environment': 0, 'human_rights': 0, 'ict': 0, 'international': 0, 'labor': 0, 'national_security': 0, 'politics': 1, 'quality_of_life': 0, 'social': 0, 'title': 'แถลงการณ์ อมธ.แนะใช้สิทธิ ตาม รธน.เข้าชื่อร้องต่อประธานวุฒิสภาถอดถอน "สมัคร" จากตำแหน่งนายกฯ', 'url': 'https://prachatai.com/print/18038'} ### Data Fields - `url`: url of the article - `date`: date the article was published - `title`: title of the article - `body_text`: body text of the article - `politics`: 1 if sample has this tag else 0 - `human_rights`: 1 if sample has this tag else 0 - `quality_of_life`: 1 if sample has this tag else 0 - `international`: 1 if sample has this tag else 0 - `social`: 1 if sample has this tag else 0 - `environment`: 1 if sample has this tag else 0 - `economics`: 1 if sample has this tag else 0 - `culture`: 1 if sample has this tag else 0 - `labor`: 1 if sample has this tag else 0 - `national_security`: 1 if sample has this tag else 0 - `ict`: 1 if sample has this tag else 0 - `education`: 1 if sample has this tag else 0 ### Data Splits | | train | valid | test | |-------------------|-------|--------|------| | # articles | 54379 | 6721 | 6789 | | politics | 31401 | 3852 | 3842 | | human_rights | 12061 | 1458 | 1511 | | quality_of_life | 9037 | 1144 | 1127 | | international | 6432 | 828 | 834 | | social | 6321 | 782 | 789 | | environment | 6157 | 764 | 772 | | economics | 3994 | 487 | 519 | | culture | 3279 | 388 | 398 | | labor | 2905 | 375 | 350 | | national_security | 2865 | 339 | 338 | | ict | 2326 | 285 | 292 | | education | 2093 | 248 | 255 | ## Dataset Creation ### Curation Rationale The data was scraped from the news site [Prachathai](prachathai.com) from August 24, 2004 to November 15, 2018. The initial intention was to use the dataset as a benchmark for Thai text classification. Due to the size of the dataset, it can also be used for language modeling. ### Source Data #### Initial Data Collection and Normalization 67,889 articles wtih 51,797 tags were scraped from the news site [Prachathai](prachathai.com) from August 24, 2004 to November 15, 2018. We filtered out those articles with less than 500 characters of body text, mostly images and cartoons. #### Who are the source language producers? Prachathai.com ### Annotations #### Annotation process Tags are annotated for the news website Prachathai.com #### Who are the annotators? We assume that the reporters who wrote the articles or other Prachathai staff gave each article its tags. ### Personal and Sensitive Information We do not expect any personal and sensitive information to be present since all data are public news articles. ## Considerations for Using the Data ### Social Impact of Dataset - classification benchmark for multi-label Thai text classification ### Discussion of Biases Prachathai.com is a left-leaning, human-right-focused news site, and thus unusual news labels such as human rights and quality of life. The news articles are expected to be left-leaning in contents. ### Other Known Limitations Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators PyThaiNLP ### Licensing Information CC-BY-NC ### Citation Information @misc{prachathai67k, author = {cstorm125, lukkiddd }, title = {prachathai67k}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, howpublished={\\url{https://github.com/PyThaiNLP/prachathai-67k}}, } ### Contributions Thanks to [@cstorm125](https://github.com/cstorm125) for adding this dataset.

prachathai67k

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["topic-classification"], "paperswithcode_id": "prachathai-67k", "pretty_name": "prachathai67k", "dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "body_text", "dtype": "string"}, {"name": "politics", "dtype": {"class_label": {"names": {"0": "neg", "1": "pos"}}}}, {"name": "human_rights", "dtype": {"class_label": {"names": {"0": "neg", "1": "pos"}}}}, {"name": "quality_of_life", "dtype": {"class_label": {"names": {"0": "neg", "1": "pos"}}}}, {"name": "international", "dtype": {"class_label": {"names": {"0": "neg", "1": "pos"}}}}, {"name": "social", "dtype": {"class_label": {"names": {"0": "neg", "1": "pos"}}}}, {"name": "environment", "dtype": {"class_label": {"names": {"0": "neg", "1": "pos"}}}}, {"name": "economics", "dtype": {"class_label": {"names": {"0": "neg", "1": "pos"}}}}, {"name": "culture", "dtype": {"class_label": {"names": {"0": "neg", "1": "pos"}}}}, {"name": "labor", "dtype": {"class_label": {"names": {"0": "neg", "1": "pos"}}}}, {"name": "national_security", "dtype": {"class_label": {"names": {"0": "neg", "1": "pos"}}}}, {"name": "ict", "dtype": {"class_label": {"names": {"0": "neg", "1": "pos"}}}}, {"name": "education", "dtype": {"class_label": {"names": {"0": "neg", "1": "pos"}}}}], "config_name": "prachathai67k", "splits": [{"name": "train", "num_bytes": 865848436, "num_examples": 54379}, {"name": "validation", "num_bytes": 108641386, "num_examples": 6721}, {"name": "test", "num_bytes": 110034036, "num_examples": 6789}], "download_size": 254240975, "dataset_size": 1084523858}}

2024-01-18T11:13:30+00:00

TAGS #task_categories-text-classification #task_ids-topic-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-unknown #region-us

Dataset Card for 'prachathai67k' ================================ Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: URL * Paper: * Leaderboard: * Point of Contact: URL ### Dataset Summary 'prachathai-67k': News Article Corpus and Multi-label Text Classificdation from URL The 'prachathai-67k' dataset was scraped from the news site Prachathai. We filtered out those articles with less than 500 characters of body text, mostly images and cartoons. It contains 67,889 articles wtih 12 curated tags from August 24, 2004 to November 15, 2018. The dataset was originally scraped by @lukkiddd and cleaned by @cstorm125. Download the dataset here. You can also see preliminary exploration in URL. This dataset is a part of pyThaiNLP Thai text classification-benchmarks. For the benchmark, we selected the following tags with substantial volume that resemble classifying types of articles: * 'การเมือง' - politics * 'สิทธิมนุษยชน' - human\_rights * 'คุณภาพชีวิต' - quality\_of\_life * 'ต่างประเทศ' - international * 'สังคม' - social * 'สิ่งแวดล้อม' - environment * 'เศรษฐกิจ' - economics * 'วัฒนธรรม' - culture * 'แรงงาน' - labor * 'ความมั่นคง' - national\_security * 'ไอซีที' - ict * 'การศึกษา' - education ### Supported Tasks and Leaderboards multi-label text classification, language modeling ### Languages Thai Dataset Structure ----------------- ### Data Instances {'body\_text': '17 พ.ย. 2558 Blognone [1] รายงานว่า กลุ่มแฮคเกอร์ Anonymous ประกาศสงครามไซเบอร์กับกลุ่มหัวรุนแรงหลังจากกลุ่ม IS ออกมาประกาศว่าเป็นผู้อยู่เบื้องหลังการโจมตีกรุงปารีสในคืนวันศุกร์ที่ผ่านมา\n\n\nภาพในคลิปใน YouTube โฆษกของกลุ่มแฮคเกอร์สวมหน้ากากที่เป็นสัญลักษณ์ของกลุ่มได้ออกมาอ่านแถลงเป็นภาษาฝรั่งเศส มีใจความว่า จากการโจมตีของกลุ่ม IS ในกรุงปารีส กลุ่ม Anonymous ทั่วโลกจะตามล่ากลุ่ม IS เหมือนที่เคยทำตอนที่มีการโจมตีสำนักพิมพ์ Charlie Hebdo และครั้งนี้จะเป็นปฏิบัติการโจมตีครั้งใหญ่ที่สุดของกลุ่ม Anonymous เลย นอกจากนี้กลุ่ม Anonymous ยังแสดงความเสียใจต่อครอบครัวผู้สูญเสียในเหตุการณ์ครั้งนี้\nกลุ่ม Anonymous เคยประกาศสงครามกับกลุ่ม IS หลังจากการโจมตีสำนักพิมพ์ Charlie Hebdo ที่ฝรั่งเศสเมื่อต้นปีที่ผ่านมา ซึ่งครั้งนั้นกลุ่ม Anonymous อ้างว่าได้ระงับบัญชีผู้ใช้งานที่เกี่ยวข้องกับ IS ไปหลายพันบัญชี (อ่านรายละเอียดเพิ่มเติม จากBlognone ที่\xa0\xa0กลุ่มแฮคเกอร์ Anonymous ประกาศสงครามไซเบอร์ขอกวาดล้างพวก ISIS [2])', 'culture': 0, 'date': '2015-11-17 18:14', 'economics': 0, 'education': 0, 'environment': 0, 'human\_rights': 0, 'ict': 1, 'international': 1, 'labor': 0, 'national\_security': 0, 'politics': 0, 'quality\_of\_life': 0, 'social': 0, 'title': 'แฮคเกอร์ Anonymous ลั่นทำสงครามไซเบอร์ครั้งใหญ่สุดกับกลุ่ม IS', 'url': 'URL {'body\_text': 'แถลงการณ์\n\n\xa0\n\nองค์การนักศึกษามหาวิทยาลัยธรรมศาสตร์\n\n\xa0\n\nมหาวิทยาลัยธรรมศาสตร์ก่อตั้งขึ้นภายใต้แนวคิดการให้การศึกษากับประชาชนเพื่อสนับสนุนการปกครองระบอบประชาธิปไตย อีกทั้งยังเป็นสถาบันหนึ่งที่อยู่เคียงข้างประชาชนมาโดยตลอด\n\n\xa0\n\nสถานการณ์สังคมไทยปัจจุบันได้เกิดความขัดแย้งทางการเมือง ทางแนวคิด จนลุกลามเป็นวิกฤตการณ์อันหาทางออกได้ยากยิ่ง องค์กรนักศึกษามหาวิทยาลัยธรรมศาสตร์ขอร้องเรียนและเสนอแนะต่อทุกฝ่าย โดยยึดหลักแนวทางตามรัฐธรรมนูญแห่งราชอาณาจักรไทย พ.ศ. ๒๕๕๐ อันเป็นกฎหมายสูงสุดในการจัดการปกครองรัฐ ที่มีผลบังคับใช้อยู่ในปัจจุบันซึ่งผ่านการประชามติจากปวงชนชาวไทยเมื่อวันที่ ๑๙ สิงหาคม พ.ศ. ๒๕๕๐ แล้วดังต่อนี้\n\n\xa0\n\n๑.การชุมชมโดยสงบและปราศจากอาวุธย่อมได้รับการคุ้มครองตามรัฐธรรมนูญ แต่หากการชุมนุมและเคลื่อนไหวของกลุ่มใดๆ มีการละเมิดสิทธิและเสรีภาพของผู้อื่นหรือก่อให้เกิดความเสียหายต่อชีวิตและทรัพย์สินของบุคคลและส่วนรวมนั้น ไม่สามารถกระทำได้ การใช้ความรุนแรง การกระทำอุกอาจต่างๆ ทั้งต่อบุคคลและทรัพย์สิน การยั่วยุ ปลุกระดมเพื่อหวังผลในการปะทะต่อสู้ จึงควรได้รับการกล่าวโทษ\n\n\xa0\n\nดังนั้นทั้งกลุ่มพันธมิตรประชาชนเพื่อประชาธิปไตย (พธม.) และกลุ่มแนวร่วมประชาธิปไตยไม่เอาเผด็จการแห่งชาติ (นปช.) จึงควรยอมรับกระบวนการตามกฎหมาย และหากถูกกล่าวหาไม่ว่ากรณีใดๆ ก็ควรพิสูจน์ความบริสุทธิ์โดยใช้กระบวนการยุติธรรม และหากจะยังชุมนุมต่อไปก็ยังคงทำได้ภายใต้บทบัญญัติแห่งกฎหมาย\n\n\xa0\n\nองค์กรนักศึกษามหาวิทยาลัยธรรมศาสตร์ จึงร้องขอให้หน่วยงานต่างๆ ที่เกี่ยวข้องดำเนินการตามกระบวนการทางกฎหมายกับการกระทำที่ผิดบทบัญญัติแห่งกฎหมายที่ทุกฝ่ายได้กระทำไป\n\n\xa0\n\n๒.นายสมัคร สุนทรเวช นายกรัฐมนตรี ไม่มีความเหมาะสมในการบริหารราชการแผ่นดินขาดหลักธรรมาภิบาล แต่ทั้งนี้นายสมัคร สุนทรเวช ยังคงยืนยันและกล่าวอ้างความชอบธรรมตามระบอบประชาธิปไตยภายใต้รัฐธรรมนูญ โดยไม่คำนึงถึงกระแสเรียกร้องใดๆ อันส่งผลให้ความขัดแย้งทางสังคมยิ่งบานปลายจนกลายเป็นวิกฤตการณ์เช่นปัจจุบัน ซึ่งก่อให้เกิดความเสียหายต่อประเทศแนวโน้มจะคลี่คลาย\n\n\xa0\n\nองค์การนักศึกษามหาวิทยาลัยธรรมศาสตร์ จึงเห็นว่า ควรใช้สิทธิตามรัฐธรรมนูญแห่งราชอาณาจักรไทย พุทธศักราช ๒๕๕๐ มาตรา ๑๖๔ โดยการเข้าชื่อเพื่อร้องต่อประธานวุฒิสภาเพื่อให้มีมติตามมาตรา ๒๗๔ ให้ถอดถอนนายสมัคร สุนทรเวช ออกจากตำแหน่งนายกรัฐมนตรีตามมาตรา ๒๗๐ ณ ลานโพ มหาวิทยาลัยธรรมศาสตร์ ท่าพระจันทร์ อาคารเรียนรวมสังคมศาสตร์ อาคารปิยชาติ และตึกกิจกรรมนักศึกษา มหาวิทยาลัยธรรมศาสตร์ ศูนย์รังสิต\n\n\xa0\n\n\xa0\n\nด้วยความสมานฉันท์\n\nองค์การนักศึกษามหาวิทยาลัยธรรมศาสตร์', 'culture': 0, 'date': '2008-09-06 03:36', 'economics': 0, 'education': 0, 'environment': 0, 'human\_rights': 0, 'ict': 0, 'international': 0, 'labor': 0, 'national\_security': 0, 'politics': 1, 'quality\_of\_life': 0, 'social': 0, 'title': 'แถลงการณ์ อมธ.แนะใช้สิทธิ ตาม รธน.เข้าชื่อร้องต่อประธานวุฒิสภาถอดถอน "สมัคร" จากตำแหน่งนายกฯ', 'url': 'URL ### Data Fields * 'url': url of the article * 'date': date the article was published * 'title': title of the article * 'body\_text': body text of the article * 'politics': 1 if sample has this tag else 0 * 'human\_rights': 1 if sample has this tag else 0 * 'quality\_of\_life': 1 if sample has this tag else 0 * 'international': 1 if sample has this tag else 0 * 'social': 1 if sample has this tag else 0 * 'environment': 1 if sample has this tag else 0 * 'economics': 1 if sample has this tag else 0 * 'culture': 1 if sample has this tag else 0 * 'labor': 1 if sample has this tag else 0 * 'national\_security': 1 if sample has this tag else 0 * 'ict': 1 if sample has this tag else 0 * 'education': 1 if sample has this tag else 0 ### Data Splits Dataset Creation ---------------- ### Curation Rationale The data was scraped from the news site Prachathai from August 24, 2004 to November 15, 2018. The initial intention was to use the dataset as a benchmark for Thai text classification. Due to the size of the dataset, it can also be used for language modeling. ### Source Data #### Initial Data Collection and Normalization 67,889 articles wtih 51,797 tags were scraped from the news site Prachathai from August 24, 2004 to November 15, 2018. We filtered out those articles with less than 500 characters of body text, mostly images and cartoons. #### Who are the source language producers? URL ### Annotations #### Annotation process Tags are annotated for the news website URL #### Who are the annotators? We assume that the reporters who wrote the articles or other Prachathai staff gave each article its tags. ### Personal and Sensitive Information We do not expect any personal and sensitive information to be present since all data are public news articles. Considerations for Using the Data --------------------------------- ### Social Impact of Dataset * classification benchmark for multi-label Thai text classification ### Discussion of Biases URL is a left-leaning, human-right-focused news site, and thus unusual news labels such as human rights and quality of life. The news articles are expected to be left-leaning in contents. ### Other Known Limitations Dataset provided for research purposes only. Please check dataset license for additional information. Additional Information ---------------------- ### Dataset Curators PyThaiNLP ### Licensing Information CC-BY-NC @misc{prachathai67k, author = {cstorm125, lukkiddd }, title = {prachathai67k}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, howpublished={\url{URL } ### Contributions Thanks to @cstorm125 for adding this dataset.

b5178e3203fb37b8556e2e5ecbef5cab9be77615

# Dataset Card for pragmeval ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@sileod](https://github.com/sileod) for adding this dataset.

pragmeval

2022-03-02T23:29:22+00:00

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"idx", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 592520, "num_examples": 5712}, {"name": "validation", "num_bytes": 65215, "num_examples": 634}, {"name": "test", "num_bytes": 251799, "num_examples": 2424}], "download_size": 5330724, "dataset_size": 909534}, {"config_name": "emobank-arousal", "features": [{"name": "sentence", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "low", "1": "high"}}}}, {"name": "idx", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 567660, "num_examples": 5470}, {"name": "validation", "num_bytes": 71221, "num_examples": 684}, {"name": "test", "num_bytes": 69276, "num_examples": 683}], "download_size": 5330724, "dataset_size": 708157}, {"config_name": "switchboard", "features": [{"name": "sentence", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Response Acknowledgement", "1": "Uninterpretable", "2": "Or-Clause", "3": "Reject", "4": "Statement-non-opinion", "5": "3rd-party-talk", "6": "Repeat-phrase", "7": "Hold Before Answer/Agreement", "8": "Signal-non-understanding", "9": "Offers, Options Commits", "10": "Agree/Accept", "11": "Dispreferred Answers", "12": "Hedge", "13": "Action-directive", "14": "Tag-Question", "15": "Self-talk", "16": "Yes-No-Question", "17": "Rhetorical-Question", "18": "No Answers", "19": "Open-Question", "20": "Conventional-closing", "21": "Other Answers", "22": "Acknowledge (Backchannel)", "23": "Wh-Question", "24": "Declarative Wh-Question", "25": "Thanking", "26": "Yes Answers", "27": "Affirmative Non-yes Answers", "28": "Declarative Yes-No-Question", "29": "Backchannel in Question Form", "30": "Apology", "31": "Downplayer", "32": "Conventional-opening", "33": "Collaborative Completion", "34": "Summarize/Reformulate", "35": "Negative Non-no Answers", "36": "Statement-opinion", "37": "Appreciation", "38": "Other", "39": "Quotation", "40": "Maybe/Accept-part"}}}}, {"name": "idx", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 1021220, "num_examples": 18930}, {"name": "validation", "num_bytes": 116058, "num_examples": 2113}, {"name": "test", "num_bytes": 34013, "num_examples": 649}], "download_size": 5330724, "dataset_size": 1171291}, {"config_name": "persuasiveness-eloquence", "features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "low", "1": "high"}}}}, {"name": "idx", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 153946, "num_examples": 725}, {"name": "validation", "num_bytes": 19376, "num_examples": 91}, {"name": "test", "num_bytes": 18379, "num_examples": 90}], "download_size": 5330724, "dataset_size": 191701}, {"config_name": "mrda", "features": [{"name": "sentence", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Declarative-Question", "1": "Statement", "2": "Reject", "3": "Or-Clause", "4": "3rd-party-talk", "5": "Continuer", "6": "Hold Before Answer/Agreement", "7": "Assessment/Appreciation", "8": "Signal-non-understanding", "9": "Floor Holder", "10": "Sympathy", "11": "Dispreferred Answers", "12": "Reformulate/Summarize", "13": "Exclamation", "14": "Interrupted/Abandoned/Uninterpretable", "15": "Expansions of y/n Answers", "16": "Action-directive", "17": "Tag-Question", "18": "Accept", "19": "Rhetorical-question Continue", "20": "Self-talk", "21": "Rhetorical-Question", "22": "Yes-No-question", "23": "Open-Question", "24": "Rising Tone", "25": "Other Answers", "26": "Commit", "27": "Wh-Question", "28": "Repeat", "29": "Follow Me", "30": "Thanking", "31": "Offer", "32": "About-task", "33": "Reject-part", "34": "Affirmative Non-yes Answers", "35": "Apology", "36": "Downplayer", "37": "Humorous Material", "38": "Accept-part", "39": "Collaborative Completion", "40": "Mimic Other", "41": "Understanding Check", "42": "Misspeak Self-Correction", "43": "Or-Question", "44": "Topic Change", "45": "Negative Non-no 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2024-01-18T11:13:32+00:00

TAGS #task_categories-text-classification #task_ids-multi-class-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #size_categories-1K<n<10K #size_categories-n<1K #source_datasets-original #language-English #license-unknown #region-us

# Dataset Card for pragmeval ## Table of Contents - Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @sileod for adding this dataset.

8c6f03845443e3ac66bcf799a26340bc5262652b

# Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Interactive Demo:** [Interactive demo](http://protoqa.com) - **Repository:** [proto_qa repository](https://github.com/iesl/protoqa-data) - **Paper:** [proto_qa paper](https://arxiv.org/pdf/2005.00771.pdf) - **Point of Contact:** [Michael Boratko](mailto:mboratko@cs.umass.edu) [Xiang Lorraine Li](mailto:xiangl@cs.umass.edu) [Tim O’Gorman](mailto:togorman@cs.umass.edu) [Rajarshi Das](mailto:rajarshi@cs.umass.edu) [Dan Le](mailto:dhle@cs.umass.edu) [Andrew McCallum](mailto:mccallum@cs.umass.edu) ### Dataset Summary This dataset is for studying computational models trained to reason about prototypical situations. It is anticipated that still would not lead to usage in a downstream task, but as a way of studying the knowledge (and biases) of prototypical situations already contained in pre-trained models. The data it is partially based on (Family Feud). Using deterministic filtering a sampling from a larger set of all transcriptions was built. Scraped data was acquired through fan transcriptions at [family feud](https://www.familyfeudinfo.com) and [family feud friends](http://familyfeudfriends.arjdesigns.com/); crowdsourced data was acquired with FigureEight (now Appen) ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The text in the dataset is in English ## Dataset Structure ### Data Instances **What do the instances that comprise the dataset represent?**<br> Each represents a survey question from Family Feud game and reported answer clusters **How many instances are there in total?**<br> 9789 instances **What data does each instance consist of?**<br> Each instance is a question, a set of answers, and a count associated with each answer. ### Data Fields **Data Files**<br> Each line is a json dictionary, in which:<br> **question** contains the question (in original and a normalized form)<br> **answerstrings** contains the original answers provided by survey respondents (when available), along with the counts for each string. Because the FamilyFeud data has only cluster names rather than strings, those cluster names are included with 0 weight.<br> **answer-clusters** list of clusters, with the count of each cluster and the strings included in that cluster. Each cluster is given a unique ID that can be linked to in the assessment files. The simplified configuration includes: - `question`: contains the original question - `normalized-question`: contains the question in normalized form - `totalcount`: unique identifier of the comment (can be used to look up the entry in the raw dataset) - `id`: unique identifier of the commen - `source`: unique identifier of the commen - `answerstrings`: unique identifier of the commen - `answer-clusters | answers-cleaned`: list clusters of: * `clusterid`: Each cluster is given a unique ID that can be linked to in the assessment files * `count`: the count of each cluster * `answers`: the strings included in that cluster In addition to the above, there is crowdsourced assessments file. The config "proto_qa_cs_assessments" provides mappings from additional human and model answers to clusters, to evaluate different assessment methods. **Assessment files**<br> The file **data/dev/crowdsource_dev.assessments.jsonl** contains mappings from additional human and model answers to clusters, to evaluate different assessment methods. Each line contains:<br> * `question`: contains the ID of the question * `assessments`: maps individual strings to one of three options, either the answer cluster id, "invalid" if the answer is judged to be bad, or "valid_new_cluster" if the answer is valid but does not match any existing clusters. ### Data Splits * proto_qa `Train` : 8781 instances for training or fine-tuning scraped from Family Feud fan sites (see paper). Scraped data has answer clusters with sizes, but only has a single string per cluster (corresponding to the original cluster name * proto_qa `Validation` : 979 instances sampled from the same Family Feud data, for use in model validation and development. * proto_qa_cs `Validation` :: 51 questions collected with exhaustive answer collection and manual clustering, matching the details of the eval test set (roughly 100 human answers per question) **data/dev/crowdsource_dev.assessments.jsonl**: assessment file (format described above) for study of assessment methods. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization **How was the data associated with each instance acquired?**<br> Scraped data was acquired through fan transcriptions at https://www.familyfeudinfo.com and http://familyfeudfriends.arjdesigns.com/ ; crowdsourced data was acquired with FigureEight (now Appen) **If the dataset is a sample from a larger set, what was the sampling strategy?**<br> Deterministic filtering was used (noted elsewhere), but no probabilistic sampling was used. **Who was involved in the data collection process (e.g., students,crowdworkers , contractors) and how were they compensated?**<br> Crowdworkers were used in the evalaution dataset. Time per task was calculated and per-task cost was set to attempt to provide a living wage **Over what timeframe was the data collected?**<br> Crowdsource answers were collected between Fall of 2018 and Spring of 2019. Scraped data covers question-answer pairs collected since the origin of the show in 1976 #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process **Was any preprocessing/cleaning/labeling of the data done?**<br> Obvious typos in the crowdsourced answer set were corrected #### Who are the annotators? The original question-answer pairs were generated by surveys of US English-speakers in a period from 1976 to present day. Crowd-sourced evaluation was constrained geographically to US English speakers but not otherwise constrained. Additional demographic data was not collected. ### Personal and Sensitive Information **Does the dataset contain data that might be considered sensitive in any way?**<br> As the questions address prototypical/stereotypical activities, models trained on more offensive material (such as large language models) may provide offensive answers to such questions. While we had found a few questions which we worried would actually encourage models to provide offensive answers, we cannot guarantee that the data is clean of such questions. Even a perfectly innocent version of this dataset would be encouraging models to express generalizations about situations, and therefore may provoke offensive material that is oontained in language models **Does the dataset contain data that might be considered confidential?**<br> The data does not concern individuals and thus does not contain any information to identify persons. Crowdsourced answers do not provide any user identifiers. ## Considerations for Using the Data ### Social Impact of Dataset **Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety?**<br> Not egregiously so (questions are all designed to be shown on television or replications thereof), ### Discussion of Biases **Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses?** <br>All original questions were written with US television audiences in mind, and therefore characterize prototypical situations with a specific lens. Any usages which deploy this to actually model prototypical situations globally will carry that bias. **Are there tasks for which the dataset should not be used?** <br>We caution regarding free-form use of this dataset for interactive "commonsense question answering" purposes without more study of the biases and stereotypes learned by such models. ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The listed authors are maintaining/supporting the dataset. They pledge to help support issues, but cannot guarantee long-term support ### Licensing Information The Proto_qa dataset is licensed under the [Creative Commons Attribution 4.0 International](https://github.com/iesl/protoqa-data/blob/master/LICENSE) ### Citation Information ``` @InProceedings{ huggingface:dataset, title = {ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning}, authors = {Michael Boratko, Xiang Lorraine Li, Tim O’Gorman, Rajarshi Das, Dan Le, Andrew McCallum}, year = {2020}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {https://github.com/iesl/protoqa-data}, } ``` ### Contributions Thanks to [@bpatidar](https://github.com/bpatidar) for adding this dataset.

proto_qa

2022-03-02T23:29:22+00:00

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2024-01-18T11:13:33+00:00

TAGS #task_categories-question-answering #task_ids-multiple-choice-qa #task_ids-open-domain-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #language_creators-other #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc-by-4.0 #arxiv-2005.00771 #region-us

# Dataset Card for [Dataset Name] ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Interactive Demo: Interactive demo - Repository: proto_qa repository - Paper: proto_qa paper - Point of Contact: Michael Boratko Xiang Lorraine Li Tim O’Gorman Rajarshi Das Dan Le Andrew McCallum ### Dataset Summary This dataset is for studying computational models trained to reason about prototypical situations. It is anticipated that still would not lead to usage in a downstream task, but as a way of studying the knowledge (and biases) of prototypical situations already contained in pre-trained models. The data it is partially based on (Family Feud). Using deterministic filtering a sampling from a larger set of all transcriptions was built. Scraped data was acquired through fan transcriptions at family feud and family feud friends; crowdsourced data was acquired with FigureEight (now Appen) ### Supported Tasks and Leaderboards ### Languages The text in the dataset is in English ## Dataset Structure ### Data Instances What do the instances that comprise the dataset represent?<br> Each represents a survey question from Family Feud game and reported answer clusters How many instances are there in total?<br> 9789 instances What data does each instance consist of?<br> Each instance is a question, a set of answers, and a count associated with each answer. ### Data Fields Data Files<br> Each line is a json dictionary, in which:<br> question contains the question (in original and a normalized form)<br> answerstrings contains the original answers provided by survey respondents (when available), along with the counts for each string. Because the FamilyFeud data has only cluster names rather than strings, those cluster names are included with 0 weight.<br> answer-clusters list of clusters, with the count of each cluster and the strings included in that cluster. Each cluster is given a unique ID that can be linked to in the assessment files. The simplified configuration includes: - 'question': contains the original question - 'normalized-question': contains the question in normalized form - 'totalcount': unique identifier of the comment (can be used to look up the entry in the raw dataset) - 'id': unique identifier of the commen - 'source': unique identifier of the commen - 'answerstrings': unique identifier of the commen - 'answer-clusters | answers-cleaned': list clusters of: * 'clusterid': Each cluster is given a unique ID that can be linked to in the assessment files * 'count': the count of each cluster * 'answers': the strings included in that cluster In addition to the above, there is crowdsourced assessments file. The config "proto_qa_cs_assessments" provides mappings from additional human and model answers to clusters, to evaluate different assessment methods. Assessment files<br> The file data/dev/crowdsource_dev.URL contains mappings from additional human and model answers to clusters, to evaluate different assessment methods. Each line contains:<br> * 'question': contains the ID of the question * 'assessments': maps individual strings to one of three options, either the answer cluster id, "invalid" if the answer is judged to be bad, or "valid_new_cluster" if the answer is valid but does not match any existing clusters. ### Data Splits * proto_qa 'Train' : 8781 instances for training or fine-tuning scraped from Family Feud fan sites (see paper). Scraped data has answer clusters with sizes, but only has a single string per cluster (corresponding to the original cluster name * proto_qa 'Validation' : 979 instances sampled from the same Family Feud data, for use in model validation and development. * proto_qa_cs 'Validation' :: 51 questions collected with exhaustive answer collection and manual clustering, matching the details of the eval test set (roughly 100 human answers per question) data/dev/crowdsource_dev.URL: assessment file (format described above) for study of assessment methods. ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization How was the data associated with each instance acquired?<br> Scraped data was acquired through fan transcriptions at URL and URL ; crowdsourced data was acquired with FigureEight (now Appen) If the dataset is a sample from a larger set, what was the sampling strategy?<br> Deterministic filtering was used (noted elsewhere), but no probabilistic sampling was used. Who was involved in the data collection process (e.g., students,crowdworkers , contractors) and how were they compensated?<br> Crowdworkers were used in the evalaution dataset. Time per task was calculated and per-task cost was set to attempt to provide a living wage Over what timeframe was the data collected?<br> Crowdsource answers were collected between Fall of 2018 and Spring of 2019. Scraped data covers question-answer pairs collected since the origin of the show in 1976 #### Who are the source language producers? ### Annotations #### Annotation process Was any preprocessing/cleaning/labeling of the data done?<br> Obvious typos in the crowdsourced answer set were corrected #### Who are the annotators? The original question-answer pairs were generated by surveys of US English-speakers in a period from 1976 to present day. Crowd-sourced evaluation was constrained geographically to US English speakers but not otherwise constrained. Additional demographic data was not collected. ### Personal and Sensitive Information Does the dataset contain data that might be considered sensitive in any way?<br> As the questions address prototypical/stereotypical activities, models trained on more offensive material (such as large language models) may provide offensive answers to such questions. While we had found a few questions which we worried would actually encourage models to provide offensive answers, we cannot guarantee that the data is clean of such questions. Even a perfectly innocent version of this dataset would be encouraging models to express generalizations about situations, and therefore may provoke offensive material that is oontained in language models Does the dataset contain data that might be considered confidential?<br> The data does not concern individuals and thus does not contain any information to identify persons. Crowdsourced answers do not provide any user identifiers. ## Considerations for Using the Data ### Social Impact of Dataset Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety?<br> Not egregiously so (questions are all designed to be shown on television or replications thereof), ### Discussion of Biases Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses? <br>All original questions were written with US television audiences in mind, and therefore characterize prototypical situations with a specific lens. Any usages which deploy this to actually model prototypical situations globally will carry that bias. Are there tasks for which the dataset should not be used? <br>We caution regarding free-form use of this dataset for interactive "commonsense question answering" purposes without more study of the biases and stereotypes learned by such models. ### Other Known Limitations ## Additional Information ### Dataset Curators The listed authors are maintaining/supporting the dataset. They pledge to help support issues, but cannot guarantee long-term support ### Licensing Information The Proto_qa dataset is licensed under the Creative Commons Attribution 4.0 International ### Contributions Thanks to @bpatidar for adding this dataset.

5b2c7649aa6be2609bdcb7d18e946c3d8cf75413

# Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://zil.ipipan.waw.pl/PolishSummariesCorpus - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The Polish Summaries Corpus contains news articles and their summaries. We used summaries of the same article as positive pairs and sampled the most similar summaries of different articles as negatives. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Polish ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - extract_text: text to summarise - summary_text: summary of extracted text - label: 1 indicates summary is similar, 0 means that it is not similar ### Data Splits Data is splitted in train and test dataset. Test dataset doesn't have label column, so -1 is set instead. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information CC BY-SA 3.0 ### Citation Information @inproceedings{ogro:kop:14:lrec, title={The {P}olish {S}ummaries {C}orpus}, author={Ogrodniczuk, Maciej and Kope{\'c}, Mateusz}, booktitle = "Proceedings of the Ninth International {C}onference on {L}anguage {R}esources and {E}valuation, {LREC}~2014", year = "2014", } ### Contributions Thanks to [@abecadel](https://github.com/abecadel) for adding this dataset.

psc

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["other"], "language": ["pl"], "license": ["cc-by-sa-3.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["summarization"], "task_ids": ["news-articles-summarization"], "pretty_name": "psc", "dataset_info": {"features": [{"name": "extract_text", "dtype": "string"}, {"name": "summary_text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}], "splits": [{"name": "train", "num_bytes": 5026582, "num_examples": 4302}, {"name": "test", "num_bytes": 1292103, "num_examples": 1078}], "download_size": 2357808, "dataset_size": 6318685}}

2024-01-18T11:13:35+00:00

TAGS #task_categories-summarization #task_ids-news-articles-summarization #annotations_creators-expert-generated #language_creators-other #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Polish #license-cc-by-sa-3.0 #region-us

# Dataset Card for [Dataset Name] ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary The Polish Summaries Corpus contains news articles and their summaries. We used summaries of the same article as positive pairs and sampled the most similar summaries of different articles as negatives. ### Supported Tasks and Leaderboards ### Languages Polish ## Dataset Structure ### Data Instances ### Data Fields - extract_text: text to summarise - summary_text: summary of extracted text - label: 1 indicates summary is similar, 0 means that it is not similar ### Data Splits Data is splitted in train and test dataset. Test dataset doesn't have label column, so -1 is set instead. ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information CC BY-SA 3.0 @inproceedings{ogro:kop:14:lrec, title={The {P}olish {S}ummaries {C}orpus}, author={Ogrodniczuk, Maciej and Kope{\'c}, Mateusz}, booktitle = "Proceedings of the Ninth International {C}onference on {L}anguage {R}esources and {E}valuation, {LREC}~2014", year = "2014", } ### Contributions Thanks to @abecadel for adding this dataset.

d29c6b61aa5ff8d6d0dc52b98da60237292c1fcc

# Dataset Card for Penn Treebank ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://catalog.ldc.upenn.edu/LDC99T42 - **Repository:** 'https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.train.txt', 'https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.valid.txt', 'https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.test.txt' - **Paper:** https://www.aclweb.org/anthology/J93-2004.pdf - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary This is the Penn Treebank Project: Release 2 CDROM, featuring a million words of 1989 Wall Street Journal material. The rare words in this version are already replaced with <unk> token. The numbers are replaced with <N> token. ### Supported Tasks and Leaderboards Language Modelling ### Languages The text in the dataset is in American English ## Dataset Structure ### Data Instances [Needs More Information] ### Data Fields [Needs More Information] ### Data Splits [Needs More Information] ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information Dataset provided for research purposes only. Please check dataset license for additional information. ### Citation Information @article{marcus-etal-1993-building, title = "Building a Large Annotated Corpus of {E}nglish: The {P}enn {T}reebank", author = "Marcus, Mitchell P. and Santorini, Beatrice and Marcinkiewicz, Mary Ann", journal = "Computational Linguistics", volume = "19", number = "2", year = "1993", url = "https://www.aclweb.org/anthology/J93-2004", pages = "313--330", } ### Contributions Thanks to [@harshalmittal4](https://github.com/harshalmittal4) for adding this dataset.

ptb_text_only

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"], "pretty_name": "Penn Treebank", "license_details": "LDC User Agreement for Non-Members", "dataset_info": {"features": [{"name": "sentence", "dtype": "string"}], "config_name": "penn_treebank", "splits": [{"name": "train", "num_bytes": 5143706, "num_examples": 42068}, {"name": "test", "num_bytes": 453710, "num_examples": 3761}, {"name": "validation", "num_bytes": 403156, "num_examples": 3370}], "download_size": 5951345, "dataset_size": 6000572}}

2024-01-18T11:13:39+00:00

TAGS #task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-other #region-us

# Dataset Card for Penn Treebank ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: 'URL 'URL 'URL - Paper: URL - Leaderboard: - Point of Contact: ### Dataset Summary This is the Penn Treebank Project: Release 2 CDROM, featuring a million words of 1989 Wall Street Journal material. The rare words in this version are already replaced with <unk> token. The numbers are replaced with <N> token. ### Supported Tasks and Leaderboards Language Modelling ### Languages The text in the dataset is in American English ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information Dataset provided for research purposes only. Please check dataset license for additional information. @article{marcus-etal-1993-building, title = "Building a Large Annotated Corpus of {E}nglish: The {P}enn {T}reebank", author = "Marcus, Mitchell P. and Santorini, Beatrice and Marcinkiewicz, Mary Ann", journal = "Computational Linguistics", volume = "19", number = "2", year = "1993", url = "URL pages = "313--330", } ### Contributions Thanks to @harshalmittal4 for adding this dataset.

8263182bec2a83be3f73706759adada1e1bfe3c6

# Dataset Card for PubMed ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** : [https://www.nlm.nih.gov/databases/download/pubmed_medline.html]() - **Documentation:** : [https://www.nlm.nih.gov/databases/download/pubmed_medline_documentation.html]() - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** [National Center for Biotechnology Information](mailto:info@ncbi.nlm.nih.gov) ### Dataset Summary PubMed comprises more than 36 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites. NLM produces a baseline set of PubMed citation records in XML format for download on an annual basis. The annual baseline is released in December of each year. - Last Updated December 15, 2023 Each day, NLM produces update files that include new, revised, and deleted citations. Source: https://ftp.ncbi.nlm.nih.gov/pubmed/README.txt ### Supported Tasks and Leaderboards [More Information Needed] ### Languages - English ## Dataset Structure Bear in mind the data comes from XML that have various tags that are hard to reflect in a concise JSON format. Tags and list are kind of non "natural" to XML documents leading this library to make some choices regarding data. "Journal" info was dropped altogether as it would have led to many fields being empty all the time. The hierarchy is also a bit unnatural but the choice was made to keep as close as possible to the original data for future releases that may change schema from NLM's side. Author has been kept and contains either "ForeName", "LastName", "Initials", or "CollectiveName". (All the fields will be present all the time, but only some will be filled) ### Data Instances ```json { "MedlineCitation": { "PMID": 0, "DateCompleted": {"Year": 0, "Month": 0, "Day": 0}, "NumberOfReferences": 0, "DateRevised": {"Year": 0, "Month": 0, "Day": 0}, "Article": { "Abstract": {"AbstractText": "Some abstract (can be missing)" }, "ArticleTitle": "Article title", "AuthorList": {"Author": [ {"FirstName": "John", "ForeName": "Doe", "Initials": "JD", "CollectiveName": ""} {"CollectiveName": "The Manhattan Project", "FirstName": "", "ForeName": "", "Initials": ""} ]}, "Language": "en", "GrantList": { "Grant": [], }, "PublicationTypeList": {"PublicationType": []}, }, "MedlineJournalInfo": {"Country": "France"}, "ChemicalList": {"Chemical": [{ "RegistryNumber": "XX", "NameOfSubstance": "Methanol" }]}, "CitationSubset": "AIM", "MeshHeadingList": { "MeshHeading": [], }, }, "PubmedData": { "ArticleIdList": {"ArticleId": "10.1002/bjs.1800650203"}, "PublicationStatus": "ppublish", "History": {"PubMedPubDate": [{"Year": 0, "Month": 0, "Day": 0}]}, "ReferenceList": [{"Citation": "Somejournal", "CitationId": 01}], }, } ``` ### Data Fields Main Fields will probably interest people are: - "MedlineCitation" > "Article" > "AuthorList" > "Author" - "MedlineCitation" > "Article" > "Abstract" > "AbstractText" - "MedlineCitation" > "Article" > "Article Title" - "MedlineCitation" > "ChemicalList" > "Chemical" - "MedlineCitation" > "NumberOfReferences" ### Data Splits There are no splits in this dataset. It is given as is. ## Dataset Creation ### Curation Rationale The use of "Medline" in an element name does not mean the record represents a citation from a MEDLINE-selected journal. When the NLM DTDs and XML elements were first created, MEDLINE records were the only data exported. Now NLM exports citations other than MEDLINE records. To minimize unnecessary disruption to users of the data, NLM has retained the original element names (e.g., MedlineCitation, MedlineJournalInfo, MedlineTA). Policies affecting data creation have evolved over the years. Some PubMed records are added or revised well after the cited article was first published. In these cases, on occasion an element that had not yet been created when the article was published may appear on the record. For example, the Abstract element was not created until 1975, but some records published before 1975 but added to PubMed after 1975 contain <Abstract>. It is also possible that an element may be treated differently from the way it would have been treated had the record been created or maintained near the time the article was published. For example, the number of <Author> occurrences can diverge from the policies stated in the NLM author indexing policy (https://pubmed.ncbi.nlm.nih.gov/help/#author-indexing-policy). Lastly, as of October 2016, the publisher of the original article has the capability to edit the PubMed record’s citation data, with the exception of MeSH data, using the PubMed Data Management system. PubMed record data for older citations, therefore, may contain data for elements that didn’t exist when the citation was created. ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [National Library of Medicine Terms and Conditions](https://www.nlm.nih.gov/databases/download/terms_and_conditions.html) Downloading PubMed data from the National Library of Medicine FTP servers indicates your acceptance of the following Terms and Conditions. No charges, usage fees or royalties are paid to NLM for these data. #### PubMed Specific Terms: NLM freely provides PubMed data. Please note some abstracts may be protected by copyright. #### General Terms and Conditions Users of the data agree to: - acknowledge NLM as the source of the data in a clear and conspicuous manner, - NOT use the PubMed wordmark or the PubMed logo in association or in connection with user's or any other party's product or service. - NOT adopt, use, or seek to register any mark or trade name confusingly similar to or suggestive of the PubMed wordmark or PubMed logo - NOT to indicate or imply that NLM/NIH/HHS has endorsed its products/services/applications. Users who republish or redistribute the data (services, products or raw data) agree to: - maintain the most current version of all distributed data, or - make known in a clear and conspicuous manner that the products/services/applications do not reflect the most current/accurate data available from NLM. These data are produced with a reasonable standard of care, but NLM makes no warranties express or implied, including no warranty of merchantability or fitness for particular purpose, regarding the accuracy or completeness of the data. Users agree to hold NLM and the U.S. Government harmless from any liability resulting from errors in the data. NLM disclaims any liability for any consequences due to use, misuse, or interpretation of information contained or not contained in the data. NLM does not provide legal advice regarding copyright, fair use, or other aspects of intellectual property rights. See the NLM Copyright page: https://www.nlm.nih.gov/web_policies.html#copyright NLM reserves the right to change the type and format of its machine-readable data. NLM will take reasonable steps to inform users of any changes to the format of the data before the data are distributed via the announcement section or subscription to email and RSS updates. The PubMed wordmark and the PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited. ### Citation Information [Courtesy of the U.S. National Library of Medicine](https://www.nlm.nih.gov/databases/download/terms_and_conditions.html). ### Contributions Thanks to [@Narsil](https://github.com/Narsil) for adding this dataset.

pubmed

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["10M<n<100M"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask", "text-classification"], "task_ids": ["language-modeling", "masked-language-modeling", "text-scoring", "topic-classification"], "paperswithcode_id": "pubmed", "pretty_name": "PubMed", "tags": ["citation-estimation"], "dataset_info": [{"config_name": "2024", "features": [{"name": "MedlineCitation", "struct": [{"name": "PMID", "dtype": "int32"}, {"name": "DateCompleted", "struct": [{"name": "Year", "dtype": "int32"}, {"name": "Month", "dtype": "int32"}, {"name": "Day", "dtype": "int32"}]}, {"name": "NumberOfReferences", "dtype": "int32"}, {"name": "DateRevised", "struct": [{"name": "Year", "dtype": "int32"}, {"name": "Month", "dtype": "int32"}, {"name": "Day", "dtype": "int32"}]}, {"name": "Article", "struct": [{"name": "Abstract", "struct": [{"name": "AbstractText", "dtype": "string"}]}, {"name": "ArticleTitle", "dtype": "string"}, {"name": "AuthorList", "struct": [{"name": "Author", "sequence": [{"name": "LastName", "dtype": "string"}, {"name": "ForeName", "dtype": "string"}, {"name": "Initials", "dtype": "string"}, {"name": "CollectiveName", "dtype": "string"}]}]}, {"name": "Language", "dtype": "string"}, {"name": "GrantList", "struct": [{"name": "Grant", "sequence": [{"name": "GrantID", "dtype": "string"}, {"name": "Agency", "dtype": "string"}, {"name": "Country", "dtype": "string"}]}]}, {"name": "PublicationTypeList", "struct": [{"name": "PublicationType", "sequence": "string"}]}]}, {"name": "MedlineJournalInfo", "struct": [{"name": "Country", "dtype": "string"}]}, {"name": "ChemicalList", "struct": [{"name": "Chemical", "sequence": [{"name": "RegistryNumber", "dtype": "string"}, {"name": "NameOfSubstance", "dtype": "string"}]}]}, {"name": "CitationSubset", "dtype": "string"}, {"name": "MeshHeadingList", "struct": [{"name": "MeshHeading", "sequence": [{"name": "DescriptorName", "dtype": "string"}, {"name": "QualifierName", "dtype": "string"}]}]}]}, {"name": "PubmedData", "struct": [{"name": "ArticleIdList", "sequence": [{"name": "ArticleId", "sequence": "string"}]}, {"name": "PublicationStatus", "dtype": "string"}, {"name": "History", "struct": [{"name": "PubMedPubDate", "sequence": [{"name": "Year", "dtype": "int32"}, {"name": "Month", "dtype": "int32"}, {"name": "Day", "dtype": "int32"}]}]}, {"name": "ReferenceList", "sequence": [{"name": "Citation", "dtype": "string"}, {"name": "CitationId", "dtype": "int32"}]}]}], "splits": [{"name": "train", "num_bytes": 54723097181, "num_examples": 36555430}], "download_size": 45202943276, "dataset_size": 54723097181}]}

2024-01-26T17:52:23+00:00

TAGS #task_categories-text-generation #task_categories-fill-mask #task_categories-text-classification #task_ids-language-modeling #task_ids-masked-language-modeling #task_ids-text-scoring #task_ids-topic-classification #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10M<n<100M #source_datasets-original #language-English #license-other #citation-estimation #region-us

# Dataset Card for PubMed ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: : [URL - Documentation: : [URL - Repository: - Paper: - Leaderboard: - Point of Contact: National Center for Biotechnology Information ### Dataset Summary PubMed comprises more than 36 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites. NLM produces a baseline set of PubMed citation records in XML format for download on an annual basis. The annual baseline is released in December of each year. - Last Updated December 15, 2023 Each day, NLM produces update files that include new, revised, and deleted citations. Source: URL ### Supported Tasks and Leaderboards ### Languages - English ## Dataset Structure Bear in mind the data comes from XML that have various tags that are hard to reflect in a concise JSON format. Tags and list are kind of non "natural" to XML documents leading this library to make some choices regarding data. "Journal" info was dropped altogether as it would have led to many fields being empty all the time. The hierarchy is also a bit unnatural but the choice was made to keep as close as possible to the original data for future releases that may change schema from NLM's side. Author has been kept and contains either "ForeName", "LastName", "Initials", or "CollectiveName". (All the fields will be present all the time, but only some will be filled) ### Data Instances ### Data Fields Main Fields will probably interest people are: - "MedlineCitation" > "Article" > "AuthorList" > "Author" - "MedlineCitation" > "Article" > "Abstract" > "AbstractText" - "MedlineCitation" > "Article" > "Article Title" - "MedlineCitation" > "ChemicalList" > "Chemical" - "MedlineCitation" > "NumberOfReferences" ### Data Splits There are no splits in this dataset. It is given as is. ## Dataset Creation ### Curation Rationale The use of "Medline" in an element name does not mean the record represents a citation from a MEDLINE-selected journal. When the NLM DTDs and XML elements were first created, MEDLINE records were the only data exported. Now NLM exports citations other than MEDLINE records. To minimize unnecessary disruption to users of the data, NLM has retained the original element names (e.g., MedlineCitation, MedlineJournalInfo, MedlineTA). Policies affecting data creation have evolved over the years. Some PubMed records are added or revised well after the cited article was first published. In these cases, on occasion an element that had not yet been created when the article was published may appear on the record. For example, the Abstract element was not created until 1975, but some records published before 1975 but added to PubMed after 1975 contain <Abstract>. It is also possible that an element may be treated differently from the way it would have been treated had the record been created or maintained near the time the article was published. For example, the number of <Author> occurrences can diverge from the policies stated in the NLM author indexing policy (URL Lastly, as of October 2016, the publisher of the original article has the capability to edit the PubMed record’s citation data, with the exception of MeSH data, using the PubMed Data Management system. PubMed record data for older citations, therefore, may contain data for elements that didn’t exist when the citation was created. ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information National Library of Medicine Terms and Conditions Downloading PubMed data from the National Library of Medicine FTP servers indicates your acceptance of the following Terms and Conditions. No charges, usage fees or royalties are paid to NLM for these data. #### PubMed Specific Terms: NLM freely provides PubMed data. Please note some abstracts may be protected by copyright. #### General Terms and Conditions Users of the data agree to: - acknowledge NLM as the source of the data in a clear and conspicuous manner, - NOT use the PubMed wordmark or the PubMed logo in association or in connection with user's or any other party's product or service. - NOT adopt, use, or seek to register any mark or trade name confusingly similar to or suggestive of the PubMed wordmark or PubMed logo - NOT to indicate or imply that NLM/NIH/HHS has endorsed its products/services/applications. Users who republish or redistribute the data (services, products or raw data) agree to: - maintain the most current version of all distributed data, or - make known in a clear and conspicuous manner that the products/services/applications do not reflect the most current/accurate data available from NLM. These data are produced with a reasonable standard of care, but NLM makes no warranties express or implied, including no warranty of merchantability or fitness for particular purpose, regarding the accuracy or completeness of the data. Users agree to hold NLM and the U.S. Government harmless from any liability resulting from errors in the data. NLM disclaims any liability for any consequences due to use, misuse, or interpretation of information contained or not contained in the data. NLM does not provide legal advice regarding copyright, fair use, or other aspects of intellectual property rights. See the NLM Copyright page: URL NLM reserves the right to change the type and format of its machine-readable data. NLM will take reasonable steps to inform users of any changes to the format of the data before the data are distributed via the announcement section or subscription to email and RSS updates. The PubMed wordmark and the PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited. Courtesy of the U.S. National Library of Medicine. ### Contributions Thanks to @Narsil for adding this dataset.

323752ad243ce751ba75c9222d34a3130d080030

# Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [PUBMED_QA homepage](https://pubmedqa.github.io/ ) - **Repository:** [PUBMED_QA repository](https://github.com/pubmedqa/pubmedqa) - **Paper:** [PUBMED_QA: A Dataset for Biomedical Research Question Answering](https://arxiv.org/abs/1909.06146) - **Leaderboard:** [PUBMED_QA: Leaderboard](https://pubmedqa.github.io/) ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@tuner007](https://github.com/tuner007) for adding this dataset.

pubmed_qa

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated", "machine-generated"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M", "10K<n<100K", "1K<n<10K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["multiple-choice-qa"], "paperswithcode_id": "pubmedqa", "pretty_name": "PubMedQA", "config_names": ["pqa_artificial", "pqa_labeled", "pqa_unlabeled"], "dataset_info": [{"config_name": "pqa_artificial", "features": [{"name": "pubid", "dtype": "int32"}, {"name": "question", "dtype": "string"}, {"name": "context", "sequence": [{"name": "contexts", "dtype": "string"}, {"name": "labels", "dtype": "string"}, {"name": "meshes", "dtype": "string"}]}, {"name": "long_answer", "dtype": "string"}, {"name": "final_decision", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 443501057, "num_examples": 211269}], "download_size": 233411194, "dataset_size": 443501057}, {"config_name": "pqa_labeled", "features": [{"name": "pubid", "dtype": "int32"}, {"name": "question", "dtype": "string"}, {"name": "context", "sequence": [{"name": "contexts", "dtype": "string"}, {"name": "labels", "dtype": "string"}, {"name": "meshes", "dtype": "string"}, {"name": "reasoning_required_pred", "dtype": "string"}, {"name": "reasoning_free_pred", "dtype": "string"}]}, {"name": "long_answer", "dtype": "string"}, {"name": "final_decision", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2088898, "num_examples": 1000}], "download_size": 1075513, "dataset_size": 2088898}, {"config_name": "pqa_unlabeled", "features": [{"name": "pubid", "dtype": "int32"}, {"name": "question", "dtype": "string"}, {"name": "context", "sequence": [{"name": "contexts", "dtype": "string"}, {"name": "labels", "dtype": "string"}, {"name": "meshes", "dtype": "string"}]}, {"name": "long_answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 125922964, "num_examples": 61249}], "download_size": 66010017, "dataset_size": 125922964}], "configs": [{"config_name": "pqa_artificial", "data_files": [{"split": "train", "path": "pqa_artificial/train-*"}]}, {"config_name": "pqa_labeled", "data_files": [{"split": "train", "path": "pqa_labeled/train-*"}]}, {"config_name": "pqa_unlabeled", "data_files": [{"split": "train", "path": "pqa_unlabeled/train-*"}]}]}

2024-01-05T16:08:56+00:00

TAGS #task_categories-question-answering #task_ids-multiple-choice-qa #annotations_creators-expert-generated #annotations_creators-machine-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-100K<n<1M #size_categories-10K<n<100K #size_categories-1K<n<10K #source_datasets-original #language-English #license-mit #arxiv-1909.06146 #region-us

# Dataset Card for [Dataset Name] ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: PUBMED_QA homepage - Repository: PUBMED_QA repository - Paper: PUBMED_QA: A Dataset for Biomedical Research Question Answering - Leaderboard: PUBMED_QA: Leaderboard ### Dataset Summary ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @tuner007 for adding this dataset.

80469d8465b38b725c9c466eb3eb5b8808c1e830

# Dataset Card for [py_ast] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **homepage**: [py150](https://www.sri.inf.ethz.ch/py150) - **Paper**: [Probabilistic Model for Code with Decision Trees](https://www.semanticscholar.org/paper/Probabilistic-model-for-code-with-decision-trees-Raychev-Bielik/62e176977d439aac2e2d7eca834a7a99016dfcaf) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The dataset consists of parsed ASTs that were used to train and evaluate the DeepSyn tool. The Python programs are collected from GitHub repositories by removing duplicate files, removing project forks (copy of another existing repository), keeping only programs that parse and have at most 30'000 nodes in the AST and we aim to remove obfuscated files ### Supported Tasks and Leaderboards Code Representation, Unsupervised Learning ### Languages Python ## Dataset Structure ### Data Instances A typical datapoint contains an AST of a python program, parsed. The main key is `ast` wherein every program's AST is stored. Each children would have, `type` which will formulate the type of the node. `children` which enumerates if a given node has children(non-empty list). `value`, if the given node has any hardcoded value(else "N/A"). An example would be, ''' [ {"type":"Module","children":[1,4]},{"type":"Assign","children":[2,3]},{"type":"NameStore","value":"x"},{"type":"Num","value":"7"}, {"type":"Print","children":[5]}, {"type":"BinOpAdd","children":[6,7]}, {"type":"NameLoad","value":"x"}, {"type":"Num","value":"1"} ] ''' ### Data Fields - `ast`: a list of dictionaries, wherein every dictionary is a node in the Abstract Syntax Tree. - `type`: explains the type of the node. - `children`: list of nodes which are children under the given - `value`: hardcoded value, if the node holds an hardcoded value. ### Data Splits The data is split into a training and test set. The final split sizes are as follows: | | train | validation | |------------------|--------:|------------:| | py_ast examples | 100000 | 50000 | ## Dataset Creation [More Information Needed] ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Raychev, V., Bielik, P., and Vechev, M ### Licensing Information MIT, BSD and Apache ### Citation Information @InProceedings{OOPSLA ’16, ACM, title = {Probabilistic Model for Code with Decision Trees.}, authors={Raychev, V., Bielik, P., and Vechev, M.}, year={2016} } ``` @inproceedings{10.1145/2983990.2984041, author = {Raychev, Veselin and Bielik, Pavol and Vechev, Martin}, title = {Probabilistic Model for Code with Decision Trees}, year = {2016}, isbn = {9781450344449}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/2983990.2984041}, doi = {10.1145/2983990.2984041}, booktitle = {Proceedings of the 2016 ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications}, pages = {731–747}, numpages = {17}, keywords = {Code Completion, Decision Trees, Probabilistic Models of Code}, location = {Amsterdam, Netherlands}, series = {OOPSLA 2016} } ``` ### Contributions Thanks to [@reshinthadithyan](https://github.com/reshinthadithyan) for adding this dataset.

py_ast

2022-03-02T23:29:22+00:00

{"annotations_creators": ["machine-generated"], "language_creators": ["found"], "language": ["code"], "license": ["bsd-2-clause", "mit"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["text2text-generation", "text-generation", "fill-mask"], "task_ids": [], "pretty_name": "PyAst", "tags": ["code-modeling", "code-generation"], "dataset_info": {"features": [{"name": "ast", "sequence": [{"name": "type", "dtype": "string"}, {"name": "value", "dtype": "string"}, {"name": "children", "sequence": "int32"}]}], "config_name": "ast", "splits": [{"name": "train", "num_bytes": 1870790180, "num_examples": 100000}, {"name": "test", "num_bytes": 907514993, "num_examples": 50000}], "download_size": 526642289, "dataset_size": 2778305173}}

2024-01-18T11:13:44+00:00

TAGS #task_categories-text2text-generation #task_categories-text-generation #task_categories-fill-mask #annotations_creators-machine-generated #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-code #license-bsd-2-clause #license-mit #code-modeling #code-generation #region-us

Dataset Card for [py\_ast] ========================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * homepage: py150 * Paper: Probabilistic Model for Code with Decision Trees * Leaderboard: * Point of Contact: ### Dataset Summary The dataset consists of parsed ASTs that were used to train and evaluate the DeepSyn tool. The Python programs are collected from GitHub repositories by removing duplicate files, removing project forks (copy of another existing repository), keeping only programs that parse and have at most 30'000 nodes in the AST and we aim to remove obfuscated files ### Supported Tasks and Leaderboards Code Representation, Unsupervised Learning ### Languages Python Dataset Structure ----------------- ### Data Instances A typical datapoint contains an AST of a python program, parsed. The main key is 'ast' wherein every program's AST is stored. Each children would have, 'type' which will formulate the type of the node. 'children' which enumerates if a given node has children(non-empty list). 'value', if the given node has any hardcoded value(else "N/A"). An example would be, ''' [ {"type":"Module","children":[1,4]},{"type":"Assign","children":[2,3]},{"type":"NameStore","value":"x"},{"type":"Num","value":"7"}, {"type":"Print","children":[5]}, {"type":"BinOpAdd","children":[6,7]}, {"type":"NameLoad","value":"x"}, {"type":"Num","value":"1"} ] ''' ### Data Fields * 'ast': a list of dictionaries, wherein every dictionary is a node in the Abstract Syntax Tree. * 'type': explains the type of the node. * 'children': list of nodes which are children under the given * 'value': hardcoded value, if the node holds an hardcoded value. ### Data Splits The data is split into a training and test set. The final split sizes are as follows: Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators Raychev, V., Bielik, P., and Vechev, M ### Licensing Information MIT, BSD and Apache @InProceedings{OOPSLA ’16, ACM, title = {Probabilistic Model for Code with Decision Trees.}, authors={Raychev, V., Bielik, P., and Vechev, M.}, year={2016} } ### Contributions Thanks to @reshinthadithyan for adding this dataset.

b15474bded16e37edd4f4681cdd49d81dd3cc41e

# Dataset Card for "qa4mre" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://nlp.uned.es/clef-qa/repository/qa4mre.php - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [QA4MRE 2011-2013: Overview of Question Answering for Machine Reading Evaluation](https://link.springer.com/chapter/10.1007/978-3-642-40802-1_29) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 5.49 MB - **Size of the generated dataset:** 48.35 MB - **Total amount of disk used:** 53.84 MB ### Dataset Summary QA4MRE dataset was created for the CLEF 2011/2012/2013 shared tasks to promote research in question answering and reading comprehension. The dataset contains a supporting passage and a set of questions corresponding to the passage. Multiple options for answers are provided for each question, of which only one is correct. The training and test datasets are available for the main track. Additional gold standard documents are available for two pilot studies: one on alzheimers data, and the other on entrance exams data. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### 2011.main.DE - **Size of downloaded dataset files:** 0.22 MB - **Size of the generated dataset:** 1.75 MB - **Total amount of disk used:** 1.97 MB An example of 'train' looks as follows. ``` ``` #### 2011.main.EN - **Size of downloaded dataset files:** 0.20 MB - **Size of the generated dataset:** 1.57 MB - **Total amount of disk used:** 1.77 MB An example of 'train' looks as follows. ``` ``` #### 2011.main.ES - **Size of downloaded dataset files:** 0.22 MB - **Size of the generated dataset:** 1.70 MB - **Total amount of disk used:** 1.91 MB An example of 'train' looks as follows. ``` ``` #### 2011.main.IT - **Size of downloaded dataset files:** 0.21 MB - **Size of the generated dataset:** 1.67 MB - **Total amount of disk used:** 1.88 MB An example of 'train' looks as follows. ``` ``` #### 2011.main.RO - **Size of downloaded dataset files:** 0.22 MB - **Size of the generated dataset:** 1.74 MB - **Total amount of disk used:** 1.96 MB An example of 'train' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### 2011.main.DE - `topic_id`: a `string` feature. - `topic_name`: a `string` feature. - `test_id`: a `string` feature. - `document_id`: a `string` feature. - `document_str`: a `string` feature. - `question_id`: a `string` feature. - `question_str`: a `string` feature. - `answer_options`: a dictionary feature containing: - `answer_id`: a `string` feature. - `answer_str`: a `string` feature. - `correct_answer_id`: a `string` feature. - `correct_answer_str`: a `string` feature. #### 2011.main.EN - `topic_id`: a `string` feature. - `topic_name`: a `string` feature. - `test_id`: a `string` feature. - `document_id`: a `string` feature. - `document_str`: a `string` feature. - `question_id`: a `string` feature. - `question_str`: a `string` feature. - `answer_options`: a dictionary feature containing: - `answer_id`: a `string` feature. - `answer_str`: a `string` feature. - `correct_answer_id`: a `string` feature. - `correct_answer_str`: a `string` feature. #### 2011.main.ES - `topic_id`: a `string` feature. - `topic_name`: a `string` feature. - `test_id`: a `string` feature. - `document_id`: a `string` feature. - `document_str`: a `string` feature. - `question_id`: a `string` feature. - `question_str`: a `string` feature. - `answer_options`: a dictionary feature containing: - `answer_id`: a `string` feature. - `answer_str`: a `string` feature. - `correct_answer_id`: a `string` feature. - `correct_answer_str`: a `string` feature. #### 2011.main.IT - `topic_id`: a `string` feature. - `topic_name`: a `string` feature. - `test_id`: a `string` feature. - `document_id`: a `string` feature. - `document_str`: a `string` feature. - `question_id`: a `string` feature. - `question_str`: a `string` feature. - `answer_options`: a dictionary feature containing: - `answer_id`: a `string` feature. - `answer_str`: a `string` feature. - `correct_answer_id`: a `string` feature. - `correct_answer_str`: a `string` feature. #### 2011.main.RO - `topic_id`: a `string` feature. - `topic_name`: a `string` feature. - `test_id`: a `string` feature. - `document_id`: a `string` feature. - `document_str`: a `string` feature. - `question_id`: a `string` feature. - `question_str`: a `string` feature. - `answer_options`: a dictionary feature containing: - `answer_id`: a `string` feature. - `answer_str`: a `string` feature. - `correct_answer_id`: a `string` feature. - `correct_answer_str`: a `string` feature. ### Data Splits | name |train| |------------|----:| |2011.main.DE| 120| |2011.main.EN| 120| |2011.main.ES| 120| |2011.main.IT| 120| |2011.main.RO| 120| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{10.1007/978-3-642-40802-1_29, author="Pe{\~{n}}as, Anselmo and Hovy, Eduard and Forner, Pamela and Rodrigo, {\'A}lvaro and Sutcliffe, Richard and Morante, Roser", editor="Forner, Pamela and M{\"u}ller, Henning and Paredes, Roberto and Rosso, Paolo and Stein, Benno", title="QA4MRE 2011-2013: Overview of Question Answering for Machine Reading Evaluation", booktitle="Information Access Evaluation. Multilinguality, Multimodality, and Visualization", year="2013", publisher="Springer Berlin Heidelberg", address="Berlin, Heidelberg", pages="303--320", isbn="978-3-642-40802-1" } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@albertvillanova](https://github.com/albertvillanova), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

qa4mre

2022-03-02T23:29:22+00:00

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"document_id", "dtype": "string"}, {"name": "document_str", "dtype": "string"}, {"name": "question_id", "dtype": "string"}, {"name": "question_str", "dtype": "string"}, {"name": "answer_options", "sequence": [{"name": "answer_id", "dtype": "string"}, {"name": "answer_str", "dtype": "string"}]}, {"name": "correct_answer_id", "dtype": "string"}, {"name": "correct_answer_str", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 180827, "num_examples": 46}], "download_size": 54598, "dataset_size": 180827}]}

2024-01-18T11:13:45+00:00

TAGS #task_categories-multiple-choice #task_ids-multiple-choice-qa #annotations_creators-other #language_creators-found #multilinguality-multilingual #size_categories-1K<n<10K #source_datasets-original #language-Arabic #language-Bulgarian #language-German #language-English #language-Spanish #language-Italian #language-Romanian #license-unknown #region-us

Dataset Card for "qa4mre" ========================= Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: QA4MRE 2011-2013: Overview of Question Answering for Machine Reading Evaluation * Point of Contact: * Size of downloaded dataset files: 5.49 MB * Size of the generated dataset: 48.35 MB * Total amount of disk used: 53.84 MB ### Dataset Summary QA4MRE dataset was created for the CLEF 2011/2012/2013 shared tasks to promote research in question answering and reading comprehension. The dataset contains a supporting passage and a set of questions corresponding to the passage. Multiple options for answers are provided for each question, of which only one is correct. The training and test datasets are available for the main track. Additional gold standard documents are available for two pilot studies: one on alzheimers data, and the other on entrance exams data. ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### URL.DE * Size of downloaded dataset files: 0.22 MB * Size of the generated dataset: 1.75 MB * Total amount of disk used: 1.97 MB An example of 'train' looks as follows. #### URL.EN * Size of downloaded dataset files: 0.20 MB * Size of the generated dataset: 1.57 MB * Total amount of disk used: 1.77 MB An example of 'train' looks as follows. #### URL.ES * Size of downloaded dataset files: 0.22 MB * Size of the generated dataset: 1.70 MB * Total amount of disk used: 1.91 MB An example of 'train' looks as follows. #### URL.IT * Size of downloaded dataset files: 0.21 MB * Size of the generated dataset: 1.67 MB * Total amount of disk used: 1.88 MB An example of 'train' looks as follows. #### URL.RO * Size of downloaded dataset files: 0.22 MB * Size of the generated dataset: 1.74 MB * Total amount of disk used: 1.96 MB An example of 'train' looks as follows. ### Data Fields The data fields are the same among all splits. #### URL.DE * 'topic\_id': a 'string' feature. * 'topic\_name': a 'string' feature. * 'test\_id': a 'string' feature. * 'document\_id': a 'string' feature. * 'document\_str': a 'string' feature. * 'question\_id': a 'string' feature. * 'question\_str': a 'string' feature. * 'answer\_options': a dictionary feature containing: + 'answer\_id': a 'string' feature. + 'answer\_str': a 'string' feature. * 'correct\_answer\_id': a 'string' feature. * 'correct\_answer\_str': a 'string' feature. #### URL.EN * 'topic\_id': a 'string' feature. * 'topic\_name': a 'string' feature. * 'test\_id': a 'string' feature. * 'document\_id': a 'string' feature. * 'document\_str': a 'string' feature. * 'question\_id': a 'string' feature. * 'question\_str': a 'string' feature. * 'answer\_options': a dictionary feature containing: + 'answer\_id': a 'string' feature. + 'answer\_str': a 'string' feature. * 'correct\_answer\_id': a 'string' feature. * 'correct\_answer\_str': a 'string' feature. #### URL.ES * 'topic\_id': a 'string' feature. * 'topic\_name': a 'string' feature. * 'test\_id': a 'string' feature. * 'document\_id': a 'string' feature. * 'document\_str': a 'string' feature. * 'question\_id': a 'string' feature. * 'question\_str': a 'string' feature. * 'answer\_options': a dictionary feature containing: + 'answer\_id': a 'string' feature. + 'answer\_str': a 'string' feature. * 'correct\_answer\_id': a 'string' feature. * 'correct\_answer\_str': a 'string' feature. #### URL.IT * 'topic\_id': a 'string' feature. * 'topic\_name': a 'string' feature. * 'test\_id': a 'string' feature. * 'document\_id': a 'string' feature. * 'document\_str': a 'string' feature. * 'question\_id': a 'string' feature. * 'question\_str': a 'string' feature. * 'answer\_options': a dictionary feature containing: + 'answer\_id': a 'string' feature. + 'answer\_str': a 'string' feature. * 'correct\_answer\_id': a 'string' feature. * 'correct\_answer\_str': a 'string' feature. #### URL.RO * 'topic\_id': a 'string' feature. * 'topic\_name': a 'string' feature. * 'test\_id': a 'string' feature. * 'document\_id': a 'string' feature. * 'document\_str': a 'string' feature. * 'question\_id': a 'string' feature. * 'question\_str': a 'string' feature. * 'answer\_options': a dictionary feature containing: + 'answer\_id': a 'string' feature. + 'answer\_str': a 'string' feature. * 'correct\_answer\_id': a 'string' feature. * 'correct\_answer\_str': a 'string' feature. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @patrickvonplaten, @albertvillanova, @mariamabarham, @thomwolf for adding this dataset.

5ec94435e9c37ea23ac8d014ac9299f2f4a4f40e

# Dataset Card for QA-SRL ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Homepage](https://dada.cs.washington.edu/qasrl/#page-top) - **Annotation Tool:** [Annotation tool](https://github.com/luheng/qasrl_annotation) - **Repository:** [Repository](https://dada.cs.washington.edu/qasrl/#dataset) - **Paper:** [Qa_srl paper](https://www.aclweb.org/anthology/D15-1076.pdf) - **Point of Contact:** [Luheng He](luheng@cs.washington.edu) ### Dataset Summary we model predicate-argument structure of a sentence with a set of question-answer pairs. our method allows practical large-scale annotation of training data. We focus on semantic rather than syntactic annotation, and introduce a scalable method for gathering data that allows both training and evaluation. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages This dataset is in english language. ## Dataset Structure ### Data Instances We use question-answer pairs to model verbal predicate-argument structure. The questions start with wh-words (Who, What, Where, What, etc.) and contains a verb predicate in the sentence; the answers are phrases in the sentence. For example: `UCD finished the 2006 championship as Dublin champions , by beating St Vincents in the final .` Predicate | Question | Answer ---|---|---| |Finished|Who finished something? | UCD |Finished|What did someone finish?|the 2006 championship |Finished|What did someone finish something as? |Dublin champions |Finished|How did someone finish something? |by beating St Vincents in the final |beating | Who beat someone? | UCD |beating|When did someone beat someone? |in the final |beating|Who did someone beat?| St Vincents ### Data Fields Annotations provided are as follows: - `sentence`: contains tokenized sentence - `sent_id`: is the sentence identifier - `predicate_idx`:the index of the predicate (its position in the sentence) - `predicate`: the predicate token - `question`: contains the question which is a list of tokens. The question always consists of seven slots, as defined in the paper. The empty slots are represented with a marker “_”. The question ends with question mark. - `answer`: list of answers to the question ### Data Splits Dataset | Sentences | Verbs | QAs --- | --- | --- |---| **newswire-train**|744|2020|4904| **newswire-dev**|249|664|1606| **newswire-test**|248|652|1599 **Wikipedia-train**|`1174`|`2647`|`6414`| **Wikipedia-dev**|`392`|`895`|`2183`| **Wikipedia-test**|`393`|`898`|`2201`| **Please note** This dataset only has wikipedia data. Newswire dataset needs CoNLL-2009 English training data to get the complete data. This training data is under license. Thus, newswire dataset is not included in this data. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization We annotated over 3000 sentences (nearly 8,000 verbs) in total across two domains: newswire (PropBank) and Wikipedia. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process non-expert annotators were given a short tutorial and a small set of sample annotations (about 10 sentences). Annotators were hired if they showed good understanding of English and the task. The entire screening process usually took less than 2 hours. #### Who are the annotators? 10 part-time, non-exper annotators from Upwork (Previously oDesk) ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [Luheng He](luheng@cs.washington.edu) ### Licensing Information [More Information Needed] ### Citation Information ``` @InProceedings{huggingface:dataset, title = {QA-SRL: Question-Answer Driven Semantic Role Labeling}, authors={Luheng He, Mike Lewis, Luke Zettlemoyer}, year={2015} publisher = {cs.washington.edu}, howpublished={\\url{https://dada.cs.washington.edu/qasrl/#page-top}}, } ``` ### Contributions Thanks to [@bpatidar](https://github.com/bpatidar) for adding this dataset.

qa_srl

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["multiple-choice-qa", "open-domain-qa"], "paperswithcode_id": "qa-srl", "pretty_name": "QA-SRL", "dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "sent_id", "dtype": "string"}, {"name": "predicate_idx", "dtype": "int32"}, {"name": "predicate", "dtype": "string"}, {"name": "question", "sequence": "string"}, {"name": "answers", "sequence": "string"}], "config_name": "plain_text", "splits": [{"name": "train", "num_bytes": 1835549, "num_examples": 6414}, {"name": "validation", "num_bytes": 632992, "num_examples": 2183}, {"name": "test", "num_bytes": 637317, "num_examples": 2201}], "download_size": 1087729, "dataset_size": 3105858}}

2024-01-18T11:13:48+00:00

TAGS #task_categories-question-answering #task_ids-multiple-choice-qa #task_ids-open-domain-qa #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-unknown #region-us

Dataset Card for QA-SRL ======================= Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: Homepage * Annotation Tool: Annotation tool * Repository: Repository * Paper: Qa\_srl paper * Point of Contact: Luheng He ### Dataset Summary we model predicate-argument structure of a sentence with a set of question-answer pairs. our method allows practical large-scale annotation of training data. We focus on semantic rather than syntactic annotation, and introduce a scalable method for gathering data that allows both training and evaluation. ### Supported Tasks and Leaderboards ### Languages This dataset is in english language. Dataset Structure ----------------- ### Data Instances We use question-answer pairs to model verbal predicate-argument structure. The questions start with wh-words (Who, What, Where, What, etc.) and contains a verb predicate in the sentence; the answers are phrases in the sentence. For example: 'UCD finished the 2006 championship as Dublin champions , by beating St Vincents in the final .' Predicate: Finished, Question: Who finished something?, Answer: UCD Predicate: Finished, Question: What did someone finish?, Answer: the 2006 championship Predicate: Finished, Question: What did someone finish something as?, Answer: Dublin champions Predicate: Finished, Question: How did someone finish something?, Answer: by beating St Vincents in the final Predicate: beating, Question: Who beat someone?, Answer: UCD Predicate: beating, Question: When did someone beat someone?, Answer: in the final Predicate: beating, Question: Who did someone beat?, Answer: St Vincents ### Data Fields Annotations provided are as follows: * 'sentence': contains tokenized sentence * 'sent\_id': is the sentence identifier * 'predicate\_idx':the index of the predicate (its position in the sentence) * 'predicate': the predicate token * 'question': contains the question which is a list of tokens. The question always consists of seven slots, as defined in the paper. The empty slots are represented with a marker “\_”. The question ends with question mark. * 'answer': list of answers to the question ### Data Splits Please note This dataset only has wikipedia data. Newswire dataset needs CoNLL-2009 English training data to get the complete data. This training data is under license. Thus, newswire dataset is not included in this data. Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization We annotated over 3000 sentences (nearly 8,000 verbs) in total across two domains: newswire (PropBank) and Wikipedia. #### Who are the source language producers? ### Annotations #### Annotation process non-expert annotators were given a short tutorial and a small set of sample annotations (about 10 sentences). Annotators were hired if they showed good understanding of English and the task. The entire screening process usually took less than 2 hours. #### Who are the annotators? 10 part-time, non-exper annotators from Upwork (Previously oDesk) ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators Luheng He ### Licensing Information ### Contributions Thanks to @bpatidar for adding this dataset.

334431e0f9a9a8b09d5c0c42520bfa54a366edf6

# Dataset Card for QaZre ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [http://nlp.cs.washington.edu/zeroshot](http://nlp.cs.washington.edu/zeroshot) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 516.06 MB - **Size of the generated dataset:** 2.09 GB - **Total amount of disk used:** 2.60 GB ### Dataset Summary A dataset reducing relation extraction to simple reading comprehension questions ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 516.06 MB - **Size of the generated dataset:** 2.09 GB - **Total amount of disk used:** 2.60 GB An example of 'validation' looks as follows. ``` { "answers": [], "context": "answer", "question": "What is XXX in this question?", "relation": "relation_name", "subject": "Some entity Here is a bit of context which will explain the question in some way" } ``` ### Data Fields The data fields are the same among all splits. #### default - `relation`: a `string` feature. - `question`: a `string` feature. - `subject`: a `string` feature. - `context`: a `string` feature. - `answers`: a `list` of `string` features. ### Data Splits | name | train | validation | test | |---------|--------:|-----------:|-------:| | default | 8400000 | 6000 | 120000 | ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information Unknown. ### Citation Information ``` @inproceedings{levy-etal-2017-zero, title = "Zero-Shot Relation Extraction via Reading Comprehension", author = "Levy, Omer and Seo, Minjoon and Choi, Eunsol and Zettlemoyer, Luke", booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)", month = aug, year = "2017", address = "Vancouver, Canada", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/K17-1034", doi = "10.18653/v1/K17-1034", pages = "333--342", } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@ghomasHudson](https://github.com/ghomasHudson), [@lewtun](https://github.com/lewtun) for adding this dataset.

qa_zre

2022-03-02T23:29:22+00:00

{"annotations_creators": ["no-annotation"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": [], "pretty_name": "QaZre", "tags": ["zero-shot-relation-extraction"], "dataset_info": {"features": [{"name": "relation", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "answers", "sequence": "string"}], "splits": [{"name": "test", "num_bytes": 29410194, "num_examples": 120000}, {"name": "validation", "num_bytes": 1481430, "num_examples": 6000}, {"name": "train", "num_bytes": 2054954011, "num_examples": 8400000}], "download_size": 516061636, "dataset_size": 2085845635}}

2024-01-18T11:13:50+00:00

TAGS #task_categories-question-answering #annotations_creators-no-annotation #language_creators-expert-generated #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-English #license-unknown #zero-shot-relation-extraction #region-us

Dataset Card for QaZre ====================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: * Point of Contact: * Size of downloaded dataset files: 516.06 MB * Size of the generated dataset: 2.09 GB * Total amount of disk used: 2.60 GB ### Dataset Summary A dataset reducing relation extraction to simple reading comprehension questions ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### default * Size of downloaded dataset files: 516.06 MB * Size of the generated dataset: 2.09 GB * Total amount of disk used: 2.60 GB An example of 'validation' looks as follows. ### Data Fields The data fields are the same among all splits. #### default * 'relation': a 'string' feature. * 'question': a 'string' feature. * 'subject': a 'string' feature. * 'context': a 'string' feature. * 'answers': a 'list' of 'string' features. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information Unknown. ### Contributions Thanks to @thomwolf, @lhoestq, @ghomasHudson, @lewtun for adding this dataset.

97becc7a03e223f22daff17ff6e89f2a22ccfffd

# Dataset Card for "qangaroo" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [http://qangaroo.cs.ucl.ac.uk/index.html](http://qangaroo.cs.ucl.ac.uk/index.html) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 1.36 GB - **Size of the generated dataset:** 981.89 MB - **Total amount of disk used:** 2.34 GB ### Dataset Summary We have created two new Reading Comprehension datasets focussing on multi-hop (alias multi-step) inference. Several pieces of information often jointly imply another fact. In multi-hop inference, a new fact is derived by combining facts via a chain of multiple steps. Our aim is to build Reading Comprehension methods that perform multi-hop inference on text, where individual facts are spread out across different documents. The two QAngaroo datasets provide a training and evaluation resource for such methods. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### masked_medhop - **Size of downloaded dataset files:** 339.84 MB - **Size of the generated dataset:** 112.63 MB - **Total amount of disk used:** 452.47 MB An example of 'validation' looks as follows. ``` ``` #### masked_wikihop - **Size of downloaded dataset files:** 339.84 MB - **Size of the generated dataset:** 391.98 MB - **Total amount of disk used:** 731.82 MB An example of 'validation' looks as follows. ``` ``` #### medhop - **Size of downloaded dataset files:** 339.84 MB - **Size of the generated dataset:** 110.42 MB - **Total amount of disk used:** 450.26 MB An example of 'validation' looks as follows. ``` ``` #### wikihop - **Size of downloaded dataset files:** 339.84 MB - **Size of the generated dataset:** 366.87 MB - **Total amount of disk used:** 706.71 MB An example of 'validation' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### masked_medhop - `query`: a `string` feature. - `supports`: a `list` of `string` features. - `candidates`: a `list` of `string` features. - `answer`: a `string` feature. - `id`: a `string` feature. #### masked_wikihop - `query`: a `string` feature. - `supports`: a `list` of `string` features. - `candidates`: a `list` of `string` features. - `answer`: a `string` feature. - `id`: a `string` feature. #### medhop - `query`: a `string` feature. - `supports`: a `list` of `string` features. - `candidates`: a `list` of `string` features. - `answer`: a `string` feature. - `id`: a `string` feature. #### wikihop - `query`: a `string` feature. - `supports`: a `list` of `string` features. - `candidates`: a `list` of `string` features. - `answer`: a `string` feature. - `id`: a `string` feature. ### Data Splits | name |train|validation| |--------------|----:|---------:| |masked_medhop | 1620| 342| |masked_wikihop|43738| 5129| |medhop | 1620| 342| |wikihop |43738| 5129| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@jplu](https://github.com/jplu), [@lewtun](https://github.com/lewtun), [@lhoestq](https://github.com/lhoestq), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset.

qangaroo

2022-03-02T23:29:22+00:00

{"language": ["en"], "pretty_name": "qangaroo", "dataset_info": [{"config_name": "medhop", "features": [{"name": "query", "dtype": "string"}, {"name": "supports", "sequence": "string"}, {"name": "candidates", "sequence": "string"}, {"name": "answer", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 93947725, "num_examples": 1620}, {"name": "validation", "num_bytes": 16463555, "num_examples": 342}], "download_size": 339843061, "dataset_size": 110411280}, {"config_name": "masked_medhop", "features": [{"name": "query", "dtype": "string"}, {"name": "supports", "sequence": "string"}, {"name": "candidates", "sequence": "string"}, {"name": "answer", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 95823986, "num_examples": 1620}, {"name": "validation", "num_bytes": 16802484, "num_examples": 342}], "download_size": 339843061, "dataset_size": 112626470}, {"config_name": "wikihop", "features": [{"name": "query", "dtype": "string"}, {"name": "supports", "sequence": "string"}, {"name": "candidates", "sequence": "string"}, {"name": "answer", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 325994029, "num_examples": 43738}, {"name": "validation", "num_bytes": 40869634, "num_examples": 5129}], "download_size": 339843061, "dataset_size": 366863663}, {"config_name": "masked_wikihop", "features": [{"name": "query", "dtype": "string"}, {"name": "supports", "sequence": "string"}, {"name": "candidates", "sequence": "string"}, {"name": "answer", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 348290479, "num_examples": 43738}, {"name": "validation", "num_bytes": 43689810, "num_examples": 5129}], "download_size": 339843061, "dataset_size": 391980289}]}

2024-01-18T11:13:54+00:00

TAGS #language-English #region-us

Dataset Card for "qangaroo" =========================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: * Point of Contact: * Size of downloaded dataset files: 1.36 GB * Size of the generated dataset: 981.89 MB * Total amount of disk used: 2.34 GB ### Dataset Summary We have created two new Reading Comprehension datasets focussing on multi-hop (alias multi-step) inference. Several pieces of information often jointly imply another fact. In multi-hop inference, a new fact is derived by combining facts via a chain of multiple steps. Our aim is to build Reading Comprehension methods that perform multi-hop inference on text, where individual facts are spread out across different documents. The two QAngaroo datasets provide a training and evaluation resource for such methods. ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### masked\_medhop * Size of downloaded dataset files: 339.84 MB * Size of the generated dataset: 112.63 MB * Total amount of disk used: 452.47 MB An example of 'validation' looks as follows. #### masked\_wikihop * Size of downloaded dataset files: 339.84 MB * Size of the generated dataset: 391.98 MB * Total amount of disk used: 731.82 MB An example of 'validation' looks as follows. #### medhop * Size of downloaded dataset files: 339.84 MB * Size of the generated dataset: 110.42 MB * Total amount of disk used: 450.26 MB An example of 'validation' looks as follows. #### wikihop * Size of downloaded dataset files: 339.84 MB * Size of the generated dataset: 366.87 MB * Total amount of disk used: 706.71 MB An example of 'validation' looks as follows. ### Data Fields The data fields are the same among all splits. #### masked\_medhop * 'query': a 'string' feature. * 'supports': a 'list' of 'string' features. * 'candidates': a 'list' of 'string' features. * 'answer': a 'string' feature. * 'id': a 'string' feature. #### masked\_wikihop * 'query': a 'string' feature. * 'supports': a 'list' of 'string' features. * 'candidates': a 'list' of 'string' features. * 'answer': a 'string' feature. * 'id': a 'string' feature. #### medhop * 'query': a 'string' feature. * 'supports': a 'list' of 'string' features. * 'candidates': a 'list' of 'string' features. * 'answer': a 'string' feature. * 'id': a 'string' feature. #### wikihop * 'query': a 'string' feature. * 'supports': a 'list' of 'string' features. * 'candidates': a 'list' of 'string' features. * 'answer': a 'string' feature. * 'id': a 'string' feature. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @thomwolf, @jplu, @lewtun, @lhoestq, @mariamabarham for adding this dataset.

4ce8f553ab877f4310656c63fcff4b3627e6192b

# Dataset Card for "qanta" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [http://www.qanta.org/](http://www.qanta.org/) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [Quizbowl: The Case for Incremental Question Answering](https://arxiv.org/abs/1904.04792) - **Point of Contact:** [Jordan Boyd-Graber](mailto:jbg@umiacs.umd.edu) - **Size of downloaded dataset files:** 170.75 MB - **Size of the generated dataset:** 147.18 MB - **Total amount of disk used:** 317.93 MB ### Dataset Summary The Qanta dataset is a question answering dataset based on the academic trivia game Quizbowl. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### mode=first,char_skip=25 - **Size of downloaded dataset files:** 170.75 MB - **Size of the generated dataset:** 147.18 MB - **Total amount of disk used:** 317.93 MB An example of 'guessdev' looks as follows. ``` This example was too long and was cropped: { "answer": "Apollo_program", "category": "History", "char_idx": -1, "dataset": "quizdb.org", "difficulty": "easy_college", "first_sentence": "As part of this program, William Anders took a photo that Galen Rowell called \"the most influential environmental photograph ever taken.\"", "fold": "guessdev", "full_question": "\"As part of this program, William Anders took a photo that Galen Rowell called \\\"the most influential environmental photograph e...", "gameplay": false, "id": "127028-first", "page": "Apollo_program", "proto_id": "", "qanta_id": 127028, "qdb_id": 126689, "raw_answer": "Apollo program [or Project Apollo; accept Apollo 8; accept Apollo 1; accept Apollo 11; prompt on landing on the moon]", "sentence_idx": -1, "subcategory": "American", "text": "As part of this program, William Anders took a photo that Galen Rowell called \"the most influential environmental photograph ever taken.\"", "tokenizations": [[0, 137], [138, 281], [282, 412], [413, 592], [593, 675]], "tournament": "ACF Fall", "year": 2016 } ``` ### Data Fields The data fields are the same among all splits. #### mode=first,char_skip=25 - `id`: a `string` feature. - `qanta_id`: a `int32` feature. - `proto_id`: a `string` feature. - `qdb_id`: a `int32` feature. - `dataset`: a `string` feature. - `text`: a `string` feature. - `full_question`: a `string` feature. - `first_sentence`: a `string` feature. - `char_idx`: a `int32` feature. - `sentence_idx`: a `int32` feature. - `tokenizations`: a dictionary feature containing: - `feature`: a `int32` feature. - `answer`: a `string` feature. - `page`: a `string` feature. - `raw_answer`: a `string` feature. - `fold`: a `string` feature. - `gameplay`: a `bool` feature. - `category`: a `string` feature. - `subcategory`: a `string` feature. - `tournament`: a `string` feature. - `difficulty`: a `string` feature. - `year`: a `int32` feature. ### Data Splits | name |adversarial|buzzdev|buzztrain|guessdev|guesstrain|buzztest|guesstest| |-----------------------|----------:|------:|--------:|-------:|---------:|-------:|--------:| |mode=first,char_skip=25| 1145| 1161| 16706| 1055| 96221| 1953| 2151| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @article{Rodriguez2019QuizbowlTC, title={Quizbowl: The Case for Incremental Question Answering}, author={Pedro Rodriguez and Shi Feng and Mohit Iyyer and He He and Jordan L. Boyd-Graber}, journal={ArXiv}, year={2019}, volume={abs/1904.04792} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset.

qanta

2022-03-02T23:29:22+00:00

{"annotations_creators": ["machine-generated"], "language_creators": ["found"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": [], "paperswithcode_id": "quizbowl", "pretty_name": "Quizbowl", "tags": ["quizbowl"], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "qanta_id", "dtype": "int32"}, {"name": "proto_id", "dtype": "string"}, {"name": "qdb_id", "dtype": "int32"}, {"name": "dataset", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "full_question", "dtype": "string"}, {"name": "first_sentence", "dtype": "string"}, {"name": "char_idx", "dtype": "int32"}, {"name": "sentence_idx", "dtype": "int32"}, {"name": "tokenizations", "sequence": {"sequence": "int32", "length": 2}}, {"name": "answer", "dtype": "string"}, {"name": "page", "dtype": "string"}, {"name": "raw_answer", "dtype": "string"}, {"name": "fold", "dtype": "string"}, {"name": "gameplay", "dtype": "bool"}, {"name": "category", "dtype": "string"}, {"name": "subcategory", "dtype": "string"}, {"name": "tournament", "dtype": "string"}, {"name": "difficulty", "dtype": "string"}, {"name": "year", "dtype": "int32"}], "config_name": "mode=first,char_skip=25", "splits": [{"name": "adversarial", "num_bytes": 1258844, "num_examples": 1145}, {"name": "buzzdev", "num_bytes": 1553636, "num_examples": 1161}, {"name": "buzztest", "num_bytes": 2653425, "num_examples": 1953}, {"name": "buzztrain", "num_bytes": 19699736, "num_examples": 16706}, {"name": "guessdev", "num_bytes": 1414882, "num_examples": 1055}, {"name": "guesstest", "num_bytes": 2997123, "num_examples": 2151}, {"name": "guesstrain", "num_bytes": 117599750, "num_examples": 96221}], "download_size": 170754918, "dataset_size": 147177396}}

2024-01-18T11:14:00+00:00

TAGS #task_categories-question-answering #annotations_creators-machine-generated #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-unknown #quizbowl #arxiv-1904.04792 #region-us

Dataset Card for "qanta" ======================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: Quizbowl: The Case for Incremental Question Answering * Point of Contact: Jordan Boyd-Graber * Size of downloaded dataset files: 170.75 MB * Size of the generated dataset: 147.18 MB * Total amount of disk used: 317.93 MB ### Dataset Summary The Qanta dataset is a question answering dataset based on the academic trivia game Quizbowl. ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### mode=first,char\_skip=25 * Size of downloaded dataset files: 170.75 MB * Size of the generated dataset: 147.18 MB * Total amount of disk used: 317.93 MB An example of 'guessdev' looks as follows. ### Data Fields The data fields are the same among all splits. #### mode=first,char\_skip=25 * 'id': a 'string' feature. * 'qanta\_id': a 'int32' feature. * 'proto\_id': a 'string' feature. * 'qdb\_id': a 'int32' feature. * 'dataset': a 'string' feature. * 'text': a 'string' feature. * 'full\_question': a 'string' feature. * 'first\_sentence': a 'string' feature. * 'char\_idx': a 'int32' feature. * 'sentence\_idx': a 'int32' feature. * 'tokenizations': a dictionary feature containing: + 'feature': a 'int32' feature. * 'answer': a 'string' feature. * 'page': a 'string' feature. * 'raw\_answer': a 'string' feature. * 'fold': a 'string' feature. * 'gameplay': a 'bool' feature. * 'category': a 'string' feature. * 'subcategory': a 'string' feature. * 'tournament': a 'string' feature. * 'difficulty': a 'string' feature. * 'year': a 'int32' feature. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @thomwolf, @patrickvonplaten, @lewtun for adding this dataset.

a34ba204eb9a33b919c10cc08f4f1c8dae5ec070

# Dataset Card for "qasc" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://allenai.org/data/qasc](https://allenai.org/data/qasc) - **Repository:** https://github.com/allenai/qasc/ - **Paper:** [QASC: A Dataset for Question Answering via Sentence Composition](https://arxiv.org/abs/1910.11473) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 1.61 MB - **Size of the generated dataset:** 5.87 MB - **Total amount of disk used:** 7.49 MB ### Dataset Summary QASC is a question-answering dataset with a focus on sentence composition. It consists of 9,980 8-way multiple-choice questions about grade school science (8,134 train, 926 dev, 920 test), and comes with a corpus of 17M sentences. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 1.61 MB - **Size of the generated dataset:** 5.87 MB - **Total amount of disk used:** 7.49 MB An example of 'validation' looks as follows. ``` { "answerKey": "F", "choices": { "label": ["A", "B", "C", "D", "E", "F", "G", "H"], "text": ["sand", "occurs over a wide range", "forests", "Global warming", "rapid changes occur", "local weather conditions", "measure of motion", "city life"] }, "combinedfact": "Climate is generally described in terms of local weather conditions", "fact1": "Climate is generally described in terms of temperature and moisture.", "fact2": "Fire behavior is driven by local weather conditions such as winds, temperature and moisture.", "formatted_question": "Climate is generally described in terms of what? (A) sand (B) occurs over a wide range (C) forests (D) Global warming (E) rapid changes occur (F) local weather conditions (G) measure of motion (H) city life", "id": "3NGI5ARFTT4HNGVWXAMLNBMFA0U1PG", "question": "Climate is generally described in terms of what?" } ``` ### Data Fields The data fields are the same among all splits. #### default - `id`: a `string` feature. - `question`: a `string` feature. - `choices`: a dictionary feature containing: - `text`: a `string` feature. - `label`: a `string` feature. - `answerKey`: a `string` feature. - `fact1`: a `string` feature. - `fact2`: a `string` feature. - `combinedfact`: a `string` feature. - `formatted_question`: a `string` feature. ### Data Splits | name |train|validation|test| |-------|----:|---------:|---:| |default| 8134| 926| 920| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information The dataset is released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license. ### Citation Information ``` @article{allenai:qasc, author = {Tushar Khot and Peter Clark and Michal Guerquin and Peter Jansen and Ashish Sabharwal}, title = {QASC: A Dataset for Question Answering via Sentence Composition}, journal = {arXiv:1910.11473v2}, year = {2020}, } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset.

qasc

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["question-answering", "multiple-choice"], "task_ids": ["extractive-qa", "multiple-choice-qa"], "paperswithcode_id": "qasc", "pretty_name": "Question Answering via Sentence Composition (QASC)", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "string"}]}, {"name": "answerKey", "dtype": "string"}, {"name": "fact1", "dtype": "string"}, {"name": "fact2", "dtype": "string"}, {"name": "combinedfact", "dtype": "string"}, {"name": "formatted_question", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4891878, "num_examples": 8134}, {"name": "test", "num_bytes": 390534, "num_examples": 920}, {"name": "validation", "num_bytes": 559180, "num_examples": 926}], "download_size": 2349698, "dataset_size": 5841592}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "validation", "path": "data/validation-*"}]}]}

2024-01-04T16:17:46+00:00

TAGS #task_categories-question-answering #task_categories-multiple-choice #task_ids-extractive-qa #task_ids-multiple-choice-qa #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc-by-4.0 #arxiv-1910.11473 #region-us

Dataset Card for "qasc" ======================= Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: URL * Paper: QASC: A Dataset for Question Answering via Sentence Composition * Point of Contact: * Size of downloaded dataset files: 1.61 MB * Size of the generated dataset: 5.87 MB * Total amount of disk used: 7.49 MB ### Dataset Summary QASC is a question-answering dataset with a focus on sentence composition. It consists of 9,980 8-way multiple-choice questions about grade school science (8,134 train, 926 dev, 920 test), and comes with a corpus of 17M sentences. ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### default * Size of downloaded dataset files: 1.61 MB * Size of the generated dataset: 5.87 MB * Total amount of disk used: 7.49 MB An example of 'validation' looks as follows. ### Data Fields The data fields are the same among all splits. #### default * 'id': a 'string' feature. * 'question': a 'string' feature. * 'choices': a dictionary feature containing: + 'text': a 'string' feature. + 'label': a 'string' feature. * 'answerKey': a 'string' feature. * 'fact1': a 'string' feature. * 'fact2': a 'string' feature. * 'combinedfact': a 'string' feature. * 'formatted\_question': a 'string' feature. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information The dataset is released under CC BY 4.0 license. ### Contributions Thanks to @thomwolf, @patrickvonplaten, @lewtun for adding this dataset.

fdc9d8214fbab5dd782958601db4d678e6934a54

# Dataset Card for Qasper ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://allenai.org/data/qasper](https://allenai.org/data/qasper) - **Demo:** [https://qasper-demo.apps.allenai.org/](https://qasper-demo.apps.allenai.org/) - **Paper:** [https://arxiv.org/abs/2105.03011](https://arxiv.org/abs/2105.03011) - **Blogpost:** [https://medium.com/ai2-blog/question-answering-on-scientific-research-papers-f6d6da9fd55c](https://medium.com/ai2-blog/question-answering-on-scientific-research-papers-f6d6da9fd55c) - **Leaderboards:** [https://paperswithcode.com/dataset/qasper](https://paperswithcode.com/dataset/qasper) ### Dataset Summary QASPER is a dataset for question answering on scientific research papers. It consists of 5,049 questions over 1,585 Natural Language Processing papers. Each question is written by an NLP practitioner who read only the title and abstract of the corresponding paper, and the question seeks information present in the full text. The questions are then answered by a separate set of NLP practitioners who also provide supporting evidence to answers. ### Supported Tasks and Leaderboards - `question-answering`: The dataset can be used to train a model for Question Answering. Success on this task is typically measured by achieving a *high* [F1 score](https://huggingface.co/metrics/f1). The [official baseline model](https://github.com/allenai/qasper-led-baseline) currently achieves 33.63 Token F1 score & uses [Longformer](https://huggingface.co/transformers/model_doc/longformer.html). This task has an active leaderboard which can be found [here](https://paperswithcode.com/sota/question-answering-on-qasper) - `evidence-selection`: The dataset can be used to train a model for Evidence Selection. Success on this task is typically measured by achieving a *high* [F1 score](https://huggingface.co/metrics/f1). The [official baseline model](https://github.com/allenai/qasper-led-baseline) currently achieves 39.37 F1 score & uses [Longformer](https://huggingface.co/transformers/model_doc/longformer.html). This task has an active leaderboard which can be found [here](https://paperswithcode.com/sota/evidence-selection-on-qasper) ### Languages English, as it is used in research papers. ## Dataset Structure ### Data Instances A typical instance in the dataset: ``` { 'id': "Paper ID (string)", 'title': "Paper Title", 'abstract': "paper abstract ...", 'full_text': { 'paragraphs':[["section1_paragraph1_text","section1_paragraph2_text",...],["section2_paragraph1_text","section2_paragraph2_text",...]], 'section_name':["section1_title","section2_title"],...}, 'qas': { 'answers':[{ 'annotation_id': ["q1_answer1_annotation_id","q1_answer2_annotation_id"] 'answer': [{ 'unanswerable':False, 'extractive_spans':["q1_answer1_extractive_span1","q1_answer1_extractive_span2"], 'yes_no':False, 'free_form_answer':"q1_answer1", 'evidence':["q1_answer1_evidence1","q1_answer1_evidence2",..], 'highlighted_evidence':["q1_answer1_highlighted_evidence1","q1_answer1_highlighted_evidence2",..] }, { 'unanswerable':False, 'extractive_spans':["q1_answer2_extractive_span1","q1_answer2_extractive_span2"], 'yes_no':False, 'free_form_answer':"q1_answer2", 'evidence':["q1_answer2_evidence1","q1_answer2_evidence2",..], 'highlighted_evidence':["q1_answer2_highlighted_evidence1","q1_answer2_highlighted_evidence2",..] }], 'worker_id':["q1_answer1_worker_id","q1_answer2_worker_id"] },{...["question2's answers"]..},{...["question3's answers"]..}], 'question':["question1","question2","question3"...], 'question_id':["question1_id","question2_id","question3_id"...], 'question_writer':["question1_writer_id","question2_writer_id","question3_writer_id"...], 'nlp_background':["question1_writer_nlp_background","question2_writer_nlp_background",...], 'topic_background':["question1_writer_topic_background","question2_writer_topic_background",...], 'paper_read': ["question1_writer_paper_read_status","question2_writer_paper_read_status",...], 'search_query':["question1_search_query","question2_search_query","question3_search_query"...], } } ``` ### Data Fields The following is an excerpt from the dataset README: Within "qas", some fields should be obvious. Here is some explanation about the others: #### Fields specific to questions: - "nlp_background" shows the experience the question writer had. The values can be "zero" (no experience), "two" (0 - 2 years of experience), "five" (2 - 5 years of experience), and "infinity" (> 5 years of experience). The field may be empty as well, indicating the writer has chosen not to share this information. - "topic_background" shows how familiar the question writer was with the topic of the paper. The values are "unfamiliar", "familiar", "research" (meaning that the topic is the research area of the writer), or null. - "paper_read", when specified shows whether the questionwriter has read the paper. - "search_query", if not empty, is the query the question writer used to find the abstract of the paper from a large pool of abstracts we made available to them. #### Fields specific to answers Unanswerable answers have "unanswerable" set to true. The remaining answers have exactly one of the following fields being non-empty. - "extractive_spans" are spans in the paper which serve as the answer. - "free_form_answer" is a written out answer. - "yes_no" is true iff the answer is Yes, and false iff the answer is No. "evidence" is the set of paragraphs, figures or tables used to arrive at the answer. Tables or figures start with the string "FLOAT SELECTED" "highlighted_evidence" is the set of sentences the answer providers selected as evidence if they chose textual evidence. The text in the "evidence" field is a mapping from these sentences to the paragraph level. That is, if you see textual evidence in the "evidence" field, it is guaranteed to be entire paragraphs, while that is not the case with "highlighted_evidence". ### Data Splits | | Train | Valid | | ----- | ------ | ----- | | Number of papers | 888 | 281 | | Number of questions | 2593 | 1005 | | Number of answers | 2675 | 1764 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data NLP papers: The full text of the papers is extracted from [S2ORC](https://huggingface.co/datasets/s2orc) (Lo et al., 2020) #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? "The annotators are NLP practitioners, not expert researchers, and it is likely that an expert would score higher" ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Crowdsourced NLP practitioners ### Licensing Information [CC BY 4.0](https://creativecommons.org/licenses/by/4.0) ### Citation Information ``` @inproceedings{Dasigi2021ADO, title={A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers}, author={Pradeep Dasigi and Kyle Lo and Iz Beltagy and Arman Cohan and Noah A. Smith and Matt Gardner}, year={2021} } ``` ### Contributions Thanks to [@cceyda](https://github.com/cceyda) for adding this dataset.

allenai/qasper

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["extended|s2orc"], "task_categories": ["question-answering"], "task_ids": ["closed-domain-qa"], "paperswithcode_id": "qasper", "pretty_name": "QASPER", "language_bcp47": ["en-US"]}

2022-10-07T21:04:11+00:00

TAGS #task_categories-question-answering #task_ids-closed-domain-qa #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|s2orc #language-English #license-cc-by-4.0 #arxiv-2105.03011 #region-us

Dataset Card for Qasper ======================= Table of Contents ----------------- * Table of Contents * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data - Initial Data Collection and Normalization - Who are the source language producers? + Annotations - Annotation process - Who are the annotators? + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Demo: URL * Paper: URL * Blogpost: URL * Leaderboards: URL ### Dataset Summary QASPER is a dataset for question answering on scientific research papers. It consists of 5,049 questions over 1,585 Natural Language Processing papers. Each question is written by an NLP practitioner who read only the title and abstract of the corresponding paper, and the question seeks information present in the full text. The questions are then answered by a separate set of NLP practitioners who also provide supporting evidence to answers. ### Supported Tasks and Leaderboards * 'question-answering': The dataset can be used to train a model for Question Answering. Success on this task is typically measured by achieving a *high* F1 score. The official baseline model currently achieves 33.63 Token F1 score & uses Longformer. This task has an active leaderboard which can be found here * 'evidence-selection': The dataset can be used to train a model for Evidence Selection. Success on this task is typically measured by achieving a *high* F1 score. The official baseline model currently achieves 39.37 F1 score & uses Longformer. This task has an active leaderboard which can be found here ### Languages English, as it is used in research papers. Dataset Structure ----------------- ### Data Instances A typical instance in the dataset: ### Data Fields The following is an excerpt from the dataset README: Within "qas", some fields should be obvious. Here is some explanation about the others: #### Fields specific to questions: * "nlp\_background" shows the experience the question writer had. The values can be "zero" (no experience), "two" (0 - 2 years of experience), "five" (2 - 5 years of experience), and "infinity" (> 5 years of experience). The field may be empty as well, indicating the writer has chosen not to share this information. * "topic\_background" shows how familiar the question writer was with the topic of the paper. The values are "unfamiliar", "familiar", "research" (meaning that the topic is the research area of the writer), or null. * "paper\_read", when specified shows whether the questionwriter has read the paper. * "search\_query", if not empty, is the query the question writer used to find the abstract of the paper from a large pool of abstracts we made available to them. #### Fields specific to answers Unanswerable answers have "unanswerable" set to true. The remaining answers have exactly one of the following fields being non-empty. * "extractive\_spans" are spans in the paper which serve as the answer. * "free\_form\_answer" is a written out answer. * "yes\_no" is true iff the answer is Yes, and false iff the answer is No. "evidence" is the set of paragraphs, figures or tables used to arrive at the answer. Tables or figures start with the string "FLOAT SELECTED" "highlighted\_evidence" is the set of sentences the answer providers selected as evidence if they chose textual evidence. The text in the "evidence" field is a mapping from these sentences to the paragraph level. That is, if you see textual evidence in the "evidence" field, it is guaranteed to be entire paragraphs, while that is not the case with "highlighted\_evidence". ### Data Splits Train: Number of papers, Valid: 888 Train: Number of questions, Valid: 2593 Train: Number of answers, Valid: 2675 Dataset Creation ---------------- ### Curation Rationale ### Source Data NLP papers: The full text of the papers is extracted from S2ORC (Lo et al., 2020) #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? "The annotators are NLP practitioners, not expert researchers, and it is likely that an expert would score higher" ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators Crowdsourced NLP practitioners ### Licensing Information CC BY 4.0 ### Contributions Thanks to @cceyda for adding this dataset.

4e98c6b3d81667900d1aabc26f4601f55ee353c9

# Dataset Card for QED ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** N/A - **Repository:** [GitHub](https://github.com/google-research-datasets/QED) - **Paper:** [QED: A Framework and Dataset for Explanations in Question Answering](https://arxiv.org/abs/2009.06354) - **Leaderboard:** N/A - **Point of Contact:** - ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.

qed

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["extended|natural_questions"], "task_categories": ["question-answering"], "task_ids": ["extractive-qa"], "paperswithcode_id": "qed", "pretty_name": "QED", "tags": ["explanations-in-question-answering"], "dataset_info": {"features": [{"name": "example_id", "dtype": "int64"}, {"name": "title_text", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "paragraph_text", "dtype": "string"}, {"name": "sentence_starts", "sequence": "int32"}, {"name": "original_nq_answers", "list": [{"name": "start", "dtype": "int32"}, {"name": "end", "dtype": "int32"}, {"name": "string", "dtype": "string"}]}, {"name": "annotation", "struct": [{"name": "referential_equalities", "list": [{"name": "question_reference", "struct": [{"name": "start", "dtype": "int32"}, {"name": "end", "dtype": "int32"}, {"name": "string", "dtype": "string"}]}, {"name": "sentence_reference", "struct": [{"name": "start", "dtype": "int32"}, {"name": "end", "dtype": "int32"}, {"name": "bridge", "dtype": "string"}, {"name": "string", "dtype": "string"}]}]}, {"name": "answer", "list": [{"name": "sentence_reference", "struct": [{"name": "start", "dtype": "int32"}, {"name": "end", "dtype": "int32"}, {"name": "bridge", "dtype": "string"}, {"name": "string", "dtype": "string"}]}, {"name": "paragraph_reference", "struct": [{"name": "start", "dtype": "int32"}, {"name": "end", "dtype": "int32"}, {"name": "string", "dtype": "string"}]}]}, {"name": "explanation_type", "dtype": "string"}, {"name": "selected_sentence", "struct": [{"name": "start", "dtype": "int32"}, {"name": "end", "dtype": "int32"}, {"name": "string", "dtype": "string"}]}]}], "config_name": "qed", "splits": [{"name": "train", "num_bytes": 8602094, "num_examples": 7638}, {"name": "validation", "num_bytes": 1584139, "num_examples": 1355}], "download_size": 14083968, "dataset_size": 10186233}}

2024-01-18T11:14:02+00:00

TAGS #task_categories-question-answering #task_ids-extractive-qa #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-extended|natural_questions #language-English #license-unknown #explanations-in-question-answering #arxiv-2009.06354 #region-us

# Dataset Card for QED ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: N/A - Repository: GitHub - Paper: QED: A Framework and Dataset for Explanations in Question Answering - Leaderboard: N/A - Point of Contact: - ### Dataset Summary ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @patil-suraj for adding this dataset.

a5c74deca15a93dd2ebc34d5f1b75601a700ca2e

# Dataset Card for QedAmara ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://opus.nlpl.eu/QED.php - **Repository:** None - **Paper:** https://www.aclweb.org/anthology/L14-1675/ - **Leaderboard:** [More Information Needed] - **Point of Contact:** [More Information Needed] ### Dataset Summary To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs. You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/QED.php E.g. `dataset = load_dataset("qed_amara", lang1="cs", lang2="nb")` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The languages in the dataset are: - aa - ab - ae - aeb - af - aka: `ak` - amh: `am` - an - ar - arq - arz - as - ase - ast - av - ay - az - ba - bam: `bm` - be - ber - bg - bh - bi - bn - bnt - bo - br - bs - bug - ca - ce - ceb - ch - cho - cku - cnh - co - cr - cs - cu - cv - cy - da - de - dv - dz - ee - efi - el - en - eo - es - et - eu - fa - ff - fi - fil - fj - fo - fr - ful: `ff` - ga - gd - gl - gn - gu - hai - hau: `ha` - haw - haz - hb: ? - hch - he - hi - ho - hr - ht - hu - hup - hus - hy - hz - ia - ibo: `ig` - id - ie - ik - inh - io - iro - is - it - iu - ja - jv - ka - kar - kau: `kr` - kik: `ki` - kin: `rw` - kj - kk - kl - km - kn - ko - ksh - ku - kv - kw - ky - la - lb - lg - li - lin: `ln` - lkt - lld - lo - lt - ltg - lu - luo - luy - lv - mad - mfe - mi - mk - ml - mlg: `mg` - mn - mni - mo: Moldavian (deprecated tag; preferred value: Romanian; Moldavian; Moldovan (`ro`)) - moh - mos - mr - ms - mt - mus - my - nb - nci - nd - ne - nl - nn - nso - nv - nya: `ny` - oc - or - orm: `om` - pam - pan: `pa` - pap - pi - pl - pnb - prs - ps - pt - que: `qu` - rm - ro - ru - run: `rn` - rup - ry: ? - sa - sc - scn - sco - sd - sg - sgn - sh - si - sk - sl - sm - sna: `sn` - som: `so` - sot: `st` - sq - sr - srp: `sr` - sv - swa: `sw` - szl - ta - te - tet - tg - th - tir: `ti` - tk - tl - tlh - to - tr - ts - tt - tw - ug - uk - umb - ur - uz - ve - vi - vls - vo - wa - wol: `wo` - xh - yaq - yi - yor: `yo` - za - zam - zh - zul: `zu` ## Dataset Structure ### Data Instances Here are some examples of questions and facts: ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.

qed_amara

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["aa", "ab", "ae", "aeb", "af", "ak", "am", "an", "ar", "arq", "arz", "as", "ase", "ast", "av", "ay", "az", "ba", "be", "ber", "bg", "bh", "bi", "bm", "bn", "bnt", "bo", "br", "bs", "bug", "ca", "ce", "ceb", "ch", "cho", "cku", "cnh", "co", "cr", "cs", "cu", "cv", "cy", "da", "de", "dv", "dz", "ee", "efi", "el", "en", "eo", "es", "et", "eu", "fa", "ff", "fi", "fil", "fj", "fo", "fr", "ga", "gd", "gl", "gn", "gu", "ha", "hai", "haw", "haz", "hch", "he", "hi", "ho", "hr", "ht", "hu", "hup", "hus", "hy", "hz", "ia", "id", "ie", "ig", "ik", "inh", "io", "iro", "is", "it", "iu", "ja", "jv", "ka", "kar", "ki", "kj", "kk", "kl", "km", "kn", "ko", "kr", "ksh", "ku", "kv", "kw", "ky", "la", "lb", "lg", "li", "lkt", "lld", "ln", "lo", "lt", "ltg", "lu", "luo", "luy", "lv", "mad", "mfe", "mg", "mi", "mk", "ml", "mn", "mni", "moh", "mos", "mr", "ms", "mt", "mus", "my", "nb", "nci", "nd", "ne", "nl", "nn", "nso", "nv", "ny", "oc", "om", "or", "pa", "pam", "pap", "pi", "pl", "pnb", "prs", "ps", "pt", "qu", "rm", "rn", "ro", "ru", "rup", "rw", "sa", "sc", "scn", "sco", "sd", "sg", "sgn", "sh", "si", "sk", "sl", "sm", "sn", "so", "sq", "sr", "st", "sv", "sw", "szl", "ta", "te", "tet", "tg", "th", "ti", "tk", "tl", "tlh", "to", "tr", "ts", "tt", "tw", "ug", "uk", "umb", "ur", "uz", "ve", "vi", "vls", "vo", "wa", "wo", "xh", "yaq", "yi", "yo", "za", "zam", "zh", "zu"], "license": ["unknown"], "multilinguality": ["multilingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["translation"], "task_ids": [], "pretty_name": "QedAmara", "dataset_info": [{"config_name": "ar-ko", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["ar", "ko"]}}}], "splits": [{"name": "train", "num_bytes": 79605277, "num_examples": 592589}], "download_size": 23410393, "dataset_size": 79605277}, {"config_name": "de-fr", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["de", "fr"]}}}], "splits": [{"name": "train", "num_bytes": 75861416, "num_examples": 407224}], "download_size": 26579871, "dataset_size": 75861416}, {"config_name": "es-it", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["es", "it"]}}}], "splits": [{"name": "train", "num_bytes": 80650321, "num_examples": 447369}], "download_size": 28344317, "dataset_size": 80650321}, {"config_name": "en-ja", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["en", "ja"]}}}], "splits": [{"name": "train", "num_bytes": 86731218, "num_examples": 497531}], "download_size": 29836171, "dataset_size": 86731218}, {"config_name": "he-nl", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["he", "nl"]}}}], "splits": [{"name": "train", "num_bytes": 51448732, "num_examples": 273165}], "download_size": 16642865, "dataset_size": 51448732}]}

2024-01-18T11:14:04+00:00

TAGS #task_categories-translation #annotations_creators-found #language_creators-found #multilinguality-multilingual #size_categories-100K<n<1M #source_datasets-original #language-Afar #language-Abkhazian #language-Avestan #language-Tunisian Arabic #language-Afrikaans #language-Akan #language-Amharic #language-Aragonese #language-Arabic #language-Algerian Arabic #language-Egyptian Arabic #language-Assamese #language-American Sign Language #language-Asturian #language-Avaric #language-Aymara #language-Azerbaijani #language-Bashkir #language-Belarusian #language-ber #language-Bulgarian #language-bh #language-Bislama #language-Bambara #language-Bengali #language-bnt #language-Tibetan #language-Breton #language-Bosnian #language-Buginese #language-Catalan #language-Chechen #language-Cebuano #language-Chamorro #language-Choctaw #language-Koasati #language-Hakha Chin #language-Corsican #language-Cree #language-Czech #language-Church Slavic #language-Chuvash #language-Welsh #language-Danish #language-German #language-Dhivehi #language-Dzongkha #language-Ewe #language-Efik #language-Modern Greek (1453-) #language-English #language-Esperanto #language-Spanish #language-Estonian #language-Basque #language-Persian #language-Fulah #language-Finnish #language-Filipino #language-Fijian #language-Faroese #language-French #language-Irish #language-Scottish Gaelic #language-Galician #language-Guarani #language-Gujarati #language-Hausa #language-Haida #language-Hawaiian #language-Hazaragi #language-Huichol #language-Hebrew #language-Hindi #language-Hiri Motu #language-Croatian #language-Haitian #language-Hungarian #language-Hupa #language-Huastec #language-Armenian #language-Herero #language-Interlingua (International Auxiliary Language Association) #language-Indonesian #language-Interlingue #language-Igbo #language-Inupiaq #language-Ingush #language-Ido #language-iro #language-Icelandic #language-Italian #language-Inuktitut #language-Japanese #language-Javanese #language-Georgian #language-kar #language-Kikuyu #language-Kuanyama #language-Kazakh #language-Kalaallisut #language-Khmer #language-Kannada #language-Korean #language-Kanuri #language-Kölsch #language-Kurdish #language-Komi #language-Cornish #language-Kirghiz #language-Latin #language-Luxembourgish #language-Ganda #language-Limburgan #language-Lakota #language-Ladin #language-Lingala #language-Lao #language-Lithuanian #language-Latgalian #language-Luba-Katanga #language-Luo (Kenya and Tanzania) #language-Luyia #language-Latvian #language-Madurese #language-Morisyen #language-Malagasy #language-Maori #language-Macedonian #language-Malayalam #language-Mongolian #language-Manipuri #language-Mohawk #language-Mossi #language-Marathi #language-Malay (macrolanguage) #language-Maltese #language-Creek #language-Burmese #language-Norwegian Bokmål #language-Classical Nahuatl #language-North Ndebele #language-Nepali (macrolanguage) #language-Dutch #language-Norwegian Nynorsk #language-Pedi #language-Navajo #language-Nyanja #language-Occitan (post 1500) #language-Oromo #language-Oriya (macrolanguage) #language-Panjabi #language-Pampanga #language-Papiamento #language-Pali #language-Polish #language-Western Panjabi #language-Dari #language-Pushto #language-Portuguese #language-Quechua #language-Romansh #language-Rundi #language-Romanian #language-Russian #language-Macedo-Romanian #language-Kinyarwanda #language-Sanskrit #language-Sardinian #language-Sicilian #language-Scots #language-Sindhi #language-Sango #language-sgn #language-Serbo-Croatian #language-Sinhala #language-Slovak #language-Slovenian #language-Samoan #language-Shona #language-Somali #language-Albanian #language-Serbian #language-Southern Sotho #language-Swedish #language-Swahili (macrolanguage) #language-Silesian #language-Tamil #language-Telugu #language-Tetum #language-Tajik #language-Thai #language-Tigrinya #language-Turkmen #language-Tagalog #language-Klingon #language-Tonga (Tonga Islands) #language-Turkish #language-Tsonga #language-Tatar #language-Twi #language-Uighur #language-Ukrainian #language-Umbundu #language-Urdu #language-Uzbek #language-Venda #language-Vietnamese #language-Vlaams #language-Volapük #language-Walloon #language-Wolof #language-Xhosa #language-Yaqui #language-Yiddish #language-Yoruba #language-Zhuang #language-Miahuatlán Zapotec #language-Chinese #language-Zulu #license-unknown #region-us

# Dataset Card for QedAmara ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: None - Paper: URL - Leaderboard: - Point of Contact: ### Dataset Summary To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs. You can find the valid pairs in Homepage section of Dataset Description: URL E.g. 'dataset = load_dataset("qed_amara", lang1="cs", lang2="nb")' ### Supported Tasks and Leaderboards ### Languages The languages in the dataset are: - aa - ab - ae - aeb - af - aka: 'ak' - amh: 'am' - an - ar - arq - arz - as - ase - ast - av - ay - az - ba - bam: 'bm' - be - ber - bg - bh - bi - bn - bnt - bo - br - bs - bug - ca - ce - ceb - ch - cho - cku - cnh - co - cr - cs - cu - cv - cy - da - de - dv - dz - ee - efi - el - en - eo - es - et - eu - fa - ff - fi - fil - fj - fo - fr - ful: 'ff' - ga - gd - gl - gn - gu - hai - hau: 'ha' - haw - haz - hb: ? - hch - he - hi - ho - hr - ht - hu - hup - hus - hy - hz - ia - ibo: 'ig' - id - ie - ik - inh - io - iro - is - it - iu - ja - jv - ka - kar - kau: 'kr' - kik: 'ki' - kin: 'rw' - kj - kk - kl - km - kn - ko - ksh - ku - kv - kw - ky - la - lb - lg - li - lin: 'ln' - lkt - lld - lo - lt - ltg - lu - luo - luy - lv - mad - mfe - mi - mk - ml - mlg: 'mg' - mn - mni - mo: Moldavian (deprecated tag; preferred value: Romanian; Moldavian; Moldovan ('ro')) - moh - mos - mr - ms - mt - mus - my - nb - nci - nd - ne - nl - nn - nso - nv - nya: 'ny' - oc - or - orm: 'om' - pam - pan: 'pa' - pap - pi - pl - pnb - prs - ps - pt - que: 'qu' - rm - ro - ru - run: 'rn' - rup - ry: ? - sa - sc - scn - sco - sd - sg - sgn - sh - si - sk - sl - sm - sna: 'sn' - som: 'so' - sot: 'st' - sq - sr - srp: 'sr' - sv - swa: 'sw' - szl - ta - te - tet - tg - th - tir: 'ti' - tk - tl - tlh - to - tr - ts - tt - tw - ug - uk - umb - ur - uz - ve - vi - vls - vo - wa - wol: 'wo' - xh - yaq - yi - yor: 'yo' - za - zam - zh - zul: 'zu' ## Dataset Structure ### Data Instances Here are some examples of questions and facts: ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @abhishekkrthakur for adding this dataset.

2c1b73f07a629a338127b6a9761c64bf3e969179

# Dataset Card for Question Answering in Context ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [QuAC](https://quac.ai/) - **Paper:** [QuAC: Question Answering in Context](https://arxiv.org/abs/1808.07036) - **Leaderboard:** [QuAC's leaderboard](https://quac.ai/) - **Point of Contact:** [Google group](https://groups.google.com/forum/#!forum/quac_ai) ### Dataset Summary Question Answering in Context is a dataset for modeling, understanding, and participating in information seeking dialog. Data instances consist of an interactive dialog between two crowd workers: (1) a student who poses a sequence of freeform questions to learn as much as possible about a hidden Wikipedia text, and (2) a teacher who answers the questions by providing short excerpts (spans) from the text. QuAC introduces challenges not found in existing machine comprehension datasets: its questions are often more open-ended, unanswerable, or only meaningful within the dialog context. ### Supported Tasks and Leaderboards The core problem involves predicting a text span to answer a question about a Wikipedia section (extractive question answering). Since QuAC questions include a dialog component, each instance includes a “dialog history” of questions and answers asked in the dialog prior to the given question, along with some additional metadata. Authors provided [an official evaluation script](https://s3.amazonaws.com/my89public/quac/scorer.py) for evaluation. ### Languages The text in the dataset is in English. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances A validation examples looks like this (one entry per dialogue): ``` { 'dialogue_id': 'C_6abd2040a75d47168a9e4cca9ca3fed5_0', 'wikipedia_page_title': 'Satchel Paige', 'background': 'Leroy Robert "Satchel" Paige (July 7, 1906 - June 8, 1982) was an American Negro league baseball and Major League Baseball (MLB) pitcher who became a legend in his own lifetime by being known as perhaps the best pitcher in baseball history, by his longevity in the game, and by attracting record crowds wherever he pitched. Paige was a right-handed pitcher, and at age 42 in 1948, he was the oldest major league rookie while playing for the Cleveland Indians. He played with the St. Louis Browns until age 47, and represented them in the All-Star Game in 1952 and 1953.', 'section_title': 'Chattanooga and Birmingham: 1926-29', 'context': 'A former friend from the Mobile slums, Alex Herman, was the player/manager for the Chattanooga White Sox of the minor Negro Southern League. In 1926 he discovered Paige and offered to pay him $250 per month, of which Paige would collect $50 with the rest going to his mother. He also agreed to pay Lula Paige a $200 advance, and she agreed to the contract. The local newspapers--the Chattanooga News and Chattanooga Times--recognized from the beginning that Paige was special. In April 1926, shortly after his arrival, he recorded nine strikeouts over six innings against the Atlanta Black Crackers. Part way through the 1927 season, Paige\'s contract was sold to the Birmingham Black Barons of the major Negro National League (NNL). According to Paige\'s first memoir, his contract was for $450 per month, but in his second he said it was for $275. Pitching for the Black Barons, Paige threw hard but was wild and awkward. In his first big game in late June 1927, against the St. Louis Stars, Paige incited a brawl when his fastball hit the hand of St. Louis catcher Mitchell Murray. Murray then charged the mound and Paige raced for the dugout, but Murray flung his bat and struck Paige above the hip. The police were summoned, and the headline of the Birmingham Reporter proclaimed a "Near Riot." Paige improved and matured as a pitcher with help from his teammates, Sam Streeter and Harry Salmon, and his manager, Bill Gatewood. He finished the 1927 season 7-1 with 69 strikeouts and 26 walks in 89 1/3 innings. Over the next two seasons, Paige went 12-5 and 10-9 while recording 176 strikeouts in 1929. (Several sources credit his 1929 strikeout total as the all-time single-season record for the Negro leagues, though there is variation among the sources about the exact number of strikeouts.) On April 29 of that season he recorded 17 strikeouts in a game against the Cuban Stars, which exceeded what was then the major league record of 16 held by Noodles Hahn and Rube Waddell. Six days later he struck out 18 Nashville Elite Giants, a number that was tied in the white majors by Bob Feller in 1938. Due to his increased earning potential, Barons owner R. T. Jackson would "rent" Paige out to other ball clubs for a game or two to draw a decent crowd, with both Jackson and Paige taking a cut. CANNOTANSWER', 'turn_ids': ['C_6abd2040a75d47168a9e4cca9ca3fed5_0_q#0', 'C_6abd2040a75d47168a9e4cca9ca3fed5_0_q#1', 'C_6abd2040a75d47168a9e4cca9ca3fed5_0_q#2', 'C_6abd2040a75d47168a9e4cca9ca3fed5_0_q#3', 'C_6abd2040a75d47168a9e4cca9ca3fed5_0_q#4', 'C_6abd2040a75d47168a9e4cca9ca3fed5_0_q#5', 'C_6abd2040a75d47168a9e4cca9ca3fed5_0_q#6', 'C_6abd2040a75d47168a9e4cca9ca3fed5_0_q#7'], 'questions': ['what did he do in Chattanooga', 'how did he discover him', 'what position did he play', 'how did they help him', 'when did he go to Birmingham', 'how did he feel about this', 'how did he do with this team', 'What made him leave the team'], 'followups': [0, 2, 0, 1, 0, 1, 0, 1], 'yesnos': [2, 2, 2, 2, 2, 2, 2, 2] 'answers': { 'answer_starts': [ [480, 39, 0, 67, 39], [2300, 2300, 2300], [848, 1023, 848, 848, 1298], [2300, 2300, 2300, 2300, 2300], [600, 600, 600, 634, 600], [2300, 2300, 2300], [939, 1431, 848, 848, 1514], [2106, 2106, 2165] ], 'texts': [ ['April 1926, shortly after his arrival, he recorded nine strikeouts over six innings against the Atlanta Black Crackers.', 'Alex Herman, was the player/manager for the Chattanooga White Sox of the minor Negro Southern League. In 1926 he discovered Paige', 'A former friend from the Mobile slums, Alex Herman, was the player/manager for the Chattanooga White Sox of the minor Negro Southern League.', 'manager for the Chattanooga White Sox of the minor Negro Southern League. In 1926 he discovered Paige and offered to pay him $250 per month,', 'Alex Herman, was the player/manager for the Chattanooga White Sox of the minor Negro Southern League. In 1926 he discovered Paige and offered to pay him $250 per month,'], ['CANNOTANSWER', 'CANNOTANSWER', 'CANNOTANSWER'], ['Pitching for the Black Barons,', 'fastball', 'Pitching for', 'Pitching', 'Paige improved and matured as a pitcher with help from his teammates,'], ['CANNOTANSWER', 'CANNOTANSWER', 'CANNOTANSWER', 'CANNOTANSWER', 'CANNOTANSWER'], ["Part way through the 1927 season, Paige's contract was sold to the Birmingham Black Barons", "Part way through the 1927 season, Paige's contract was sold to the Birmingham Black Barons", "Part way through the 1927 season, Paige's contract was sold to the Birmingham Black Barons", "Paige's contract was sold to the Birmingham Black Barons of the major Negro National League (NNL", "Part way through the 1927 season, Paige's contract was sold to the Birmingham Black Barons"], ['CANNOTANSWER', 'CANNOTANSWER', 'CANNOTANSWER'], ['game in late June 1927, against the St. Louis Stars, Paige incited a brawl when his fastball hit the hand of St. Louis catcher Mitchell Murray.', 'He finished the 1927 season 7-1 with 69 strikeouts and 26 walks in 89 1/3 innings.', 'Pitching for the Black Barons, Paige threw hard but was wild and awkward.', 'Pitching for the Black Barons, Paige threw hard but was wild and awkward.', 'Over the next two seasons, Paige went 12-5 and 10-9 while recording 176 strikeouts in 1929. ('], ['Due to his increased earning potential, Barons owner R. T. Jackson would "rent" Paige out to other ball clubs', 'Due to his increased earning potential, Barons owner R. T. Jackson would "rent" Paige out to other ball clubs for a game or two to draw a decent crowd,', 'Jackson would "rent" Paige out to other ball clubs for a game or two to draw a decent crowd, with both Jackson and Paige taking a cut.'] ] }, 'orig_answers': { 'answer_starts': [39, 2300, 1298, 2300, 600, 2300, 1514, 2165], 'texts': ['Alex Herman, was the player/manager for the Chattanooga White Sox of the minor Negro Southern League. In 1926 he discovered Paige and offered to pay him $250 per month,', 'CANNOTANSWER', 'Paige improved and matured as a pitcher with help from his teammates,', 'CANNOTANSWER', "Part way through the 1927 season, Paige's contract was sold to the Birmingham Black Barons", 'CANNOTANSWER', 'Over the next two seasons, Paige went 12-5 and 10-9 while recording 176 strikeouts in 1929. (', 'Jackson would "rent" Paige out to other ball clubs for a game or two to draw a decent crowd, with both Jackson and Paige taking a cut.'] }, } ``` ### Data Fields - `dialogue_id`: ID of the dialogue. - `wikipedia_page_title`: title of the Wikipedia page. - `background`: first paragraph of the main Wikipedia article. - `section_tile`: Wikipedia section title. - `context`: Wikipedia section text. - `turn_ids`: list of identification of dialogue turns. One list of ids per dialogue. - `questions`: list of questions in the dialogue. One list of questions per dialogue. - `followups`: list of followup actions in the dialogue. One list of followups per dialogue. `y`: follow, `m`: maybe follow yp, `n`: don't follow up. - `yesnos`: list of yes/no in the dialogue. One list of yes/nos per dialogue. `y`: yes, `n`: no, `x`: neither. - `answers`: dictionary of answers to the questions (validation step of data collection) - `answer_starts`: list of list of starting offsets. For training, list of single element lists (one answer per question). - `texts`: list of list of span texts answering questions. For training, list of single element lists (one answer per question). - `orig_answers`: dictionary of original answers (the ones provided by the teacher in the dialogue) - `answer_starts`: list of starting offsets - `texts`: list of span texts answering questions. ### Data Splits QuAC contains 98,407 QA pairs from 13,594 dialogs. The dialogs were conducted on 8,854 unique sections from 3,611 unique Wikipedia articles, and every dialog contains between four and twelve questions. The dataset comes with a train/dev split such that there is no overlap in sections across splits. Furthermore, the dev and test sets only include one dialog per section, in contrast to the training set which can have multiple dialogs per section. Dev and test instances come with five reference answers instead of just one as in the training set; we obtain the extra references to improve the reliability of our evaluations, as questions can have multiple valid answer spans. The test set is not publicly available; instead, researchers must submit their models to the [leaderboard](http://quac.ai), which will run the model on our hidden test set. The training set contains 83,568 questions (11,567 dialogues), while 7,354 (1,000) and 7,353 (1,002) separate questions are reserved for the dev and test set respectively. ## Dataset Creation ### Curation Rationale Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset. ### Source Data Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset. #### Initial Data Collection and Normalization Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset. #### Who are the source language producers? Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset. ### Annotations Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset. #### Annotation process Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset. #### Who are the annotators? Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset. ### Personal and Sensitive Information Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset. ## Considerations for Using the Data ### Social Impact of Dataset Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset. ### Discussion of Biases Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset. ### Other Known Limitations Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset. ## Additional Information ### Dataset Curators Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset. ### Licensing Information The dataset is distributed under the MIT license. ### Citation Information Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example: ``` @inproceedings{choi-etal-2018-quac, title = "{Q}u{AC}: Question Answering in Context", author = "Choi, Eunsol and He, He and Iyyer, Mohit and Yatskar, Mark and Yih, Wen-tau and Choi, Yejin and Liang, Percy and Zettlemoyer, Luke", booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing", month = oct # "-" # nov, year = "2018", address = "Brussels, Belgium", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D18-1241", doi = "10.18653/v1/D18-1241", pages = "2174--2184", abstract = "We present QuAC, a dataset for Question Answering in Context that contains 14K information-seeking QA dialogs (100K questions in total). The dialogs involve two crowd workers: (1) a student who poses a sequence of freeform questions to learn as much as possible about a hidden Wikipedia text, and (2) a teacher who answers the questions by providing short excerpts from the text. QuAC introduces challenges not found in existing machine comprehension datasets: its questions are often more open-ended, unanswerable, or only meaningful within the dialog context, as we show in a detailed qualitative evaluation. We also report results for a number of reference models, including a recently state-of-the-art reading comprehension architecture extended to model dialog context. Our best model underperforms humans by 20 F1, suggesting that there is significant room for future work on this data. Dataset, baseline, and leaderboard available at \url{http://quac.ai}.", } ``` ### Contributions Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset.

quac

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced", "found"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["extended|wikipedia"], "task_categories": ["question-answering", "text-generation", "fill-mask"], "task_ids": ["dialogue-modeling", "extractive-qa"], "paperswithcode_id": "quac", "pretty_name": "Question Answering in Context", "dataset_info": {"features": [{"name": "dialogue_id", "dtype": "string"}, {"name": "wikipedia_page_title", "dtype": "string"}, {"name": "background", "dtype": "string"}, {"name": "section_title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "turn_ids", "sequence": "string"}, {"name": "questions", "sequence": "string"}, {"name": "followups", "sequence": {"class_label": {"names": {"0": "y", "1": "n", "2": "m"}}}}, {"name": "yesnos", "sequence": {"class_label": {"names": {"0": "y", "1": "n", "2": "x"}}}}, {"name": "answers", "sequence": [{"name": "texts", "sequence": "string"}, {"name": "answer_starts", "sequence": "int32"}]}, {"name": "orig_answers", "struct": [{"name": "texts", "sequence": "string"}, {"name": "answer_starts", "sequence": "int32"}]}], "config_name": "plain_text", "splits": [{"name": "train", "num_bytes": 58174754, "num_examples": 11567}, {"name": "validation", "num_bytes": 7375938, "num_examples": 1000}], "download_size": 77043986, "dataset_size": 65550692}}

2024-01-18T11:14:05+00:00

TAGS #task_categories-question-answering #task_categories-text-generation #task_categories-fill-mask #task_ids-dialogue-modeling #task_ids-extractive-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|wikipedia #language-English #license-mit #arxiv-1808.07036 #region-us

# Dataset Card for Question Answering in Context ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: QuAC - Paper: QuAC: Question Answering in Context - Leaderboard: QuAC's leaderboard - Point of Contact: Google group ### Dataset Summary Question Answering in Context is a dataset for modeling, understanding, and participating in information seeking dialog. Data instances consist of an interactive dialog between two crowd workers: (1) a student who poses a sequence of freeform questions to learn as much as possible about a hidden Wikipedia text, and (2) a teacher who answers the questions by providing short excerpts (spans) from the text. QuAC introduces challenges not found in existing machine comprehension datasets: its questions are often more open-ended, unanswerable, or only meaningful within the dialog context. ### Supported Tasks and Leaderboards The core problem involves predicting a text span to answer a question about a Wikipedia section (extractive question answering). Since QuAC questions include a dialog component, each instance includes a “dialog history” of questions and answers asked in the dialog prior to the given question, along with some additional metadata. Authors provided an official evaluation script for evaluation. ### Languages The text in the dataset is in English. The associated BCP-47 code is 'en'. ## Dataset Structure ### Data Instances A validation examples looks like this (one entry per dialogue): ### Data Fields - 'dialogue_id': ID of the dialogue. - 'wikipedia_page_title': title of the Wikipedia page. - 'background': first paragraph of the main Wikipedia article. - 'section_tile': Wikipedia section title. - 'context': Wikipedia section text. - 'turn_ids': list of identification of dialogue turns. One list of ids per dialogue. - 'questions': list of questions in the dialogue. One list of questions per dialogue. - 'followups': list of followup actions in the dialogue. One list of followups per dialogue. 'y': follow, 'm': maybe follow yp, 'n': don't follow up. - 'yesnos': list of yes/no in the dialogue. One list of yes/nos per dialogue. 'y': yes, 'n': no, 'x': neither. - 'answers': dictionary of answers to the questions (validation step of data collection) - 'answer_starts': list of list of starting offsets. For training, list of single element lists (one answer per question). - 'texts': list of list of span texts answering questions. For training, list of single element lists (one answer per question). - 'orig_answers': dictionary of original answers (the ones provided by the teacher in the dialogue) - 'answer_starts': list of starting offsets - 'texts': list of span texts answering questions. ### Data Splits QuAC contains 98,407 QA pairs from 13,594 dialogs. The dialogs were conducted on 8,854 unique sections from 3,611 unique Wikipedia articles, and every dialog contains between four and twelve questions. The dataset comes with a train/dev split such that there is no overlap in sections across splits. Furthermore, the dev and test sets only include one dialog per section, in contrast to the training set which can have multiple dialogs per section. Dev and test instances come with five reference answers instead of just one as in the training set; we obtain the extra references to improve the reliability of our evaluations, as questions can have multiple valid answer spans. The test set is not publicly available; instead, researchers must submit their models to the leaderboard, which will run the model on our hidden test set. The training set contains 83,568 questions (11,567 dialogues), while 7,354 (1,000) and 7,353 (1,002) separate questions are reserved for the dev and test set respectively. ## Dataset Creation ### Curation Rationale Please refer to the Datasheet from the authors of the dataset. ### Source Data Please refer to the Datasheet from the authors of the dataset. #### Initial Data Collection and Normalization Please refer to the Datasheet from the authors of the dataset. #### Who are the source language producers? Please refer to the Datasheet from the authors of the dataset. ### Annotations Please refer to the Datasheet from the authors of the dataset. #### Annotation process Please refer to the Datasheet from the authors of the dataset. #### Who are the annotators? Please refer to the Datasheet from the authors of the dataset. ### Personal and Sensitive Information Please refer to the Datasheet from the authors of the dataset. ## Considerations for Using the Data ### Social Impact of Dataset Please refer to the Datasheet from the authors of the dataset. ### Discussion of Biases Please refer to the Datasheet from the authors of the dataset. ### Other Known Limitations Please refer to the Datasheet from the authors of the dataset. ## Additional Information ### Dataset Curators Please refer to the Datasheet from the authors of the dataset. ### Licensing Information The dataset is distributed under the MIT license. Provide the BibTex-formatted reference for the dataset. For example: ### Contributions Thanks to @VictorSanh for adding this dataset.

2bd9d7f90a532fe1a910b70972cef5fda341c8fe

# Dataset Card for "quail" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://text-machine-lab.github.io/blog/2020/quail/](https://text-machine-lab.github.io/blog/2020/quail/) - **Repository:** https://github.com/text-machine-lab/quail - **Paper:** [Getting Closer to AI Complete Question Answering: A Set of Prerequisite Real Tasks](https://doi.org/10.1609/aaai.v34i05.6398 ) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 6.41 MB - **Size of the generated dataset:** 29.62 MB - **Total amount of disk used:** 36.03 MB ### Dataset Summary QuAIL is a reading comprehension dataset. QuAIL contains 15K multi-choice questions in texts 300-350 tokens long 4 domains (news, user stories, fiction, blogs).QuAIL is balanced and annotated for question types. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### quail - **Size of downloaded dataset files:** 6.41 MB - **Size of the generated dataset:** 29.62 MB - **Total amount of disk used:** 36.03 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "answers": ["the cousin is not friendly", "the cousin could have been pretier", "not enough information", "the cousin was too nice"], "context": "\"That fall came and I went back to Michigan and the school year went by and summer came and I never really thought about it. I'm...", "context_id": "f001", "correct_answer_id": 0, "domain": "fiction", "id": "f001_19", "metadata": { "author": "Joseph Devon", "title": "Black Eyed Susan", "url": "http://manybooks.net/pages/devonjother08black_eyed_susan/0.html" }, "question": "After the events in the text what does the author think about the cousin?", "question_id": "19", "question_type": "Subsequent_state" } ``` ### Data Fields The data fields are the same among all splits. #### quail - `id`: a `string` feature. - `context_id`: a `string` feature. - `question_id`: a `string` feature. - `domain`: a `string` feature. - `author`: a `string` feature. - `title`: a `string` feature. - `url`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `question_type`: a `string` feature. - `answers`: a `list` of `string` features. - `correct_answer_id`: a `int32` feature. ### Data Splits |name |train|challenge|validation| |-----|----:|--------:|---------:| |quail|10246| 556| 2164| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{DBLP:conf/aaai/RogersKDR20, author = {Anna Rogers and Olga Kovaleva and Matthew Downey and Anna Rumshisky}, title = {Getting Closer to {AI} Complete Question Answering: {A} Set of Prerequisite Real Tasks}, booktitle = {The Thirty-Fourth {AAAI} Conference on Artificial Intelligence, {AAAI} 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, {IAAI} 2020, The Tenth {AAAI} Symposium on Educational Advances in Artificial Intelligence, {EAAI} 2020, New York, NY, USA, February 7-12, 2020}, pages = {8722--8731}, publisher = {{AAAI} Press}, year = {2020}, url = {https://aaai.org/ojs/index.php/AAAI/article/view/6398}, timestamp = {Thu, 04 Jun 2020 13:18:48 +0200}, biburl = {https://dblp.org/rec/conf/aaai/RogersKDR20.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` ### Contributions Thanks to [@sai-prasanna](https://github.com/sai-prasanna), [@ngdodd](https://github.com/ngdodd) for adding this dataset.

quail

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-nc-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["multiple-choice"], "task_ids": ["multiple-choice-qa"], "paperswithcode_id": "quail", "pretty_name": "Question Answering for Artificial Intelligence (QuAIL)", "dataset_info": {"config_name": "quail", "features": [{"name": "id", "dtype": "string"}, {"name": "context_id", "dtype": "string"}, {"name": "question_id", "dtype": "string"}, {"name": "domain", "dtype": "string"}, {"name": "metadata", "struct": [{"name": "author", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "question_type", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "correct_answer_id", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 23432601, "num_examples": 10246}, {"name": "validation", "num_bytes": 4989531, "num_examples": 2164}, {"name": "challenge", "num_bytes": 1199792, "num_examples": 556}], "download_size": 2286403, "dataset_size": 29621924}, "configs": [{"config_name": "quail", "data_files": [{"split": "train", "path": "quail/train-*"}, {"split": "validation", "path": "quail/validation-*"}, {"split": "challenge", "path": "quail/challenge-*"}], "default": true}]}

2024-01-04T16:18:32+00:00

TAGS #task_categories-multiple-choice #task_ids-multiple-choice-qa #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-nc-sa-4.0 #region-us

Dataset Card for "quail" ======================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: URL * Paper: Getting Closer to AI Complete Question Answering: A Set of Prerequisite Real Tasks * Point of Contact: * Size of downloaded dataset files: 6.41 MB * Size of the generated dataset: 29.62 MB * Total amount of disk used: 36.03 MB ### Dataset Summary QuAIL is a reading comprehension dataset. QuAIL contains 15K multi-choice questions in texts 300-350 tokens long 4 domains (news, user stories, fiction, blogs).QuAIL is balanced and annotated for question types. ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### quail * Size of downloaded dataset files: 6.41 MB * Size of the generated dataset: 29.62 MB * Total amount of disk used: 36.03 MB An example of 'train' looks as follows. ### Data Fields The data fields are the same among all splits. #### quail * 'id': a 'string' feature. * 'context\_id': a 'string' feature. * 'question\_id': a 'string' feature. * 'domain': a 'string' feature. * 'author': a 'string' feature. * 'title': a 'string' feature. * 'url': a 'string' feature. * 'context': a 'string' feature. * 'question': a 'string' feature. * 'question\_type': a 'string' feature. * 'answers': a 'list' of 'string' features. * 'correct\_answer\_id': a 'int32' feature. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @sai-prasanna, @ngdodd for adding this dataset.

aeb2fe9509a7aaaccc43c3631609b809eb32b45f

# Dataset Card for "quarel" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://allenai.org/data/quarel](https://allenai.org/data/quarel) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 0.63 MB - **Size of the generated dataset:** 1.53 MB - **Total amount of disk used:** 2.17 MB ### Dataset Summary QuaRel is a crowdsourced dataset of 2771 multiple-choice story questions, including their logical forms. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 0.63 MB - **Size of the generated dataset:** 1.53 MB - **Total amount of disk used:** 2.17 MB An example of 'train' looks as follows. ``` { "answer_index": 0, "id": "QuaRel_V1_B5_1403", "logical_form_pretty": "qrel(time, lower, world1) -> qrel(distance, higher, world2) ; qrel(distance, higher, world1)", "logical_forms": ["(infer (time lower world1) (distance higher world2) (distance higher world1))", "(infer (time lower world2) (distance higher world1) (distance higher world2))"], "question": "John and Rita are going for a run. Rita gets tired and takes a break on the park bench. After twenty minutes in the park, who has run farther? (A) John (B) Rita", "world_literals": { "world1": ["Rita"], "world2": ["John"] } } ``` ### Data Fields The data fields are the same among all splits. #### default - `id`: a `string` feature. - `answer_index`: a `int32` feature. - `logical_forms`: a `list` of `string` features. - `logical_form_pretty`: a `string` feature. - `world_literals`: a dictionary feature containing: - `world1`: a `string` feature. - `world2`: a `string` feature. - `question`: a `string` feature. ### Data Splits | name |train|validation|test| |-------|----:|---------:|---:| |default| 1941| 278| 552| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{quarel_v1, title={QuaRel: A Dataset and Models for Answering Questions about Qualitative Relationships}, author={Oyvind Tafjord, Peter Clark, Matt Gardner, Wen-tau Yih, Ashish Sabharwal}, year={2018}, journal={arXiv:1805.05377v1} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@mariamabarham](https://github.com/mariamabarham), [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

quarel

2022-03-02T23:29:22+00:00

{"language": ["en"], "paperswithcode_id": "quarel", "pretty_name": "QuaRel", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "answer_index", "dtype": "int32"}, {"name": "logical_forms", "sequence": "string"}, {"name": "logical_form_pretty", "dtype": "string"}, {"name": "world_literals", "sequence": [{"name": "world1", "dtype": "string"}, {"name": "world2", "dtype": "string"}]}, {"name": "question", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1072874, "num_examples": 1941}, {"name": "test", "num_bytes": 307588, "num_examples": 552}, {"name": "validation", "num_bytes": 154308, "num_examples": 278}], "download_size": 631370, "dataset_size": 1534770}}

2024-01-18T11:14:08+00:00

TAGS #language-English #region-us

Dataset Card for "quarel" ========================= Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: * Point of Contact: * Size of downloaded dataset files: 0.63 MB * Size of the generated dataset: 1.53 MB * Total amount of disk used: 2.17 MB ### Dataset Summary QuaRel is a crowdsourced dataset of 2771 multiple-choice story questions, including their logical forms. ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### default * Size of downloaded dataset files: 0.63 MB * Size of the generated dataset: 1.53 MB * Total amount of disk used: 2.17 MB An example of 'train' looks as follows. ### Data Fields The data fields are the same among all splits. #### default * 'id': a 'string' feature. * 'answer\_index': a 'int32' feature. * 'logical\_forms': a 'list' of 'string' features. * 'logical\_form\_pretty': a 'string' feature. * 'world\_literals': a dictionary feature containing: + 'world1': a 'string' feature. + 'world2': a 'string' feature. * 'question': a 'string' feature. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @thomwolf, @lewtun, @mariamabarham, @lhoestq, @patrickvonplaten for adding this dataset.

28c1dbb56caf81799296cb17892fa73402e23464

# Dataset Card for "quartz" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://allenai.org/data/quartz](https://allenai.org/data/quartz) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 0.49 MB - **Size of the generated dataset:** 1.72 MB - **Total amount of disk used:** 2.22 MB ### Dataset Summary QuaRTz is a crowdsourced dataset of 3864 multiple-choice questions about open domain qualitative relationships. Each question is paired with one of 405 different background sentences (sometimes short paragraphs). The QuaRTz dataset V1 contains 3864 questions about open domain qualitative relationships. Each question is paired with one of 405 different background sentences (sometimes short paragraphs). The dataset is split into train (2696), dev (384) and test (784). A background sentence will only appear in a single split. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 0.49 MB - **Size of the generated dataset:** 1.72 MB - **Total amount of disk used:** 2.22 MB An example of 'train' looks as follows. ``` { "answerKey": "A", "choices": { "label": ["A", "B"], "text": ["higher", "lower"] }, "id": "QRQA-10116-3", "para": "Electrons at lower energy levels, which are closer to the nucleus, have less energy.", "para_anno": { "cause_dir_sign": "LESS", "cause_dir_str": "closer", "cause_prop": "distance from a nucleus", "effect_dir_sign": "LESS", "effect_dir_str": "less", "effect_prop": "energy" }, "para_id": "QRSent-10116", "question": "Electrons further away from a nucleus have _____ energy levels than close ones.", "question_anno": { "less_cause_dir": "electron energy levels", "less_cause_prop": "nucleus", "less_effect_dir": "lower", "less_effect_prop": "electron energy levels", "more_effect_dir": "higher", "more_effect_prop": "electron energy levels" } } ``` ### Data Fields The data fields are the same among all splits. #### default - `id`: a `string` feature. - `question`: a `string` feature. - `choices`: a dictionary feature containing: - `text`: a `string` feature. - `label`: a `string` feature. - `answerKey`: a `string` feature. - `para`: a `string` feature. - `para_id`: a `string` feature. - `effect_prop`: a `string` feature. - `cause_dir_str`: a `string` feature. - `effect_dir_str`: a `string` feature. - `cause_dir_sign`: a `string` feature. - `effect_dir_sign`: a `string` feature. - `cause_prop`: a `string` feature. - `more_effect_dir`: a `string` feature. - `less_effect_dir`: a `string` feature. - `less_cause_prop`: a `string` feature. - `more_effect_prop`: a `string` feature. - `less_effect_prop`: a `string` feature. - `less_cause_dir`: a `string` feature. ### Data Splits | name |train|validation|test| |-------|----:|---------:|---:| |default| 2696| 384| 784| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information The dataset is licensed under Creative Commons [Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/). ### Citation Information ``` @InProceedings{quartz, author = {Oyvind Tafjord and Matt Gardner and Kevin Lin and Peter Clark}, title = {"QUARTZ: An Open-Domain Dataset of Qualitative Relationship Questions"}, year = {"2019"}, } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

quartz

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["extractive-qa", "open-domain-qa"], "paperswithcode_id": "quartz", "pretty_name": "QuaRTz", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "string"}]}, {"name": "answerKey", "dtype": "string"}, {"name": "para", "dtype": "string"}, {"name": "para_id", "dtype": "string"}, {"name": "para_anno", "struct": [{"name": "effect_prop", "dtype": "string"}, {"name": "cause_dir_str", "dtype": "string"}, {"name": "effect_dir_str", "dtype": "string"}, {"name": "cause_dir_sign", "dtype": "string"}, {"name": "effect_dir_sign", "dtype": "string"}, {"name": "cause_prop", "dtype": "string"}]}, {"name": "question_anno", "struct": [{"name": "more_effect_dir", "dtype": "string"}, {"name": "less_effect_dir", "dtype": "string"}, {"name": "less_cause_prop", "dtype": "string"}, {"name": "more_effect_prop", "dtype": "string"}, {"name": "less_effect_prop", "dtype": "string"}, {"name": "less_cause_dir", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 1188342, "num_examples": 2696}, {"name": "test", "num_bytes": 348644, "num_examples": 784}, {"name": "validation", "num_bytes": 174491, "num_examples": 384}], "download_size": 569255, "dataset_size": 1711477}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "validation", "path": "data/validation-*"}]}]}

2024-01-04T16:19:05+00:00

TAGS #task_categories-question-answering #task_ids-extractive-qa #task_ids-open-domain-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc-by-4.0 #region-us

Dataset Card for "quartz" ========================= Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: * Point of Contact: * Size of downloaded dataset files: 0.49 MB * Size of the generated dataset: 1.72 MB * Total amount of disk used: 2.22 MB ### Dataset Summary QuaRTz is a crowdsourced dataset of 3864 multiple-choice questions about open domain qualitative relationships. Each question is paired with one of 405 different background sentences (sometimes short paragraphs). The QuaRTz dataset V1 contains 3864 questions about open domain qualitative relationships. Each question is paired with one of 405 different background sentences (sometimes short paragraphs). The dataset is split into train (2696), dev (384) and test (784). A background sentence will only appear in a single split. ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### default * Size of downloaded dataset files: 0.49 MB * Size of the generated dataset: 1.72 MB * Total amount of disk used: 2.22 MB An example of 'train' looks as follows. ### Data Fields The data fields are the same among all splits. #### default * 'id': a 'string' feature. * 'question': a 'string' feature. * 'choices': a dictionary feature containing: + 'text': a 'string' feature. + 'label': a 'string' feature. * 'answerKey': a 'string' feature. * 'para': a 'string' feature. * 'para\_id': a 'string' feature. * 'effect\_prop': a 'string' feature. * 'cause\_dir\_str': a 'string' feature. * 'effect\_dir\_str': a 'string' feature. * 'cause\_dir\_sign': a 'string' feature. * 'effect\_dir\_sign': a 'string' feature. * 'cause\_prop': a 'string' feature. * 'more\_effect\_dir': a 'string' feature. * 'less\_effect\_dir': a 'string' feature. * 'less\_cause\_prop': a 'string' feature. * 'more\_effect\_prop': a 'string' feature. * 'less\_effect\_prop': a 'string' feature. * 'less\_cause\_dir': a 'string' feature. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information The dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0). ### Contributions Thanks to @patrickvonplaten, @lewtun, @thomwolf for adding this dataset.