Datasets:

librarian-bots

/

dataset_cards_with_metadata_with_embeddings

like 1

gmongaras/BERT_Base_Cased_512_Dataset_Mapped

gmongaras

--- dataset_info: features: - name: input_ids sequence: int32 - name: token_type_ids sequence: int8 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 52875464012.02522 num_examples: 136226984 download_size: 17583618282 dataset_size: 52875464012.02522 configs: - config_name: default data_files: - split: train path: data/train-* --- Dataset using the bert-cased tokenizer, cutoff sentences to 512 length (not sentence pairs), all sentence pairs extracted. Original datasets: https://huggingface.co/datasets/bookcorpus https://huggingface.co/datasets/wikipedia Variant: 20220301.en Mapped from: https://huggingface.co/datasets/gmongaras/BERT_Base_Cased_512_Dataset

Graphcore/gqa

Graphcore

--- language: - en license: - cc-by-4.0 ---

dart

--- annotations_creators: - crowdsourced - machine-generated language_creators: - crowdsourced - machine-generated language: - en license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended|wikitable_questions - extended|wikisql - extended|web_nlg - extended|cleaned_e2e task_categories: - tabular-to-text task_ids: - rdf-to-text paperswithcode_id: dart pretty_name: DART dataset_info: features: - name: tripleset sequence: sequence: string - name: subtree_was_extended dtype: bool - name: annotations sequence: - name: source dtype: string - name: text dtype: string splits: - name: train num_bytes: 12966443 num_examples: 30526 - name: validation num_bytes: 1458106 num_examples: 2768 - name: test num_bytes: 2657644 num_examples: 5097 download_size: 29939366 dataset_size: 17082193 --- # Dataset Card for DART ## 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:** [homepahe](https://github.com/Yale-LILY/dart) - **Repository:** [github](https://github.com/Yale-LILY/dart) - **Paper:** [paper](https://arxiv.org/abs/2007.02871) - **Leaderboard:** [leaderboard](https://github.com/Yale-LILY/dart#leaderboard) ### Dataset Summary DART is a large dataset for open-domain structured data record to text generation. We consider the structured data record input as a set of RDF entity-relation triples, a format widely used for knowledge representation and semantics description. DART consists of 82,191 examples across different domains with each input being a semantic RDF triple set derived from data records in tables and the tree ontology of the schema, annotated with sentence descriptions that cover all facts in the triple set. This hierarchical, structured format with its open-domain nature differentiates DART from other existing table-to-text corpora. ### Supported Tasks and Leaderboards The task associated to DART is text generation from data records that are RDF triplets: - `rdf-to-text`: The dataset can be used to train a model for text generation from RDF triplets, which consists in generating textual description of structured data. Success on this task is typically measured by achieving a *high* [BLEU](https://huggingface.co/metrics/bleu), [METEOR](https://huggingface.co/metrics/meteor), [BLEURT](https://huggingface.co/metrics/bleurt), [TER](https://huggingface.co/metrics/ter), [MoverScore](https://huggingface.co/metrics/mover_score), and [BERTScore](https://huggingface.co/metrics/bert_score). The ([BART-large model](https://huggingface.co/facebook/bart-large) from [BART](https://huggingface.co/transformers/model_doc/bart.html)) model currently achieves the following scores: | | BLEU | METEOR | TER | MoverScore | BERTScore | BLEURT | | ----- | ----- | ------ | ---- | ----------- | ---------- | ------ | | BART | 37.06 | 0.36 | 0.57 | 0.44 | 0.92 | 0.22 | This task has an active leaderboard which can be found [here](https://github.com/Yale-LILY/dart#leaderboard) and ranks models based on the above metrics while also reporting. ### Languages The dataset is in english (en). ## Dataset Structure ### Data Instances Here is an example from the dataset: ``` {'annotations': {'source': ['WikiTableQuestions_mturk'], 'text': ['First Clearing\tbased on Callicoon, New York and location at On NYS 52 1 Mi. Youngsville']}, 'subtree_was_extended': False, 'tripleset': [['First Clearing', 'LOCATION', 'On NYS 52 1 Mi. Youngsville'], ['On NYS 52 1 Mi. Youngsville', 'CITY_OR_TOWN', 'Callicoon, New York']]} ``` It contains one annotation where the textual description is 'First Clearing\tbased on Callicoon, New York and location at On NYS 52 1 Mi. Youngsville'. The RDF triplets considered to generate this description are in tripleset and are formatted as subject, predicate, object. ### Data Fields The different fields are: - `annotations`: - `text`: list of text descriptions of the triplets - `source`: list of sources of the RDF triplets (WikiTable, e2e, etc.) - `subtree_was_extended`: boolean, if the subtree condidered during the dataset construction was extended. Sometimes this field is missing, and therefore set to `None` - `tripleset`: RDF triplets as a list of triplets of strings (subject, predicate, object) ### Data Splits There are three splits, train, validation and test: | | train | validation | test | | ----- |------:|-----------:|-----:| | N. Examples | 30526 | 2768 | 6959 | ## Dataset Creation ### Curation Rationale Automatically generating textual descriptions from structured data inputs is crucial to improving the accessibility of knowledge bases to lay users. ### Source Data DART comes from existing datasets that cover a variety of different domains while allowing to build a tree ontology and form RDF triple sets as semantic representations. The datasets used are WikiTableQuestions, WikiSQL, WebNLG and Cleaned E2E. #### Initial Data Collection and Normalization DART is constructed using multiple complementary methods: (1) human annotation on open-domain Wikipedia tables from WikiTableQuestions (Pasupat and Liang, 2015) and WikiSQL (Zhong et al., 2017), (2) automatic conversion of questions in WikiSQL to declarative sentences, and (3) incorporation of existing datasets including WebNLG 2017 (Gardent et al., 2017a,b; Shimorina and Gardent, 2018) and Cleaned E2E (Novikova et al., 2017b; Dušek et al., 2018, 2019) #### Who are the source language producers? [More Information Needed] ### Annotations DART is constructed using multiple complementary methods: (1) human annotation on open-domain Wikipedia tables from WikiTableQuestions (Pasupat and Liang, 2015) and WikiSQL (Zhong et al., 2017), (2) automatic conversion of questions in WikiSQL to declarative sentences, and (3) incorporation of existing datasets including WebNLG 2017 (Gardent et al., 2017a,b; Shimorina and Gardent, 2018) and Cleaned E2E (Novikova et al., 2017b; Dušek et al., 2018, 2019) #### Annotation process The two stage annotation process for constructing tripleset sentence pairs is based on a tree-structured ontology of each table. First, internal skilled annotators denote the parent column for each column header. Then, a larger number of annotators provide a sentential description of an automatically-chosen subset of table cells in a row. #### 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 Under MIT license (see [here](https://github.com/Yale-LILY/dart/blob/master/LICENSE)) ### Citation Information ``` @article{radev2020dart, title={DART: Open-Domain Structured Data Record to Text Generation}, author={Dragomir Radev and Rui Zhang and Amrit Rau and Abhinand Sivaprasad and Chiachun Hsieh and Nazneen Fatema Rajani and Xiangru Tang and Aadit Vyas and Neha Verma and Pranav Krishna and Yangxiaokang Liu and Nadia Irwanto and Jessica Pan and Faiaz Rahman and Ahmad Zaidi and Murori Mutuma and Yasin Tarabar and Ankit Gupta and Tao Yu and Yi Chern Tan and Xi Victoria Lin and Caiming Xiong and Richard Socher}, journal={arXiv preprint arXiv:2007.02871}, year={2020} ``` ### Contributions Thanks to [@lhoestq](https://github.com/lhoestq) for adding this dataset.

tweets_hate_speech_detection

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - gpl-3.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification pretty_name: Tweets Hate Speech Detection dataset_info: features: - name: label dtype: class_label: names: '0': no-hate-speech '1': hate-speech - name: tweet dtype: string splits: - name: train num_bytes: 3191888 num_examples: 31962 - name: test num_bytes: 1711606 num_examples: 17197 download_size: 4738708 dataset_size: 4903494 train-eval-index: - config: default task: text-classification task_id: binary_classification splits: train_split: train col_mapping: tweet: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 binary args: average: binary - 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 --- # Dataset Card for Tweets Hate Speech Detection ## 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:** [Home](https://github.com/sharmaroshan/Twitter-Sentiment-Analysis) - **Repository:** [Repo](https://github.com/sharmaroshan/Twitter-Sentiment-Analysis/blob/master/train_tweet.csv) - **Paper:** - **Leaderboard:** - **Point of Contact:** [Darshan Gandhi](darshangandhi1151@gmail.com) ### Dataset Summary The objective of this task is to detect hate speech in tweets. For the sake of simplicity, we say a tweet contains hate speech if it has a racist or sexist sentiment associated with it. So, the task is to classify racist or sexist tweets from other tweets. Formally, given a training sample of tweets and labels, where label ‘1’ denotes the tweet is racist/sexist and label ‘0’ denotes the tweet is not racist/sexist, your objective is to predict the labels on the given test dataset. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The tweets are primarily in English Language. ## Dataset Structure ### Data Instances The dataset contains a label denoting is the tweet a hate speech or not ``` {'label': 0, # not a hate speech 'tweet': ' @user when a father is dysfunctional and is so selfish he drags his kids into his dysfunction. #run'} ``` ### Data Fields * label : 1 - it is a hate speech, 0 - not a hate speech. * tweet: content of the tweet as a string. ### Data Splits The data contains training data with :31962 entries ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization Crowdsourced from tweets of users #### Who are the source language producers? Cwodsourced from twitter ### Annotations #### Annotation process The data has been precprocessed and a model has been trained to assign the relevant label to the tweet #### Who are the annotators? The data has been provided by Roshan Sharma ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset With the help of this dataset, one can understand more about the human sentiments and also analye the situations when a particular person intends to make use of hatred/racist comments ### Discussion of Biases The data could be cleaned up further for additional purposes such as applying a better feature extraction techniques [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Roshan Sharma ### Licensing Information [Information](https://github.com/sharmaroshan/Twitter-Sentiment-Analysis/blob/master/LICENSE) ### Citation Information [Citation](https://github.com/sharmaroshan/Twitter-Sentiment-Analysis/blob/master/CONTRIBUTING.md) ### Contributions Thanks to [@darshan-gandhi](https://github.com/darshan-gandhi) for adding this dataset.

higgsfield/dsml_original_loc

higgsfield

--- dataset_info: features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 243684731 num_examples: 32477 download_size: 27760890 dataset_size: 243684731 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "dsml_original_loc" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

vwxyzjn/summarize_from_feedback_tldr_3_filtered_oai_preprocessing_pythia-160m_48

vwxyzjn

--- dataset_info: features: - name: id dtype: string - name: subreddit dtype: string - name: title dtype: string - name: post dtype: string - name: summary dtype: string - name: query_token sequence: int64 - name: query dtype: string - name: reference_response dtype: string - name: reference_response_token sequence: int64 - name: reference_response_token_len dtype: int64 splits: - name: train num_bytes: 887163319 num_examples: 116722 - name: validation num_bytes: 49018373 num_examples: 6447 - name: test num_bytes: 49853305 num_examples: 6553 download_size: 339424718 dataset_size: 986034997 --- # Dataset Card for "summarize_from_feedback_tldr_3_filtered_oai_preprocessing_pythia-160m_48" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

sberquad

--- annotations_creators: - crowdsourced language_creators: - found - crowdsourced language: - ru license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - extractive-qa paperswithcode_id: sberquad pretty_name: SberQuAD dataset_info: config_name: sberquad features: - name: id dtype: int32 - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: train num_bytes: 71631541 num_examples: 45328 - name: validation num_bytes: 7972953 num_examples: 5036 - name: test num_bytes: 36397776 num_examples: 23936 download_size: 10491714 dataset_size: 116002270 --- # Dataset Card for sberquad ## 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:** [Needs More Information] - **Repository:** https://github.com/sberbank-ai/data-science-journey-2017 - **Paper:** https://arxiv.org/abs/1912.09723 - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary Sber Question Answering Dataset (SberQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Russian original analogue presented in Sberbank Data Science Journey 2017. ### Supported Tasks and Leaderboards [Needs More Information] ### Languages Russian ## Dataset Structure ### Data Instances ``` { "context": "Первые упоминания о строении человеческого тела встречаются в Древнем Египте...", "id": 14754, "qas": [ { "id": 60544, "question": "Где встречаются первые упоминания о строении человеческого тела?", "answers": [{"answer_start": 60, "text": "в Древнем Египте"}], } ] } ``` ### Data Fields - id: a int32 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 |validation|test | |----------|-----:|---------:|-----| |plain_text|45328 | 5036 |23936| ## 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{sberquad, doi = {10.1007/978-3-030-58219-7_1}, author = {Pavel Efimov and Andrey Chertok and Leonid Boytsov and Pavel Braslavski}, title = {SberQuAD -- Russian Reading Comprehension Dataset: Description and Analysis}, booktitle = {Experimental IR Meets Multilinguality, Multimodality, and Interaction}, year = {2020}, publisher = {Springer International Publishing}, pages = {3--15} } ``` ### Contributions Thanks to [@alenusch](https://github.com/Alenush) for adding this dataset.

lince

--- paperswithcode_id: lince pretty_name: Linguistic Code-switching Evaluation Dataset dataset_info: - config_name: lid_spaeng features: - name: idx dtype: int32 - name: words sequence: string - name: lid sequence: string splits: - name: train num_bytes: 4745003 num_examples: 21030 - name: validation num_bytes: 739950 num_examples: 3332 - name: test num_bytes: 1337727 num_examples: 8289 download_size: 1188861 dataset_size: 6822680 - config_name: lid_hineng features: - name: idx dtype: int32 - name: words sequence: string - name: lid sequence: string splits: - name: train num_bytes: 1662284 num_examples: 4823 - name: validation num_bytes: 268930 num_examples: 744 - name: test num_bytes: 456850 num_examples: 1854 download_size: 432854 dataset_size: 2388064 - config_name: lid_msaea features: - name: idx dtype: int32 - name: words sequence: string - name: lid sequence: string splits: - name: train num_bytes: 3804156 num_examples: 8464 - name: validation num_bytes: 490566 num_examples: 1116 - name: test num_bytes: 590488 num_examples: 1663 download_size: 803806 dataset_size: 4885210 - config_name: lid_nepeng features: - name: idx dtype: int32 - name: words sequence: string - name: lid sequence: string splits: - name: train num_bytes: 2239014 num_examples: 8451 - name: validation num_bytes: 351649 num_examples: 1332 - name: test num_bytes: 620512 num_examples: 3228 download_size: 545342 dataset_size: 3211175 - config_name: pos_spaeng features: - name: idx dtype: int32 - name: words sequence: string - name: lid sequence: string - name: pos sequence: string splits: - name: train num_bytes: 5467832 num_examples: 27893 - name: validation num_bytes: 840593 num_examples: 4298 - name: test num_bytes: 1758626 num_examples: 10720 download_size: 819657 dataset_size: 8067051 - config_name: pos_hineng features: - name: idx dtype: int32 - name: words sequence: string - name: lid sequence: string - name: pos sequence: string splits: - name: train num_bytes: 537541 num_examples: 1030 - name: validation num_bytes: 80886 num_examples: 160 - name: test num_bytes: 131192 num_examples: 299 download_size: 113872 dataset_size: 749619 - config_name: ner_spaeng features: - name: idx dtype: int32 - name: words sequence: string - name: lid sequence: string - name: ner sequence: string splits: - name: train num_bytes: 9836312 num_examples: 33611 - name: validation num_bytes: 2980990 num_examples: 10085 - name: test num_bytes: 6530956 num_examples: 23527 download_size: 3075520 dataset_size: 19348258 - config_name: ner_msaea features: - name: idx dtype: int32 - name: words sequence: string - name: ner sequence: string splits: - name: train num_bytes: 3887684 num_examples: 10103 - name: validation num_bytes: 431414 num_examples: 1122 - name: test num_bytes: 367310 num_examples: 1110 download_size: 938671 dataset_size: 4686408 - config_name: ner_hineng features: - name: idx dtype: int32 - name: words sequence: string - name: lid sequence: string - name: ner sequence: string splits: - name: train num_bytes: 474639 num_examples: 1243 - name: validation num_bytes: 121403 num_examples: 314 - name: test num_bytes: 185220 num_examples: 522 download_size: 141285 dataset_size: 781262 - config_name: sa_spaeng features: - name: idx dtype: int32 - name: words sequence: string - name: lid sequence: string - name: sa dtype: string splits: - name: train num_bytes: 3587783 num_examples: 12194 - name: validation num_bytes: 546692 num_examples: 1859 - name: test num_bytes: 1349407 num_examples: 4736 download_size: 1031412 dataset_size: 5483882 --- # Dataset Card for "lince" ## 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://ritual.uh.edu/lince](http://ritual.uh.edu/lince) - **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:** 9.09 MB - **Size of the generated dataset:** 56.42 MB - **Total amount of disk used:** 65.52 MB ### Dataset Summary LinCE is a centralized Linguistic Code-switching Evaluation benchmark (https://ritual.uh.edu/lince/) that contains data for training and evaluating NLP systems on code-switching tasks. ### 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 #### lid_hineng - **Size of downloaded dataset files:** 0.43 MB - **Size of the generated dataset:** 2.39 MB - **Total amount of disk used:** 2.82 MB An example of 'validation' looks as follows. ``` { "idx": 0, "lid": ["other", "other", "lang1", "lang1", "lang1", "other", "lang1", "lang1", "lang1", "lang1", "lang1", "lang1", "lang1", "mixed", "lang1", "lang1", "other"], "words": ["@ZahirJ", "@BinyavangaW", "Loved", "the", "ending", "!", "I", "could", "have", "offered", "you", "some", "ironic", "chai-tea", "for", "it", ";)"] } ``` #### lid_msaea - **Size of downloaded dataset files:** 0.81 MB - **Size of the generated dataset:** 4.89 MB - **Total amount of disk used:** 5.69 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "idx": 0, "lid": ["ne", "lang2", "other", "lang2", "lang2", "other", "other", "lang2", "lang2", "lang2", "lang2", "lang2", "lang2", "lang2", "lang2", "lang2", "lang2", "lang2", "lang2", "lang2", "lang2", "other", "lang2", "lang2", "lang2", "ne", "lang2", "lang2"], "words": "[\"علاء\", \"بخير\", \"،\", \"معنوياته\", \"كويسة\", \".\", \"..\", \"اسخف\", \"حاجة\", \"بس\", \"ان\", \"كل\", \"واحد\", \"منهم\", \"بييقى\", \"مقفول\", \"عليه\"..." } ``` #### lid_nepeng - **Size of downloaded dataset files:** 0.55 MB - **Size of the generated dataset:** 3.21 MB - **Total amount of disk used:** 3.75 MB An example of 'validation' looks as follows. ``` { "idx": 1, "lid": ["other", "lang2", "lang2", "lang2", "lang2", "lang1", "lang1", "lang1", "lang1", "lang1", "lang2", "lang2", "other", "mixed", "lang2", "lang2", "other", "other", "other", "other"], "words": ["@nirvikdada", "la", "hamlai", "bhetna", "paayeko", "will", "be", "your", "greatest", "gift", "ni", "dada", ";P", "#TreatChaiyo", "j", "hos", ";)", "@zappylily", "@AsthaGhm", "@ayacs_asis"] } ``` #### lid_spaeng - **Size of downloaded dataset files:** 1.18 MB - **Size of the generated dataset:** 6.83 MB - **Total amount of disk used:** 8.01 MB An example of 'train' looks as follows. ``` { "idx": 0, "lid": ["other", "other", "lang1", "lang1", "lang1", "other", "lang1", "lang1"], "words": ["11:11", ".....", "make", "a", "wish", ".......", "night", "night"] } ``` #### ner_hineng - **Size of downloaded dataset files:** 0.14 MB - **Size of the generated dataset:** 0.79 MB - **Total amount of disk used:** 0.92 MB An example of 'train' looks as follows. ``` { "idx": 1, "lid": ["en", "en", "en", "en", "en", "en", "hi", "hi", "hi", "hi", "hi", "hi", "hi", "en", "en", "en", "en", "rest"], "ner": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-PERSON", "I-PERSON", "O", "O", "O", "B-PERSON", "I-PERSON"], "words": ["I", "liked", "a", "@YouTube", "video", "https://t.co/DmVqhZbdaI", "Kabhi", "Palkon", "Pe", "Aasoon", "Hai-", "Kishore", "Kumar", "-Vocal", "Cover", "By", "Stephen", "Qadir"] } ``` ### Data Fields The data fields are the same among all splits. #### lid_hineng - `idx`: a `int32` feature. - `words`: a `list` of `string` features. - `lid`: a `list` of `string` features. #### lid_msaea - `idx`: a `int32` feature. - `words`: a `list` of `string` features. - `lid`: a `list` of `string` features. #### lid_nepeng - `idx`: a `int32` feature. - `words`: a `list` of `string` features. - `lid`: a `list` of `string` features. #### lid_spaeng - `idx`: a `int32` feature. - `words`: a `list` of `string` features. - `lid`: a `list` of `string` features. #### ner_hineng - `idx`: a `int32` feature. - `words`: a `list` of `string` features. - `lid`: a `list` of `string` features. - `ner`: a `list` of `string` features. ### Data Splits | name |train|validation|test| |----------|----:|---------:|---:| |lid_hineng| 4823| 744|1854| |lid_msaea | 8464| 1116|1663| |lid_nepeng| 8451| 1332|3228| |lid_spaeng|21030| 3332|8289| |ner_hineng| 1243| 314| 522| ## 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{aguilar-etal-2020-lince, title = "{L}in{CE}: A Centralized Benchmark for Linguistic Code-switching Evaluation", author = "Aguilar, Gustavo and Kar, Sudipta and Solorio, Thamar", 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-1.223", pages = "1803--1813", language = "English", ISBN = "979-10-95546-34-4", } ``` Note that each LinCE dataset has its own citation too. Please see [here](https://ritual.uh.edu/lince/datasets) for the correct citation on each dataset. ### Contributions Thanks to [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf), [@gaguilar](https://github.com/gaguilar) for adding this dataset.

ai4bharat/samanantar

ai4bharat

--- annotations_creators: - no-annotation language_creators: - found language: - en - as - bn - gu - hi - kn - ml - mr - or - pa - ta - te license: - cc-by-nc-4.0 multilinguality: - translation size_categories: - unknown source_datasets: - original task_categories: - text-generation - translation task_ids: [] pretty_name: Samanantar tags: - conditional-text-generation --- # Dataset Card for Samanantar ## 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:** https://indicnlp.ai4bharat.org/samanantar/ - **Repository:** - **Paper:** [Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages](https://arxiv.org/abs/2104.05596) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary Samanantar is the largest publicly available parallel corpora collection for Indic language: Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Oriya, Punjabi, Tamil, Telugu. The corpus has 49.6M sentence pairs between English to Indian Languages. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Samanantar contains parallel sentences between English (`en`) and 11 Indic language: - Assamese (`as`), - Bengali (`bn`), - Gujarati (`gu`), - Hindi (`hi`), - Kannada (`kn`), - Malayalam (`ml`), - Marathi (`mr`), - Odia (`or`), - Punjabi (`pa`), - Tamil (`ta`) and - Telugu (`te`). ## Dataset Structure ### Data Instances ``` { 'idx': 0, 'src': 'Prime Minister Narendra Modi met Her Majesty Queen Maxima of the Kingdom of the Netherlands today.', 'tgt': 'নতুন দিল্লিতে সোমবার প্রধানমন্ত্রী শ্রী নরেন্দ্র মোদীর সঙ্গে নেদারন্যান্ডসের মহারানী ম্যাক্সিমা সাক্ষাৎ করেন।', 'data_source': 'pmi' } ``` ### Data Fields - `idx` (int): ID. - `src` (string): Sentence in source language (English). - `tgt` (string): Sentence in destination language (one of the 11 Indic languages). - `data_source` (string): Source of the data. For created data sources, depending on the destination language, it might be one of: - anuvaad_catchnews - anuvaad_DD_National - anuvaad_DD_sports - anuvaad_drivespark - anuvaad_dw - anuvaad_financialexpress - anuvaad-general_corpus - anuvaad_goodreturns - anuvaad_indianexpress - anuvaad_mykhel - anuvaad_nativeplanet - anuvaad_newsonair - anuvaad_nouns_dictionary - anuvaad_ocr - anuvaad_oneindia - anuvaad_pib - anuvaad_pib_archives - anuvaad_prothomalo - anuvaad_timesofindia - asianetnews - betterindia - bridge - business_standard - catchnews - coursera - dd_national - dd_sports - dwnews - drivespark - fin_express - goodreturns - gu_govt - jagran-business - jagran-education - jagran-sports - ie_business - ie_education - ie_entertainment - ie_general - ie_lifestyle - ie_news - ie_sports - ie_tech - indiccorp - jagran-entertainment - jagran-lifestyle - jagran-news - jagran-tech - khan_academy - Kurzgesagt - marketfeed - mykhel - nativeplanet - nptel - ocr - oneindia - pa_govt - pmi - pranabmukherjee - sakshi - sentinel - thewire - toi - tribune - vsauce - wikipedia - zeebiz ### 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 [Creative Commons Attribution-NonCommercial 4.0 International](https://creativecommons.org/licenses/by-nc/4.0/). ### Citation Information ``` @misc{ramesh2021samanantar, title={Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages}, author={Gowtham Ramesh and Sumanth Doddapaneni and Aravinth Bheemaraj and Mayank Jobanputra and Raghavan AK and Ajitesh Sharma and Sujit Sahoo and Harshita Diddee and Mahalakshmi J and Divyanshu Kakwani and Navneet Kumar and Aswin Pradeep and Srihari Nagaraj and Kumar Deepak and Vivek Raghavan and Anoop Kunchukuttan and Pratyush Kumar and Mitesh Shantadevi Khapra}, year={2021}, eprint={2104.05596}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.

bigbio/pubmed_qa

bigbio

--- language: - en bigbio_language: - English license: mit multilinguality: monolingual bigbio_license_shortname: MIT pretty_name: PubMedQA homepage: https://github.com/pubmedqa/pubmedqa bigbio_pubmed: True bigbio_public: True bigbio_tasks: - QUESTION_ANSWERING --- # Dataset Card for PubMedQA ## Dataset Description - **Homepage:** https://github.com/pubmedqa/pubmedqa - **Pubmed:** True - **Public:** True - **Tasks:** QA PubMedQA is a novel biomedical question answering (QA) dataset collected from PubMed abstracts. The task of PubMedQA is to answer research biomedical questions with yes/no/maybe using the corresponding abstracts. PubMedQA has 1k expert-annotated (PQA-L), 61.2k unlabeled (PQA-U) and 211.3k artificially generated QA instances (PQA-A). Each PubMedQA instance is composed of: (1) a question which is either an existing research article title or derived from one, (2) a context which is the corresponding PubMed abstract without its conclusion, (3) a long answer, which is the conclusion of the abstract and, presumably, answers the research question, and (4) a yes/no/maybe answer which summarizes the conclusion. PubMedQA is the first QA dataset where reasoning over biomedical research texts, especially their quantitative contents, is required to answer the questions. PubMedQA datasets comprise of 3 different subsets: (1) PubMedQA Labeled (PQA-L): A labeled PubMedQA subset comprises of 1k manually annotated yes/no/maybe QA data collected from PubMed articles. (2) PubMedQA Artificial (PQA-A): An artificially labelled PubMedQA subset comprises of 211.3k PubMed articles with automatically generated questions from the statement titles and yes/no answer labels generated using a simple heuristic. (3) PubMedQA Unlabeled (PQA-U): An unlabeled PubMedQA subset comprises of 61.2k context-question pairs data collected from PubMed articles. ## Citation Information ``` @inproceedings{jin2019pubmedqa, title={PubMedQA: A Dataset for Biomedical Research Question Answering}, author={Jin, Qiao and Dhingra, Bhuwan and Liu, Zhengping and Cohen, William and Lu, Xinghua}, booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)}, pages={2567--2577}, year={2019} } ```

HuggingFaceM4/VQAv2_modif_10k

HuggingFaceM4

Invalid username or password.

nielsr/ade20k-panoptic-demo

nielsr

--- dataset_info: features: - name: image dtype: image - name: label dtype: image - name: segments_info list: - name: area dtype: int64 - name: bbox sequence: int64 - name: category_id dtype: int64 - name: id dtype: int64 - name: iscrowd dtype: int64 splits: - name: train num_bytes: 492746.0 num_examples: 10 - name: validation num_bytes: 461402.0 num_examples: 10 download_size: 949392 dataset_size: 954148.0 --- # Dataset Card for "ade20k-panoptic-demo" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

nthngdy/oscar-small

nthngdy

--- annotations_creators: - no-annotation language_creators: - found language: - af - am - ar - arz - as - az - azb - ba - be - bg - bn - bo - br - ca - ce - ceb - ckb - cs - cv - cy - da - de - dv - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - gl - gu - he - hi - hr - hu - hy - id - is - it - ja - ka - kk - km - kn - ko - ku - ky - la - lb - lo - lt - lv - mg - mhr - mk - ml - mn - mr - ms - mt - my - nds - ne - nl - nn - 'no' - or - os - pa - pl - pnb - ps - pt - ro - ru - sa - sah - sd - sh - si - sk - sl - sq - sr - sv - sw - ta - te - tg - th - tk - tl - tr - tt - ug - uk - ur - uz - vi - yi - zh license: - cc0-1.0 multilinguality: - multilingual source_datasets: - oscar task_categories: - text-generation task_ids: - language-modeling paperswithcode_id: oscar pretty_name: OSCAR --- ## WARNING: this dataset is an extract of the OSCAR dataset published here to simulate the use of the full dataset in low-resource contexts. Using this dataset is equivalent to using a processed version of OSCAR legally speaking. I take no credit for the gathering of the original data and hence refer entirely to the original dataset in the card below. # 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. ### 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. ## 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.

acronym_identification

--- annotations_creators: - expert-generated language_creators: - found language: - en license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - token-classification task_ids: [] paperswithcode_id: acronym-identification pretty_name: Acronym Identification Dataset tags: - acronym-identification dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: labels sequence: class_label: names: '0': B-long '1': B-short '2': I-long '3': I-short '4': O splits: - name: train num_bytes: 7792803 num_examples: 14006 - name: validation num_bytes: 952705 num_examples: 1717 - name: test num_bytes: 987728 num_examples: 1750 download_size: 8556464 dataset_size: 9733236 train-eval-index: - config: default task: token-classification task_id: entity_extraction splits: eval_split: test col_mapping: tokens: tokens labels: tags --- # Dataset Card for Acronym Identification 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:** https://sites.google.com/view/sdu-aaai21/shared-task - **Repository:** https://github.com/amirveyseh/AAAI-21-SDU-shared-task-1-AI - **Paper:** [What Does This Acronym Mean? Introducing a New Dataset for Acronym Identification and Disambiguation](https://arxiv.org/pdf/2010.14678v1.pdf) - **Leaderboard:** https://competitions.codalab.org/competitions/26609 - **Point of Contact:** [More Information Needed] ### Dataset Summary This dataset contains the training, validation, and test data for the **Shared Task 1: Acronym Identification** of the AAAI-21 Workshop on Scientific Document Understanding. ### Supported Tasks and Leaderboards The dataset supports an `acronym-identification` task, where the aim is to predic which tokens in a pre-tokenized sentence correspond to acronyms. The dataset was released for a Shared Task which supported a [leaderboard](https://competitions.codalab.org/competitions/26609). ### Languages The sentences in the dataset are in English (`en`). ## Dataset Structure ### Data Instances A sample from the training set is provided below: ``` {'id': 'TR-0', 'labels': [4, 4, 4, 4, 0, 2, 2, 4, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4], 'tokens': ['What', 'is', 'here', 'called', 'controlled', 'natural', 'language', '(', 'CNL', ')', 'has', 'traditionally', 'been', 'given', 'many', 'different', 'names', '.']} ``` Please note that in test set sentences only the `id` and `tokens` fields are available. `labels` can be ignored for test set. Labels in the test set are all `O` ### Data Fields The data instances have the following fields: - `id`: a `string` variable representing the example id, unique across the full dataset - `tokens`: a list of `string` variables representing the word-tokenized sentence - `labels`: a list of `categorical` variables with possible values `["B-long", "B-short", "I-long", "I-short", "O"]` corresponding to a BIO scheme. `-long` corresponds to the expanded acronym, such as *controlled natural language* here, and `-short` to the abbrviation, `CNL` here. ### Data Splits The training, validation, and test set contain `14,006`, `1,717`, and `1750` sentences respectively. ## Dataset Creation ### Curation Rationale > First, most of the existing datasets for acronym identification (AI) are either limited in their sizes or created using simple rule-based methods. > This is unfortunate as rules are in general not able to capture all the diverse forms to express acronyms and their long forms in text. > Second, most of the existing datasets are in the medical domain, ignoring the challenges in other scientific domains. > In order to address these limitations this paper introduces two new datasets for Acronym Identification. > Notably, our datasets are annotated by human to achieve high quality and have substantially larger numbers of examples than the existing AI datasets in the non-medical domain. ### Source Data #### Initial Data Collection and Normalization > In order to prepare a corpus for acronym annotation, we collect a corpus of 6,786 English papers from arXiv. > These papers consist of 2,031,592 sentences that would be used for data annotation for AI in this work. The dataset paper does not report the exact tokenization method. #### Who are the source language producers? The language was comes from papers hosted on the online digital archive [arXiv](https://arxiv.org/). No more information is available on the selection process or identity of the writers. ### Annotations #### Annotation process > Each sentence for annotation needs to contain at least one word in which more than half of the characters in are capital letters (i.e., acronym candidates). > Afterward, we search for a sub-sequence of words in which the concatenation of the first one, two or three characters of the words (in the order of the words in the sub-sequence could form an acronym candidate. > We call the sub-sequence a long form candidate. If we cannot find any long form candidate, we remove the sentence. > Using this process, we end up with 17,506 sentences to be annotated manually by the annotators from Amazon Mechanical Turk (MTurk). > In particular, we create a HIT for each sentence and ask the workers to annotate the short forms and the long forms in the sentence. > In case of disagreements, if two out of three workers agree on an annotation, we use majority voting to decide the correct annotation. > Otherwise, a fourth annotator is hired to resolve the conflict #### Who are the annotators? Workers were recruited through Amazon MEchanical Turk and paid $0.05 per annotation. No further demographic information is provided. ### Personal and Sensitive Information Papers published on arXiv are unlikely to contain much personal information, although some do include some poorly chosen examples revealing personal details, so the data should be used with care. ## 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 The dataset provided for this shared task is licensed under CC BY-NC-SA 4.0 international license. ### Citation Information ``` @inproceedings{Veyseh2020, author = {Amir Pouran Ben Veyseh and Franck Dernoncourt and Quan Hung Tran and Thien Huu Nguyen}, editor = {Donia Scott and N{\'{u}}ria Bel and Chengqing Zong}, title = {What Does This Acronym Mean? Introducing a New Dataset for Acronym Identification and Disambiguation}, booktitle = {Proceedings of the 28th International Conference on Computational Linguistics, {COLING} 2020, Barcelona, Spain (Online), December 8-13, 2020}, pages = {3285--3301}, publisher = {International Committee on Computational Linguistics}, year = {2020}, url = {https://doi.org/10.18653/v1/2020.coling-main.292}, doi = {10.18653/v1/2020.coling-main.292} } ``` ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.

bilgeyucel/seven-wonders

bilgeyucel

--- language: - en size_categories: - n<1K ---

frgfm/imagenette

frgfm

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - apache-2.0 multilinguality: [] size_categories: - 1K<n<10K source_datasets: - extended task_categories: - image-classification task_ids: [] paperswithcode_id: imagenette pretty_name: Imagenette --- # Dataset Card for Imagenette ## 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:** https://github.com/fastai/imagenette - **Repository:** https://github.com/fastai/imagenette - **Leaderboard:** https://paperswithcode.com/sota/image-classification-on-imagenette ### Dataset Summary A smaller subset of 10 easily classified classes from [Imagenet](https://huggingface.co/datasets/imagenet-1k#dataset-summary), and a little more French. This dataset was created by [Jeremy Howard](https://twitter.com/jeremyphoward), and this repository is only there to share his work on this platform. The repository owner takes no credit of any kind in the creation, curation or packaging of the dataset. ### Supported Tasks and Leaderboards - `image-classification`: The dataset can be used to train a model for Image Classification. ### Languages The class labels in the dataset are in English. ## Dataset Structure ### Data Instances A data point comprises an image URL and its classification label. ``` { 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=320x320 at 0x19FA12186D8>, 'label': 'tench', } ``` ### Data Fields - `image`: A `PIL.Image.Image` object containing the image. - `label`: the expected class label of the image. ### Data Splits | |train|validation| |----------|----:|---------:| |imagenette| 9469| 3925| ## Dataset Creation ### Curation Rationale cf. https://huggingface.co/datasets/imagenet-1k#curation-rationale ### Source Data #### Initial Data Collection and Normalization Imagenette is a subset of [ImageNet](https://huggingface.co/datasets/imagenet-1k). Information about data collection of the source data can be found [here](https://huggingface.co/datasets/imagenet-1k#initial-data-collection-and-normalization). ### Annotations #### Annotation process cf. https://huggingface.co/datasets/imagenet-1k#annotation-process #### Who are the annotators? cf. https://huggingface.co/datasets/imagenet-1k#who-are-the-annotators ### Personal and Sensitive Information cf. https://huggingface.co/datasets/imagenet-1k#personal-and-sensitive-information ## Considerations for Using the Data ### Social Impact of Dataset cf. https://huggingface.co/datasets/imagenet-1k#social-impact-of-dataset ### Discussion of Biases cf. https://huggingface.co/datasets/imagenet-1k#discussion-of-biases ### Other Known Limitations cf. https://huggingface.co/datasets/imagenet-1k#other-known-limitations ## Additional Information ### Dataset Curators cf. https://huggingface.co/datasets/imagenet-1k#dataset-curators and Jeremy Howard ### Licensing Information [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). ### Citation Information ``` @software{Howard_Imagenette_2019, title={Imagenette: A smaller subset of 10 easily classified classes from Imagenet}, author={Jeremy Howard}, year={2019}, month={March}, publisher = {GitHub}, url = {https://github.com/fastai/imagenette} } ``` ### Contributions This dataset was created by [Jeremy Howard](https://twitter.com/jeremyphoward) and published on [Github](https://github.com/fastai/imagenette). It was then only integrated into HuggingFace Datasets by [@frgfm](https://huggingface.co/frgfm).

cos_e

--- annotations_creators: - crowdsourced language: - en language_creators: - crowdsourced license: - unknown multilinguality: - monolingual pretty_name: Commonsense Explanations size_categories: - 10K<n<100K source_datasets: - extended|commonsense_qa task_categories: - question-answering task_ids: - open-domain-qa paperswithcode_id: cos-e dataset_info: - config_name: v1.0 features: - name: id dtype: string - name: question dtype: string - name: choices sequence: string - name: answer dtype: string - name: abstractive_explanation dtype: string - name: extractive_explanation dtype: string splits: - name: train num_bytes: 2077517 num_examples: 7610 - name: validation num_bytes: 261887 num_examples: 950 download_size: 4295320 dataset_size: 2339404 - config_name: v1.11 features: - name: id dtype: string - name: question dtype: string - name: choices sequence: string - name: answer dtype: string - name: abstractive_explanation dtype: string - name: extractive_explanation dtype: string splits: - name: train num_bytes: 2717420 num_examples: 9741 - name: validation num_bytes: 331760 num_examples: 1221 download_size: 6535534 dataset_size: 3049180 --- # Dataset Card for "cos_e" ## 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:** https://github.com/salesforce/cos-e - **Paper:** [Explain Yourself! Leveraging Language Models for Commonsense Reasoning](https://arxiv.org/abs/1906.02361) - **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:** 10.83 MB - **Size of the generated dataset:** 5.39 MB - **Total amount of disk used:** 16.22 MB ### Dataset Summary Common Sense Explanations (CoS-E) allows for training language models to automatically generate explanations that can be used during training and inference in a novel Commonsense Auto-Generated Explanation (CAGE) framework. ### 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 #### v1.0 - **Size of downloaded dataset files:** 4.30 MB - **Size of the generated dataset:** 2.34 MB - **Total amount of disk used:** 6.64 MB An example of 'train' looks as follows. ``` { "abstractive_explanation": "this is open-ended", "answer": "b", "choices": ["a", "b", "c"], "extractive_explanation": "this is selected train", "id": "42", "question": "question goes here." } ``` #### v1.11 - **Size of downloaded dataset files:** 6.53 MB - **Size of the generated dataset:** 3.05 MB - **Total amount of disk used:** 9.58 MB An example of 'train' looks as follows. ``` { "abstractive_explanation": "this is open-ended", "answer": "b", "choices": ["a", "b", "c"], "extractive_explanation": "this is selected train", "id": "42", "question": "question goes here." } ``` ### Data Fields The data fields are the same among all splits. #### v1.0 - `id`: a `string` feature. - `question`: a `string` feature. - `choices`: a `list` of `string` features. - `answer`: a `string` feature. - `abstractive_explanation`: a `string` feature. - `extractive_explanation`: a `string` feature. #### v1.11 - `id`: a `string` feature. - `question`: a `string` feature. - `choices`: a `list` of `string` features. - `answer`: a `string` feature. - `abstractive_explanation`: a `string` feature. - `extractive_explanation`: a `string` feature. ### Data Splits |name |train|validation| |-----|----:|---------:| |v1.0 | 7610| 950| |v1.11| 9741| 1221| ## 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{rajani2019explain, title = "Explain Yourself! Leveraging Language models for Commonsense Reasoning", author = "Rajani, Nazneen Fatema and McCann, Bryan and Xiong, Caiming and Socher, Richard", year="2019", booktitle = "Proceedings of the 2019 Conference of the Association for Computational Linguistics (ACL2019)", url ="https://arxiv.org/abs/1906.02361" } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham), [@patrickvonplaten](https://github.com/patrickvonplaten), [@albertvillanova](https://github.com/albertvillanova), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

allocine

--- annotations_creators: - no-annotation language_creators: - found language: - fr license: - mit multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification paperswithcode_id: allocine pretty_name: Allociné dataset_info: features: - name: review dtype: string - name: label dtype: class_label: names: '0': neg '1': pos config_name: allocine splits: - name: train num_bytes: 91330696 num_examples: 160000 - name: validation num_bytes: 11546250 num_examples: 20000 - name: test num_bytes: 11547697 num_examples: 20000 download_size: 66625305 dataset_size: 114424643 train-eval-index: - config: allocine task: text-classification task_id: multi_class_classification splits: train_split: train eval_split: test col_mapping: review: 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 --- # Dataset Card for Allociné ## 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:** [Allociné dataset repository](https://github.com/TheophileBlard/french-sentiment-analysis-with-bert/tree/master/allocine_dataset) - **Paper:** - **Leaderboard:** - **Point of Contact:** [Théophile Blard](mailto:theophile.blard@gmail.com) ### Dataset Summary The Allociné dataset is a French-language dataset for sentiment analysis. The texts are movie reviews written between 2006 and 2020 by members of the [Allociné.fr](https://www.allocine.fr/) community for various films. It contains 100k positive and 100k negative reviews divided into train (160k), validation (20k), and test (20k). ### Supported Tasks and Leaderboards - `text-classification`, `sentiment-classification`: The dataset can be used to train a model for sentiment classification. The model performance is evaluated based on the accuracy of the predicted labels as compared to the given labels in the dataset. A BERT-based model, [tf-allociné](https://huggingface.co/tblard/tf-allocine), achieves 97.44% accuracy on the test set. ### Languages The text is in French, as spoken by users of the [Allociné.fr](https://www.allocine.fr/) website. The BCP-47 code for French is fr. ## Dataset Structure ### Data Instances Each data instance contains the following features: _review_ and _label_. In the Hugging Face distribution of the dataset, the _label_ has 2 possible values, _0_ and _1_, which correspond to _negative_ and _positive_ respectively. See the [Allociné corpus viewer](https://huggingface.co/datasets/viewer/?dataset=allocine) to explore more examples. An example from the Allociné train set looks like the following: ``` {'review': 'Premier film de la saga Kozure Okami, "Le Sabre de la vengeance" est un très bon film qui mêle drame et action, et qui, en 40 ans, n'a pas pris une ride.', 'label': 1} ``` ### Data Fields - 'review': a string containing the review text - 'label': an integer, either _0_ or _1_, indicating a _negative_ or _positive_ review, respectively ### Data Splits The Allociné dataset has 3 splits: _train_, _validation_, and _test_. The splits contain disjoint sets of movies. The following table contains the number of reviews in each split and the percentage of positive and negative reviews. | Dataset Split | Number of Instances in Split | Percent Negative Reviews | Percent Positive Reviews | | ------------- | ---------------------------- | ------------------------ | ------------------------ | | Train | 160,000 | 49.6% | 50.4% | | Validation | 20,000 | 51.0% | 49.0% | | Test | 20,000 | 52.0% | 48.0% | ## Dataset Creation ### Curation Rationale The Allociné dataset was developed to support large-scale sentiment analysis in French. It was released alongside the [tf-allociné](https://huggingface.co/tblard/tf-allocine) model and used to compare the performance of several language models on this task. ### Source Data #### Initial Data Collection and Normalization The reviews and ratings were collected using a list of [film page urls](https://github.com/TheophileBlard/french-sentiment-analysis-with-bert/blob/master/allocine_dataset/allocine_films_urls.txt) and the [allocine_scraper.py](https://github.com/TheophileBlard/french-sentiment-analysis-with-bert/blob/master/allocine_dataset/allocine_scraper.py) tool. Up to 30 reviews were collected for each film. The reviews were originally labeled with a rating from 0.5 to 5.0 with a step of 0.5 between each rating. Ratings less than or equal to 2 are labeled as negative and ratings greater than or equal to 4 are labeled as positive. Only reviews with less than 2000 characters are included in the dataset. #### Who are the source language producers? The dataset contains movie reviews produced by the online community of the [Allociné.fr](https://www.allocine.fr/) website. ### Annotations The dataset does not contain any additional annotations. #### Annotation process [N/A] #### Who are the annotators? [N/A] ### Personal and Sensitive Information Reviewer usernames or personal information were not collected with the reviews, but could potentially be recovered. The content of each review may include information and opinions about the film's actors, film crew, and plot. ## Considerations for Using the Data ### Social Impact of Dataset Sentiment classification is a complex task which requires sophisticated language understanding skills. Successful models can support decision-making based on the outcome of the sentiment analysis, though such models currently require a high degree of domain specificity. It should be noted that the community represented in the dataset may not represent any downstream application's potential users, and the observed behavior of a model trained on this dataset may vary based on the domain and use case. ### Discussion of Biases The Allociné website lists a number of topics which violate their [terms of service](https://www.allocine.fr/service/conditions.html#charte). Further analysis is needed to determine the extent to which moderators have successfully removed such content. ### Other Known Limitations The limitations of the Allociné dataset have not yet been investigated, however [Staliūnaitė and Bonfil (2017)](https://www.aclweb.org/anthology/W17-5410.pdf) detail linguistic phenomena that are generally present in sentiment analysis but difficult for models to accurately label, such as negation, adverbial modifiers, and reviewer pragmatics. ## Additional Information ### Dataset Curators The Allociné dataset was collected by Théophile Blard. ### Licensing Information The Allociné dataset is licensed under the [MIT License](https://opensource.org/licenses/MIT). ### Citation Information > Théophile Blard, French sentiment analysis with BERT, (2020), GitHub repository, <https://github.com/TheophileBlard/french-sentiment-analysis-with-bert> ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@TheophileBlard](https://github.com/TheophileBlard), [@lewtun](https://github.com/lewtun) and [@mcmillanmajora](https://github.com/mcmillanmajora) for adding this dataset.

cbt

--- pretty_name: Children’s Book Test (CBT) annotations_creators: - machine-generated language_creators: - found language: - en license: - gfdl multilinguality: - monolingual size_categories: - 100K<n<1M - n<1K source_datasets: - original task_categories: - other - question-answering task_ids: - multiple-choice-qa paperswithcode_id: cbt dataset_info: - config_name: raw features: - name: title dtype: string - name: content dtype: string splits: - name: train num_bytes: 25741580 num_examples: 98 - name: test num_bytes: 1528704 num_examples: 5 - name: validation num_bytes: 1182657 num_examples: 5 download_size: 120547669 dataset_size: 28452941 - config_name: V features: - name: sentences sequence: string - name: question dtype: string - name: answer dtype: string - name: options sequence: string splits: - name: train num_bytes: 252177649 num_examples: 105825 - name: test num_bytes: 5806625 num_examples: 2500 - name: validation num_bytes: 4556425 num_examples: 2000 download_size: 120547669 dataset_size: 262540699 - config_name: P features: - name: sentences sequence: string - name: question dtype: string - name: answer dtype: string - name: options sequence: string splits: - name: train num_bytes: 852852601 num_examples: 334030 - name: test num_bytes: 6078048 num_examples: 2500 - name: validation num_bytes: 4776981 num_examples: 2000 download_size: 120547669 dataset_size: 863707630 - config_name: NE features: - name: sentences sequence: string - name: question dtype: string - name: answer dtype: string - name: options sequence: string splits: - name: train num_bytes: 253551931 num_examples: 108719 - name: test num_bytes: 5707734 num_examples: 2500 - name: validation num_bytes: 4424316 num_examples: 2000 download_size: 120547669 dataset_size: 263683981 - config_name: CN features: - name: sentences sequence: string - name: question dtype: string - name: answer dtype: string - name: options sequence: string splits: - name: train num_bytes: 301730151 num_examples: 120769 - name: test num_bytes: 6138376 num_examples: 2500 - name: validation num_bytes: 4737257 num_examples: 2000 download_size: 120547669 dataset_size: 312605784 config_names: - CN - NE - P - V - raw --- # Dataset Card for CBT ## 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 bAbI project](https://research.fb.com/downloads/babi/) - **Repository:** - **Paper:** [arXiv Paper](https://arxiv.org/pdf/1511.02301.pdf) - **Leaderboard:** - **Point of Contact:** [Felix Hill](mailto:felix.hill@cl.cam.ac.uk) or [Antoine Bordes](mailto:abordes@fb.com). ### Dataset Summary The Children’s Book Test (CBT) is designed to measure directly how well language models can exploit wider linguistic context. The CBT is built from books that are freely available. This dataset contains four different configurations: - `V`: where the answers to the questions are verbs. - `P`: where the answers to the questions are pronouns. - `NE`: where the answers to the questions are named entities. - `CN`: where the answers to the questions are common nouns. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The data is present in English language as written by authors Lucy Maud Montgomery, Charles Dickens,Andrew Lang, etc. in story books for children. ## Dataset Structure ### Data Instances An instance from the `V` config: ``` {'answer': 'said', 'options': ['christening', 'existed', 'hear', 'knows', 'read', 'remarked', 'said', 'sitting', 'talking', 'wearing'], 'question': "`` They are very kind old ladies in their way , '' XXXXX the king ; `` and were nice to me when I was a boy . ''", 'sentences': ['This vexed the king even more than the queen , who was very clever and learned , and who had hated dolls when she was a child .', 'However , she , too in spite of all the books she read and all the pictures she painted , would have been glad enough to be the mother of a little prince .', 'The king was anxious to consult the fairies , but the queen would not hear of such a thing .', 'She did not believe in fairies : she said that they had never existed ; and that she maintained , though The History of the Royal Family was full of chapters about nothing else .', 'Well , at long and at last they had a little boy , who was generally regarded as the finest baby that had ever been seen .', 'Even her majesty herself remarked that , though she could never believe all the courtiers told her , yet he certainly was a fine child -- a very fine child .', 'Now , the time drew near for the christening party , and the king and queen were sitting at breakfast in their summer parlour talking over it .', 'It was a splendid room , hung with portraits of the royal ancestors .', 'There was Cinderella , the grandmother of the reigning monarch , with her little foot in her glass slipper thrust out before her .', 'There was the Marquis de Carabas , who , as everyone knows , was raised to the throne as prince consort after his marriage with the daughter of the king of the period .', 'On the arm of the throne was seated his celebrated cat , wearing boots .', 'There , too , was a portrait of a beautiful lady , sound asleep : this was Madame La Belle au Bois-dormant , also an ancestress of the royal family .', 'Many other pictures of celebrated persons were hanging on the walls .', "`` You have asked all the right people , my dear ? ''", 'said the king .', "`` Everyone who should be asked , '' answered the queen .", "`` People are so touchy on these occasions , '' said his majesty .", "`` You have not forgotten any of our aunts ? ''", "`` No ; the old cats ! ''", "replied the queen ; for the king 's aunts were old-fashioned , and did not approve of her , and she knew it ."]} ``` ### Data Fields For the `raw` config, the data fields are: - `title`: a `string` feature containing the title of the book present in the dataset. - `content`: a `string` feature containing the content of the book present in the dataset. For all other configs, the data fields are: - `sentences`: a `list` of `string` features containing 20 sentences from a book. - `question`: a `string` feature containing a question with blank marked as `XXXX` which is to be filled with one of the options. - `answer`: a `string` feature containing the answer. - `options`: a `list` of `string` features containing the options for the question. ### Data Splits The splits and corresponding sizes are: | |train |test |validation| |:--|------:|----:|---------:| |raw|98 |5 |5 | |V |105825 |2500 |2000 | |P |334030 |2500 |2000 | |CN |120769 |2500 |2000 | |NE |108719 |2500 |2000 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? Children's Book Authors ### Annotations #### Annotation process From the [homepage](https://research.fb.com/downloads/babi/): >After allocating books to either training, validation or test sets, we formed example ‘questions’ from chapters in the book by enumerating 21 consecutive sentences. In each question, the first 20 sentences form the context, and a word is removed from the 21st sentence, which becomes the query. Models must identify the answer word among a selection of 10 candidate answers appearing in the context sentences and the query. For finer-grained analyses, we evaluated four classes of question by removing distinct types of word: Named Entities, (Common) Nouns, Verbs and Prepositions. #### 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 Free Documentation License v1.3 ``` ### Citation Information ``` @misc{hill2016goldilocks, title={The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations}, author={Felix Hill and Antoine Bordes and Sumit Chopra and Jason Weston}, year={2016}, eprint={1511.02301}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@gchhablani](https://github.com/gchhablani) for adding this dataset.

evanarlian/imagenet_1k_resized_256

evanarlian

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - other multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - derived task_categories: - image-classification task_ids: - multi-class-image-classification paperswithcode_id: imagenet pretty_name: ImageNet Resized 256 license_details: imagenet-agreement extra_gated_prompt: https://huggingface.co/datasets/imagenet-1k extra_gated_fields: I have agreed to the original ImageNet dataset: checkbox dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': tench, Tinca tinca '1': goldfish, Carassius auratus '2': great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias '3': tiger shark, Galeocerdo cuvieri '4': hammerhead, hammerhead shark '5': electric ray, crampfish, numbfish, torpedo '6': stingray '7': cock '8': hen '9': ostrich, Struthio camelus '10': brambling, Fringilla montifringilla '11': goldfinch, Carduelis carduelis '12': house finch, linnet, Carpodacus mexicanus '13': junco, snowbird '14': indigo bunting, indigo finch, indigo bird, Passerina cyanea '15': robin, American robin, Turdus migratorius '16': bulbul '17': jay '18': magpie '19': chickadee '20': water ouzel, dipper '21': kite '22': bald eagle, American eagle, Haliaeetus leucocephalus '23': vulture '24': great grey owl, great gray owl, Strix nebulosa '25': European fire salamander, Salamandra salamandra '26': common newt, Triturus vulgaris '27': eft '28': spotted salamander, Ambystoma maculatum '29': axolotl, mud puppy, Ambystoma mexicanum '30': bullfrog, Rana catesbeiana '31': tree frog, tree-frog '32': tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui '33': loggerhead, loggerhead turtle, Caretta caretta '34': leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea '35': mud turtle '36': terrapin '37': box turtle, box tortoise '38': banded gecko '39': common iguana, iguana, Iguana iguana '40': American chameleon, anole, Anolis carolinensis '41': whiptail, whiptail lizard '42': agama '43': frilled lizard, Chlamydosaurus kingi '44': alligator lizard '45': Gila monster, Heloderma suspectum '46': green lizard, Lacerta viridis '47': African chameleon, Chamaeleo chamaeleon '48': Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis '49': African crocodile, Nile crocodile, Crocodylus niloticus '50': American alligator, Alligator mississipiensis '51': triceratops '52': thunder snake, worm snake, Carphophis amoenus '53': ringneck snake, ring-necked snake, ring snake '54': hognose snake, puff adder, sand viper '55': green snake, grass snake '56': king snake, kingsnake '57': garter snake, grass snake '58': water snake '59': vine snake '60': night snake, Hypsiglena torquata '61': boa constrictor, Constrictor constrictor '62': rock python, rock snake, Python sebae '63': Indian cobra, Naja naja '64': green mamba '65': sea snake '66': horned viper, cerastes, sand viper, horned asp, Cerastes cornutus '67': diamondback, diamondback rattlesnake, Crotalus adamanteus '68': sidewinder, horned rattlesnake, Crotalus cerastes '69': trilobite '70': harvestman, daddy longlegs, Phalangium opilio '71': scorpion '72': black and gold garden spider, Argiope aurantia '73': barn spider, Araneus cavaticus '74': garden spider, Aranea diademata '75': black widow, Latrodectus mactans '76': tarantula '77': wolf spider, hunting spider '78': tick '79': centipede '80': black grouse '81': ptarmigan '82': ruffed grouse, partridge, Bonasa umbellus '83': prairie chicken, prairie grouse, prairie fowl '84': peacock '85': quail '86': partridge '87': African grey, African gray, Psittacus erithacus '88': macaw '89': sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita '90': lorikeet '91': coucal '92': bee eater '93': hornbill '94': hummingbird '95': jacamar '96': toucan '97': drake '98': red-breasted merganser, Mergus serrator '99': goose '100': black swan, Cygnus atratus '101': tusker '102': echidna, spiny anteater, anteater '103': platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus '104': wallaby, brush kangaroo '105': koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus '106': wombat '107': jellyfish '108': sea anemone, anemone '109': brain coral '110': flatworm, platyhelminth '111': nematode, nematode worm, roundworm '112': conch '113': snail '114': slug '115': sea slug, nudibranch '116': chiton, coat-of-mail shell, sea cradle, polyplacophore '117': chambered nautilus, pearly nautilus, nautilus '118': Dungeness crab, Cancer magister '119': rock crab, Cancer irroratus '120': fiddler crab '121': king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica '122': American lobster, Northern lobster, Maine lobster, Homarus americanus '123': spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish '124': crayfish, crawfish, crawdad, crawdaddy '125': hermit crab '126': isopod '127': white stork, Ciconia ciconia '128': black stork, Ciconia nigra '129': spoonbill '130': flamingo '131': little blue heron, Egretta caerulea '132': American egret, great white heron, Egretta albus '133': bittern '134': crane '135': limpkin, Aramus pictus '136': European gallinule, Porphyrio porphyrio '137': American coot, marsh hen, mud hen, water hen, Fulica americana '138': bustard '139': ruddy turnstone, Arenaria interpres '140': red-backed sandpiper, dunlin, Erolia alpina '141': redshank, Tringa totanus '142': dowitcher '143': oystercatcher, oyster catcher '144': pelican '145': king penguin, Aptenodytes patagonica '146': albatross, mollymawk '147': grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus '148': killer whale, killer, orca, grampus, sea wolf, Orcinus orca '149': dugong, Dugong dugon '150': sea lion '151': Chihuahua '152': Japanese spaniel '153': Maltese dog, Maltese terrier, Maltese '154': Pekinese, Pekingese, Peke '155': Shih-Tzu '156': Blenheim spaniel '157': papillon '158': toy terrier '159': Rhodesian ridgeback '160': Afghan hound, Afghan '161': basset, basset hound '162': beagle '163': bloodhound, sleuthhound '164': bluetick '165': black-and-tan coonhound '166': Walker hound, Walker foxhound '167': English foxhound '168': redbone '169': borzoi, Russian wolfhound '170': Irish wolfhound '171': Italian greyhound '172': whippet '173': Ibizan hound, Ibizan Podenco '174': Norwegian elkhound, elkhound '175': otterhound, otter hound '176': Saluki, gazelle hound '177': Scottish deerhound, deerhound '178': Weimaraner '179': Staffordshire bullterrier, Staffordshire bull terrier '180': American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier '181': Bedlington terrier '182': Border terrier '183': Kerry blue terrier '184': Irish terrier '185': Norfolk terrier '186': Norwich terrier '187': Yorkshire terrier '188': wire-haired fox terrier '189': Lakeland terrier '190': Sealyham terrier, Sealyham '191': Airedale, Airedale terrier '192': cairn, cairn terrier '193': Australian terrier '194': Dandie Dinmont, Dandie Dinmont terrier '195': Boston bull, Boston terrier '196': miniature schnauzer '197': giant schnauzer '198': standard schnauzer '199': Scotch terrier, Scottish terrier, Scottie '200': Tibetan terrier, chrysanthemum dog '201': silky terrier, Sydney silky '202': soft-coated wheaten terrier '203': West Highland white terrier '204': Lhasa, Lhasa apso '205': flat-coated retriever '206': curly-coated retriever '207': golden retriever '208': Labrador retriever '209': Chesapeake Bay retriever '210': German short-haired pointer '211': vizsla, Hungarian pointer '212': English setter '213': Irish setter, red setter '214': Gordon setter '215': Brittany spaniel '216': clumber, clumber spaniel '217': English springer, English springer spaniel '218': Welsh springer spaniel '219': cocker spaniel, English cocker spaniel, cocker '220': Sussex spaniel '221': Irish water spaniel '222': kuvasz '223': schipperke '224': groenendael '225': malinois '226': briard '227': kelpie '228': komondor '229': Old English sheepdog, bobtail '230': Shetland sheepdog, Shetland sheep dog, Shetland '231': collie '232': Border collie '233': Bouvier des Flandres, Bouviers des Flandres '234': Rottweiler '235': German shepherd, German shepherd dog, German police dog, alsatian '236': Doberman, Doberman pinscher '237': miniature pinscher '238': Greater Swiss Mountain dog '239': Bernese mountain dog '240': Appenzeller '241': EntleBucher '242': boxer '243': bull mastiff '244': Tibetan mastiff '245': French bulldog '246': Great Dane '247': Saint Bernard, St Bernard '248': Eskimo dog, husky '249': malamute, malemute, Alaskan malamute '250': Siberian husky '251': dalmatian, coach dog, carriage dog '252': affenpinscher, monkey pinscher, monkey dog '253': basenji '254': pug, pug-dog '255': Leonberg '256': Newfoundland, Newfoundland dog '257': Great Pyrenees '258': Samoyed, Samoyede '259': Pomeranian '260': chow, chow chow '261': keeshond '262': Brabancon griffon '263': Pembroke, Pembroke Welsh corgi '264': Cardigan, Cardigan Welsh corgi '265': toy poodle '266': miniature poodle '267': standard poodle '268': Mexican hairless '269': timber wolf, grey wolf, gray wolf, Canis lupus '270': white wolf, Arctic wolf, Canis lupus tundrarum '271': red wolf, maned wolf, Canis rufus, Canis niger '272': coyote, prairie wolf, brush wolf, Canis latrans '273': dingo, warrigal, warragal, Canis dingo '274': dhole, Cuon alpinus '275': African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus '276': hyena, hyaena '277': red fox, Vulpes vulpes '278': kit fox, Vulpes macrotis '279': Arctic fox, white fox, Alopex lagopus '280': grey fox, gray fox, Urocyon cinereoargenteus '281': tabby, tabby cat '282': tiger cat '283': Persian cat '284': Siamese cat, Siamese '285': Egyptian cat '286': cougar, puma, catamount, mountain lion, painter, panther, Felis concolor '287': lynx, catamount '288': leopard, Panthera pardus '289': snow leopard, ounce, Panthera uncia '290': jaguar, panther, Panthera onca, Felis onca '291': lion, king of beasts, Panthera leo '292': tiger, Panthera tigris '293': cheetah, chetah, Acinonyx jubatus '294': brown bear, bruin, Ursus arctos '295': American black bear, black bear, Ursus americanus, Euarctos americanus '296': ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus '297': sloth bear, Melursus ursinus, Ursus ursinus '298': mongoose '299': meerkat, mierkat '300': tiger beetle '301': ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle '302': ground beetle, carabid beetle '303': long-horned beetle, longicorn, longicorn beetle '304': leaf beetle, chrysomelid '305': dung beetle '306': rhinoceros beetle '307': weevil '308': fly '309': bee '310': ant, emmet, pismire '311': grasshopper, hopper '312': cricket '313': walking stick, walkingstick, stick insect '314': cockroach, roach '315': mantis, mantid '316': cicada, cicala '317': leafhopper '318': lacewing, lacewing fly '319': dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk '320': damselfly '321': admiral '322': ringlet, ringlet butterfly '323': monarch, monarch butterfly, milkweed butterfly, Danaus plexippus '324': cabbage butterfly '325': sulphur butterfly, sulfur butterfly '326': lycaenid, lycaenid butterfly '327': starfish, sea star '328': sea urchin '329': sea cucumber, holothurian '330': wood rabbit, cottontail, cottontail rabbit '331': hare '332': Angora, Angora rabbit '333': hamster '334': porcupine, hedgehog '335': fox squirrel, eastern fox squirrel, Sciurus niger '336': marmot '337': beaver '338': guinea pig, Cavia cobaya '339': sorrel '340': zebra '341': hog, pig, grunter, squealer, Sus scrofa '342': wild boar, boar, Sus scrofa '343': warthog '344': hippopotamus, hippo, river horse, Hippopotamus amphibius '345': ox '346': water buffalo, water ox, Asiatic buffalo, Bubalus bubalis '347': bison '348': ram, tup '349': bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis '350': ibex, Capra ibex '351': hartebeest '352': impala, Aepyceros melampus '353': gazelle '354': Arabian camel, dromedary, Camelus dromedarius '355': llama '356': weasel '357': mink '358': polecat, fitch, foulmart, foumart, Mustela putorius '359': black-footed ferret, ferret, Mustela nigripes '360': otter '361': skunk, polecat, wood pussy '362': badger '363': armadillo '364': three-toed sloth, ai, Bradypus tridactylus '365': orangutan, orang, orangutang, Pongo pygmaeus '366': gorilla, Gorilla gorilla '367': chimpanzee, chimp, Pan troglodytes '368': gibbon, Hylobates lar '369': siamang, Hylobates syndactylus, Symphalangus syndactylus '370': guenon, guenon monkey '371': patas, hussar monkey, Erythrocebus patas '372': baboon '373': macaque '374': langur '375': colobus, colobus monkey '376': proboscis monkey, Nasalis larvatus '377': marmoset '378': capuchin, ringtail, Cebus capucinus '379': howler monkey, howler '380': titi, titi monkey '381': spider monkey, Ateles geoffroyi '382': squirrel monkey, Saimiri sciureus '383': Madagascar cat, ring-tailed lemur, Lemur catta '384': indri, indris, Indri indri, Indri brevicaudatus '385': Indian elephant, Elephas maximus '386': African elephant, Loxodonta africana '387': lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens '388': giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca '389': barracouta, snoek '390': eel '391': coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch '392': rock beauty, Holocanthus tricolor '393': anemone fish '394': sturgeon '395': gar, garfish, garpike, billfish, Lepisosteus osseus '396': lionfish '397': puffer, pufferfish, blowfish, globefish '398': abacus '399': abaya '400': academic gown, academic robe, judge's robe '401': accordion, piano accordion, squeeze box '402': acoustic guitar '403': aircraft carrier, carrier, flattop, attack aircraft carrier '404': airliner '405': airship, dirigible '406': altar '407': ambulance '408': amphibian, amphibious vehicle '409': analog clock '410': apiary, bee house '411': apron '412': ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin '413': assault rifle, assault gun '414': backpack, back pack, knapsack, packsack, rucksack, haversack '415': bakery, bakeshop, bakehouse '416': balance beam, beam '417': balloon '418': ballpoint, ballpoint pen, ballpen, Biro '419': Band Aid '420': banjo '421': bannister, banister, balustrade, balusters, handrail '422': barbell '423': barber chair '424': barbershop '425': barn '426': barometer '427': barrel, cask '428': barrow, garden cart, lawn cart, wheelbarrow '429': baseball '430': basketball '431': bassinet '432': bassoon '433': bathing cap, swimming cap '434': bath towel '435': bathtub, bathing tub, bath, tub '436': beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon '437': beacon, lighthouse, beacon light, pharos '438': beaker '439': bearskin, busby, shako '440': beer bottle '441': beer glass '442': bell cote, bell cot '443': bib '444': bicycle-built-for-two, tandem bicycle, tandem '445': bikini, two-piece '446': binder, ring-binder '447': binoculars, field glasses, opera glasses '448': birdhouse '449': boathouse '450': bobsled, bobsleigh, bob '451': bolo tie, bolo, bola tie, bola '452': bonnet, poke bonnet '453': bookcase '454': bookshop, bookstore, bookstall '455': bottlecap '456': bow '457': bow tie, bow-tie, bowtie '458': brass, memorial tablet, plaque '459': brassiere, bra, bandeau '460': breakwater, groin, groyne, mole, bulwark, seawall, jetty '461': breastplate, aegis, egis '462': broom '463': bucket, pail '464': buckle '465': bulletproof vest '466': bullet train, bullet '467': butcher shop, meat market '468': cab, hack, taxi, taxicab '469': caldron, cauldron '470': candle, taper, wax light '471': cannon '472': canoe '473': can opener, tin opener '474': cardigan '475': car mirror '476': carousel, carrousel, merry-go-round, roundabout, whirligig '477': carpenter's kit, tool kit '478': carton '479': car wheel '480': cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM '481': cassette '482': cassette player '483': castle '484': catamaran '485': CD player '486': cello, violoncello '487': cellular telephone, cellular phone, cellphone, cell, mobile phone '488': chain '489': chainlink fence '490': chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour '491': chain saw, chainsaw '492': chest '493': chiffonier, commode '494': chime, bell, gong '495': china cabinet, china closet '496': Christmas stocking '497': church, church building '498': cinema, movie theater, movie theatre, movie house, picture palace '499': cleaver, meat cleaver, chopper '500': cliff dwelling '501': cloak '502': clog, geta, patten, sabot '503': cocktail shaker '504': coffee mug '505': coffeepot '506': coil, spiral, volute, whorl, helix '507': combination lock '508': computer keyboard, keypad '509': confectionery, confectionary, candy store '510': container ship, containership, container vessel '511': convertible '512': corkscrew, bottle screw '513': cornet, horn, trumpet, trump '514': cowboy boot '515': cowboy hat, ten-gallon hat '516': cradle '517': crane2 '518': crash helmet '519': crate '520': crib, cot '521': Crock Pot '522': croquet ball '523': crutch '524': cuirass '525': dam, dike, dyke '526': desk '527': desktop computer '528': dial telephone, dial phone '529': diaper, nappy, napkin '530': digital clock '531': digital watch '532': dining table, board '533': dishrag, dishcloth '534': dishwasher, dish washer, dishwashing machine '535': disk brake, disc brake '536': dock, dockage, docking facility '537': dogsled, dog sled, dog sleigh '538': dome '539': doormat, welcome mat '540': drilling platform, offshore rig '541': drum, membranophone, tympan '542': drumstick '543': dumbbell '544': Dutch oven '545': electric fan, blower '546': electric guitar '547': electric locomotive '548': entertainment center '549': envelope '550': espresso maker '551': face powder '552': feather boa, boa '553': file, file cabinet, filing cabinet '554': fireboat '555': fire engine, fire truck '556': fire screen, fireguard '557': flagpole, flagstaff '558': flute, transverse flute '559': folding chair '560': football helmet '561': forklift '562': fountain '563': fountain pen '564': four-poster '565': freight car '566': French horn, horn '567': frying pan, frypan, skillet '568': fur coat '569': garbage truck, dustcart '570': gasmask, respirator, gas helmet '571': gas pump, gasoline pump, petrol pump, island dispenser '572': goblet '573': go-kart '574': golf ball '575': golfcart, golf cart '576': gondola '577': gong, tam-tam '578': gown '579': grand piano, grand '580': greenhouse, nursery, glasshouse '581': grille, radiator grille '582': grocery store, grocery, food market, market '583': guillotine '584': hair slide '585': hair spray '586': half track '587': hammer '588': hamper '589': hand blower, blow dryer, blow drier, hair dryer, hair drier '590': hand-held computer, hand-held microcomputer '591': handkerchief, hankie, hanky, hankey '592': hard disc, hard disk, fixed disk '593': harmonica, mouth organ, harp, mouth harp '594': harp '595': harvester, reaper '596': hatchet '597': holster '598': home theater, home theatre '599': honeycomb '600': hook, claw '601': hoopskirt, crinoline '602': horizontal bar, high bar '603': horse cart, horse-cart '604': hourglass '605': iPod '606': iron, smoothing iron '607': jack-o'-lantern '608': jean, blue jean, denim '609': jeep, landrover '610': jersey, T-shirt, tee shirt '611': jigsaw puzzle '612': jinrikisha, ricksha, rickshaw '613': joystick '614': kimono '615': knee pad '616': knot '617': lab coat, laboratory coat '618': ladle '619': lampshade, lamp shade '620': laptop, laptop computer '621': lawn mower, mower '622': lens cap, lens cover '623': letter opener, paper knife, paperknife '624': library '625': lifeboat '626': lighter, light, igniter, ignitor '627': limousine, limo '628': liner, ocean liner '629': lipstick, lip rouge '630': Loafer '631': lotion '632': loudspeaker, speaker, speaker unit, loudspeaker system, speaker system '633': loupe, jeweler's loupe '634': lumbermill, sawmill '635': magnetic compass '636': mailbag, postbag '637': mailbox, letter box '638': maillot '639': maillot, tank suit '640': manhole cover '641': maraca '642': marimba, xylophone '643': mask '644': matchstick '645': maypole '646': maze, labyrinth '647': measuring cup '648': medicine chest, medicine cabinet '649': megalith, megalithic structure '650': microphone, mike '651': microwave, microwave oven '652': military uniform '653': milk can '654': minibus '655': miniskirt, mini '656': minivan '657': missile '658': mitten '659': mixing bowl '660': mobile home, manufactured home '661': Model T '662': modem '663': monastery '664': monitor '665': moped '666': mortar '667': mortarboard '668': mosque '669': mosquito net '670': motor scooter, scooter '671': mountain bike, all-terrain bike, off-roader '672': mountain tent '673': mouse, computer mouse '674': mousetrap '675': moving van '676': muzzle '677': nail '678': neck brace '679': necklace '680': nipple '681': notebook, notebook computer '682': obelisk '683': oboe, hautboy, hautbois '684': ocarina, sweet potato '685': odometer, hodometer, mileometer, milometer '686': oil filter '687': organ, pipe organ '688': oscilloscope, scope, cathode-ray oscilloscope, CRO '689': overskirt '690': oxcart '691': oxygen mask '692': packet '693': paddle, boat paddle '694': paddlewheel, paddle wheel '695': padlock '696': paintbrush '697': pajama, pyjama, pj's, jammies '698': palace '699': panpipe, pandean pipe, syrinx '700': paper towel '701': parachute, chute '702': parallel bars, bars '703': park bench '704': parking meter '705': passenger car, coach, carriage '706': patio, terrace '707': pay-phone, pay-station '708': pedestal, plinth, footstall '709': pencil box, pencil case '710': pencil sharpener '711': perfume, essence '712': Petri dish '713': photocopier '714': pick, plectrum, plectron '715': pickelhaube '716': picket fence, paling '717': pickup, pickup truck '718': pier '719': piggy bank, penny bank '720': pill bottle '721': pillow '722': ping-pong ball '723': pinwheel '724': pirate, pirate ship '725': pitcher, ewer '726': plane, carpenter's plane, woodworking plane '727': planetarium '728': plastic bag '729': plate rack '730': plow, plough '731': plunger, plumber's helper '732': Polaroid camera, Polaroid Land camera '733': pole '734': police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria '735': poncho '736': pool table, billiard table, snooker table '737': pop bottle, soda bottle '738': pot, flowerpot '739': potter's wheel '740': power drill '741': prayer rug, prayer mat '742': printer '743': prison, prison house '744': projectile, missile '745': projector '746': puck, hockey puck '747': punching bag, punch bag, punching ball, punchball '748': purse '749': quill, quill pen '750': quilt, comforter, comfort, puff '751': racer, race car, racing car '752': racket, racquet '753': radiator '754': radio, wireless '755': radio telescope, radio reflector '756': rain barrel '757': recreational vehicle, RV, R.V. '758': reel '759': reflex camera '760': refrigerator, icebox '761': remote control, remote '762': restaurant, eating house, eating place, eatery '763': revolver, six-gun, six-shooter '764': rifle '765': rocking chair, rocker '766': rotisserie '767': rubber eraser, rubber, pencil eraser '768': rugby ball '769': rule, ruler '770': running shoe '771': safe '772': safety pin '773': saltshaker, salt shaker '774': sandal '775': sarong '776': sax, saxophone '777': scabbard '778': scale, weighing machine '779': school bus '780': schooner '781': scoreboard '782': screen, CRT screen '783': screw '784': screwdriver '785': seat belt, seatbelt '786': sewing machine '787': shield, buckler '788': shoe shop, shoe-shop, shoe store '789': shoji '790': shopping basket '791': shopping cart '792': shovel '793': shower cap '794': shower curtain '795': ski '796': ski mask '797': sleeping bag '798': slide rule, slipstick '799': sliding door '800': slot, one-armed bandit '801': snorkel '802': snowmobile '803': snowplow, snowplough '804': soap dispenser '805': soccer ball '806': sock '807': solar dish, solar collector, solar furnace '808': sombrero '809': soup bowl '810': space bar '811': space heater '812': space shuttle '813': spatula '814': speedboat '815': spider web, spider's web '816': spindle '817': sports car, sport car '818': spotlight, spot '819': stage '820': steam locomotive '821': steel arch bridge '822': steel drum '823': stethoscope '824': stole '825': stone wall '826': stopwatch, stop watch '827': stove '828': strainer '829': streetcar, tram, tramcar, trolley, trolley car '830': stretcher '831': studio couch, day bed '832': stupa, tope '833': submarine, pigboat, sub, U-boat '834': suit, suit of clothes '835': sundial '836': sunglass '837': sunglasses, dark glasses, shades '838': sunscreen, sunblock, sun blocker '839': suspension bridge '840': swab, swob, mop '841': sweatshirt '842': swimming trunks, bathing trunks '843': swing '844': switch, electric switch, electrical switch '845': syringe '846': table lamp '847': tank, army tank, armored combat vehicle, armoured combat vehicle '848': tape player '849': teapot '850': teddy, teddy bear '851': television, television system '852': tennis ball '853': thatch, thatched roof '854': theater curtain, theatre curtain '855': thimble '856': thresher, thrasher, threshing machine '857': throne '858': tile roof '859': toaster '860': tobacco shop, tobacconist shop, tobacconist '861': toilet seat '862': torch '863': totem pole '864': tow truck, tow car, wrecker '865': toyshop '866': tractor '867': trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi '868': tray '869': trench coat '870': tricycle, trike, velocipede '871': trimaran '872': tripod '873': triumphal arch '874': trolleybus, trolley coach, trackless trolley '875': trombone '876': tub, vat '877': turnstile '878': typewriter keyboard '879': umbrella '880': unicycle, monocycle '881': upright, upright piano '882': vacuum, vacuum cleaner '883': vase '884': vault '885': velvet '886': vending machine '887': vestment '888': viaduct '889': violin, fiddle '890': volleyball '891': waffle iron '892': wall clock '893': wallet, billfold, notecase, pocketbook '894': wardrobe, closet, press '895': warplane, military plane '896': washbasin, handbasin, washbowl, lavabo, wash-hand basin '897': washer, automatic washer, washing machine '898': water bottle '899': water jug '900': water tower '901': whiskey jug '902': whistle '903': wig '904': window screen '905': window shade '906': Windsor tie '907': wine bottle '908': wing '909': wok '910': wooden spoon '911': wool, woolen, woollen '912': worm fence, snake fence, snake-rail fence, Virginia fence '913': wreck '914': yawl '915': yurt '916': web site, website, internet site, site '917': comic book '918': crossword puzzle, crossword '919': street sign '920': traffic light, traffic signal, stoplight '921': book jacket, dust cover, dust jacket, dust wrapper '922': menu '923': plate '924': guacamole '925': consomme '926': hot pot, hotpot '927': trifle '928': ice cream, icecream '929': ice lolly, lolly, lollipop, popsicle '930': French loaf '931': bagel, beigel '932': pretzel '933': cheeseburger '934': hotdog, hot dog, red hot '935': mashed potato '936': head cabbage '937': broccoli '938': cauliflower '939': zucchini, courgette '940': spaghetti squash '941': acorn squash '942': butternut squash '943': cucumber, cuke '944': artichoke, globe artichoke '945': bell pepper '946': cardoon '947': mushroom '948': Granny Smith '949': strawberry '950': orange '951': lemon '952': fig '953': pineapple, ananas '954': banana '955': jackfruit, jak, jack '956': custard apple '957': pomegranate '958': hay '959': carbonara '960': chocolate sauce, chocolate syrup '961': dough '962': meat loaf, meatloaf '963': pizza, pizza pie '964': potpie '965': burrito '966': red wine '967': espresso '968': cup '969': eggnog '970': alp '971': bubble '972': cliff, drop, drop-off '973': coral reef '974': geyser '975': lakeside, lakeshore '976': promontory, headland, head, foreland '977': sandbar, sand bar '978': seashore, coast, seacoast, sea-coast '979': valley, vale '980': volcano '981': ballplayer, baseball player '982': groom, bridegroom '983': scuba diver '984': rapeseed '985': daisy '986': yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum '987': corn '988': acorn '989': hip, rose hip, rosehip '990': buckeye, horse chestnut, conker '991': coral fungus '992': agaric '993': gyromitra '994': stinkhorn, carrion fungus '995': earthstar '996': hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa '997': bolete '998': ear, spike, capitulum '999': toilet tissue, toilet paper, bathroom tissue splits: - name: train num_bytes: 25757593104.281 num_examples: 1281167 - name: val num_bytes: 752578700.0 num_examples: 50000 - name: test num_bytes: 1748575400.0 num_examples: 100000 download_size: 25143755650 dataset_size: 28258747204.281 configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* - split: test path: data/test-* --- # Dataset Card for "imagenet_1k_resized_256" ## Dataset summary The same ImageNet dataset but all the smaller side resized to 256. A lot of pretraining workflows contain resizing images to 256 and random cropping to 224x224, this is why 256 is chosen. The resized dataset can also be downloaded much faster and consume less space than the original one. See [here](https://huggingface.co/datasets/imagenet-1k) for detailed readme. ## Dataset Structure Below is the example of one row of data. Note that the labels in the test split are all -1. ``` { 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=256x384 at 0x276021C5EB8>, 'label': 23 } ``` The number of rows per split is the same as the original ImageNet. | |train |validation| test | |-------------|------:|---------:|------:| |# of examples|1281167|50000 |100000 | ## Licensing Information In exchange for permission to use the ImageNet database (the "Database") at Princeton University and Stanford University, Researcher hereby agrees to the following terms and conditions: 1. Researcher shall use the Database only for non-commercial research and educational purposes. 1. Princeton University and Stanford University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose. 1. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the ImageNet team, Princeton University, and Stanford University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database. 1. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions. 1. Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time. 1. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer. 1. The law of the State of New Jersey shall apply to all disputes under this agreement. ## Citation Information ```bibtex @article{imagenet15russakovsky, Author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei}, Title = { {ImageNet Large Scale Visual Recognition Challenge} }, Year = {2015}, journal = {International Journal of Computer Vision (IJCV)}, doi = {10.1007/s11263-015-0816-y}, volume={115}, number={3}, pages={211-252} } ```

pcuenq/oxford-pets

pcuenq

--- tags: - pets - oxford license: cc-by-sa-4.0 license_details: https://www.robots.ox.ac.uk/~vgg/data/pets/ pretty_name: Oxford-IIIT Pet Dataset (no annotations) source_datasets: https://www.robots.ox.ac.uk/~vgg/data/pets/ task_categories: - image-classification --- # Oxford-IIIT Pet Dataset Images from [The Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/). Only images and labels have been pushed, segmentation annotations were ignored. - **Homepage:** https://www.robots.ox.ac.uk/~vgg/data/pets/ License: Same as the original dataset.

climate_fever

--- annotations_creators: - crowdsourced - expert-generated language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - extended|wikipedia - original task_categories: - text-classification - text-retrieval task_ids: - text-scoring - fact-checking - fact-checking-retrieval - semantic-similarity-scoring - multi-input-text-classification paperswithcode_id: climate-fever pretty_name: ClimateFever dataset_info: features: - name: claim_id dtype: string - name: claim dtype: string - name: claim_label dtype: class_label: names: '0': SUPPORTS '1': REFUTES '2': NOT_ENOUGH_INFO '3': DISPUTED - name: evidences list: - name: evidence_id dtype: string - name: evidence_label dtype: class_label: names: '0': SUPPORTS '1': REFUTES '2': NOT_ENOUGH_INFO - name: article dtype: string - name: evidence dtype: string - name: entropy dtype: float32 - name: votes list: string splits: - name: test num_bytes: 2429272 num_examples: 1535 download_size: 687133 dataset_size: 2429272 --- # Dataset Card for ClimateFever ## 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:** [CLIMATE-FEVER homepage](http://climatefever.ai) - **Repository:** [CLIMATE-FEVER repository](https://github.com/tdiggelm/climate-fever-dataset) - **Paper:** [CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims](https://arxiv.org/abs/2012.00614) - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Thomas Diggelmann](mailto:thomasdi@student.ethz.ch) ### Dataset Summary A dataset adopting the FEVER methodology that consists of 1,535 real-world claims regarding climate-change collected on the internet. Each claim is accompanied by five manually annotated evidence sentences retrieved from the English Wikipedia that support, refute or do not give enough information to validate the claim totalling in 7,675 claim-evidence pairs. The dataset features challenging claims that relate multiple facets and disputed cases of claims where both supporting and refuting evidence are present. ### Supported Tasks and Leaderboards [Needs More Information] ### Languages The text in the dataset is in English, as found in real-world claims about climate-change on the Internet. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances ``` { "claim_id": "0", "claim": "Global warming is driving polar bears toward extinction", "claim_label": 0, # "SUPPORTS" "evidences": [ { "evidence_id": "Extinction risk from global warming:170", "evidence_label": 2, # "NOT_ENOUGH_INFO" "article": "Extinction risk from global warming", "evidence": "\"Recent Research Shows Human Activity Driving Earth Towards Global Extinction Event\".", "entropy": 0.6931471805599453, "votes": [ "SUPPORTS", "NOT_ENOUGH_INFO", null, null, null ] }, { "evidence_id": "Global warming:14", "evidence_label": 0, # "SUPPORTS" "article": "Global warming", "evidence": "Environmental impacts include the extinction or relocation of many species as their ecosystems change, most immediately the environments of coral reefs, mountains, and the Arctic.", "entropy": 0.0, "votes": [ "SUPPORTS", "SUPPORTS", null, null, null ] }, { "evidence_id": "Global warming:178", "evidence_label": 2, # "NOT_ENOUGH_INFO" "article": "Global warming", "evidence": "Rising temperatures push bees to their physiological limits, and could cause the extinction of bee populations.", "entropy": 0.6931471805599453, "votes": [ "SUPPORTS", "NOT_ENOUGH_INFO", null, null, null ] }, { "evidence_id": "Habitat destruction:61", "evidence_label": 0, # "SUPPORTS" "article": "Habitat destruction", "evidence": "Rising global temperatures, caused by the greenhouse effect, contribute to habitat destruction, endangering various species, such as the polar bear.", "entropy": 0.0, "votes": [ "SUPPORTS", "SUPPORTS", null, null, null ] }, { "evidence_id": "Polar bear:1328", "evidence_label": 2, # "NOT_ENOUGH_INFO" "article": "Polar bear", "evidence": "\"Bear hunting caught in global warming debate\".", "entropy": 0.6931471805599453, "votes": [ "SUPPORTS", "NOT_ENOUGH_INFO", null, null, null ] } ] } ``` ### Data Fields - `claim_id`: a `string` feature, unique claim identifier. - `claim`: a `string` feature, claim text. - `claim_label`: a `int` feature, overall label assigned to claim (based on evidence majority vote). The label correspond to 0: "supports", 1: "refutes", 2: "not enough info" and 3: "disputed". - `evidences`: a list of evidences with fields: - `evidence_id`: a `string` feature, unique evidence identifier. - `evidence_label`: a `int` feature, micro-verdict label. The label correspond to 0: "supports", 1: "refutes" and 2: "not enough info". - `article`: a `string` feature, title of source article (Wikipedia page). - `evidence`: a `string` feature, evidence sentence. - `entropy`: a `float32` feature, entropy reflecting uncertainty of `evidence_label`. - `votes`: a `list` of `string` features, corresponding to individual votes. ### Data Splits This benchmark dataset currently consists of a single data split `test` that consists of 1,535 claims or 7,675 claim-evidence pairs. ## 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 ```bibtex @misc{diggelmann2020climatefever, title={CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims}, author={Thomas Diggelmann and Jordan Boyd-Graber and Jannis Bulian and Massimiliano Ciaramita and Markus Leippold}, year={2020}, eprint={2012.00614}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@tdiggelm](https://github.com/tdiggelm) for adding this dataset.

pharaouk/dharma-1

pharaouk

--- configs: - config_name: default data_files: - split: 'dharma_1_full' path: dharma_1_full* - split: 'dharma_1_mini' path: dharma_1_mini* - split: 'dharma_1_micro' path: dharma_1_micro* - split: 'dharma_1_unshuffled' path: dharma_eval_unshuffled* --- # "Dharma-1" A new carefully curated benchmark set, designed for a new era where the true end user uses LLM's for zero-shot and one-shot tasks, for a vast majority of the time. Stop training your models on mindless targets (eval_loss, train_loss), start training your LLM on lightweight Dharma as an eval target. A mix of all the top benchmarks. Formed to have an equal distribution of some of the most trusted benchmarks used by those developing SOTA LLMs, comprised of only 3,000 examples for the largest size, as well as 450 and 90 for Dharma-mini and Dharma-micro respectively. The current version of Dharma is comprised of a curated sampling of the following benchmarks: - AGIEval - Bigbench - MMLU - Winogrande - Arc-C - Arc- E - OBQA - TruthfulQA - Bool-q Each of these original benchmark datasets have their own subsections, careful work has gone into also choosing an equal distribution of the important subsections of each these, to have the best representation of the original benchmark creators goals. Dharma-1 is now integrated into Axolotl as well!, so you can focus on optimizing the other aspects of your training pipeline, model architecture and/or dataset, as opposed to worrying about what is the best evaluation measurement or optimization target that will best represent capabilities for the end user. Benchmarking for top base model will be listed here when completed and verified. Special thanks to @LDJnr for their contributions. Check out their Puffin dataset here: https://huggingface.co/LDJnr [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

ivelin/ui_refexp_saved

ivelin

--- dataset_info: features: - name: image dtype: image - name: image_id dtype: string - name: image_file_path dtype: string - name: prompt dtype: string - name: target_bounding_box dtype: string splits: - name: train num_bytes: 1910805137.216 num_examples: 15624 - name: validation num_bytes: 60403386 num_examples: 471 - name: test num_bytes: 69078983 num_examples: 565 download_size: 1246541216 dataset_size: 2040287506.216 license: cc-by-4.0 task_categories: - image-to-text language: - en pretty_name: UIBert Referring Expressions Dataset size_categories: - 10K<n<100K --- # Dataset Card for "ui_refexp_saved_Jan2023" This is a saved snapshot of the dynamically generated [UI Bert](https://huggingface.co/datasets/ivelin/ui_refexp) dataset. Much faster download time than the dynamic version which pulls and filters large data files from remote sources.

shunk031/livedoor-news-corpus

shunk031

--- annotations_creators: [] language: - ja language_creators: - found license: - cc-by-nd-4.0 multilinguality: - monolingual pretty_name: livedoor-news-corpus size_categories: [] source_datasets: [] tags: [] task_categories: - text-classification task_ids: - multi-class-classification --- # Dataset Card for Livedoor News Corpus [](https://github.com/shunk031/huggingface-datasets_livedoor-news-corpus/actions/workflows/ci.yaml)  ## 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://www.rondhuit.com/download.html#ldcc - **Repository:** https://github.com/shunk031/huggingface-datasets_livedoor-news-corpus ### Dataset Summary > 本コーパスは、NHN Japan 株式会社が運営する「livedoor ニュース」のうち、下記のクリエイティブ・コモンズライセンスが適用されるニュース記事を収集し、可能な限り HTML タグを取り除いて作成したものです。 ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances ```python from datasets import load_dataset dataset = load_dataset( "shunk031/livedoor-news-corpus", train_ratio=0.8, val_ratio=0.1, test_ratio=0.1, random_state=42, shuffle=True, ) print(dataset) # DatasetDict({ # train: Dataset({ # features: ['url', 'date', 'title', 'content', 'category'], # num_rows: 5894 # }) # validation: Dataset({ # features: ['url', 'date', 'title', 'content', 'category'], # num_rows: 737 # }) # test: Dataset({ # features: ['url', 'date', 'title', 'content', 'category'], # num_rows: 736 # }) # }) ``` ### 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 > 各記事ファイルにはクリエイティブ・コモンズライセンス「表示 – 改変禁止」が適用されます。 クレジット表示についてはニュースカテゴリにより異なるため、ダウンロードしたファイルを展開したサブディレクトリにあるそれぞれの LICENSE.txt をご覧ください。 livedoor は NHN Japan 株式会社の登録商標です。 ### Citation Information [More Information Needed] ### Contributions Thanks to [RONDHUIT Co., Ltd.](https://www.rondhuit.com/) for creating this dataset.

biosses

--- annotations_creators: - expert-generated language_creators: - found language: - en license: - gpl-3.0 multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - text-classification task_ids: - text-scoring - semantic-similarity-scoring paperswithcode_id: biosses pretty_name: BIOSSES dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: score dtype: float32 splits: - name: train num_bytes: 32783 num_examples: 100 download_size: 36324 dataset_size: 32783 --- # Dataset Card for BIOSSES ## 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://tabilab.cmpe.boun.edu.tr/BIOSSES/DataSet.html - **Repository:** https://github.com/gizemsogancioglu/biosses - **Paper:** [BIOSSES: a semantic sentence similarity estimation system for the biomedical domain](https://academic.oup.com/bioinformatics/article/33/14/i49/3953954) - **Point of Contact:** [Gizem Soğancıoğlu](gizemsogancioglu@gmail.com) and [Arzucan Özgür](gizemsogancioglu@gmail.com) ### Dataset Summary BIOSSES is a benchmark dataset for biomedical sentence similarity estimation. The dataset comprises 100 sentence pairs, in which each sentence was selected from the [TAC (Text Analysis Conference) Biomedical Summarization Track Training Dataset](https://tac.nist.gov/2014/BiomedSumm/) containing articles from the biomedical domain. The sentence pairs in BIOSSES were selected from citing sentences, i.e. sentences that have a citation to a reference article. The sentence pairs were evaluated by five different human experts that judged their similarity and gave scores ranging from 0 (no relation) to 4 (equivalent). In the original paper the mean of the scores assigned by the five human annotators was taken as the gold standard. The Pearson correlation between the gold standard scores and the scores estimated by the models was used as the evaluation metric. The strength of correlation can be assessed by the general guideline proposed by Evans (1996) as follows: - very strong: 0.80–1.00 - strong: 0.60–0.79 - moderate: 0.40–0.59 - weak: 0.20–0.39 - very weak: 0.00–0.19 ### Supported Tasks and Leaderboards Biomedical Semantic Similarity Scoring. ### Languages English. ## Dataset Structure ### Data Instances For each instance, there are two sentences (i.e. sentence 1 and 2), and its corresponding similarity score (the mean of the scores assigned by the five human annotators). ``` {'sentence 1': 'Here, looking for agents that could specifically kill KRAS mutant cells, they found that knockdown of GATA2 was synthetically lethal with KRAS mutation' 'sentence 2': 'Not surprisingly, GATA2 knockdown in KRAS mutant cells resulted in a striking reduction of active GTP-bound RHO proteins, including the downstream ROCK kinase' 'score': 2.2} ``` ### Data Fields - `sentence 1`: string - `sentence 2`: string - `score`: float ranging from 0 (no relation) to 4 (equivalent) ### Data Splits No data splits provided. ## Dataset Creation ### Curation Rationale ### Source Data The [TAC (Text Analysis Conference) Biomedical Summarization Track Training Dataset](https://tac.nist.gov/2014/BiomedSumm/). #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process The sentence pairs were evaluated by five different human experts that judged their similarity and gave scores ranging from 0 (no relation) to 4 (equivalent). The score range was described based on the guidelines of SemEval 2012 Task 6 on STS (Agirre et al., 2012). Besides the annotation instructions, example sentences from the biomedical literature were provided to the annotators for each of the similarity degrees. The table below shows the Pearson correlation of the scores of each annotator with respect to the average scores of the remaining four annotators. It is observed that there is strong association among the scores of the annotators. The lowest correlations are 0.902, which can be considered as an upper bound for an algorithmic measure evaluated on this dataset. | |Correlation r | |----------:|--------------:| |Annotator A| 0.952| |Annotator B| 0.958| |Annotator C| 0.917| |Annotator D| 0.902| |Annotator E| 0.941| #### 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 - Gizem Soğancıoğlu, gizemsogancioglu@gmail.com - Hakime Öztürk, hakime.ozturk@boun.edu.tr - Arzucan Özgür, gizemsogancioglu@gmail.com Bogazici University, Istanbul, Turkey ### Licensing Information BIOSSES is made available under the terms of [The GNU Common Public License v.3.0](https://www.gnu.org/licenses/gpl-3.0.en.html). ### Citation Information @article{souganciouglu2017biosses, title={BIOSSES: a semantic sentence similarity estimation system for the biomedical domain}, author={So{\u{g}}anc{\i}o{\u{g}}lu, Gizem and {\"O}zt{\"u}rk, Hakime and {\"O}zg{\"u}r, Arzucan}, journal={Bioinformatics}, volume={33}, number={14}, pages={i49--i58}, year={2017}, publisher={Oxford University Press} } ### Contributions Thanks to [@bwang482](https://github.com/bwang482) for adding this dataset.

cardiffnlp/tweet_sentiment_multilingual

cardiffnlp

--- language: - en - ar - fr - de - hi - it - pt - es multilinguality: - multilingual size_categories: - 10K<n<100K source_datasets: - extended|other-tweet-datasets task_categories: - text-classification task_ids: - sentiment-classification paperswithcode_id: tweet_sentiment_multilingual pretty_name: Tweet Sentiment Multilingual train-eval-index: - config: sentiment task: text-classification task_id: multi_class_classification splits: train_split: train eval_split: test col_mapping: 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 configs: - arabic - english - french - german - hindi - italian - portuguese - spanish dataset_info: - config_name: sentiment features: - name: text dtype: string - name: label dtype: class_label: names: 0: negative 1: neutral 2: positive --- # Dataset Card for cardiffnlp/tweet_sentiment_multilingual ## Dataset Description - **Homepage:** [https://github.com/cardiffnlp/xlm-t](https://github.com/cardiffnlp/xlm-t) - **Repository:** - **Homepage:** [https://github.com/cardiffnlp/xlm-t](https://github.com/cardiffnlp/xlm-t) - **Paper:** [https://aclanthology.org/2022.lrec-1.27/](https://aclanthology.org/2022.lrec-1.27/) - **Point of Contact:** [Asahi Ushio](https://asahiushio.com/) ### Dataset Summary Tweet Sentiment Multilingual consists of sentiment analysis dataset on Twitter in 8 different lagnuages. - arabic - english - french - german - hindi - italian - portuguese - spanish ### Supported Tasks and Leaderboards - `text_classification`: The dataset can be trained using a SentenceClassification model from HuggingFace transformers. ## Dataset Structure ### Data Instances An instance from `sentiment` config: ``` {'label': 2, 'text': '"QT @user In the original draft of the 7th book, Remus Lupin survived the Battle of Hogwarts. #HappyBirthdayRemusLupin"'} ``` ### Data Fields For `sentiment` config: - `text`: a `string` feature containing the tweet. - `label`: an `int` classification label with the following mapping: `0`: negative `1`: neutral `2`: positive ### Data Splits - arabic - english - french - german - hindi - italian - portuguese - spanish | name | train | validation | test | | --------------- | ----- | ---------- | ----- | | arabic | 1838 | 323 | 869 | | english | 1838 | 323 | 869 | | french | 1838 | 323 | 869 | | german | 1838 | 323 | 869 | | hindi | 1838 | 323 | 869 | | italian | 1838 | 323 | 869 | | portuguese | 1838 | 323 | 869 | | spanish | 1838 | 323 | 869 | ### Dataset Curators Francesco Barbieri, Jose Camacho-Collados, Luis Espiinosa-Anke and Leonardo Neves through Cardiff NLP. ### Licensing Information [Creative Commons Attribution 3.0 Unported License](https://groups.google.com/g/semevaltweet/c/k5DDcvVb_Vo/m/zEOdECFyBQAJ), and all of the datasets require complying with Twitter [Terms Of Service](https://twitter.com/tos) and Twitter API [Terms Of Service](https://developer.twitter.com/en/developer-terms/agreement-and-policy) ### Citation Information ``` @inproceedings{barbieri-etal-2022-xlm, title = "{XLM}-{T}: Multilingual Language Models in {T}witter for Sentiment Analysis and Beyond", author = "Barbieri, Francesco and Espinosa Anke, Luis and Camacho-Collados, Jose", booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference", month = jun, year = "2022", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2022.lrec-1.27", pages = "258--266", abstract = "Language models are ubiquitous in current NLP, and their multilingual capacity has recently attracted considerable attention. However, current analyses have almost exclusively focused on (multilingual variants of) standard benchmarks, and have relied on clean pre-training and task-specific corpora as multilingual signals. In this paper, we introduce XLM-T, a model to train and evaluate multilingual language models in Twitter. In this paper we provide: (1) a new strong multilingual baseline consisting of an XLM-R (Conneau et al. 2020) model pre-trained on millions of tweets in over thirty languages, alongside starter code to subsequently fine-tune on a target task; and (2) a set of unified sentiment analysis Twitter datasets in eight different languages and a XLM-T model trained on this dataset.", } ```

llm-book/JGLUE

llm-book

--- annotations_creators: - crowdsourced language: - ja language_creators: - crowdsourced - found license: - cc-by-4.0 multilinguality: - monolingual pretty_name: JGLUE size_categories: [] source_datasets: - original tags: - MARC - STS - NLI - SQuAD - CommonsenseQA task_categories: - multiple-choice - question-answering - sentence-similarity - text-classification task_ids: - multiple-choice-qa - open-domain-qa - multi-class-classification - sentiment-classification --- # Dataset Card for JGLUE [](https://aclanthology.org/2022.lrec-1.317) 書籍『大規模言語モデル入門』で使用する、JGLUEのデータセットです。 [オリジナルのリポジトリ](https://github.com/yahoojapan/JGLUE)で公開されているデータセットを利用しています。 ### Licence コードのライセンスは Creative Commons Attribution-ShareAlike 4.0 International License です。 データそのもののライセンスは[配布元](https://github.com/yahoojapan/JGLUE)のライセンスに従ってください。 ### Citation ```bibtex @inproceedings{kurihara-etal-2022-jglue, title = "{JGLUE}: {J}apanese General Language Understanding Evaluation", author = "Kurihara, Kentaro and Kawahara, Daisuke and Shibata, Tomohide", booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference", month = jun, year = "2022", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2022.lrec-1.317", pages = "2957--2966", abstract = "To develop high-performance natural language understanding (NLU) models, it is necessary to have a benchmark to evaluate and analyze NLU ability from various perspectives. While the English NLU benchmark, GLUE, has been the forerunner, benchmarks are now being released for languages other than English, such as CLUE for Chinese and FLUE for French; but there is no such benchmark for Japanese. We build a Japanese NLU benchmark, JGLUE, from scratch without translation to measure the general NLU ability in Japanese. We hope that JGLUE will facilitate NLU research in Japanese.", } ``` ```bibtex @InProceedings{Kurihara_nlp2022, author = "栗原健太郎 and 河原大輔 and 柴田知秀", title = "JGLUE: 日本語言語理解ベンチマーク", booktitle = "言語処理学会第 28 回年次大会", year = "2022", url = "https://www.anlp.jp/proceedings/annual_meeting/2022/pdf_dir/E8-4.pdf" note= "in Japanese" } ``` ### Contributions データセット作成者である [Kentaro Kurihara](https://twitter.com/kkurihara_cs), [Daisuke Kawahara](https://twitter.com/daisukekawahar1), [Tomohide Shibata](https://twitter.com/stomohide) に感謝を申し上げます。 また本リポジトリのコードは [Shunsuke Kitada](https://twitter.com/shunk031)の[こちらのリポジトリ](https://huggingface.co/datasets/shunk031/JGLUE)を基に作成されたものです。

imodels/credit-card

imodels

--- annotations_creators: [] language: [] language_creators: [] license: [] multilinguality: [] pretty_name: credit-card size_categories: - 10K<n<100K source_datasets: [] tags: - interpretability - fairness - medicine task_categories: - tabular-classification task_ids: [] --- Port of the credit-card dataset from UCI (link [here](https://www.kaggle.com/datasets/uciml/default-of-credit-card-clients-dataset)). See details there and use carefully. Basic preprocessing done by the [imodels team](https://github.com/csinva/imodels) in [this notebook](https://github.com/csinva/imodels-data/blob/master/notebooks_fetch_data/00_get_datasets_custom.ipynb). The target is the binary outcome `default.payment.next.month`. ### Sample usage Load the data: ``` from datasets import load_dataset dataset = load_dataset("imodels/credit-card") df = pd.DataFrame(dataset['train']) X = df.drop(columns=['default.payment.next.month']) y = df['default.payment.next.month'].values ``` Fit a model: ``` import imodels import numpy as np m = imodels.FIGSClassifier(max_rules=5) m.fit(X, y) print(m) ``` Evaluate: ``` df_test = pd.DataFrame(dataset['test']) X_test = df.drop(columns=['default.payment.next.month']) y_test = df['default.payment.next.month'].values print('accuracy', np.mean(m.predict(X_test) == y_test)) ```

AI-Sweden/SuperLim

AI-Sweden

--- language: - sv multilinguality: - monolingual pretty_name: SuperLim task_categories: - question-answering - text-classification - sequence-modeling - other --- # Dataset Card for SuperLim ## 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 Structure/Creation/Use/Additional Information](#dataset-structurecreationuseadditional-information) - [Dalaj](#dalaj) - [SweAna](#sweana) - [SweDiag](#swediag) - [SweFaq](#swefaq) - [SweFracas](#swefracas) - [SwePar](#swepar) - [SweSat](#swesat) - [SweSim](#swesim) - [SweWgr](#swewgr) - [SweWic](#swewic) - [SweWsc](#swewsc) ## Dataset Description - **Homepage:** [Språkbanken](https://spraakbanken.gu.se/en/resources/superlim) - **Repository:** / - **Paper:** / - **Leaderboard:** / - **Point of Contact:** [Contact Us](mailto:severine.verlinden@ai.se) ### Dataset Summary A standardized suite for evaluation and analysis of Swedish natural language understanding systems. ### Supported Tasks and Leaderboards Work in progress ### Languages Swedish ## Dataset Structure/Creation/Use/Additional Information ### Dalaj [dataset documentation](https://svn.spraakdata.gu.se/sb-arkiv/pub/dalaj/dalaj_documentation.tsv) ### SweAna [dataset documentation](https://svn.spraakdata.gu.se/sb-arkiv/pub/swedish_analogy/analogy_documentation_sheet.tsv) #### SweDiag work in progress ### SweFaq [dataset documentation](https://svn.spraakdata.gu.se/sb-arkiv/pub/faq/faq_documentation_sheet.tsv) ### SweFracas [dataset documentation](https://svn.spraakdata.gu.se/sb-arkiv/pub/swefracas/swefracas_documentation_sheet.tsv) ### SwePar [dataset documentation](https://svn.spraakdata.gu.se/sb-arkiv/pub/sweparaphrase/sweparaphrase_documentation.tsv) ### SweSat [dataset documentation](https://svn.spraakdata.gu.se/sb-arkiv/pub/swesat/swesat-synonyms_documentation_sheet.tsv) ### SweSim [dataset documentation](https://demo.spraakbanken.gu.se/gerlof/SuperSim/supersim-superlim_documentation_sheet.txt) ### SweWgr [dataset documentation](https://demo.spraakbanken.gu.se/gerlof/SweWinogender/swewinogender_documentation_sheet.txt) ### SweWic [dataset documentation](https://demo.spraakbanken.gu.se/gerlof/SweWiC/swewic_documentation_sheet.txt) ### SweWsc [dataset documentation](https://demo.spraakbanken.gu.se/gerlof/SweWinograd/swewinograd_documentation_sheet.txt)

alzoubi36/piextract

alzoubi36

--- dataset_info: features: - name: COLLECT struct: - name: subtask dtype: string - name: tags sequence: string - name: tokens sequence: string - name: NOT_COLLECT struct: - name: subtask dtype: string - name: tags sequence: string - name: tokens sequence: string - name: NOT_SHARE struct: - name: subtask dtype: string - name: tags sequence: string - name: tokens sequence: string - name: SHARE struct: - name: subtask dtype: string - name: tags sequence: string - name: tokens sequence: string splits: - name: train num_bytes: 3453408 num_examples: 2579 - name: test num_bytes: 1580498 num_examples: 1029 - name: validation num_bytes: 662810 num_examples: 456 download_size: 1013894 dataset_size: 5696716 --- # Dataset for the PI-Extract task in the [PrivacyGLUE](https://github.com/infsys-lab/privacy-glue) dataset

bible-nlp/biblenlp-corpus

bible-nlp

--- annotations_creators: - no-annotation language_creators: - expert-generated language: - aai - aak - aau - aaz - abt - abx - aby - acf - acr - acu - adz - aer - aey - agd - agg - agm - agn - agr - agt - agu - aia - aii - aka - ake - alp - alq - als - aly - ame - amf - amk - amm - amn - amo - amp - amr - amu - amx - anh - anv - aoi - aoj - aom - aon - apb - ape - apn - apr - apu - apw - apz - arb - are - arl - arn - arp - asm - aso - ata - atb - atd - atg - att - auc - aui - auy - avt - awb - awk - awx - azb - azg - azz - bao - bba - bbb - bbr - bch - bco - bdd - bea - bef - bel - ben - beo - beu - bgs - bgt - bhg - bhl - big - bjk - bjp - bjr - bjv - bjz - bkd - bki - bkq - bkx - bla - blw - blz - bmh - bmk - bmr - bmu - bnp - boa - boj - bon - box - bpr - bps - bqc - bqp - bre - bsj - bsn - bsp - bss - buk - bus - bvd - bvr - bxh - byr - byx - bzd - bzh - bzj - caa - cab - cac - caf - cak - cao - cap - car - cav - cax - cbc - cbi - cbk - cbr - cbs - cbt - cbu - cbv - cco - ceb - cek - ces - cgc - cha - chd - chf - chk - chq - chz - cjo - cjv - ckb - cle - clu - cme - cmn - cni - cnl - cnt - cof - con - cop - cot - cpa - cpb - cpc - cpu - cpy - crn - crx - cso - csy - cta - cth - ctp - ctu - cub - cuc - cui - cuk - cut - cux - cwe - cya - daa - dad - dah - dan - ded - deu - dgc - dgr - dgz - dhg - dif - dik - dji - djk - djr - dob - dop - dov - dwr - dww - dwy - ebk - eko - emi - emp - eng - enq - epo - eri - ese - esk - etr - ewe - faa - fai - far - ffm - for - fra - fue - fuf - fuh - gah - gai - gam - gaw - gdn - gdr - geb - gfk - ghs - glk - gmv - gng - gnn - gnw - gof - grc - gub - guh - gui - guj - gul - gum - gun - guo - gup - gux - gvc - gvf - gvn - gvs - gwi - gym - gyr - hat - hau - haw - hbo - hch - heb - heg - hin - hix - hla - hlt - hmo - hns - hop - hot - hrv - hto - hub - hui - hun - hus - huu - huv - hvn - ian - ign - ikk - ikw - ilo - imo - inb - ind - ino - iou - ipi - isn - ita - iws - ixl - jac - jae - jao - jic - jid - jiv - jni - jpn - jvn - kan - kaq - kbc - kbh - kbm - kbq - kdc - kde - kdl - kek - ken - kew - kgf - kgk - kgp - khs - khz - kik - kiw - kiz - kje - kjn - kjs - kkc - kkl - klt - klv - kmg - kmh - kmk - kmo - kms - kmu - kne - knf - knj - knv - kos - kpf - kpg - kpj - kpr - kpw - kpx - kqa - kqc - kqf - kql - kqw - ksd - ksj - ksr - ktm - kto - kud - kue - kup - kvg - kvn - kwd - kwf - kwi - kwj - kyc - kyf - kyg - kyq - kyz - kze - lac - lat - lbb - lbk - lcm - leu - lex - lgl - lid - lif - lin - lit - llg - lug - luo - lww - maa - maj - mal - mam - maq - mar - mau - mav - maz - mbb - mbc - mbh - mbj - mbl - mbs - mbt - mca - mcb - mcd - mcf - mco - mcp - mcq - mcr - mdy - med - mee - mek - meq - met - meu - mgc - mgh - mgw - mhl - mib - mic - mie - mig - mih - mil - mio - mir - mit - miz - mjc - mkj - mkl - mkn - mks - mle - mlh - mlp - mmo - mmx - mna - mop - mox - mph - mpj - mpm - mpp - mps - mpt - mpx - mqb - mqj - msb - msc - msk - msm - msy - mti - mto - mux - muy - mva - mvn - mwc - mwe - mwf - mwp - mxb - mxp - mxq - mxt - mya - myk - myu - myw - myy - mzz - nab - naf - nak - nas - nay - nbq - nca - nch - ncj - ncl - ncu - ndg - ndj - nfa - ngp - ngu - nhe - nhg - nhi - nho - nhr - nhu - nhw - nhy - nif - nii - nin - nko - nld - nlg - nmw - nna - nnq - noa - nop - not - nou - npi - npl - nsn - nss - ntj - ntp - ntu - nuy - nvm - nwi - nya - nys - nyu - obo - okv - omw - ong - ons - ood - opm - ory - ote - otm - otn - otq - ots - pab - pad - pah - pan - pao - pes - pib - pio - pir - piu - pjt - pls - plu - pma - poe - poh - poi - pol - pon - por - poy - ppo - prf - pri - ptp - ptu - pwg - qub - quc - quf - quh - qul - qup - qvc - qve - qvh - qvm - qvn - qvs - qvw - qvz - qwh - qxh - qxn - qxo - rai - reg - rgu - rkb - rmc - rmy - ron - roo - rop - row - rro - ruf - rug - rus - rwo - sab - san - sbe - sbk - sbs - seh - sey - sgb - sgz - shj - shp - sim - sja - sll - smk - snc - snn - snp - snx - sny - som - soq - soy - spa - spl - spm - spp - sps - spy - sri - srm - srn - srp - srq - ssd - ssg - ssx - stp - sua - sue - sus - suz - swe - swh - swp - sxb - tac - taj - tam - tav - taw - tbc - tbf - tbg - tbl - tbo - tbz - tca - tcs - tcz - tdt - tee - tel - ter - tet - tew - tfr - tgk - tgl - tgo - tgp - tha - thd - tif - tim - tiw - tiy - tke - tku - tlf - tmd - tna - tnc - tnk - tnn - tnp - toc - tod - tof - toj - ton - too - top - tos - tpa - tpi - tpt - tpz - trc - tsw - ttc - tte - tuc - tue - tuf - tuo - tur - tvk - twi - txq - txu - tzj - tzo - ubr - ubu - udu - uig - ukr - uli - ulk - upv - ura - urb - urd - uri - urt - urw - usa - usp - uvh - uvl - vid - vie - viv - vmy - waj - wal - wap - wat - wbi - wbp - wed - wer - wim - wiu - wiv - wmt - wmw - wnc - wnu - wol - wos - wrk - wro - wrs - wsk - wuv - xav - xbi - xed - xla - xnn - xon - xsi - xtd - xtm - yaa - yad - yal - yap - yaq - yby - ycn - yka - yle - yml - yon - yor - yrb - yre - yss - yuj - yut - yuw - yva - zaa - zab - zac - zad - zai - zaj - zam - zao - zap - zar - zas - zat - zav - zaw - zca - zga - zia - ziw - zlm - zos - zpc - zpl - zpm - zpo - zpq - zpu - zpv - zpz - zsr - ztq - zty - zyp - be - br - cs - ch - zh - de - en - eo - fr - ht - he - hr - id - it - ja - la - nl - ru - sa - so - es - sr - sv - to - uk - vi license: - cc-by-4.0 - other multilinguality: - translation - multilingual pretty_name: biblenlp-corpus size_categories: - 1M<n<10M source_datasets: - original task_categories: - translation task_ids: [] --- # Dataset Card for BibleNLP Corpus ### Dataset Summary Partial and complete Bible translations in 833 languages, aligned by verse. ### Languages aai, aak, aau, aaz, abt, abx, aby, acf, acr, acu, adz, aer, aey, agd, agg, agm, agn, agr, agt, agu, aia, aii, aka, ake, alp, alq, als, aly, ame, amf, amk, amm, amn, amo, amp, amr, amu, amx, anh, anv, aoi, aoj, aom, aon, apb, ape, apn, apr, apu, apw, apz, arb, are, arl, arn, arp, asm, aso, ata, atb, atd, atg, att, auc, aui, auy, avt, awb, awk, awx, azb, azg, azz, bao, bba, bbb, bbr, bch, bco, bdd, bea, bef, bel, ben, beo, beu, bgs, bgt, bhg, bhl, big, bjk, bjp, bjr, bjv, bjz, bkd, bki, bkq, bkx, bla, blw, blz, bmh, bmk, bmr, bmu, bnp, boa, boj, bon, box, bpr, bps, bqc, bqp, bre, bsj, bsn, bsp, bss, buk, bus, bvd, bvr, bxh, byr, byx, bzd, bzh, bzj, caa, cab, cac, caf, cak, cao, cap, car, cav, cax, cbc, cbi, cbk, cbr, cbs, cbt, cbu, cbv, cco, ceb, cek, ces, cgc, cha, chd, chf, chk, chq, chz, cjo, cjv, ckb, cle, clu, cme, cmn, cni, cnl, cnt, cof, con, cop, cot, cpa, cpb, cpc, cpu, cpy, crn, crx, cso, csy, cta, cth, ctp, ctu, cub, cuc, cui, cuk, cut, cux, cwe, cya, daa, dad, dah, dan, ded, deu, dgc, dgr, dgz, dhg, dif, dik, dji, djk, djr, dob, dop, dov, dwr, dww, dwy, ebk, eko, emi, emp, eng, enq, epo, eri, ese, esk, etr, ewe, faa, fai, far, ffm, for, fra, fue, fuf, fuh, gah, gai, gam, gaw, gdn, gdr, geb, gfk, ghs, glk, gmv, gng, gnn, gnw, gof, grc, gub, guh, gui, guj, gul, gum, gun, guo, gup, gux, gvc, gvf, gvn, gvs, gwi, gym, gyr, hat, hau, haw, hbo, hch, heb, heg, hin, hix, hla, hlt, hmo, hns, hop, hot, hrv, hto, hub, hui, hun, hus, huu, huv, hvn, ian, ign, ikk, ikw, ilo, imo, inb, ind, ino, iou, ipi, isn, ita, iws, ixl, jac, jae, jao, jic, jid, jiv, jni, jpn, jvn, kan, kaq, kbc, kbh, kbm, kbq, kdc, kde, kdl, kek, ken, kew, kgf, kgk, kgp, khs, khz, kik, kiw, kiz, kje, kjn, kjs, kkc, kkl, klt, klv, kmg, kmh, kmk, kmo, kms, kmu, kne, knf, knj, knv, kos, kpf, kpg, kpj, kpr, kpw, kpx, kqa, kqc, kqf, kql, kqw, ksd, ksj, ksr, ktm, kto, kud, kue, kup, kvg, kvn, kwd, kwf, kwi, kwj, kyc, kyf, kyg, kyq, kyz, kze, lac, lat, lbb, lbk, lcm, leu, lex, lgl, lid, lif, lin, lit, llg, lug, luo, lww, maa, maj, mal, mam, maq, mar, mau, mav, maz, mbb, mbc, mbh, mbj, mbl, mbs, mbt, mca, mcb, mcd, mcf, mco, mcp, mcq, mcr, mdy, med, mee, mek, meq, met, meu, mgc, mgh, mgw, mhl, mib, mic, mie, mig, mih, mil, mio, mir, mit, miz, mjc, mkj, mkl, mkn, mks, mle, mlh, mlp, mmo, mmx, mna, mop, mox, mph, mpj, mpm, mpp, mps, mpt, mpx, mqb, mqj, msb, msc, msk, msm, msy, mti, mto, mux, muy, mva, mvn, mwc, mwe, mwf, mwp, mxb, mxp, mxq, mxt, mya, myk, myu, myw, myy, mzz, nab, naf, nak, nas, nay, nbq, nca, nch, ncj, ncl, ncu, ndg, ndj, nfa, ngp, ngu, nhe, nhg, nhi, nho, nhr, nhu, nhw, nhy, nif, nii, nin, nko, nld, nlg, nmw, nna, nnq, noa, nop, not, nou, npi, npl, nsn, nss, ntj, ntp, ntu, nuy, nvm, nwi, nya, nys, nyu, obo, okv, omw, ong, ons, ood, opm, ory, ote, otm, otn, otq, ots, pab, pad, pah, pan, pao, pes, pib, pio, pir, piu, pjt, pls, plu, pma, poe, poh, poi, pol, pon, por, poy, ppo, prf, pri, ptp, ptu, pwg, qub, quc, quf, quh, qul, qup, qvc, qve, qvh, qvm, qvn, qvs, qvw, qvz, qwh, qxh, qxn, qxo, rai, reg, rgu, rkb, rmc, rmy, ron, roo, rop, row, rro, ruf, rug, rus, rwo, sab, san, sbe, sbk, sbs, seh, sey, sgb, sgz, shj, shp, sim, sja, sll, smk, snc, snn, snp, snx, sny, som, soq, soy, spa, spl, spm, spp, sps, spy, sri, srm, srn, srp, srq, ssd, ssg, ssx, stp, sua, sue, sus, suz, swe, swh, swp, sxb, tac, taj, tam, tav, taw, tbc, tbf, tbg, tbl, tbo, tbz, tca, tcs, tcz, tdt, tee, tel, ter, tet, tew, tfr, tgk, tgl, tgo, tgp, tha, thd, tif, tim, tiw, tiy, tke, tku, tlf, tmd, tna, tnc, tnk, tnn, tnp, toc, tod, tof, toj, ton, too, top, tos, tpa, tpi, tpt, tpz, trc, tsw, ttc, tte, tuc, tue, tuf, tuo, tur, tvk, twi, txq, txu, tzj, tzo, ubr, ubu, udu, uig, ukr, uli, ulk, upv, ura, urb, urd, uri, urt, urw, usa, usp, uvh, uvl, vid, vie, viv, vmy, waj, wal, wap, wat, wbi, wbp, wed, wer, wim, wiu, wiv, wmt, wmw, wnc, wnu, wol, wos, wrk, wro, wrs, wsk, wuv, xav, xbi, xed, xla, xnn, xon, xsi, xtd, xtm, yaa, yad, yal, yap, yaq, yby, ycn, yka, yle, yml, yon, yor, yrb, yre, yss, yuj, yut, yuw, yva, zaa, zab, zac, zad, zai, zaj, zam, zao, zap, zar, zas, zat, zav, zaw, zca, zga, zia, ziw, zlm, zos, zpc, zpl, zpm, zpo, zpq, zpu, zpv, zpz, zsr, ztq, zty, zyp ## Dataset Structure ### Data Fields **translation** - **languages** - an N length list of the languages of the translations, sorted alphabetically - **translation** - an N length list with the translations each corresponding to the language specified in the above field **files** - **lang** - an N length list of the languages of the files, in order of input - **file** - an N length list of the filenames from the corpus on github, each corresponding with the lang above **ref** - the verse(s) contained in the record, as a list, with each represented with: ``<a three letter book code> <chapter number>:<verse number>`` **licenses** - an N length list of licenses, corresponding to the list of files above **copyrights** - information on copyright holders, corresponding to the list of files above ### Usage The dataset loading script requires installation of tqdm, ijson, and numpy Specify the languages to be paired with a list and ISO 693-3 language codes, such as ``languages = ['eng', 'fra']``. By default, the script will return individual verse pairs, as well as verses covering a full range. If only the individual verses is desired, use ``pair='single'``. If only the maximum range pairing is desired use ``pair='range'`` (for example, if one text uses the verse range covering GEN 1:1-3, all texts would return only the full length pairing). ## Sources https://github.com/BibleNLP/ebible-corpus

bigcode/the-stack-smol

bigcode

--- annotations_creators: [] language_creators: - crowdsourced language: ["code"] multilinguality: - multilingual size_categories: - unknown source_datasets: [] task_categories: - text-generation task_ids: - language-modeling extra_gated_prompt: |- ## Terms of Use for The Stack The Stack dataset is a collection of 3.1 TB of source code in 30 programming languages. We ask that you read and acknowledge the following points before using the dataset: 1. The Stack is a collection of source code from repositories with various licenses. Any use of all or part of the code gathered in The Stack must abide by the terms of the original licenses, including attribution clauses when relevant. We facilitate this by providing provenance information for each data point. 2. The Stack is regularly updated to enact validated data removal requests. By clicking on "Access repository", you agree to update your own version of The Stack to the most recent usable version specified by the maintainers in [the following thread](https://huggingface.co/datasets/bigcode/the-stack/discussions/7). If you have questions about dataset versions and allowed uses, please also ask them in the dataset’s [community discussions](https://huggingface.co/datasets/bigcode/the-stack/discussions/new). We will also notify users via email when the latest usable version changes. 3. To host, share, or otherwise provide access to The Stack dataset, you must include [these Terms of Use](https://huggingface.co/datasets/bigcode/the-stack#terms-of-use-for-the-stack) and require users to agree to it. By clicking on "Access repository" below, you accept that your contact information (email address and username) can be shared with the dataset maintainers as well. extra_gated_fields: Email: text I have read the License and agree with its terms: checkbox --- ## Dataset Description  A small subset (~0.1%) of [the-stack](https://huggingface.co/datasets/bigcode/the-stack) dataset, each programming language has 10,000 random samples from the original dataset. The dataset has 2.6GB of text (code). ## Languages The dataset contains 30 programming languages: ```` "assembly", "batchfile", "c++", "c", "c-sharp", "cmake", "css", "dockerfile", "fortran", "go", "haskell", "html", "java", "javascript", "julia", "lua", "makefile", "markdown", "perl", "php", "powershell", "python", "ruby", "rust", "scala", "shell", "sql", "tex", "typescript", "visual-basic" ````` ## Dataset Structure ```python from datasets import load_dataset load_dataset("bigcode/the-stack-smol") DatasetDict({ train: Dataset({ features: ['content', 'avg_line_length', 'max_line_length', 'alphanum_fraction', 'licenses', 'repository_name', 'path', 'size', 'lang'], num_rows: 300000 }) }) ``` ### How to use it You can either load the whole dataset like above, or load a specific language such as python by specifying the folder directory: ```python load_dataset("bigcode/the-stack-smol", data_dir="data/python") DatasetDict({ train: Dataset({ features: ['content', 'avg_line_length', 'max_line_length', 'alphanum_fraction', 'licenses', 'repository_name', 'path', 'size', 'lang'], num_rows: 10000 }) }) ```

coastalcph/fairlex

coastalcph

--- annotations_creators: - found - machine-generated language_creators: - found language: - en - en - de - fr - it - zh license: - cc-by-nc-sa-4.0 multilinguality: ecthr: - monolingual scotus: - monolingual fscs: - multilingual cail: - monolingual size_categories: ecthr: - 10K<n<100K scotus: - 1K<n<10K fscs: - 10K<n<100K cail: - 100K<n<1M source_datasets: - extended task_categories: - text-classification task_ids: - multi-label-classification - multi-class-classification - topic-classification pretty_name: FairLex tags: - bias - gender-bias --- # Dataset Card for "FairLex" ## 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/coastalcph/fairlex - **Repository:** https://github.com/coastalcph/fairlex - **Paper:** https://aclanthology.org/2022.acl-long.301/ - **Leaderboard:** - - **Point of Contact:** [Ilias Chalkidis](mailto:ilias.chalkidis@di.ku.dk) ### Dataset Summary We present a benchmark suite of four datasets for evaluating the fairness of pre-trained legal language models and the techniques used to fine-tune them for downstream tasks. Our benchmarks cover four jurisdictions (European Council, USA, Swiss, and Chinese), five languages (English, German, French, Italian, and Chinese), and fairness across five attributes (gender, age, nationality/region, language, and legal area). In our experiments, we evaluate pre-trained language models using several group-robust fine-tuning techniques and show that performance group disparities are vibrant in many cases, while none of these techniques guarantee fairness, nor consistently mitigate group disparities. Furthermore, we provide a quantitative and qualitative analysis of our results, highlighting open challenges in the development of robustness methods in legal NLP. For the purpose of this work, we release four domain-specific BERT models with continued pre-training on the corpora of the examined datasets (ECtHR, SCOTUS, FSCS, CAIL). We train mini-sized BERT models with 6 Transformer blocks, 384 hidden units, and 12 attention heads. We warm-start all models from the public MiniLMv2 (Wang et al., 2021) using the distilled version of RoBERTa (Liu et al., 2019). For the English datasets (ECtHR, SCOTUS) and the one distilled from XLM-R (Conneau et al., 2021) for the rest (trilingual FSCS, and Chinese CAIL). [[Link to Models](https://huggingface.co/models?search=fairlex)] ### Supported Tasks and Leaderboards The supported tasks are the following: <table> <tr><td>Dataset</td><td>Source</td><td>Sub-domain</td><td>Language</td><td>Task Type</td><td>Classes</td><tr> <tr><td>ECtHR</td><td> <a href="https://aclanthology.org/P19-1424/">Chalkidis et al. (2019)</a> </td><td>ECHR</td><td>en</td><td>Multi-label classification</td><td>10+1</td></tr> <tr><td>SCOTUS</td><td> <a href="http://scdb.wustl.edu">Spaeth et al. (2020)</a></td><td>US Law</td><td>en</td><td>Multi-class classification</td><td>11</td></tr> <tr><td>FSCS</td><td> <a href="https://aclanthology.org/2021.nllp-1.3/">Niklaus et al. (2021)</a></td><td>Swiss Law</td><td>en, fr , it</td><td>Binary classification</td><td>2</td></tr> <tr><td>CAIL</td><td> <a href="https://arxiv.org/abs/2103.13868">Wang et al. (2021)</a></td><td>Chinese Law</td><td>zh</td><td>Multi-class classification</td><td>6</td></tr> </table> #### ecthr The European Court of Human Rights (ECtHR) hears allegations that a state has breached human rights provisions of the European Convention of Human Rights (ECHR). We use the dataset of Chalkidis et al. (2021), which contains 11K cases from ECtHR's public database. Each case is mapped to *articles* of the ECHR that were violated (if any). This is a multi-label text classification task. Given the facts of a case, the goal is to predict the ECHR articles that were violated, if any, as decided (ruled) by the court. The cases are chronologically split into training (9k, 2001--16), development (1k, 2016--17), and test (1k, 2017--19) sets. To facilitate the study of the fairness of text classifiers, we record for each case the following attributes: (a) The _defendant states_, which are the European states that allegedly violated the ECHR. The defendant states for each case is a subset of the 47 Member States of the Council of Europe; To have statistical support, we group defendant states in two groups: Central-Eastern European states, on one hand, and all other states, as classified by the EuroVoc thesaurus. (b) The _applicant's age_ at the time of the decision. We extract the birth year of the applicant from the case facts, if possible, and classify its case in an age group (<=35, <=64, or older); and (c) the _applicant's gender_, extracted from the facts, if possible based on pronouns, classified in two categories (male, female). #### scotus The US Supreme Court (SCOTUS) is the highest federal court in the United States of America and generally hears only the most controversial or otherwise complex cases that have not been sufficiently well solved by lower courts. We combine information from SCOTUS opinions with the Supreme Court DataBase (SCDB) (Spaeth, 2020). SCDB provides metadata (e.g., date of publication, decisions, issues, decision directions, and many more) for all cases. We consider the available 14 thematic issue areas (e.g, Criminal Procedure, Civil Rights, Economic Activity, etc.). This is a single-label multi-class document classification task. Given the court's opinion, the goal is to predict the issue area whose focus is on the subject matter of the controversy (dispute). SCOTUS contains a total of 9,262 cases that we split chronologically into 80% for training (7.4k, 1946--1982), 10% for development (914, 1982--1991) and 10% for testing (931, 1991--2016). From SCDB, we also use the following attributes to study fairness: (a) the _type of respondent_, which is a manual categorization of respondents (defendants) in five categories (person, public entity, organization, facility, and other); and (c) the _direction of the decision_, i.e., whether the decision is liberal, or conservative, provided by SCDB. #### fscs The Federal Supreme Court of Switzerland (FSCS) is the last level of appeal in Switzerland and similarly to SCOTUS, the court generally hears only the most controversial or otherwise complex cases which have not been sufficiently well solved by lower courts. The court often focuses only on small parts of the previous decision, where they discuss possible wrong reasoning by the lower court. The Swiss-Judgment-Predict dataset (Niklaus et al., 2021) contains more than 85K decisions from the FSCS written in one of three languages (50K German, 31K French, 4K Italian) from the years 2000 to 2020. The dataset is not parallel, i.e., all cases are unique and decisions are written only in a single language. The dataset provides labels for a simplified binary (_approval_, _dismissal_) classification task. Given the facts of the case, the goal is to predict if the plaintiff's request is valid or partially valid. The cases are also chronologically split into training (59.7k, 2000-2014), development (8.2k, 2015-2016), and test (17.4k, 2017-2020) sets. The dataset provides three additional attributes: (a) the _language_ of the FSCS written decision, in either German, French, or Italian; (b) the _legal area_ of the case (public, penal, social, civil, or insurance law) derived from the chambers where the decisions were heard; and (c) the _region_ that denotes in which federal region was the case originated. #### cail The Supreme People's Court of China (CAIL) is the last level of appeal in China and considers cases that originated from the high people's courts concerning matters of national importance. The Chinese AI and Law challenge (CAIL) dataset (Xiao et al., 2018) is a Chinese legal NLP dataset for judgment prediction and contains over 1m criminal cases. The dataset provides labels for *relevant article of criminal code* prediction, *charge* (type of crime) prediction, imprisonment *term* (period) prediction, and monetary *penalty* prediction. The publication of the original dataset has been the topic of an active debate in the NLP community(Leins et al., 2020; Tsarapatsanis and Aletras, 2021; Bender, 2021). Recently, Wang et al. (2021) re-annotated a subset of approx. 100k cases with demographic attributes. Specifically, the new dataset has been annotated with: (a) the _applicant's gender_, classified in two categories (male, female); and (b) the _region_ of the court that denotes in which out of the 7 provincial-level administrative regions was the case judged. We re-split the dataset chronologically into training (80k, 2013-2017), development (12k, 2017-2018), and test (12k, 2018) sets. In our study, we re-frame the imprisonment _term_ prediction and examine a soft version, dubbed _crime severity_ prediction task, a multi-class classification task, where given the facts of a case, the goal is to predict how severe was the committed crime with respect to the imprisonment term. We approximate crime severity by the length of imprisonment term, split in 6 clusters (0, <=12, <=36, <=60, <=120, >120 months). ### Languages We consider datasets in English, German, French, Italian, and Chinese. ## Dataset Structure ### Data Instances #### ecthr An example of 'train' looks as follows. ```json { "text": "1. At the beginning of the events relevant to the application, K. had a daughter, P., and a son, M., born in 1986 and 1988 respectively. ... ", "labels": [4], "defendant_state": 1, "applicant_gender": 0, "applicant_age": 0 } ``` #### scotus An example of 'train' looks as follows. ```json { "text": "United States Supreme Court MICHIGAN NAT. BANK v. MICHIGAN(1961) No. 155 Argued: Decided: March 6, 1961 </s> R. S. 5219 permits States to tax the shares of national banks, but not at a greater rate than . . . other moneyed capital . . . coming into competition with the business of national banks ...", "label": 9, "decision_direction": 0, "respondent_type": 3 } ``` #### fscs An example of 'train' looks as follows. ```json { "text": "A.- Der 1955 geborene V._ war seit 1. September 1986 hauptberuflich als technischer Kaufmann bei der Firma A._ AG tätig und im Rahmen einer Nebenbeschäftigung (Nachtarbeit) ab Mai 1990 bei einem Bewachungsdienst angestellt gewesen, als er am 10....", "label": 0, "decision_language": 0, "legal_are": 5, "court_region": 2 } ``` #### cail An example of 'train' looks as follows. ```json { "text": "南宁市兴宁区人民检察院指控，2012年1月1日19时许，被告人蒋满德在南宁市某某路某号某市场内，因经营问题与被害人杨某某发生争吵并推打 ...", "label": 0, "defendant_gender": 0, "court_region": 5 } ``` ### Data Fields #### ecthr_a - `text`: a `string` feature (factual paragraphs (facts) from the case description). - `labels`: a list of classification labels (a list of violated ECHR articles, if any). The ECHR articles considered are 2, 3, 5, 6, 8, 9, 11, 14, P1-1. - `defendant_state`: Defendant State group (C.E. European, Rest of Europe) - `applicant_gender`: The gender of the applicant (N/A, Male, Female) - `applicant_age`: The age group of the applicant (N/A, <=35, <=64, or older) #### scotus - `text`: a `string` feature (the court opinion). - `label`: a classification label (the relevant issue area). The issue areas are: (1, Criminal Procedure), (2, Civil Rights), (3, First Amendment), (4, Due Process), (5, Privacy), (6, Attorneys), (7, Unions), (8, Economic Activity), (9, Judicial Power), (10, Federalism), (11, Interstate Relations), (12, Federal Taxation), (13, Miscellaneous), (14, Private Action). - `respondent_type`: the type of respondent, which is a manual categorization (clustering) of respondents (defendants) in five categories (person, public entity, organization, facility, and other). - `decision_direction`: the direction of the decision, i.e., whether the decision is liberal, or conservative, provided by SCDB. #### fscs - `text`: a `string` feature (an EU law). - `label`: a classification label (approval or dismissal of the appeal). - `language`: the language of the FSCS written decision, (German, French, or Italian). - `legal_area`: the legal area of the case (public, penal, social, civil, or insurance law) derived from the chambers where the decisions were heard. - `region`: the region that denotes in which federal region was the case originated. #### cail - `text`: a `string` feature (the factual description of the case). - `label`: a classification label (crime severity derived by the imprisonment term). - `defendant_gender`: the gender of the defendant (Male or Female). - `court_region`: the region of the court that denotes in which out of the 7 provincial-level administrative regions was the case judged. ### Data Splits <table> <tr><td>Dataset </td><td>Training</td><td>Development</td><td>Test</td><td>Total</td></tr> <tr><td>ECtHR</td><td>9000</td><td>1000</td><td>1000</td><td>11000</td></tr> <tr><td>SCOTUS</td><td>7417</td><td>914</td><td>931</td><td>9262</td></tr> <tr><td>FSCS</td><td>59709</td><td>8208</td><td>17357</td><td>85274</td></tr> <tr><td>CAIL</td><td>80000</td><td>12000</td><td>12000</td><td>104000</td></tr> </table> ## 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 <table> <tr><td>Dataset</td><td>Source</td><td>Sub-domain</td><td>Language</td><td>Task Type</td><td>Classes</td><tr> <tr><td>ECtHR</td><td> <a href="https://aclanthology.org/P19-1424/">Chalkidis et al. (2019)</a> </td><td>ECHR</td><td>en</td><td>Multi-label classification</td><td>10+1</td></tr> <tr><td>SCOTUS</td><td> <a href="http://scdb.wustl.edu">Spaeth et al. (2020)</a></td><td>US Law</td><td>en</td><td>Multi-class classification</td><td>14</td></tr> <tr><td>FSCS</td><td> <a href="https://aclanthology.org/2021.nllp-1.3/">Niklaus et al. (2021)</a></td><td>Swiss Law</td><td>en, fr , it</td><td>Binary classification</td><td>2</td></tr> <tr><td>CAIL</td><td> <a href="https://arxiv.org/abs/2105.03887">Wang et al. (2021)</a></td><td>Chinese Law</td><td>zh</td><td>Multi-class classification</td><td>6</td></tr> </table> #### Initial Data Collection and Normalization We standardize and put together four datasets: ECtHR (Chalkidis et al., 2021), SCOTUS (Spaeth et al., 2020), FSCS (Niklaus et al., 2021), and CAIL (Xiao et al., 2018; Wang et al., 2021) that are already publicly available. The benchmark is not a blind stapling of pre-existing resources, we augment previous datasets. In the case of ECtHR, previously unavailable demographic attributes have been released to make the original dataset amenable for fairness research. For SCOTUS, two resources (court opinions with SCDB) have been combined for the very same reason, while the authors provide a manual categorization (clustering) of respondents. All datasets, except SCOTUS, are publicly available and have been previously published. If datasets or the papers where they were introduced were not compiled or written by the authors, the original work is referenced and authors encourage FairLex users to do so as well. In fact, this work should only be referenced, in addition to citing the original work, when jointly experimenting with multiple FairLex datasets and using the FairLex evaluation framework and infrastructure, or using any newly introduced annotations (ECtHR, SCOTUS). Otherwise only the original work should be cited. #### 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? All classification labels rely on legal decisions (ECtHR, FSCS, CAIL), or are part of archival procedures (SCOTUS). The demographic attributes and other metadata are either provided by the legal databases or have been extracted automatically from the text by means of Regular Expressions. Consider the **Dataset Description** and **Discussion of Biases** sections, and the original publication for detailed information. ### Personal and Sensitive Information The data is in general partially anonymized in accordance with the applicable national law. The data is considered to be in the public sphere from a privacy perspective. This is a very sensitive matter, as the courts try to keep a balance between transparency (the public's right to know) and privacy (respect for private and family life). ECtHR cases are partially annonymized by the court. Its data is processed and made public in accordance with the European Data Protection Law. SCOTUS cases may also contain personal information and the data is processed and made available by the US Supreme Court, whose proceedings are public. While this ensures compliance with US law, it is very likely that similarly to the ECtHR any processing could be justified by either implied consent or legitimate interest under European law. In FSCS, the names of the parties have been redacted by the courts according to the official guidelines. CAIL cases are also partially anonymized by the courts according to the courts' policy. Its data is processed and made public in accordance with Chinese Law. ## Considerations for Using the Data ### Social Impact of Dataset This work can help practitioners to build assisting technology for legal professionals - with respect to the legal framework (jurisdiction) they operate -; technology that does not only rely on performance on majority groups but also considering minorities and the robustness of the developed models across them. This is an important application field, where more research should be conducted (Tsarapatsanis and Aletras, 2021) in order to improve legal services and democratize law, but more importantly, highlight (inform the audience on) the various multi-aspect shortcomings seeking a responsible and ethical (fair) deployment of technology. ### Discussion of Biases The current version of FairLex covers a very small fraction of legal applications, jurisdictions, and protected attributes. The benchmark inevitably cannot cover "_everything in the whole wide (legal) world_" (Raji et al., 2021), but nonetheless, we believe that the published resources will help critical research in the area of fairness. Some protected attributes within the datasets are extracted automatically, i.e., the gender and the age of the ECtHR dataset, by means of Regular Expressions, or manually clustered by the authors, such as the defendant state in the ECtHR dataset and the respondent attribute in the SCOTUS dataset. Those assumptions and simplifications can hold in an experimental setting only and by no means should be used in real-world applications where some simplifications, e.g., binary gender, would not be appropriate. By no means, do the authors or future users have to endorse the law standards or framework of the examined datasets, to any degree rather than the publication and use of the data. ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Dataset Curators *Ilias Chalkidis, Tommaso Pasini, Sheng Zhang, Letizia Tomada, Letizia, Sebastian Felix Schwemer, Anders Søgaard.* *FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing.* *2022. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland.* **Note:** The original datasets have been originally curated by others, and further curated (updated) by means of this benchmark. ### Licensing Information The benchmark is released under a [Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/) license. The licensing is compatible with the licensing of former material (remixed, transformed datasets). ### Citation Information [*Ilias Chalkidis, Tommaso Pasini, Sheng Zhang, Letizia Tomada, Letizia, Sebastian Felix Schwemer, Anders Søgaard.* *FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing.* *2022. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland.*](https://arxiv.org/abs/2203.07228) ``` @inproceedings{chalkidis-etal-2022-fairlex, author={Chalkidis, Ilias and Passini, Tommaso and Zhang, Sheng and Tomada, Letizia and Schwemer, Sebastian Felix and Søgaard, Anders}, title={FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing}, booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics}, year={2022}, address={Dublin, Ireland} } ``` **Note:** Please consider citing and giving credits to all publications releasing the examined datasets. ### Contributions Thanks to [@iliaschalkidis](https://github.com/iliaschalkidis) for adding this dataset.

HuggingFaceH4/testing_self_instruct_small

HuggingFaceH4

--- dataset_info: features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 20379 num_examples: 100 - name: test num_bytes: 26586 num_examples: 100 download_size: 35875 dataset_size: 46965 --- # Dataset Card for "testing_self_instruct_small" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Gustavosta/Stable-Diffusion-Prompts

Gustavosta

--- license: - unknown annotations_creators: - no-annotation language_creators: - found language: - en source_datasets: - original --- # Stable Diffusion Dataset This is a set of about 80,000 prompts filtered and extracted from the image finder for Stable Diffusion: "[Lexica.art](https://lexica.art/)". It was a little difficult to extract the data, since the search engine still doesn't have a public API without being protected by cloudflare. If you want to test the model with a demo, you can go to: "[spaces/Gustavosta/MagicPrompt-Stable-Diffusion](https://huggingface.co/spaces/Gustavosta/MagicPrompt-Stable-Diffusion)". If you want to see the model, go to: "[Gustavosta/MagicPrompt-Stable-Diffusion](https://huggingface.co/Gustavosta/MagicPrompt-Stable-Diffusion)".

DKYoon/SlimPajama-6B

DKYoon

--- language: - en size_categories: - 1M<n<10M task_categories: - text-generation pretty_name: SlimPajama-6B configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: text dtype: string - name: meta struct: - name: redpajama_set_name dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 23918118724 num_examples: 5489000 - name: validation num_bytes: 39109042 num_examples: 9347 - name: test num_bytes: 40114950 num_examples: 9346 download_size: 14048972121 dataset_size: 23997342716 --- Sampled version of [cerebras/SlimPajama-627B](https://huggingface.co/datasets/cerebras/SlimPajama-627B). [Since the original data was shuffled before chunking](https://huggingface.co/datasets/cerebras/SlimPajama-627B/discussions/4), I only downloaded train/chunk1 (of 10 total) and further sampled 10%. This should result in roughly 6B tokens, hence SlimPajama-6B. The dataset is 24GBs in storage size when decompressed (original dataset is over 2TBs) and has 5489000 rows. The validation set and test set were sampled as well. --- #### Data source proportions for SlimPajama-627B and SlimPajama-6B For sanity purpose, I caluclated the byte proportion of the sampled version. | Data source | SlimPajama-627B | SlimPajama-6B | | ------------- | ---------- | --------- | | Commoncrawl | 52.2% | 54.1% | | C4 | 26.7% | 28.7% | | GitHub | 5.2% | 4.2% | | Books | 4.2% | 3.7% | | ArXiv | 4.6% | 3.4% | | Wikpedia | 3.8% | 3.1% | | StackExchange | 3.3% | 2.8% | --- Please refer to the original dataset for other info. ``` @misc{cerebras2023slimpajama, author = {Soboleva, Daria and Al-Khateeb, Faisal and Myers, Robert and Steeves, Jacob R and Hestness, Joel and Dey, Nolan}, title = {{SlimPajama: A 627B token cleaned and deduplicated version of RedPajama}}, month = June, year = 2023, howpublished = {\url{https://www.cerebras.net/blog/slimpajama-a-627b-token-cleaned-and-deduplicated-version-of-redpajama}}, url = {https://huggingface.co/datasets/cerebras/SlimPajama-627B}, } ```

coqa

--- annotations_creators: - crowdsourced language: - en language_creators: - found license: - other multilinguality: - monolingual pretty_name: 'CoQA: Conversational Question Answering Challenge' size_categories: - 1K<n<10K source_datasets: - extended|race - extended|cnn_dailymail - extended|wikipedia - extended|other task_categories: - question-answering task_ids: - extractive-qa paperswithcode_id: coqa tags: - conversational-qa dataset_info: features: - name: source dtype: string - name: story dtype: string - name: questions sequence: string - name: answers sequence: - name: input_text dtype: string - name: answer_start dtype: int32 - name: answer_end dtype: int32 splits: - name: train num_bytes: 17981459 num_examples: 7199 - name: validation num_bytes: 1225518 num_examples: 500 download_size: 58092681 dataset_size: 19206977 --- # Dataset Card for "coqa" ## 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://stanfordnlp.github.io/coqa/](https://stanfordnlp.github.io/coqa/) - **Repository:** https://github.com/stanfordnlp/coqa-baselines - **Paper:** [CoQA: A Conversational Question Answering Challenge](https://arxiv.org/abs/1808.07042) - **Point of Contact:** [Google Group](https://groups.google.com/forum/#!forum/coqa), [Siva Reddy](mailto:siva.reddy@mila.quebec), [Danqi Chen](mailto:danqic@cs.princeton.edu) - **Size of downloaded dataset files:** 58.09 MB - **Size of the generated dataset:** 19.24 MB - **Total amount of disk used:** 77.33 MB ### Dataset Summary CoQA is a large-scale dataset for building Conversational Question Answering systems. Our dataset contains 127k questions with answers, obtained from 8k conversations about text passages from seven diverse domains. The questions are conversational, and the answers are free-form text with their corresponding evidence highlighted in the passage. ### 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:** 58.09 MB - **Size of the generated dataset:** 19.24 MB - **Total amount of disk used:** 77.33 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "answers": "{\"answer_end\": [179, 494, 511, 545, 879, 1127, 1128, 94, 150, 412, 1009, 1046, 643, -1, 764, 724, 125, 1384, 881, 910], \"answer_...", "questions": "[\"When was the Vat formally opened?\", \"what is the library for?\", \"for what subjects?\", \"and?\", \"what was started in 2014?\", \"ho...", "source": "wikipedia", "story": "\"The Vatican Apostolic Library (), more commonly called the Vatican Library or simply the Vat, is the library of the Holy See, l..." } ``` ### Data Fields The data fields are the same among all splits. #### default - `source`: a `string` feature. - `story`: a `string` feature. - `questions`: a `list` of `string` features. - `answers`: a dictionary feature containing: - `input_text`: a `string` feature. - `answer_start`: a `int32` feature. - `answer_end`: a `int32` feature. ### Data Splits | name |train|validation| |-------|----:|---------:| |default| 7199| 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 CoQA contains passages from seven domains. We make five of these public under the following licenses: - Literature and Wikipedia passages are shared under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) license. - Children's stories are collected from [MCTest](https://www.microsoft.com/en-us/research/publication/mctest-challenge-dataset-open-domain-machine-comprehension-text/) which comes with [MSR-LA](https://github.com/mcobzarenco/mctest/blob/master/data/MCTest/LICENSE.pdf) license. - Middle/High school exam passages are collected from [RACE](https://arxiv.org/abs/1704.04683) which comes with its [own](http://www.cs.cmu.edu/~glai1/data/race/) license. - News passages are collected from the [DeepMind CNN dataset](https://arxiv.org/abs/1506.03340) which comes with [Apache](https://github.com/deepmind/rc-data/blob/master/LICENSE) license. ### Citation Information ``` @article{reddy-etal-2019-coqa, title = "{C}o{QA}: A Conversational Question Answering Challenge", author = "Reddy, Siva and Chen, Danqi and Manning, Christopher D.", journal = "Transactions of the Association for Computational Linguistics", volume = "7", year = "2019", address = "Cambridge, MA", publisher = "MIT Press", url = "https://aclanthology.org/Q19-1016", doi = "10.1162/tacl_a_00266", pages = "249--266", } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham), [@ojasaar](https://github.com/ojasaar), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

mstz/heart_failure

mstz

--- language: - en tags: - heart failure - tabular_classification - binary_classification - UCI pretty_name: Heart failure size_categories: - n<1K task_categories: - tabular-classification configs: - death license: cc --- # Heart failure The [Heart failure dataset](https://www.kaggle.com/datasets/andrewmvd/heart-failure-clinical-data) from Kaggle. Predict patient death from earth failure given some personal medical data . # Configurations and tasks | **Configuration** | **Task** | **Description** | |-------------------|---------------------------|-----------------------------------------------------------------| | death | Binary classification | Did the patient die? | # Usage ```python from datasets import load_dataset dataset = load_dataset("mstz/heart_failure", "death")["train"] ``` # Features |**Feature** |**Type** | |----------------------------------------------------|-----------| |`age` |`int8` | |`has_anaemia` |`int8` | |`creatinine_phosphokinase_concentration_in_blood` |`float64` | |`has_diabetes` |`int8` | |`heart_ejection_fraction` |`float64` | |`has_high_blood_pressure` |`int8` | |`platelets_concentration_in_blood` |`float64` | |`serum_creatinine_concentration_in_blood` |`float64` | |`serum_sodium_concentration_in_blood` |`float64` | |`sex` |`int8` | |`is_smoker` |`int8` | |`days_in_study` |`int64` |

alt

--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - bn - en - fil - hi - id - ja - km - lo - ms - my - th - vi - zh license: - cc-by-4.0 multilinguality: - multilingual - translation size_categories: - 100K<n<1M - 10K<n<100K source_datasets: - original task_categories: - translation - token-classification task_ids: - parsing paperswithcode_id: alt pretty_name: Asian Language Treebank dataset_info: - config_name: alt-parallel features: - name: SNT.URLID dtype: string - name: SNT.URLID.SNTID dtype: string - name: url dtype: string - name: translation dtype: translation: languages: - bg - en - en_tok - fil - hi - id - ja - khm - lo - ms - my - th - vi - zh splits: - name: train num_bytes: 68445916 num_examples: 18088 - name: validation num_bytes: 3710979 num_examples: 1000 - name: test num_bytes: 3814431 num_examples: 1019 download_size: 21285784 dataset_size: 75971326 - config_name: alt-en features: - name: SNT.URLID dtype: string - name: SNT.URLID.SNTID dtype: string - name: url dtype: string - name: status dtype: string - name: value dtype: string splits: - name: train num_bytes: 10075569 num_examples: 17889 - name: validation num_bytes: 544719 num_examples: 988 - name: test num_bytes: 567272 num_examples: 1017 download_size: 3871379 dataset_size: 11187560 - config_name: alt-jp features: - name: SNT.URLID dtype: string - name: SNT.URLID.SNTID dtype: string - name: url dtype: string - name: status dtype: string - name: value dtype: string - name: word_alignment dtype: string - name: jp_tokenized dtype: string - name: en_tokenized dtype: string splits: - name: train num_bytes: 21888277 num_examples: 17202 - name: validation num_bytes: 1181555 num_examples: 953 - name: test num_bytes: 1175592 num_examples: 931 download_size: 13191239 dataset_size: 24245424 - config_name: alt-my features: - name: SNT.URLID dtype: string - name: SNT.URLID.SNTID dtype: string - name: url dtype: string - name: value dtype: string splits: - name: train num_bytes: 20433275 num_examples: 18088 - name: validation num_bytes: 1111410 num_examples: 1000 - name: test num_bytes: 1135209 num_examples: 1018 download_size: 3028302 dataset_size: 22679894 - config_name: alt-km features: - name: SNT.URLID dtype: string - name: SNT.URLID.SNTID dtype: string - name: url dtype: string - name: km_pos_tag dtype: string - name: km_tokenized dtype: string splits: - name: train num_bytes: 12015411 num_examples: 18088 - name: validation num_bytes: 655232 num_examples: 1000 - name: test num_bytes: 673753 num_examples: 1018 download_size: 2410832 dataset_size: 13344396 - config_name: alt-my-transliteration features: - name: en dtype: string - name: my sequence: string splits: - name: train num_bytes: 4249424 num_examples: 84022 download_size: 1232127 dataset_size: 4249424 - config_name: alt-my-west-transliteration features: - name: en dtype: string - name: my sequence: string splits: - name: train num_bytes: 7412043 num_examples: 107121 download_size: 2830071 dataset_size: 7412043 config_names: - alt-en - alt-jp - alt-km - alt-my - alt-my-transliteration - alt-my-west-transliteration - alt-parallel --- # Dataset Card for Asian Language Treebank (ALT) ## 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://www2.nict.go.jp/astrec-att/member/mutiyama/ALT/ - **Leaderboard:** - **Paper:** [Introduction of the Asian Language Treebank](https://ieeexplore.ieee.org/abstract/document/7918974) - **Point of Contact:** [ALT info](alt-info@khn.nict.go.jp) ### Dataset Summary The ALT project aims to advance the state-of-the-art Asian natural language processing (NLP) techniques through the open collaboration for developing and using ALT. It was first conducted by NICT and UCSY as described in Ye Kyaw Thu, Win Pa Pa, Masao Utiyama, Andrew Finch and Eiichiro Sumita (2016). Then, it was developed under [ASEAN IVO](https://www.nict.go.jp/en/asean_ivo/index.html) as described in this Web page. The process of building ALT began with sampling about 20,000 sentences from English Wikinews, and then these sentences were translated into the other languages. ### Supported Tasks and Leaderboards Machine Translation, Dependency Parsing ### Languages It supports 13 language: * Bengali * English * Filipino * Hindi * Bahasa Indonesia * Japanese * Khmer * Lao * Malay * Myanmar (Burmese) * Thai * Vietnamese * Chinese (Simplified Chinese). ## Dataset Structure ### Data Instances #### ALT Parallel Corpus ``` { "SNT.URLID": "80188", "SNT.URLID.SNTID": "1", "url": "http://en.wikinews.org/wiki/2007_Rugby_World_Cup:_Italy_31_-_5_Portugal", "bg": "[translated sentence]", "en": "[translated sentence]", "en_tok": "[translated sentence]", "fil": "[translated sentence]", "hi": "[translated sentence]", "id": "[translated sentence]", "ja": "[translated sentence]", "khm": "[translated sentence]", "lo": "[translated sentence]", "ms": "[translated sentence]", "my": "[translated sentence]", "th": "[translated sentence]", "vi": "[translated sentence]", "zh": "[translated sentence]" } ``` #### ALT Treebank ``` { "SNT.URLID": "80188", "SNT.URLID.SNTID": "1", "url": "http://en.wikinews.org/wiki/2007_Rugby_World_Cup:_Italy_31_-_5_Portugal", "status": "draft/reviewed", "value": "(S (S (BASENP (NNP Italy)) (VP (VBP have) (VP (VP (VP (VBN defeated) (BASENP (NNP Portugal))) (ADVP (RB 31-5))) (PP (IN in) (NP (BASENP (NNP Pool) (NNP C)) (PP (IN of) (NP (BASENP (DT the) (NN 2007) (NNP Rugby) (NNP World) (NNP Cup)) (PP (IN at) (NP (BASENP (NNP Parc) (FW des) (NNP Princes)) (COMMA ,) (BASENP (NNP Paris) (COMMA ,) (NNP France))))))))))) (PERIOD .))" } ``` #### ALT Myanmar transliteration ``` { "en": "CASINO", "my": [ "ကက်စီနို", "ကစီနို", "ကာစီနို", "ကာဆီနို" ] } ``` ### Data Fields #### ALT Parallel Corpus - SNT.URLID: URL link to the source article listed in [URL.txt](https://www2.nict.go.jp/astrec-att/member/mutiyama/ALT/ALT-Parallel-Corpus-20191206/URL.txt) - SNT.URLID.SNTID: index number from 1 to 20000. It is a seletected sentence from `SNT.URLID` and bg, en, fil, hi, id, ja, khm, lo, ms, my, th, vi, zh correspond to the target language #### ALT Treebank - status: it indicates how a sentence is annotated; `draft` sentences are annotated by one annotater and `reviewed` sentences are annotated by two annotater The annotatation is different from language to language, please see [their guildlines](https://www2.nict.go.jp/astrec-att/member/mutiyama/ALT/) for more detail. ### Data Splits | | train | valid | test | |-----------|-------|-------|-------| | # articles | 1698 | 98 | 97 | | # sentences | 18088 | 1000 | 1018 | ## Dataset Creation ### Curation Rationale The ALT project was initiated by the [National Institute of Information and Communications Technology, Japan](https://www.nict.go.jp/en/) (NICT) in 2014. NICT started to build Japanese and English ALT and worked with the University of Computer Studies, Yangon, Myanmar (UCSY) to build Myanmar ALT in 2014. Then, the Badan Pengkajian dan Penerapan Teknologi, Indonesia (BPPT), the Institute for Infocomm Research, Singapore (I2R), the Institute of Information Technology, Vietnam (IOIT), and the National Institute of Posts, Telecoms and ICT, Cambodia (NIPTICT) joined to make ALT for Indonesian, Malay, Vietnamese, and Khmer in 2015. ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? The dataset is sampled from the English Wikinews in 2014. These will be annotated with word segmentation, POS tags, and syntax information, in addition to the word alignment information by linguistic experts from * National Institute of Information and Communications Technology, Japan (NICT) for Japanses and English * University of Computer Studies, Yangon, Myanmar (UCSY) for Myanmar * the Badan Pengkajian dan Penerapan Teknologi, Indonesia (BPPT) for Indonesian * the Institute for Infocomm Research, Singapore (I2R) for Malay * the Institute of Information Technology, Vietnam (IOIT) for Vietnamese * the National Institute of Posts, Telecoms and ICT, Cambodia for Khmer ### 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 * National Institute of Information and Communications Technology, Japan (NICT) for Japanses and English * University of Computer Studies, Yangon, Myanmar (UCSY) for Myanmar * the Badan Pengkajian dan Penerapan Teknologi, Indonesia (BPPT) for Indonesian * the Institute for Infocomm Research, Singapore (I2R) for Malay * the Institute of Information Technology, Vietnam (IOIT) for Vietnamese * the National Institute of Posts, Telecoms and ICT, Cambodia for Khmer ### Licensing Information [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) ### Citation Information Please cite the following if you make use of the dataset: Hammam Riza, Michael Purwoadi, Gunarso, Teduh Uliniansyah, Aw Ai Ti, Sharifah Mahani Aljunied, Luong Chi Mai, Vu Tat Thang, Nguyen Phuong Thai, Vichet Chea, Rapid Sun, Sethserey Sam, Sopheap Seng, Khin Mar Soe, Khin Thandar Nwet, Masao Utiyama, Chenchen Ding. (2016) "Introduction of the Asian Language Treebank" Oriental COCOSDA. BibTeX: ``` @inproceedings{riza2016introduction, title={Introduction of the asian language treebank}, author={Riza, Hammam and Purwoadi, Michael and Uliniansyah, Teduh and Ti, Aw Ai and Aljunied, Sharifah Mahani and Mai, Luong Chi and Thang, Vu Tat and Thai, Nguyen Phuong and Chea, Vichet and Sam, Sethserey and others}, booktitle={2016 Conference of The Oriental Chapter of International Committee for Coordination and Standardization of Speech Databases and Assessment Techniques (O-COCOSDA)}, pages={1--6}, year={2016}, organization={IEEE} } ``` ### Contributions Thanks to [@chameleonTK](https://github.com/chameleonTK) for adding this dataset.

codah

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - question-answering task_ids: - multiple-choice-qa paperswithcode_id: codah pretty_name: COmmonsense Dataset Adversarially-authored by Humans dataset_info: - config_name: codah features: - name: id dtype: int32 - name: question_category dtype: class_label: names: '0': Idioms '1': Reference '2': Polysemy '3': Negation '4': Quantitative '5': Others - name: question_propmt dtype: string - name: candidate_answers sequence: string - name: correct_answer_idx dtype: int32 splits: - name: train num_bytes: 571208 num_examples: 2776 download_size: 485130 dataset_size: 571208 - config_name: fold_0 features: - name: id dtype: int32 - name: question_category dtype: class_label: names: '0': Idioms '1': Reference '2': Polysemy '3': Negation '4': Quantitative '5': Others - name: question_propmt dtype: string - name: candidate_answers sequence: string - name: correct_answer_idx dtype: int32 splits: - name: train num_bytes: 344912 num_examples: 1665 - name: validation num_bytes: 114211 num_examples: 556 - name: test num_bytes: 112109 num_examples: 555 download_size: 485130 dataset_size: 571232 - config_name: fold_1 features: - name: id dtype: int32 - name: question_category dtype: class_label: names: '0': Idioms '1': Reference '2': Polysemy '3': Negation '4': Quantitative '5': Others - name: question_propmt dtype: string - name: candidate_answers sequence: string - name: correct_answer_idx dtype: int32 splits: - name: train num_bytes: 340990 num_examples: 1665 - name: validation num_bytes: 114211 num_examples: 556 - name: test num_bytes: 116031 num_examples: 555 download_size: 485130 dataset_size: 571232 - config_name: fold_2 features: - name: id dtype: int32 - name: question_category dtype: class_label: names: '0': Idioms '1': Reference '2': Polysemy '3': Negation '4': Quantitative '5': Others - name: question_propmt dtype: string - name: candidate_answers sequence: string - name: correct_answer_idx dtype: int32 splits: - name: train num_bytes: 342293 num_examples: 1665 - name: validation num_bytes: 114211 num_examples: 556 - name: test num_bytes: 114728 num_examples: 555 download_size: 485130 dataset_size: 571232 - config_name: fold_3 features: - name: id dtype: int32 - name: question_category dtype: class_label: names: '0': Idioms '1': Reference '2': Polysemy '3': Negation '4': Quantitative '5': Others - name: question_propmt dtype: string - name: candidate_answers sequence: string - name: correct_answer_idx dtype: int32 splits: - name: train num_bytes: 342844 num_examples: 1665 - name: validation num_bytes: 114211 num_examples: 556 - name: test num_bytes: 114177 num_examples: 555 download_size: 485130 dataset_size: 571232 - config_name: fold_4 features: - name: id dtype: int32 - name: question_category dtype: class_label: names: '0': Idioms '1': Reference '2': Polysemy '3': Negation '4': Quantitative '5': Others - name: question_propmt dtype: string - name: candidate_answers sequence: string - name: correct_answer_idx dtype: int32 splits: - name: train num_bytes: 342844 num_examples: 1665 - name: validation num_bytes: 114177 num_examples: 555 - name: test num_bytes: 114211 num_examples: 556 download_size: 485130 dataset_size: 571232 --- # Dataset Card for COmmonsense Dataset Adversarially-authored by Humans ## 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:** [Add homepage URL here if available (unless it's a GitHub repository)]() - **Repository:** [If the dataset is hosted on github or has a github homepage, add URL here]() - **Paper:** [If the dataset was introduced by a paper or there was a paper written describing the dataset, add URL here (landing page for Arxiv paper preferred)]() - **Leaderboard:** [If the dataset supports an active leaderboard, add link here]() - **Point of Contact:** [If known, name and email of at least one person the reader can contact for questions about the dataset.]() ### 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.

reuters21578

--- language: - en license: other paperswithcode_id: reuters-21578 pretty_name: Reuters-21578 Text Categorization Collection dataset_info: - config_name: ModApte features: - name: text dtype: string - name: text_type dtype: string - name: topics sequence: string - name: lewis_split dtype: string - name: cgis_split dtype: string - name: old_id dtype: string - name: new_id dtype: string - name: places sequence: string - name: people sequence: string - name: orgs sequence: string - name: exchanges sequence: string - name: date dtype: string - name: title dtype: string splits: - name: test num_bytes: 2971653 num_examples: 3299 - name: train num_bytes: 9161179 num_examples: 9603 - name: unused num_bytes: 948244 num_examples: 722 download_size: 8150596 dataset_size: 13081076 - config_name: ModHayes features: - name: text dtype: string - name: text_type dtype: string - name: topics sequence: string - name: lewis_split dtype: string - name: cgis_split dtype: string - name: old_id dtype: string - name: new_id dtype: string - name: places sequence: string - name: people sequence: string - name: orgs sequence: string - name: exchanges sequence: string - name: date dtype: string - name: title dtype: string splits: - name: test num_bytes: 948244 num_examples: 722 - name: train num_bytes: 19071106 num_examples: 20856 download_size: 8150596 dataset_size: 20019350 - config_name: ModLewis features: - name: text dtype: string - name: text_type dtype: string - name: topics sequence: string - name: lewis_split dtype: string - name: cgis_split dtype: string - name: old_id dtype: string - name: new_id dtype: string - name: places sequence: string - name: people sequence: string - name: orgs sequence: string - name: exchanges sequence: string - name: date dtype: string - name: title dtype: string splits: - name: test num_bytes: 5400506 num_examples: 6188 - name: train num_bytes: 12994591 num_examples: 13625 - name: unused num_bytes: 948244 num_examples: 722 download_size: 8150596 dataset_size: 19343341 --- # Dataset Card for "reuters21578" ## 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://archive.ics.uci.edu/dataset/137/reuters+21578+text+categorization+collection - **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:** 24.45 MB - **Size of the generated dataset:** 52.22 MB - **Total amount of disk used:** 76.67 MB ### Dataset Summary The Reuters-21578 dataset is one of the most widely used data collections for text categorization research. It is collected from the Reuters financial newswire service in 1987. ### 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 #### ModApte - **Size of downloaded dataset files:** 8.15 MB - **Size of the generated dataset:** 13.05 MB - **Total amount of disk used:** 21.21 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "cgis_split": "\"TRAINING-SET\"", "date": "19-MAR-1987 06:17:22.36", "exchanges": [], "lewis_split": "\"TRAIN\"", "new_id": "\"7001\"", "old_id": "\"11914\"", "orgs": [], "people": [], "places": ["australia"], "text": "\"Media group John Fairfax Ltd &lt;FFXA.S>\\nsaid that its flat first half net profit partly reflected the\\nimpact of changes in t...", "title": "FAIRFAX SAYS HIGHER TAX HITS FIRST HALF EARNINGS", "topics": ["earn"] } ``` #### ModHayes - **Size of downloaded dataset files:** 8.15 MB - **Size of the generated dataset:** 19.79 MB - **Total amount of disk used:** 27.93 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "cgis_split": "\"TRAINING-SET\"", "date": "19-OCT-1987 23:49:31.45", "exchanges": [], "lewis_split": "\"TEST\"", "new_id": "\"20001\"", "old_id": "\"20596\"", "orgs": [], "people": [], "places": ["japan", "usa"], "text": "\"If the dollar goes the way of Wall Street,\\nJapanese will finally move out of dollar investments in a\\nserious way, Japan inves...", "title": "IF DOLLAR FOLLOWS WALL STREET JAPANESE WILL DIVEST", "topics": ["money-fx"] } ``` #### ModLewis - **Size of downloaded dataset files:** 8.15 MB - **Size of the generated dataset:** 19.38 MB - **Total amount of disk used:** 27.54 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "cgis_split": "\"TRAINING-SET\"", "date": "19-MAR-1987 06:17:22.36", "exchanges": [], "lewis_split": "\"TRAIN\"", "new_id": "\"7001\"", "old_id": "\"11914\"", "orgs": [], "people": [], "places": ["australia"], "text": "\"Media group John Fairfax Ltd &lt;FFXA.S>\\nsaid that its flat first half net profit partly reflected the\\nimpact of changes in t...", "title": "FAIRFAX SAYS HIGHER TAX HITS FIRST HALF EARNINGS", "topics": ["earn"] } ``` ### Data Fields The data fields are the same among all splits. #### ModApte - `text`: a `string` feature. - `topics`: a `list` of `string` features. - `lewis_split`: a `string` feature. - `cgis_split`: a `string` feature. - `old_id`: a `string` feature. - `new_id`: a `string` feature. - `places`: a `list` of `string` features. - `people`: a `list` of `string` features. - `orgs`: a `list` of `string` features. - `exchanges`: a `list` of `string` features. - `date`: a `string` feature. - `title`: a `string` feature. #### ModHayes - `text`: a `string` feature. - `topics`: a `list` of `string` features. - `lewis_split`: a `string` feature. - `cgis_split`: a `string` feature. - `old_id`: a `string` feature. - `new_id`: a `string` feature. - `places`: a `list` of `string` features. - `people`: a `list` of `string` features. - `orgs`: a `list` of `string` features. - `exchanges`: a `list` of `string` features. - `date`: a `string` feature. - `title`: a `string` feature. #### ModLewis - `text`: a `string` feature. - `topics`: a `list` of `string` features. - `lewis_split`: a `string` feature. - `cgis_split`: a `string` feature. - `old_id`: a `string` feature. - `new_id`: a `string` feature. - `places`: a `list` of `string` features. - `people`: a `list` of `string` features. - `orgs`: a `list` of `string` features. - `exchanges`: a `list` of `string` features. - `date`: a `string` feature. - `title`: a `string` feature. ### Data Splits #### ModApte | |train|unused|test| |-------|----:|-----:|---:| |ModApte| 8762| 720|3009| #### ModHayes | |train|test| |--------|----:|---:| |ModHayes|18323| 720| #### ModLewis | |train|unused|test| |--------|----:|-----:|---:| |ModLewis|12449| 720|5458| ## 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 According to the dataset website (https://archive.ics.uci.edu/dataset/137/reuters+21578+text+categorization+collection), this dataset is licensed under [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode) (C BY 4.0) license. However, the source data file contains a `README.txt` file with the following information under the **Copyright & Notification** section: > The copyright for the text of newswire articles and Reuters annotations in the Reuters-21578 collection resides with Reuters Ltd. Reuters Ltd. and Carnegie Group, Inc. have agreed to allow the free distribution of this data *for research purposes only*. > If you publish results based on this data set, please acknowledge its use, refer to the data set by the name "Reuters-21578, Distribution 1.0", and inform your readers of the current location of the data set (see "Availability & Questions"). ### Citation Information ``` @article{APTE94, author = {Chidanand Apt{'{e}} and Fred Damerau and Sholom M. Weiss}, title = {Automated Learning of Decision Rules for Text Categorization}, journal = {ACM Transactions on Information Systems}, year = {1994}, note = {To appear.} } @inproceedings{APTE94b, author = {Chidanand Apt{'{e}} and Fred Damerau and Sholom M. Weiss}, title = {Toward Language Independent Automated Learning of Text Categorization Models}, booktitle = {sigir94}, year = {1994}, note = {To appear.} } @inproceedings{HAYES8}, author = {Philip J. Hayes and Peggy M. Anderson and Irene B. Nirenburg and Linda M. Schmandt}, title = {{TCS}: A Shell for Content-Based Text Categorization}, booktitle = {IEEE Conference on Artificial Intelligence Applications}, year = {1990} } @inproceedings{HAYES90b, author = {Philip J. Hayes and Steven P. Weinstein}, title = {{CONSTRUE/TIS:} A System for Content-Based Indexing of a Database of News Stories}, booktitle = {Second Annual Conference on Innovative Applications of Artificial Intelligence}, year = {1990} } @incollection{HAYES92 , author = {Philip J. Hayes}, title = {Intelligent High-Volume Text Processing using Shallow, Domain-Specific Techniques}, booktitle = {Text-Based Intelligent Systems}, publisher = {Lawrence Erlbaum}, address = {Hillsdale, NJ}, year = {1992}, editor = {Paul S. Jacobs} } @inproceedings{LEWIS91c , author = {David D. Lewis}, title = {Evaluating Text Categorization}, booktitle = {Proceedings of Speech and Natural Language Workshop}, year = {1991}, month = {feb}, organization = {Defense Advanced Research Projects Agency}, publisher = {Morgan Kaufmann}, pages = {312--318} } @phdthesis{LEWIS91d, author = {David Dolan Lewis}, title = {Representation and Learning in Information Retrieval}, school = {Computer Science Dept.; Univ. of Massachusetts; Amherst, MA 01003}, year = 1992}, note = {Technical Report 91--93.} } @inproceedings{LEWIS91e, author = {David D. Lewis}, title = {Data Extraction as Text Categorization: An Experiment with the {MUC-3} Corpus}, booktitle = {Proceedings of the Third Message Understanding Evaluation and Conference}, year = {1991}, month = {may}, organization = {Defense Advanced Research Projects Agency}, publisher = {Morgan Kaufmann}, address = {Los Altos, CA} } @inproceedings{LEWIS92b, author = {David D. Lewis}, title = {An Evaluation of Phrasal and Clustered Representations on a Text Categorization Task}, booktitle = {Fifteenth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval}, year = {1992}, pages = {37--50} } @inproceedings{LEWIS92d , author = {David D. Lewis and Richard M. Tong}, title = {Text Filtering in {MUC-3} and {MUC-4}}, booktitle = {Proceedings of the Fourth Message Understanding Conference ({MUC-4})}, year = {1992}, month = {jun}, organization = {Defense Advanced Research Projects Agency}, publisher = {Morgan Kaufmann}, address = {Los Altos, CA} } @inproceedings{LEWIS92e, author = {David D. Lewis}, title = {Feature Selection and Feature Extraction for Text Categorization}, booktitle = {Proceedings of Speech and Natural Language Workshop}, year = {1992}, month = {feb} , organization = {Defense Advanced Research Projects Agency}, publisher = {Morgan Kaufmann}, pages = {212--217} } @inproceedings{LEWIS94b, author = {David D. Lewis and Marc Ringuette}, title = {A Comparison of Two Learning Algorithms for Text Categorization}, booktitle = {Symposium on Document Analysis and Information Retrieval}, year = {1994}, organization = {ISRI; Univ. of Nevada, Las Vegas}, address = {Las Vegas, NV}, month = {apr}, pages = {81--93} } @article{LEWIS94d, author = {David D. Lewis and Philip J. Hayes}, title = {Guest Editorial}, journal = {ACM Transactions on Information Systems}, year = {1994}, volume = {12}, number = {3}, pages = {231}, month = {jul} } @article{SPARCKJONES76, author = {K. {Sparck Jones} and C. J. {van Rijsbergen}}, title = {Information Retrieval Test Collections}, journal = {Journal of Documentation}, year = {1976}, volume = {32}, number = {1}, pages = {59--75} } @book{WEISS91, author = {Sholom M. Weiss and Casimir A. Kulikowski}, title = {Computer Systems That Learn}, publisher = {Morgan Kaufmann}, year = {1991}, address = {San Mateo, CA} } ``` ### Contributions Thanks to [@jplu](https://github.com/jplu), [@jbragg](https://github.com/jbragg), [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

silicone

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M - 10K<n<100K - 1K<n<10K source_datasets: - original task_categories: - text-generation - fill-mask - text-classification task_ids: - dialogue-modeling - language-modeling - masked-language-modeling - sentiment-classification - text-scoring pretty_name: SILICONE Benchmark tags: - emotion-classification - dialogue-act-classification dataset_info: - config_name: dyda_da features: - name: Utterance dtype: string - name: Dialogue_Act dtype: string - name: Dialogue_ID dtype: string - name: Label dtype: class_label: names: '0': commissive '1': directive '2': inform '3': question - name: Idx dtype: int32 splits: - name: train num_bytes: 8346638 num_examples: 87170 - name: validation num_bytes: 764277 num_examples: 8069 - name: test num_bytes: 740226 num_examples: 7740 download_size: 8874925 dataset_size: 9851141 - config_name: dyda_e features: - name: Utterance dtype: string - name: Emotion dtype: string - name: Dialogue_ID dtype: string - name: Label dtype: class_label: names: '0': anger '1': disgust '2': fear '3': happiness '4': no emotion '5': sadness '6': surprise - name: Idx dtype: int32 splits: - name: train num_bytes: 8547111 num_examples: 87170 - name: validation num_bytes: 781445 num_examples: 8069 - name: test num_bytes: 757670 num_examples: 7740 download_size: 8874925 dataset_size: 10086226 - config_name: iemocap features: - name: Dialogue_ID dtype: string - name: Utterance_ID dtype: string - name: Utterance dtype: string - name: Emotion dtype: string - name: Label dtype: class_label: names: '0': ang '1': dis '2': exc '3': fea '4': fru '5': hap '6': neu '7': oth '8': sad '9': sur '10': xxx - name: Idx dtype: int32 splits: - name: train num_bytes: 908180 num_examples: 7213 - name: validation num_bytes: 100969 num_examples: 805 - name: test num_bytes: 254248 num_examples: 2021 download_size: 1158778 dataset_size: 1263397 - config_name: maptask features: - name: Speaker dtype: string - name: Utterance dtype: string - name: Dialogue_Act dtype: string - name: Label dtype: class_label: names: '0': acknowledge '1': align '2': check '3': clarify '4': explain '5': instruct '6': query_w '7': query_yn '8': ready '9': reply_n '10': reply_w '11': reply_y - name: Idx dtype: int32 splits: - name: train num_bytes: 1260413 num_examples: 20905 - name: validation num_bytes: 178184 num_examples: 2963 - name: test num_bytes: 171806 num_examples: 2894 download_size: 1048357 dataset_size: 1610403 - config_name: meld_e features: - name: Utterance dtype: string - name: Speaker dtype: string - name: Emotion dtype: string - name: Dialogue_ID dtype: string - name: Utterance_ID dtype: string - name: Label dtype: class_label: names: '0': anger '1': disgust '2': fear '3': joy '4': neutral '5': sadness '6': surprise - name: Idx dtype: int32 splits: - name: train num_bytes: 916337 num_examples: 9989 - name: validation num_bytes: 100234 num_examples: 1109 - name: test num_bytes: 242352 num_examples: 2610 download_size: 1553014 dataset_size: 1258923 - config_name: meld_s features: - name: Utterance dtype: string - name: Speaker dtype: string - name: Sentiment dtype: string - name: Dialogue_ID dtype: string - name: Utterance_ID dtype: string - name: Label dtype: class_label: names: '0': negative '1': neutral '2': positive - name: Idx dtype: int32 splits: - name: train num_bytes: 930405 num_examples: 9989 - name: validation num_bytes: 101801 num_examples: 1109 - name: test num_bytes: 245873 num_examples: 2610 download_size: 1553014 dataset_size: 1278079 - config_name: mrda features: - name: Utterance_ID dtype: string - name: Dialogue_Act dtype: string - name: Channel_ID dtype: string - name: Speaker dtype: string - name: Dialogue_ID dtype: string - name: Utterance dtype: string - name: Label dtype: class_label: names: '0': s '1': d '2': b '3': f '4': q - name: Idx dtype: int32 splits: - name: train num_bytes: 9998857 num_examples: 83943 - name: validation num_bytes: 1143286 num_examples: 9815 - name: test num_bytes: 1807462 num_examples: 15470 download_size: 10305848 dataset_size: 12949605 - config_name: oasis features: - name: Speaker dtype: string - name: Utterance dtype: string - name: Dialogue_Act dtype: string - name: Label dtype: class_label: names: '0': accept '1': ackn '2': answ '3': answElab '4': appreciate '5': backch '6': bye '7': complete '8': confirm '9': correct '10': direct '11': directElab '12': echo '13': exclaim '14': expressOpinion '15': expressPossibility '16': expressRegret '17': expressWish '18': greet '19': hold '20': identifySelf '21': inform '22': informCont '23': informDisc '24': informIntent '25': init '26': negate '27': offer '28': pardon '29': raiseIssue '30': refer '31': refuse '32': reqDirect '33': reqInfo '34': reqModal '35': selfTalk '36': suggest '37': thank '38': informIntent-hold '39': correctSelf '40': expressRegret-inform '41': thank-identifySelf - name: Idx dtype: int32 splits: - name: train num_bytes: 887018 num_examples: 12076 - name: validation num_bytes: 112185 num_examples: 1513 - name: test num_bytes: 119254 num_examples: 1478 download_size: 802002 dataset_size: 1118457 - config_name: sem features: - name: Utterance dtype: string - name: NbPairInSession dtype: string - name: Dialogue_ID dtype: string - name: SpeechTurn dtype: string - name: Speaker dtype: string - name: Sentiment dtype: string - name: Label dtype: class_label: names: '0': Negative '1': Neutral '2': Positive - name: Idx dtype: int32 splits: - name: train num_bytes: 496168 num_examples: 4264 - name: validation num_bytes: 57896 num_examples: 485 - name: test num_bytes: 100072 num_examples: 878 download_size: 513689 dataset_size: 654136 - config_name: swda features: - name: Utterance dtype: string - name: Dialogue_Act dtype: string - name: From_Caller dtype: string - name: To_Caller dtype: string - name: Topic dtype: string - name: Dialogue_ID dtype: string - name: Conv_ID dtype: string - name: Label dtype: class_label: names: '0': sd '1': b '2': sv '3': '%' '4': aa '5': ba '6': fc '7': qw '8': nn '9': bk '10': h '11': qy^d '12': bh '13': ^q '14': bf '15': fo_o_fw_"_by_bc '16': fo_o_fw_by_bc_" '17': na '18': ad '19': ^2 '20': b^m '21': qo '22': qh '23': ^h '24': ar '25': ng '26': br '27': 'no' '28': fp '29': qrr '30': arp_nd '31': t3 '32': oo_co_cc '33': aap_am '34': t1 '35': bd '36': ^g '37': qw^d '38': fa '39': ft '40': + '41': x '42': ny '43': sv_fx '44': qy_qr '45': ba_fe - name: Idx dtype: int32 splits: - name: train num_bytes: 20499788 num_examples: 190709 - name: validation num_bytes: 2265898 num_examples: 21203 - name: test num_bytes: 291471 num_examples: 2714 download_size: 16227500 dataset_size: 23057157 config_names: - dyda_da - dyda_e - iemocap - maptask - meld_e - meld_s - mrda - oasis - sem - swda --- # Dataset Card for SILICONE Benchmark ## 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:** https://github.com/eusip/SILICONE-benchmark - **Paper:** https://arxiv.org/abs/2009.11152 - **Leaderboard:** [N/A] - **Point of Contact:** [Ebenge Usip](ebenge.usip@telecom-paris.fr) ### Dataset Summary The Sequence labellIng evaLuatIon benChmark fOr spoken laNguagE (SILICONE) benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems specifically designed for spoken language. All datasets are in the English language and covers a variety of domains including daily life, scripted scenarios, joint task completion, phone call conversations, and televsion dialogue. Some datasets additionally include emotion and/or sentimant labels. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English. ## Dataset Structure ### Data Instances #### DailyDialog Act Corpus (Dialogue Act) For the `dyda_da` configuration one example from the dataset is: ``` { 'Utterance': "the taxi drivers are on strike again .", 'Dialogue_Act': 2, # "inform" 'Dialogue_ID': "2" } ``` #### DailyDialog Act Corpus (Emotion) For the `dyda_e` configuration one example from the dataset is: ``` { 'Utterance': "'oh , breaktime flies .'", 'Emotion': 5, # "sadness" 'Dialogue_ID': "997" } ``` #### Interactive Emotional Dyadic Motion Capture (IEMOCAP) database For the `iemocap` configuration one example from the dataset is: ``` { 'Dialogue_ID': "Ses04F_script03_2", 'Utterance_ID': "Ses04F_script03_2_F025", 'Utterance': "You're quite insufferable. I expect it's because you're drunk.", 'Emotion': 0, # "ang" } ``` #### HCRC MapTask Corpus For the `maptask` configuration one example from the dataset is: ``` { 'Speaker': "f", 'Utterance': "i think that would bring me over the crevasse", 'Dialogue_Act': 4, # "explain" } ``` #### Multimodal EmotionLines Dataset (Emotion) For the `meld_e` configuration one example from the dataset is: ``` { 'Utterance': "'Push 'em out , push 'em out , harder , harder .'", 'Speaker': "Joey", 'Emotion': 3, # "joy" 'Dialogue_ID': "1", 'Utterance_ID': "2" } ``` #### Multimodal EmotionLines Dataset (Sentiment) For the `meld_s` configuration one example from the dataset is: ``` { 'Utterance': "'Okay , y'know what ? There is no more left , left !'", 'Speaker': "Rachel", 'Sentiment': 0, # "negative" 'Dialogue_ID': "2", 'Utterance_ID': "4" } ``` #### ICSI MRDA Corpus For the `mrda` configuration one example from the dataset is: ``` { 'Utterance_ID': "Bed006-c2_0073656_0076706", 'Dialogue_Act': 0, # "s" 'Channel_ID': "Bed006-c2", 'Speaker': "mn015", 'Dialogue_ID': "Bed006", 'Utterance': "keith is not technically one of us yet ." } ``` #### BT OASIS Corpus For the `oasis` configuration one example from the dataset is: ``` { 'Speaker': "b", 'Utterance': "when i rang up um when i rang to find out why she said oh well your card's been declined", 'Dialogue_Act': 21, # "inform" } ``` #### SEMAINE database For the `sem` configuration one example from the dataset is: ``` { 'Utterance': "can you think of somebody who is like that ?", 'NbPairInSession': "11", 'Dialogue_ID': "59", 'SpeechTurn': "674", 'Speaker': "Agent", 'Sentiment': 1, # "Neutral" } ``` #### Switchboard Dialog Act (SwDA) Corpus For the `swda` configuration one example from the dataset is: ``` { 'Utterance': "but i 'd probably say that 's roughly right .", 'Dialogue_Act': 33, # "aap_am" 'From_Caller': "1255", 'To_Caller': "1087", 'Topic': "CRIME", 'Dialogue_ID': "818", 'Conv_ID': "sw2836", } ``` ### Data Fields For the `dyda_da` configuration, the different fields are: - `Utterance`: Utterance as a string. - `Dialogue_Act`: Dialog act label of the utterance. It can be one of "commissive" (0), "directive" (1), "inform" (2) or "question" (3). - `Dialogue_ID`: identifier of the dialogue as a string. For the `dyda_e` configuration, the different fields are: - `Utterance`: Utterance as a string. - `Dialogue_Act`: Dialog act label of the utterance. It can be one of "anger" (0), "disgust" (1), "fear" (2), "happiness" (3), "no emotion" (4), "sadness" (5) or "surprise" (6). - `Dialogue_ID`: identifier of the dialogue as a string. For the `iemocap` configuration, the different fields are: - `Dialogue_ID`: identifier of the dialogue as a string. - `Utterance_ID`: identifier of the utterance as a string. - `Utterance`: Utterance as a string. - `Emotion`: Emotion label of the utterance. It can be one of "Anger" (0), "Disgust" (1), "Excitement" (2), "Fear" (3), "Frustration" (4), "Happiness" (5), "Neutral" (6), "Other" (7), "Sadness" (8), "Surprise" (9) or "Unknown" (10). For the `maptask` configuration, the different fields are: - `Speaker`: identifier of the speaker as a string. - `Utterance`: Utterance as a string. - `Dialogue_Act`: Dialog act label of the utterance. It can be one of "acknowledge" (0), "align" (1), "check" (2), "clarify" (3), "explain" (4), "instruct" (5), "query_w" (6), "query_yn" (7), "ready" (8), "reply_n" (9), "reply_w" (10) or "reply_y" (11). For the `meld_e` configuration, the different fields are: - `Utterance`: Utterance as a string. - `Speaker`: Speaker as a string. - `Emotion`: Emotion label of the utterance. It can be one of "anger" (0), "disgust" (1), "fear" (2), "joy" (3), "neutral" (4), "sadness" (5) or "surprise" (6). - `Dialogue_ID`: identifier of the dialogue as a string. - `Utterance_ID`: identifier of the utterance as a string. For the `meld_s` configuration, the different fields are: - `Utterance`: Utterance as a string. - `Speaker`: Speaker as a string. - `Sentiment`: Sentiment label of the utterance. It can be one of "negative" (0), "neutral" (1) or "positive" (2). - `Dialogue_ID`: identifier of the dialogue as a string. - `Utterance_ID`: identifier of the utterance as a string. For the `mrda` configuration, the different fields are: - `Utterance_ID`: identifier of the utterance as a string. - `Dialogue_Act`: Dialog act label of the utterance. It can be one of "s" (0) [Statement/Subjective Statement], "d" (1) [Declarative Question], "b" (2) [Backchannel], "f" (3) [Follow-me] or "q" (4) [Question]. - `Channel_ID`: identifier of the channel as a string. - `Speaker`: identifier of the speaker as a string. - `Dialogue_ID`: identifier of the channel as a string. - `Utterance`: Utterance as a string. For the `oasis` configuration, the different fields are: - `Speaker`: identifier of the speaker as a string. - `Utterance`: Utterance as a string. - `Dialogue_Act`: Dialog act label of the utterance. It can be one of "accept" (0), "ackn" (1), "answ" (2), "answElab" (3), "appreciate" (4), "backch" (5), "bye" (6), "complete" (7), "confirm" (8), "correct" (9), "direct" (10), "directElab" (11), "echo" (12), "exclaim" (13), "expressOpinion"(14), "expressPossibility" (15), "expressRegret" (16), "expressWish" (17), "greet" (18), "hold" (19), "identifySelf" (20), "inform" (21), "informCont" (22), "informDisc" (23), "informIntent" (24), "init" (25), "negate" (26), "offer" (27), "pardon" (28), "raiseIssue" (29), "refer" (30), "refuse" (31), "reqDirect" (32), "reqInfo" (33), "reqModal" (34), "selfTalk" (35), "suggest" (36), "thank" (37), "informIntent-hold" (38), "correctSelf" (39), "expressRegret-inform" (40) or "thank-identifySelf" (41). For the `sem` configuration, the different fields are: - `Utterance`: Utterance as a string. - `NbPairInSession`: number of utterance pairs in a dialogue. - `Dialogue_ID`: identifier of the dialogue as a string. - `SpeechTurn`: SpeakerTurn as a string. - `Speaker`: Speaker as a string. - `Sentiment`: Sentiment label of the utterance. It can be "Negative", "Neutral" or "Positive". For the `swda` configuration, the different fields are: `Utterance`: Utterance as a string. `Dialogue_Act`: Dialogue act label of the utterance. It can be "sd" (0) [Statement-non-opinion], "b" (1) [Acknowledge (Backchannel)], "sv" (2) [Statement-opinion], "%" (3) [Uninterpretable], "aa" (4) [Agree/Accept], "ba" (5) [Appreciation], "fc" (6) [Conventional-closing], "qw" (7) [Wh-Question], "nn" (8) [No Answers], "bk" (9) [Response Acknowledgement], "h" (10) [Hedge], "qy^d" (11) [Declarative Yes-No-Question], "bh" (12) [Backchannel in Question Form], "^q" (13) [Quotation], "bf" (14) [Summarize/Reformulate], 'fo_o_fw_"_by_bc' (15) [Other], 'fo_o_fw_by_bc_"' (16) [Other], "na" (17) [Affirmative Non-yes Answers], "ad" (18) [Action-directive], "^2" (19) [Collaborative Completion], "b^m" (20) [Repeat-phrase], "qo" (21) [Open-Question], "qh" (22) [Rhetorical-Question], "^h" (23) [Hold Before Answer/Agreement], "ar" (24) [Reject], "ng" (25) [Negative Non-no Answers], "br" (26) [Signal-non-understanding], "no" (27) [Other Answers], "fp" (28) [Conventional-opening], "qrr" (29) [Or-Clause], "arp_nd" (30) [Dispreferred Answers], "t3" (31) [3rd-party-talk], "oo_co_cc" (32) [Offers, Options Commits], "aap_am" (33) [Maybe/Accept-part], "t1" (34) [Downplayer], "bd" (35) [Self-talk], "^g" (36) [Tag-Question], "qw^d" (37) [Declarative Wh-Question], "fa" (38) [Apology], "ft" (39) [Thanking], "+" (40) [Unknown], "x" (41) [Unknown], "ny" (42) [Unknown], "sv_fx" (43) [Unknown], "qy_qr" (44) [Unknown] or "ba_fe" (45) [Unknown]. `From_Caller`: identifier of the from caller as a string. `To_Caller`: identifier of the to caller as a string. `Topic`: Topic as a string. `Dialogue_ID`: identifier of the dialogue as a string. `Conv_ID`: identifier of the conversation as a string. ### Data Splits | Dataset name | Train | Valid | Test | | ------------ | ----- | ----- | ---- | | dyda_da | 87170 | 8069 | 7740 | | dyda_e | 87170 | 8069 | 7740 | | iemocap | 7213 | 805 | 2021 | | maptask | 20905 | 2963 | 2894 | | meld_e | 9989 | 1109 | 2610 | | meld_s | 9989 | 1109 | 2610 | | mrda | 83944 | 9815 | 15470 | | oasis | 12076 | 1513 | 1478 | | sem | 4264 | 485 | 878 | | swda | 190709 | 21203 | 2714 | ## 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 ### Benchmark Curators Emile Chapuis, Pierre Colombo, Ebenge Usip. ### Licensing Information This work is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 Unported License](https://creativecommons.org/licenses/by-sa/4.0/). ### Citation Information ``` @inproceedings{chapuis-etal-2020-hierarchical, title = "Hierarchical Pre-training for Sequence Labelling in Spoken Dialog", author = "Chapuis, Emile and Colombo, Pierre and Manica, Matteo and Labeau, Matthieu and Clavel, Chlo{\'e}", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.findings-emnlp.239", doi = "10.18653/v1/2020.findings-emnlp.239", pages = "2636--2648", abstract = "Sequence labelling tasks like Dialog Act and Emotion/Sentiment identification are a key component of spoken dialog systems. In this work, we propose a new approach to learn generic representations adapted to spoken dialog, which we evaluate on a new benchmark we call Sequence labellIng evaLuatIon benChmark fOr spoken laNguagE benchmark (SILICONE). SILICONE is model-agnostic and contains 10 different datasets of various sizes. We obtain our representations with a hierarchical encoder based on transformer architectures, for which we extend two well-known pre-training objectives. Pre-training is performed on OpenSubtitles: a large corpus of spoken dialog containing over 2.3 billion of tokens. We demonstrate how hierarchical encoders achieve competitive results with consistently fewer parameters compared to state-of-the-art models and we show their importance for both pre-training and fine-tuning.", } ``` ### Contributions Thanks to [@eusip](https://github.com/eusip) and [@lhoestq](https://github.com/lhoestq) for adding this dataset.

cppe-5

--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - object-detection task_ids: [] paperswithcode_id: cppe-5 pretty_name: CPPE - 5 tags: - medical-personal-protective-equipment-detection dataset_info: features: - name: image_id dtype: int64 - name: image dtype: image - name: width dtype: int32 - name: height dtype: int32 - name: objects sequence: - name: id dtype: int64 - name: area dtype: int64 - name: bbox sequence: float32 length: 4 - name: category dtype: class_label: names: '0': Coverall '1': Face_Shield '2': Gloves '3': Goggles '4': Mask splits: - name: train num_bytes: 240481257 num_examples: 1000 - name: test num_bytes: 4172715 num_examples: 29 download_size: 238482705 dataset_size: 244653972 --- # Dataset Card for CPPE - 5 ## 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:** https://github.com/Rishit-dagli/CPPE-Dataset - **Paper:** [CPPE-5: Medical Personal Protective Equipment Dataset](https://arxiv.org/abs/2112.09569) - **Leaderboard:** https://paperswithcode.com/sota/object-detection-on-cppe-5 - **Point of Contact:** rishit.dagli@gmail.com ### Dataset Summary CPPE - 5 (Medical Personal Protective Equipment) is a new challenging dataset with the goal to allow the study of subordinate categorization of medical personal protective equipments, which is not possible with other popular data sets that focus on broad level categories. Some features of this dataset are: * high quality images and annotations (~4.6 bounding boxes per image) * real-life images unlike any current such dataset * majority of non-iconic images (allowing easy deployment to real-world environments) ### Supported Tasks and Leaderboards - `object-detection`: The dataset can be used to train a model for Object Detection. This task has an active leaderboard which can be found at https://paperswithcode.com/sota/object-detection-on-cppe-5. The metrics for this task are adopted from the COCO detection evaluation criteria, and include the mean Average Precision (AP) across IoU thresholds ranging from 0.50 to 0.95 at different scales. ### Languages English ## Dataset Structure ### Data Instances A data point comprises an image and its object annotations. ``` { 'image_id': 15, 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=943x663 at 0x2373B065C18>, 'width': 943, 'height': 663, 'objects': { 'id': [114, 115, 116, 117], 'area': [3796, 1596, 152768, 81002], 'bbox': [ [302.0, 109.0, 73.0, 52.0], [810.0, 100.0, 57.0, 28.0], [160.0, 31.0, 248.0, 616.0], [741.0, 68.0, 202.0, 401.0] ], 'category': [4, 4, 0, 0] } } ``` ### Data Fields - `image`: the image id - `image`: `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]` - `width`: the image width - `height`: the image height - `objects`: a dictionary containing bounding box metadata for the objects present on the image - `id`: the annotation id - `area`: the area of the bounding box - `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format) - `category`: the object's category, with possible values including `Coverall` (0),`Face_Shield` (1),`Gloves` (2),`Goggles` (3) and `Mask` (4) ### Data Splits The data is split into training and testing set. The training set contains 1000 images and test set 29 images. ## Dataset Creation ### Curation Rationale From the paper: > With CPPE-5 dataset, we hope to facilitate research and use in applications at multiple public places to autonomously identify if a PPE (Personal Protective Equipment) kit has been worn and also which part of the PPE kit has been worn. One of the main aims with this dataset was to also capture a higher ratio of non-iconic images or non-canonical perspectives [5] of the objects in this dataset. We further hope to see high use of this dataset to aid in medical scenarios which would have a huge effect worldwide. ### Source Data #### Initial Data Collection and Normalization The images in the CPPE-5 dataset were collected using the following process: * Obtain Images from Flickr: Following the object categories we identified earlier, we first download images from Flickr and save them at the "Original" size. On Flickr, images are served at multiple different sizes (Square 75, Small 240, Large 1024, X-Large 4K etc.), the "Original" size is an exact copy of the image uploaded by author. * Extract relevant metadata: Flickr contains images each with searchable metadata, we extract the following relevant metadata: * A direct link to the original image on Flickr * Width and height of the image * Title given to the image by the author * Date and time the image was uploaded on * Flickr username of the author of the image * Flickr Name of the author of the image * Flickr profile of the author of the image * The License image is licensed under * MD5 hash of the original image * Obtain Images from Google Images: Due to the reasons we mention earlier, we only collect a very small proportion of images from Google Images. For these set of images we extract the following metadata: * A direct link to the original image * Width and height of the image * MD5 hash of the original image * Filter inappropriate images: Though very rare in the collected images, we also remove images containing inappropriate content using the safety filters on Flickr and Google Safe Search. * Filter near-similar images: We then remove near-duplicate images in the dataset using GIST descriptors #### Who are the source language producers? The images for this dataset were collected from Flickr and Google Images. ### Annotations #### Annotation process The dataset was labelled in two phases: the first phase included labelling 416 images and the second phase included labelling 613 images. For all the images in the dataset volunteers were provided the following table: |Item |Description | |------------|--------------------------------------------------------------------- | |coveralls | Coveralls are hospital gowns worn by medical professionals as in order to provide a barrier between patient and professional, these usually cover most of the exposed skin surfaces of the professional medics.| |mask | Mask prevents airborne transmission of infections between patients and/or treating personnel by blocking the movement of pathogens (primarily bacteria and viruses) shed in respiratory droplets and aerosols into and from the wearer’s mouth and nose.| face shield | Face shield aims to protect the wearer’s entire face (or part of it) from hazards such as flying objects and road debris, chemical splashes (in laboratories or in industry), or potentially infectious materials (in medical and laboratory environments).| gloves | Gloves are used during medical examinations and procedures to help prevent cross-contamination between caregivers and patients.| |goggles | Goggles, or safety glasses, are forms of protective eye wear that usually enclose or protect the area surrounding the eye in order to prevent particulates, water or chemicals from striking the eyes.| as well as examples of: correctly labelled images, incorrectly labelled images, and not applicable images. Before the labelling task, each volunteer was provided with an exercise to verify if the volunteer was able to correctly identify categories as well as identify if an annotated image is correctly labelled, incorrectly labelled, or not applicable. The labelling process first involved two volunteers independently labelling an image from the dataset. In any of the cases that: the number of bounding boxes are different, the labels for on or more of the bounding boxes are different or two volunteer annotations are sufficiently different; a third volunteer compiles the result from the two annotations to come up with a correctly labelled image. After this step, a volunteer verifies the bounding box annotations. Following this method of labelling the dataset we ensured that all images were labelled accurately and contained exhaustive annotations. As a result of this, our dataset consists of 1029 high-quality, majorly non-iconic, and accurately annotated images. #### Who are the annotators? In both the phases crowd-sourcing techniques were used with multiple volunteers labelling the dataset using the open-source tool LabelImg. ### 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 Dagli, Rishit, and Ali Mustufa Shaikh. ### Licensing Information [More Information Needed] ### Citation Information ``` @misc{dagli2021cppe5, title={CPPE-5: Medical Personal Protective Equipment Dataset}, author={Rishit Dagli and Ali Mustufa Shaikh}, year={2021}, eprint={2112.09569}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ### Contributions Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset.

arabic_billion_words

--- annotations_creators: - found language_creators: - found language: - ar license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M - 10K<n<100K - 1M<n<10M source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: null pretty_name: Arabic Billion Words dataset_info: - config_name: Alittihad features: - name: url dtype: string - name: head_line dtype: string - name: date dtype: string - name: text dtype: string splits: - name: train num_bytes: 1601790302 num_examples: 349342 download_size: 348259999 dataset_size: 1601790302 - config_name: Almasryalyoum features: - name: url dtype: string - name: head_line dtype: string - name: date dtype: string - name: text dtype: string splits: - name: train num_bytes: 1056197870 num_examples: 291723 download_size: 242604438 dataset_size: 1056197870 - config_name: Almustaqbal features: - name: url dtype: string - name: head_line dtype: string - name: date dtype: string - name: text dtype: string splits: - name: train num_bytes: 1545659336 num_examples: 446873 download_size: 350826797 dataset_size: 1545659336 - config_name: Alqabas features: - name: url dtype: string - name: head_line dtype: string - name: date dtype: string - name: text dtype: string splits: - name: train num_bytes: 2631729746 num_examples: 817274 download_size: 595274646 dataset_size: 2631729746 - config_name: Echoroukonline features: - name: url dtype: string - name: head_line dtype: string - name: date dtype: string - name: text dtype: string splits: - name: train num_bytes: 464386206 num_examples: 139732 download_size: 108184378 dataset_size: 464386206 - config_name: Ryiadh features: - name: url dtype: string - name: head_line dtype: string - name: date dtype: string - name: text dtype: string splits: - name: train num_bytes: 3101294859 num_examples: 858188 download_size: 691264971 dataset_size: 3101294859 - config_name: Sabanews features: - name: url dtype: string - name: head_line dtype: string - name: date dtype: string - name: text dtype: string splits: - name: train num_bytes: 198019614 num_examples: 92149 download_size: 38214558 dataset_size: 198019614 - config_name: SaudiYoum features: - name: url dtype: string - name: head_line dtype: string - name: date dtype: string - name: text dtype: string splits: - name: train num_bytes: 2723291416 num_examples: 888068 download_size: 605537923 dataset_size: 2723291416 - config_name: Techreen features: - name: url dtype: string - name: head_line dtype: string - name: date dtype: string - name: text dtype: string splits: - name: train num_bytes: 1103458209 num_examples: 314597 download_size: 252976781 dataset_size: 1103458209 - config_name: Youm7 features: - name: url dtype: string - name: head_line dtype: string - name: date dtype: string - name: text dtype: string splits: - name: train num_bytes: 3004689464 num_examples: 1172136 download_size: 617708074 dataset_size: 3004689464 config_names: - Alittihad - Almasryalyoum - Almustaqbal - Alqabas - Echoroukonline - Ryiadh - Sabanews - SaudiYoum - Techreen - Youm7 --- # Dataset Card for Arabic Billion Words 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://www.abuelkhair.net/index.php/en/arabic/abu-el-khair-corpus - **Repository:** - **Paper:** https://arxiv.org/pdf/1611.04033 - **Leaderboard:** - **Point of Contact:**[Ibrahim Abu El-Khair](iabuelkhair@gmail.com) ### Dataset Summary Abu El-Khair Corpus is an Arabic text corpus, that includes more than five million newspaper articles. It contains over a billion and a half words in total, out of which, there are about three million unique words. The corpus is encoded with two types of encoding, namely: UTF-8, and Windows CP-1256. Also it was marked with two mark-up languages, namely: SGML, and XML. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Arabic ## Dataset Structure ### Data Instances This is an example of the "Almasryalyoum" configuration subset: ```python { "url": "http://today.almasryalyoum.com/printerfriendly.aspx?ArticleID=61300", "head_line": "رئيس وزراء المجر: عنصرية جماهير أوجبيست جلبت العار للبلاد", "date": "19/5/2007", "text": """قال متحدث باسم الحكومة المجرية: إن رئيس الوزراء فيرنك جيوركساني رحب بقرار اتحاد كرة القدم المجري بخصم ثلاث نقاط من نادي أوجبيست بسبب السلوك العنصري الذي صدر من جماهيره. وعاقب الاتحاد المجري فريق أوجبيست بعد أن سخرت جماهيره من إبراهيم سيديبي مهاجم فريق ديبرينسين الأسود أثناء مباراة الفريقين أوائل مايو الجاري. يذكر أن الاتحاد فرض أيضا غرامة مالية قدرها 20 ألف دولار علي أوجبيست في عام 2005 بعد أن رددت جماهيره شعارات معادية للسامية خلال مباراة بالدوري المجري. وأوضح جيوركساني في خطاب إلي إيستفان كيستليكي رئيس الاتحاد المجري لكرة القدم، أن هذا السلوك العنصري من الجماهير «جلب العار لكرة القدم وللمجر». يذكر أن المجر بها مجموعة من مشجعي كرة القدم المشاغبين «الهوليجانز»، وشارك الكثير منهم في أعمال شغب معادية للحكومة في العام الماضي.""", } ``` ### Data Fields The data fields are: - "url": string, original url of the article, - "head_line": string, headline of the article, - "date": string, date of the article, - "text": string, text content of the article, ### Data Splits There is only one "training" split for all configuration subsets, containing the following number of examples: | | Number of examples | |:---------------|-------------------:| | Alittihad | 349342 | | Almasryalyoum | 291723 | | Almustaqbal | 446873 | | Alqabas | 817274 | | Echoroukonline | 139732 | | Ryiadh | 858188 | | Sabanews | 92149 | | SaudiYoum | 888068 | | Techreen | 314597 | | Youm7 | 1172136 | ## 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 ``` @article{el20161, title={1.5 billion words arabic corpus}, author={El-Khair, Ibrahim Abu}, journal={arXiv preprint arXiv:1611.04033}, year={2016} } ``` ### Contributions Thanks to [@zaidalyafeai](https://github.com/zaidalyafeai) and [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.

bigcode/commitpack

bigcode

--- license: mit pretty_name: CommitPack language: - code ---  # Dataset Card for CommitPack ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [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) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** https://github.com/bigcode-project/octopack - **Paper:** [OctoPack: Instruction Tuning Code Large Language Models](https://arxiv.org/abs/2308.07124) - **Point of Contact:** [Niklas Muennighoff](mailto:n.muennighoff@gmail.com) ### Dataset Summary > CommitPack is is a 4TB dataset of commits scraped from GitHub repositories that are permissively licensed. - **Creation:** The dataset can be recreated using instructions available [here](https://github.com/bigcode-project/octopack). - **Languages:** 350 - **OctoPack🐙🎒:** <table> <tr> <th>Data</t> <td><a href=https://huggingface.co/datasets/bigcode/commitpack>CommitPack</a></td> <td>4TB of GitHub commits across 350 programming languages</td> </tr> <tr> <th></t> <td><a href=https://huggingface.co/datasets/bigcode/commitpackft>CommitPackFT</a></td> <td>Filtered version of CommitPack for high-quality commit messages that resemble instructions</td> </tr> <tr> <th>Model</t> <td><a href=https://huggingface.co/bigcode/octocoder>OctoCoder</a></td> <td>StarCoder (16B parameters) instruction tuned on CommitPackFT + OASST</td> </tr> <tr> <th></t> <td><a href=https://huggingface.co/bigcode/octogeex>OctoGeeX</a></td> <td>CodeGeeX2 (6B parameters) instruction tuned on CommitPackFT + OASST</td> </tr> <tr> <th>Evaluation</t> <td><a href=https://huggingface.co/datasets/bigcode/humanevalpack>HumanEvalPack</a></td> <td>Extension of OpenAI's HumanEval to cover 3 scenarios across 6 languages</td> </tr> </table> ## Dataset Structure ### Data Instances An example looks as follows: ```json { 'commit': '0c17311f7fd511f5dae8f8e4acc2dce1a2de3cf5', 'old_file': 'main.py', 'new_file': 'main.py', 'old_contents': "import numpy as np\nimport matplotlib.pyplot as plt\n\n# generate sample data\nx_data = np.linspace(-5, 5, 20)\ny_data = np.random.normal(0.0, 1.0, x_data.size)\n\nplt.plot(x_data, y_data, 'o')\nplt.show()\n", 'new_contents': "import math\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# generate sample data\nx_data = np.linspace(-math.pi, math.pi, 30)\ny_data = np.sin(x_data) + np.random.normal(0.0, 0.1, x_data.size)\n\nplt.plot(x_data, y_data, 'o')\nplt.show()\n\n", 'subject': 'Change to sin() function with noise', 'message': 'Change to sin() function with noise\n', 'lang': 'Python', 'license': 'mit', 'repos': 'MorganR/basic-gaussian-process', 'returncode': 0, 'stderr': '' } ``` ### Data Fields The data fields are the same among all splits: - `commit`: unique commit id - `old_file`: name of the file before the commit - `new_file`: name of the file after the commit - `old_contents`: contents of the file before the commit - `new_contents`: contents of the file after the commit - `subject`: subject of the commit (this is used for all experiments in the paper) - `message`: message of the commit (commonly the same as the subject) - `lang`: programming language - `license`: license of the repository the code stems from, one of `['mit', 'artistic-2.0', 'isc', 'cc0-1.0', 'epl-1.0', 'mpl-2.0', 'unlicense', 'unknown', 'apache-2.0', 'bsd-3-clause', 'agpl-3.0', 'lgpl-2.1', 'bsd-2-clause']` - `repos`: name of the the repository the code stems from (if multiple, they are comma separated) - `returncode`: if applicable errorcode during scraping (0 = no error) - 'stderr': if applicable the error that occured during scraping (empty = no error) ### Data Splits | Name | Megabytes | % of total | Samples | % of total | | --- | --- | --- | --- | --- | | total | 3709175.78 | 100.0% | 57700105 | 100.0% | | json | 583293.816 | 15.7257% | 3495038 | 6.0572% | | xml | 279208.676 | 7.5275% | 1923159 | 3.333% | | text | 270662.596 | 7.2971% | 1389525 | 2.4082% | | javascript | 262824.844 | 7.0858% | 5401937 | 9.3621% | | objective-c++ | 239009.3 | 6.4437% | 32227 | 0.0559% | | python | 234311.564 | 6.3171% | 6189601 | 10.7272% | | c | 200876.804 | 5.4157% | 2779478 | 4.8171% | | c++ | 186585.256 | 5.0304% | 2402294 | 4.1634% | | markdown | 171849.952 | 4.6331% | 7645354 | 13.2502% | | java | 127103.448 | 3.4267% | 3744377 | 6.4894% | | html | 105305.284 | 2.839% | 2366841 | 4.102% | | yaml | 100466.64 | 2.7086% | 2592787 | 4.4936% | | go | 86444.624 | 2.3306% | 1183612 | 2.0513% | | csv | 82946.192 | 2.2362% | 79268 | 0.1374% | | php | 74961.64 | 2.021% | 2555419 | 4.4288% | | jupyter-notebook | 66854.08 | 1.8024% | 94000 | 0.1629% | | gettext-catalog | 62296.88 | 1.6795% | 168327 | 0.2917% | | sql | 56802.764 | 1.5314% | 132772 | 0.2301% | | unity3d-asset | 39535.008 | 1.0659% | 17867 | 0.031% | | typescript | 39254.804 | 1.0583% | 572136 | 0.9916% | | web-ontology-language | 36435.464 | 0.9823% | 7458 | 0.0129% | | ruby | 35830.74 | 0.966% | 2928702 | 5.0757% | | c# | 33669.652 | 0.9077% | 923157 | 1.5999% | | nix | 33547.92 | 0.9045% | 221281 | 0.3835% | | shell | 25109.952 | 0.677% | 1017977 | 1.7643% | | perl | 21148.928 | 0.5702% | 374266 | 0.6486% | | tex | 17471.108 | 0.471% | 89283 | 0.1547% | | css | 16306.632 | 0.4396% | 548818 | 0.9512% | | restructuredtext | 15613.888 | 0.421% | 494037 | 0.8562% | | rust | 15011.296 | 0.4047% | 296214 | 0.5134% | | groff | 12020.188 | 0.3241% | 32923 | 0.0571% | | ini | 8375.164 | 0.2258% | 297100 | 0.5149% | | scala | 8325.96 | 0.2245% | 316064 | 0.5478% | | coffeescript | 6795.14 | 0.1832% | 292446 | 0.5068% | | haskell | 6306.12 | 0.17% | 217325 | 0.3766% | | swift | 5902.716 | 0.1591% | 319289 | 0.5534% | | lua | 5763.12 | 0.1554% | 139091 | 0.2411% | | svg | 5645.44 | 0.1522% | 27095 | 0.047% | | gas | 5585.384 | 0.1506% | 15121 | 0.0262% | | ocaml | 5355.4 | 0.1444% | 81360 | 0.141% | | erlang | 5043.32 | 0.136% | 93685 | 0.1624% | | makefile | 4238.512 | 0.1143% | 343379 | 0.5951% | | asciidoc | 4138.588 | 0.1116% | 96671 | 0.1675% | | emacs-lisp | 3988.652 | 0.1075% | 83228 | 0.1442% | | scss | 3944.936 | 0.1064% | 288190 | 0.4995% | | clojure | 3523.408 | 0.095% | 158674 | 0.275% | | org | 3126.22 | 0.0843% | 30198 | 0.0523% | | common-lisp | 2954.904 | 0.0797% | 74628 | 0.1293% | | diff | 2586.048 | 0.0697% | 21021 | 0.0364% | | groovy | 2569.14 | 0.0693% | 110057 | 0.1907% | | html+erb | 2450.676 | 0.0661% | 225379 | 0.3906% | | nesc | 2439.564 | 0.0658% | 473 | 0.0008% | | dart | 2395.796 | 0.0646% | 56873 | 0.0986% | | powershell | 2289.276 | 0.0617% | 55381 | 0.096% | | f# | 2289.236 | 0.0617% | 66840 | 0.1158% | | dm | 2223.144 | 0.0599% | 55584 | 0.0963% | | kotlin | 2219.248 | 0.0598% | 124266 | 0.2154% | | pascal | 2194.676 | 0.0592% | 42511 | 0.0737% | | jsx | 2124.744 | 0.0573% | 139148 | 0.2412% | | viml | 1948.208 | 0.0525% | 74062 | 0.1284% | | actionscript | 1844.148 | 0.0497% | 28819 | 0.0499% | | cython | 1736.588 | 0.0468% | 25927 | 0.0449% | | turtle | 1698.948 | 0.0458% | 3882 | 0.0067% | | less | 1616.564 | 0.0436% | 88634 | 0.1536% | | mathematica | 1475.044 | 0.0398% | 925 | 0.0016% | | xslt | 1441.456 | 0.0389% | 27956 | 0.0485% | | scheme | 1249.244 | 0.0337% | 30546 | 0.0529% | | perl6 | 1223.16 | 0.033% | 12167 | 0.0211% | | edn | 1186.94 | 0.032% | 2289 | 0.004% | | fortran | 1178.548 | 0.0318% | 13463 | 0.0233% | | java-server-pages | 1173.072 | 0.0316% | 53574 | 0.0928% | | standard-ml | 1133.476 | 0.0306% | 20097 | 0.0348% | | cmake | 1132.068 | 0.0305% | 58446 | 0.1013% | | json5 | 1108.2 | 0.0299% | 1827 | 0.0032% | | vala | 1104.512 | 0.0298% | 14822 | 0.0257% | | vue | 1093.8 | 0.0295% | 68967 | 0.1195% | | freemarker | 1032.332 | 0.0278% | 36216 | 0.0628% | | graphql | 1004.844 | 0.0271% | 2009 | 0.0035% | | twig | 958.96 | 0.0259% | 39588 | 0.0686% | | tcl | 869.832 | 0.0235% | 16407 | 0.0284% | | pod | 859.016 | 0.0232% | 14922 | 0.0259% | | dockerfile | 849.728 | 0.0229% | 259379 | 0.4495% | | yacc | 845.704 | 0.0228% | 8230 | 0.0143% | | postscript | 800.728 | 0.0216% | 903 | 0.0016% | | racket | 796.64 | 0.0215% | 16615 | 0.0288% | | eagle | 785.684 | 0.0212% | 2237 | 0.0039% | | haxe | 772.896 | 0.0208% | 28447 | 0.0493% | | julia | 752.068 | 0.0203% | 22695 | 0.0393% | | handlebars | 740.816 | 0.02% | 49842 | 0.0864% | | smarty | 720.944 | 0.0194% | 41065 | 0.0712% | | visual-basic | 681.516 | 0.0184% | 10511 | 0.0182% | | literate-haskell | 673.74 | 0.0182% | 10729 | 0.0186% | | smalltalk | 665.892 | 0.018% | 11741 | 0.0203% | | isabelle | 655.82 | 0.0177% | 8359 | 0.0145% | | nimrod | 652.86 | 0.0176% | 12023 | 0.0208% | | zig | 621.384 | 0.0168% | 4290 | 0.0074% | | m4 | 603.584 | 0.0163% | 12465 | 0.0216% | | max | 603.56 | 0.0163% | 2259 | 0.0039% | | elixir | 558.116 | 0.015% | 35473 | 0.0615% | | mako | 543.012 | 0.0146% | 8943 | 0.0155% | | arduino | 534.176 | 0.0144% | 32350 | 0.0561% | | jade | 531.4 | 0.0143% | 46993 | 0.0814% | | haml | 502.012 | 0.0135% | 74792 | 0.1296% | | elm | 481.968 | 0.013% | 18542 | 0.0321% | | purebasic | 474.276 | 0.0128% | 36 | 0.0001% | | coldfusion | 470.78 | 0.0127% | 9263 | 0.0161% | | lean | 470.032 | 0.0127% | 7507 | 0.013% | | r | 454.32 | 0.0122% | 12858 | 0.0223% | | cuda | 437.668 | 0.0118% | 11450 | 0.0198% | | textile | 425.116 | 0.0115% | 18491 | 0.032% | | robotframework | 421.612 | 0.0114% | 9211 | 0.016% | | abap | 409.62 | 0.011% | 1955 | 0.0034% | | rdoc | 397.028 | 0.0107% | 38760 | 0.0672% | | llvm | 382.2 | 0.0103% | 10727 | 0.0186% | | ada | 380.7 | 0.0103% | 13258 | 0.023% | | batchfile | 372.16 | 0.01% | 43674 | 0.0757% | | qml | 361.452 | 0.0097% | 19360 | 0.0336% | | jasmin | 359.82 | 0.0097% | 4782 | 0.0083% | | assembly | 343.62 | 0.0093% | 8126 | 0.0141% | | g-code | 334.964 | 0.009% | 3690 | 0.0064% | | cucumber | 331.38 | 0.0089% | 26677 | 0.0462% | | html+php | 323.348 | 0.0087% | 18381 | 0.0319% | | kicad | 321.936 | 0.0087% | 759 | 0.0013% | | api-blueprint | 317.852 | 0.0086% | 4765 | 0.0083% | | eiffel | 311.48 | 0.0084% | 373 | 0.0006% | | toml | 292.676 | 0.0079% | 63517 | 0.1101% | | modelica | 284.616 | 0.0077% | 2611 | 0.0045% | | bitbake | 277.576 | 0.0075% | 43239 | 0.0749% | | lex | 275.96 | 0.0074% | 705 | 0.0012% | | stylus | 273.056 | 0.0074% | 21967 | 0.0381% | | protocol-buffer | 254.124 | 0.0069% | 9202 | 0.0159% | | unknown | 252.228 | 0.0068% | 30570 | 0.053% | | nit | 244.54 | 0.0066% | 4951 | 0.0086% | | factor | 241.192 | 0.0065% | 15378 | 0.0267% | | xs | 239.04 | 0.0064% | 3215 | 0.0056% | | sass | 230.648 | 0.0062% | 23144 | 0.0401% | | parrot-internal-representation | 230.196 | 0.0062% | 6231 | 0.0108% | | html+django | 217.04 | 0.0059% | 10535 | 0.0183% | | mediawiki | 214.324 | 0.0058% | 10188 | 0.0177% | | logos | 212.296 | 0.0057% | 1733 | 0.003% | | genshi | 209.3 | 0.0056% | 956 | 0.0017% | | coldfusion-cfc | 208.164 | 0.0056% | 4410 | 0.0076% | | xtend | 179.544 | 0.0048% | 7775 | 0.0135% | | sqf | 168.656 | 0.0045% | 7778 | 0.0135% | | vhdl | 155.948 | 0.0042% | 2185 | 0.0038% | | antlr | 143.548 | 0.0039% | 3651 | 0.0063% | | systemverilog | 140.192 | 0.0038% | 3944 | 0.0068% | | hcl | 136.752 | 0.0037% | 13379 | 0.0232% | | asp | 136.104 | 0.0037% | 4286 | 0.0074% | | nsis | 129.124 | 0.0035% | 4048 | 0.007% | | inform-7 | 120.188 | 0.0032% | 184 | 0.0003% | | slim | 119.036 | 0.0032% | 18726 | 0.0325% | | groovy-server-pages | 117.368 | 0.0032% | 6695 | 0.0116% | | ceylon | 116.144 | 0.0031% | 7256 | 0.0126% | | fish | 111.28 | 0.003% | 15351 | 0.0266% | | processing | 108.58 | 0.0029% | 5912 | 0.0102% | | component-pascal | 105.5 | 0.0028% | 43 | 0.0001% | | lasso | 104.168 | 0.0028% | 67 | 0.0001% | | glsl | 99.488 | 0.0027% | 9478 | 0.0164% | | saltstack | 98.196 | 0.0026% | 12314 | 0.0213% | | xbase | 94.424 | 0.0025% | 1670 | 0.0029% | | autohotkey | 94.22 | 0.0025% | 1452 | 0.0025% | | liquid | 93.792 | 0.0025% | 2651 | 0.0046% | | purescript | 92.412 | 0.0025% | 5024 | 0.0087% | | agda | 92.06 | 0.0025% | 4956 | 0.0086% | | inno-setup | 91.36 | 0.0025% | 3014 | 0.0052% | | oz | 90.476 | 0.0024% | 1551 | 0.0027% | | chapel | 89.62 | 0.0024% | 26447 | 0.0458% | | arc | 87.212 | 0.0024% | 758 | 0.0013% | | opencl | 86.432 | 0.0023% | 2489 | 0.0043% | | graphviz-dot | 85.804 | 0.0023% | 1525 | 0.0026% | | pawn | 85.424 | 0.0023% | 580 | 0.001% | | jsoniq | 75.152 | 0.002% | 1343 | 0.0023% | | bluespec | 72.38 | 0.002% | 2500 | 0.0043% | | smali | 71.38 | 0.0019% | 174 | 0.0003% | | krl | 69.868 | 0.0019% | 1879 | 0.0033% | | maple | 68.284 | 0.0018% | 1311 | 0.0023% | | unrealscript | 67.668 | 0.0018% | 585 | 0.001% | | ooc | 63.188 | 0.0017% | 3416 | 0.0059% | | pure-data | 62.624 | 0.0017% | 603 | 0.001% | | xquery | 61.956 | 0.0017% | 2237 | 0.0039% | | digital-command-language | 59.644 | 0.0016% | 833 | 0.0014% | | moonscript | 59.208 | 0.0016% | 1951 | 0.0034% | | awk | 57.176 | 0.0015% | 2206 | 0.0038% | | pike | 52.872 | 0.0014% | 1262 | 0.0022% | | livescript | 51.228 | 0.0014% | 5194 | 0.009% | | solidity | 50.856 | 0.0014% | 3689 | 0.0064% | | monkey | 48.256 | 0.0013% | 1367 | 0.0024% | | jsonld | 48.012 | 0.0013% | 462 | 0.0008% | | zephir | 42.684 | 0.0012% | 1265 | 0.0022% | | crystal | 41.924 | 0.0011% | 4217 | 0.0073% | | rhtml | 41.02 | 0.0011% | 4551 | 0.0079% | | stata | 40.684 | 0.0011% | 1344 | 0.0023% | | idris | 39.896 | 0.0011% | 3025 | 0.0052% | | raml | 39.388 | 0.0011% | 948 | 0.0016% | | openscad | 37.732 | 0.001% | 2178 | 0.0038% | | red | 35.26 | 0.001% | 1108 | 0.0019% | | c2hs-haskell | 34.472 | 0.0009% | 1021 | 0.0018% | | cycript | 33.96 | 0.0009% | 197 | 0.0003% | | applescript | 33.512 | 0.0009% | 1304 | 0.0023% | | mupad | 32.488 | 0.0009% | 178 | 0.0003% | | literate-agda | 31.384 | 0.0008% | 567 | 0.001% | | boo | 31.172 | 0.0008% | 26289 | 0.0456% | | sourcepawn | 29.528 | 0.0008% | 717 | 0.0012% | | qmake | 29.508 | 0.0008% | 3632 | 0.0063% | | ragel-in-ruby-host | 28.296 | 0.0008% | 888 | 0.0015% | | io | 27.952 | 0.0008% | 1247 | 0.0022% | | desktop | 27.648 | 0.0007% | 5021 | 0.0087% | | propeller-spin | 26.772 | 0.0007% | 625 | 0.0011% | | thrift | 26.748 | 0.0007% | 1007 | 0.0017% | | volt | 25.052 | 0.0007% | 1660 | 0.0029% | | xproc | 24.212 | 0.0007% | 914 | 0.0016% | | igor-pro | 23.748 | 0.0006% | 388 | 0.0007% | | lolcode | 23.74 | 0.0006% | 24861 | 0.0431% | | html+eex | 21.412 | 0.0006% | 2100 | 0.0036% | | logtalk | 20.428 | 0.0006% | 1035 | 0.0018% | | mirah | 20.104 | 0.0005% | 706 | 0.0012% | | gnuplot | 19.676 | 0.0005% | 889 | 0.0015% | | literate-coffeescript | 19.016 | 0.0005% | 1041 | 0.0018% | | jflex | 18.608 | 0.0005% | 555 | 0.001% | | emberscript | 18.392 | 0.0005% | 1024 | 0.0018% | | cobol | 17.0 | 0.0005% | 24953 | 0.0432% | | yang | 16.94 | 0.0005% | 597 | 0.001% | | rebol | 16.468 | 0.0004% | 239 | 0.0004% | | linker-script | 16.084 | 0.0004% | 1604 | 0.0028% | | cartocss | 15.916 | 0.0004% | 555 | 0.001% | | urweb | 13.068 | 0.0004% | 304 | 0.0005% | | rmarkdown | 13.032 | 0.0004% | 750 | 0.0013% | | darcs-patch | 13.008 | 0.0004% | 80 | 0.0001% | | csound | 12.852 | 0.0003% | 229 | 0.0004% | | squirrel | 12.844 | 0.0003% | 531 | 0.0009% | | apl | 12.56 | 0.0003% | 586 | 0.001% | | hlsl | 12.168 | 0.0003% | 1529 | 0.0026% | | latte | 11.888 | 0.0003% | 1380 | 0.0024% | | pony | 11.836 | 0.0003% | 624 | 0.0011% | | ioke | 10.86 | 0.0003% | 373 | 0.0006% | | hy | 10.512 | 0.0003% | 879 | 0.0015% | | uno | 10.356 | 0.0003% | 628 | 0.0011% | | pan | 10.336 | 0.0003% | 637 | 0.0011% | | xojo | 10.308 | 0.0003% | 642 | 0.0011% | | papyrus | 10.256 | 0.0003% | 130 | 0.0002% | | stan | 10.252 | 0.0003% | 540 | 0.0009% | | slash | 9.904 | 0.0003% | 640 | 0.0011% | | supercollider | 9.796 | 0.0003% | 318 | 0.0006% | | vcl | 9.456 | 0.0003% | 747 | 0.0013% | | smt | 9.032 | 0.0002% | 117 | 0.0002% | | glyph | 8.948 | 0.0002% | 7 | 0.0% | | wisp | 8.736 | 0.0002% | 262 | 0.0005% | | renpy | 8.3 | 0.0002% | 421 | 0.0007% | | clips | 7.728 | 0.0002% | 450 | 0.0008% | | dns-zone | 7.56 | 0.0002% | 54 | 0.0001% | | sas | 7.536 | 0.0002% | 269 | 0.0005% | | rouge | 7.196 | 0.0002% | 396 | 0.0007% | | ec | 7.032 | 0.0002% | 94 | 0.0002% | | dylan | 6.82 | 0.0002% | 280 | 0.0005% | | tcsh | 6.524 | 0.0002% | 748 | 0.0013% | | aspectj | 6.332 | 0.0002% | 451 | 0.0008% | | netlogo | 6.304 | 0.0002% | 140 | 0.0002% | | gap | 6.096 | 0.0002% | 46 | 0.0001% | | fancy | 5.952 | 0.0002% | 675 | 0.0012% | | coq | 5.744 | 0.0002% | 80 | 0.0001% | | click | 5.74 | 0.0002% | 9 | 0.0% | | capn-proto | 5.644 | 0.0002% | 330 | 0.0006% | | flux | 5.572 | 0.0002% | 47 | 0.0001% | | forth | 5.512 | 0.0001% | 265 | 0.0005% | | ats | 5.424 | 0.0001% | 383 | 0.0007% | | netlinx | 5.172 | 0.0001% | 144 | 0.0002% | | clean | 5.068 | 0.0001% | 171 | 0.0003% | | parrot-assembly | 4.664 | 0.0001% | 227 | 0.0004% | | alloy | 4.644 | 0.0001% | 203 | 0.0004% | | lfe | 4.576 | 0.0001% | 287 | 0.0005% | | gdscript | 4.488 | 0.0001% | 460 | 0.0008% | | augeas | 4.444 | 0.0001% | 395 | 0.0007% | | sparql | 4.404 | 0.0001% | 1036 | 0.0018% | | lilypond | 4.308 | 0.0001% | 265 | 0.0005% | | scilab | 4.088 | 0.0001% | 375 | 0.0006% | | autoit | 4.06 | 0.0001% | 279 | 0.0005% | | myghty | 3.864 | 0.0001% | 105 | 0.0002% | | blitzmax | 3.74 | 0.0001% | 220 | 0.0004% | | creole | 3.416 | 0.0001% | 337 | 0.0006% | | harbour | 3.336 | 0.0001% | 107 | 0.0002% | | piglatin | 3.168 | 0.0001% | 513 | 0.0009% | | opa | 3.164 | 0.0001% | 211 | 0.0004% | | sage | 3.032 | 0.0001% | 414 | 0.0007% | | ston | 2.848 | 0.0001% | 414 | 0.0007% | | maxscript | 2.8 | 0.0001% | 47 | 0.0001% | | lsl | 2.68 | 0.0001% | 74 | 0.0001% | | gentoo-ebuild | 2.576 | 0.0001% | 601 | 0.001% | | nu | 2.38 | 0.0001% | 170 | 0.0003% | | bro | 2.34 | 0.0001% | 333 | 0.0006% | | xc | 2.02 | 0.0001% | 88 | 0.0002% | | j | 1.808 | 0.0% | 142 | 0.0002% | | metal | 1.724 | 0.0% | 151 | 0.0003% | | module-management-system | 1.544 | 0.0% | 91 | 0.0002% | | webidl | 1.508 | 0.0% | 96 | 0.0002% | | tea | 1.468 | 0.0% | 29 | 0.0001% | | redcode | 1.272 | 0.0% | 149 | 0.0003% | | shen | 1.2 | 0.0% | 71 | 0.0001% | | pov-ray-sdl | 1.136 | 0.0% | 104 | 0.0002% | | x10 | 1.008 | 0.0% | 33 | 0.0001% | | brainfuck | 0.964 | 0.0% | 167 | 0.0003% | | ninja | 0.952 | 0.0% | 187 | 0.0003% | | golo | 0.896 | 0.0% | 115 | 0.0002% | | webassembly | 0.86 | 0.0% | 83 | 0.0001% | | self | 0.824 | 0.0% | 15 | 0.0% | | labview | 0.808 | 0.0% | 61 | 0.0001% | | octave | 0.804 | 0.0% | 12 | 0.0% | | pogoscript | 0.804 | 0.0% | 74 | 0.0001% | | d | 0.796 | 0.0% | 20 | 0.0% | | http | 0.736 | 0.0% | 140 | 0.0002% | | ecl | 0.664 | 0.0% | 48 | 0.0001% | | chuck | 0.584 | 0.0% | 99 | 0.0002% | | gosu | 0.524 | 0.0% | 60 | 0.0001% | | parrot | 0.52 | 0.0% | 17 | 0.0% | | opal | 0.472 | 0.0% | 69 | 0.0001% | | objective-j | 0.456 | 0.0% | 37 | 0.0001% | | kit | 0.412 | 0.0% | 48 | 0.0001% | | gams | 0.376 | 0.0% | 18 | 0.0% | | prolog | 0.276 | 0.0% | 35 | 0.0001% | | clarion | 0.268 | 0.0% | 13 | 0.0% | | mask | 0.252 | 0.0% | 37 | 0.0001% | | brightscript | 0.244 | 0.0% | 28 | 0.0% | | scaml | 0.184 | 0.0% | 31 | 0.0001% | | matlab | 0.164 | 0.0% | 29 | 0.0001% | | idl | 0.148 | 0.0% | 1 | 0.0% | | ags-script | 0.124 | 0.0% | 31 | 0.0001% | | lookml | 0.12 | 0.0% | 10 | 0.0% | | apacheconf | 0.108 | 0.0% | 59 | 0.0001% | | oxygene | 0.104 | 0.0% | 9 | 0.0% | | txl | 0.096 | 0.0% | 3 | 0.0% | | grammatical-framework | 0.088 | 0.0% | 39 | 0.0001% | | renderscript | 0.064 | 0.0% | 54 | 0.0001% | | mtml | 0.052 | 0.0% | 13 | 0.0% | | unified-parallel-c | 0.052 | 0.0% | 6 | 0.0% | | dogescript | 0.04 | 0.0% | 10 | 0.0% | | gentoo-eclass | 0.04 | 0.0% | 6 | 0.0% | | zimpl | 0.04 | 0.0% | 7 | 0.0% | | irc-log | 0.036 | 0.0% | 9 | 0.0% | | fantom | 0.028 | 0.0% | 11 | 0.0% | | numpy | 0.028 | 0.0% | 1 | 0.0% | | cirru | 0.024 | 0.0% | 4 | 0.0% | | xpages | 0.024 | 0.0% | 7 | 0.0% | | nginx | 0.02 | 0.0% | 6 | 0.0% | | objdump | 0.02 | 0.0% | 1 | 0.0% | | python-traceback | 0.02 | 0.0% | 10 | 0.0% | | realbasic | 0.012 | 0.0% | 1 | 0.0% | | befunge | 0.008 | 0.0% | 2 | 0.0% | | bison | 0.008 | 0.0% | 1 | 0.0% | | m | 0.008 | 0.0% | 1 | 0.0% | | omgrofl | 0.008 | 0.0% | 1 | 0.0% | ## Additional Information ### Licensing Information Each sample comes from a code repository with a permissive license. The license is provided by the `license` field for each sample. ### Citation Information ```bibtex @article{muennighoff2023octopack, title={OctoPack: Instruction Tuning Code Large Language Models}, author={Niklas Muennighoff and Qian Liu and Armel Zebaze and Qinkai Zheng and Binyuan Hui and Terry Yue Zhuo and Swayam Singh and Xiangru Tang and Leandro von Werra and Shayne Longpre}, journal={arXiv preprint arXiv:2308.07124}, year={2023} } ```

datablations/c4-subsets

datablations

--- license: cc0-1.0 language: - en --- ## Dataset Description - **Repository:** https://github.com/huggingface/datablations - **Paper:** [Scaling Data-Constrained Language Models](https://arxiv.org/abs/2305.16264) - **Point of Contact:** [Niklas Muennighoff](mailto:n.muennighoff@gmail.com) ### Dataset Summary Various subsets of [C4](https://huggingface.co/datasets/allenai/c4) with different numbers of tokens measured with the GPT2Tokenizer. This data is used in the paper [Scaling Data-Constrained Language Models](https://arxiv.org/abs/2305.16264). Please refer to [our GitHub repository](https://github.com/huggingface/datablations) for more details. ```bibtex @article{muennighoff2023scaling, title={Scaling Data-Constrained Language Models}, author={Muennighoff, Niklas and Rush, Alexander M and Barak, Boaz and Scao, Teven Le and Piktus, Aleksandra and Tazi, Nouamane and Pyysalo, Sampo and Wolf, Thomas and Raffel, Colin}, journal={arXiv preprint arXiv:2305.16264}, year={2023} } ```

mteb/biorxiv-clustering-s2s

mteb

--- language: - en ---

BeIR/nfcorpus

BeIR

--- annotations_creators: [] language_creators: [] language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual paperswithcode_id: beir pretty_name: BEIR Benchmark size_categories: msmarco: - 1M<n<10M trec-covid: - 100k<n<1M nfcorpus: - 1K<n<10K nq: - 1M<n<10M hotpotqa: - 1M<n<10M fiqa: - 10K<n<100K arguana: - 1K<n<10K touche-2020: - 100K<n<1M cqadupstack: - 100K<n<1M quora: - 100K<n<1M dbpedia: - 1M<n<10M scidocs: - 10K<n<100K fever: - 1M<n<10M climate-fever: - 1M<n<10M scifact: - 1K<n<10K source_datasets: [] task_categories: - text-retrieval - zero-shot-retrieval - information-retrieval - zero-shot-information-retrieval task_ids: - passage-retrieval - entity-linking-retrieval - fact-checking-retrieval - tweet-retrieval - citation-prediction-retrieval - duplication-question-retrieval - argument-retrieval - news-retrieval - biomedical-information-retrieval - question-answering-retrieval --- # Dataset Card for BEIR Benchmark ## 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/UKPLab/beir - **Repository:** https://github.com/UKPLab/beir - **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ - **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns - **Point of Contact:** nandan.thakur@uwaterloo.ca ### Dataset Summary BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks: - Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact) - Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/) - Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) - News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html) - Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data) - Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) - Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs) - Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html) - Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/) All these datasets have been preprocessed and can be used for your experiments. ```python ``` ### Supported Tasks and Leaderboards The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia. The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/). ### Languages All tasks are in English (`en`). ## Dataset Structure All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format: - `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}` - `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}` - `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1` ### Data Instances A high level example of any beir dataset: ```python corpus = { "doc1" : { "title": "Albert Einstein", "text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \ one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \ its influence on the philosophy of science. He is best known to the general public for his mass–energy \ equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \ Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \ of the photoelectric effect', a pivotal step in the development of quantum theory." }, "doc2" : { "title": "", # Keep title an empty string if not present "text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \ malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\ with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)." }, } queries = { "q1" : "Who developed the mass-energy equivalence formula?", "q2" : "Which beer is brewed with a large proportion of wheat?" } qrels = { "q1" : {"doc1": 1}, "q2" : {"doc2": 1}, } ``` ### Data Fields Examples from all configurations have the following features: ### Corpus - `corpus`: a `dict` feature representing the document title and passage text, made up of: - `_id`: a `string` feature representing the unique document id - `title`: a `string` feature, denoting the title of the document. - `text`: a `string` feature, denoting the text of the document. ### Queries - `queries`: a `dict` feature representing the query, made up of: - `_id`: a `string` feature representing the unique query id - `text`: a `string` feature, denoting the text of the query. ### Qrels - `qrels`: a `dict` feature representing the query document relevance judgements, made up of: - `_id`: a `string` feature representing the query id - `_id`: a `string` feature, denoting the document id. - `score`: a `int32` feature, denoting the relevance judgement between query and document. ### Data Splits | Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 | | -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:| | MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` | | TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` | | NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` | | BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) | | NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` | | HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` | | FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` | | Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) | | TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) | | ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` | | Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` | | CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` | | Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` | | DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` | | SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` | | FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` | | Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` | | SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` | | Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) | ## 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 Cite as: ``` @inproceedings{ thakur2021beir, title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models}, author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych}, booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)}, year={2021}, url={https://openreview.net/forum?id=wCu6T5xFjeJ} } ``` ### Contributions Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.

HuggingFaceH4/testing_h4

HuggingFaceH4

--- dataset_info: features: - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: prompt dtype: string - name: prompt_id dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: test_holdout_rm num_bytes: 26133 num_examples: 10 - name: test_ift num_bytes: 23368 num_examples: 10 - name: test_rl num_bytes: 28324 num_examples: 10 - name: test_rm num_bytes: 37997 num_examples: 10 - name: train_ift num_bytes: 26975 num_examples: 10 - name: train_rl num_bytes: 20943 num_examples: 10 - name: train_rm num_bytes: 33531 num_examples: 10 download_size: 186492 dataset_size: 197271 --- # Dataset Card for "testing_h4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

zjunlp/Mol-Instructions

zjunlp

--- language: - en size_categories: - 100M<n<1B license: cc-by-4.0 tags: - chemistry - biology - molecule - protein - instructions --- <h1 align="center"> 🧪 Mol-Instructions </h1> <h3 align="center"> An open, large-scale biomolecular instruction dataset for large language models. </h3> > Please refer to our [repository](https://github.com/zjunlp/Mol-Instructions) and [paper](https://arxiv.org/abs/2306.08018) for more details.  ## 📌 Contents - [Overview](#1) - [Data Stats](#1-1) - [Data Construction](#1-2) - [Data Release](#1-3) - [Tasks](#2) - [Molecule-oriented](#2-1) - [Protein-oriented](#2-2) - [Biomolecule text](#2-3) - [Demo](#3) - [Model Weight Release](#3-1) - [Model Usage Guide](#3-2) - [FAQ](#3-3) - [Notices](#4) - [Usage and License](#4-1) - [Limitations](#4-2) - [About](#5) - [References](#5-1) - [Acknowledgements](#5-2) <h2 id="1">1. Overview</h2> <h3 id="1-1"> 📊 1.1 Data Stats</h3>  **Mol-Instructions** comprises three cardinal components: - 🔬 *Molecule-oriented instructions:* This component delves into the world of small molecules, emphasizing their inherent properties and behaviors. It sheds light on the fundamental challenges of diverse chemical reactions and molecular design, with 148,4K instructions across six tasks. - 🧬 *Protein-oriented instructions:* Rooted in the biosciences, this component presents 505K instructions across five distinct categories of tasks. These tasks aim to predict the structure, function, and activity of proteins, and facilitate protein design based on textual directives. - 🥼 *Biomolecular text instructions:* Predominantly designed to cater to NLP tasks within the fields of bioinformatics and chemoinformatics, this part encapsulates six information extraction and Q\&A tasks represented through 53K instructions. <h3 id="1-2"> 🛠️ 1.2 Data Construction</h3>  - 🤖️ *Human-AI Collaboration Task Description Creation*: In real-world applications, task instructions must be able to accommodate the varied and dynamic nature of human needs and queries. We emulate this diversity by starting with a clear, human-crafted description for each task, which is then used as an input to GPT-3.5-turbo. - 📖 *Information Derivation from Existing Data*: Biomolecular data often requires specialist laboratory experiments and expert analysis, making authoritative and recognized biochemistry databases an ideal source of our data. With suitable processing, these resources enable us to extract the required instruction data. - 📜 *Template-based Conversion of Biological Data into Textual Format*: To facilitate the transformation of these structured annotations into a textual format, we design a diverse array of templates. Each resulting text-based annotation serves as a guideline for protein design. - ✅ *Quality Control*: To expedite the model's ability to generate precise biomolecules, we implement stringent quality control measures for our biomolecular data. <h3 id="1-3"> 🤗 1.3 Data Release</h3> We release the dataset on Hugging Face at [zjunlp/Mol-Instructions](https://huggingface.co/datasets/zjunlp/Mol-Instructions). <h2 id="2">2. Tasks</h2> <h3 id="2-1"> 🔬 2.1 Molecule-oriented</h3> <details> <summary><b>Molecule description generation</b></summary> - *Please give me some details about this molecule:* [C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][=Branch1][C][=O][O][C@H1][Branch2][Ring1][=Branch1][C][O][C][=Branch1][C][=O][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][O][P][=Branch1][C][=O][Branch1][C][O][O][C][C@@H1][Branch1][=Branch1][C][=Branch1][C][=O][O][N] ``` The molecule is a 3-sn-phosphatidyl-L-serine in which the phosphatidyl acyl groups at positions 1 and 2 are specified as stearoyl and arachidonoyl respectively. It is functionally related to an arachidonic acid and an octadecanoic acid. ``` </details> <details> <summary><b>Description-guided molecule design</b></summary> - *Create a molecule with the structure as the one described:* The molecule is a primary arylamine in which an amino functional group is substituted for one of the benzene hydrogens. It is a primary arylamine and a member of anilines. ``` [N][C][=C][C][=C][C][=C][Ring1][=Branch1] ``` </details> <details> <summary><b>Forward reaction prediction</b></summary> - *With the provided reactants and reagents, propose a potential product:* [O][=N+1][Branch1][C][O-1][C][=C][N][=C][Branch1][C][Cl][C][Branch1][C][I][=C][Ring1][Branch2].[Fe] ``` [N][C][=C][N][=C][Branch1][C][Cl][C][Branch1][C][I][=C][Ring1][Branch2] ``` </details> <details> <summary><b>Retrosynthesis</b></summary> - *Please suggest potential reactants used in the synthesis of the provided product:* [C][=C][C][C][N][C][=Branch1][C][=O][O][C][Branch1][C][C][Branch1][C][C][C] ``` [C][=C][C][C][N].[C][C][Branch1][C][C][Branch1][C][C][O][C][=Branch1][C][=O][O][C][=Branch1][C][=O][O][C][Branch1][C][C][Branch1][C][C][C] ``` </details> <details> <summary><b>Reagent prediction</b></summary> - *Please provide possible reagents based on the following chemical reaction:* [C][C][=C][C][=C][Branch1][C][N][C][=N][Ring1][#Branch1].[O][=C][Branch1][C][Cl][C][Cl]>>[C][C][=C][C][=C][Branch1][Branch2][N][C][=Branch1][C][=O][C][Cl][C][=N][Ring1][O] ``` [C][C][C][O][C][Ring1][Branch1].[C][C][N][Branch1][Ring1][C][C][C][C].[O] ``` </details> <details> <summary><b>Property prediction</b></summary> - *Please provide the HOMO energy value for this molecule:* [C][C][O][C][C][Branch1][C][C][C][Branch1][C][C][C] ``` -0.2482 ``` </details> <h3 id="2-2"> 🧬 2.2 Protein-oriented</h3> <details> <summary><b>Protein design</b></summary> - *Design a protein sequence that exhibits the desired activity and specificity.* 1. The presence of Mg(2+) is necessary for the protein to function in the desired environment. 2. The AMP, (6S)-NADPHX binding site should be located in a region of the protein that is accessible to the ligand. 3. The designed protein should have ATP binding, NADPHX epimerase activity, metal ion binding, ADP-dependent NAD(P)H-hydrate dehydratase activity to facilitate nicotinamide nucleotide metabolic process. 4. For general function, the protein need meet that Catalyzes the epimerization of the S- and R-forms of NAD(P)HX, a damaged form of NAD(P)H that is a result of enzymatic or heat-dependent hydration ``` MSNELVLSREQVRRVDQRAIEAYGVPGIVLMENAGRGAAEIIRAACPSAQRVLIACGPGNNGGDGFVIARHLANAGWMVELLLACPADRITGDAQGNHEIIRRMNLPCAVMADARDLEAANDRFATADVIVDALLGTGASGPPREPIASLIRAINEAHRRVSAQPAPSVFAVDIPSGLDCDTGEAANPTVRADHTITFVARKIGFRNPAARDLLGRVHVVDIGAPRAAIQDALTGKSG ``` </details> <details> <summary><b>Catalytic activity prediction</b></summary> - *Please evaluate the following protein sequence and provide an explanation of the enzyme's catalytic activity, including the chemical reaction it facilitates:* MDKVAVAGFLPEELCASLSLSPSFRGNQIFQWIGKGVDSFDAMTNLSAELRASLAEKAILRSTRVSDVLKADDGTVKLQIQTEDDLAVETVLLTDKAARKTACVSCQAGCAMGCAFCKTGTLGLARNLSAAEIVEQFLYLEKHAGALDNIVFMGMGEPLLNLDALRKAIAVLTDKRGRNLSSRRITVSTVGIVSGIYDLANNGPDVRLAVSLTTADETLRRELMPASLTNPLSDLRQAISYYIEKTGKRVTLEAVLLSGKNTSEKNADSLIAFAKGLDVHVNLIPWNPVEGLSFVTPDPEETAQFVSRLEKGGLNVTLRMHRGKSISGACGQLGKTNPYA ``` Based on the provided protein sequence, the enzyme appears to facilitate the chemical reaction: adenosine(37) in tRNA + 2 reduced [2Fe-2S]-[ferredoxin] + 2 S- adenosyl-L-methionine = 2-methyladenosine(37) in tRNA + 5'- deoxyadenosine + L-methionine + 2 oxidized [2Fe-2S]-[ferredoxin] + S- adenosyl-L-homocysteine. ``` </details> <details> <summary><b>Protein function prediction</b></summary> - *Analyze the following amino acid sequence, and determine the function of the resulting protein, its subcellular localization, and any biological processes it may be part of:* MNGTVNASAPSKMSEVAVERLSNDKALKVIFVLGGPGSGKGTQCAKIAKHFGFTHLSVGDLLRAEINSGSKNGTMIESMINEGKIVRSEVTIKLLQRAMHESGNDKFLIDGFPRNEENRAAFENLEKIEPEFVLFFDCPMEEMERRILNRNQGRDDDKMETIRKRFKVFIESTLPVIEFYNLKGKLYKIDACKPADEVFEDVKAIFSRFRAKEDSSQQTNICTAKRFELVMCLIKRLFREIKRMWSSFFCKAL ``` The protein characterized by the amino acid sequence demonstrates ATP binding, cytidylate kinase activity, uridylate kinase activity and is implicated in the 'de novo' pyrimidine nucleobase biosynthetic process, phosphorylation, pyrimidine nucleotide biosynthetic process. Its subcellular localization is primarily within the cytoplasm, nucleus. ``` </details> <details> <summary><b>Functional description generation</b></summary> - *Examine the given protein sequence and share a brief overview of its attributes:* MKIVLASNNQGKLAELKAMLAPLGVQLLRQAELGIPEAAEPFRTFVENALAKARHASALSGLPALADDAGLCVEAFGGLPGVDTAFYAVQFGYAKGDANNVKALLAQMAGITDRRAALVSTLVAVRSAEDPEPLIACGRVAGEVALEPMGSNGFGFDPVMFIPEFGQTFAQLPVEVKNANSHRGKATRQMMALMRERWIT ``` A concise description of the protein with the specified amino acid sequence includes: Pyrophosphatase that catalyzes the hydrolysis of nucleoside triphosphates to their monophosphate derivatives, with a high preference for the non-canonical purine nucleotides XTP (xanthosine triphosphate), dITP (deoxyinosine triphosphate) and ITP. Seems to function as a house-cleaning enzyme that removes non-canonical purine nucleotides from the nucleotide pool, thus preventing their incorporation into DNA/RNA and avoiding chromosomal lesions. ``` </details> <details> <summary><b>Domain/Motif prediction</b></summary> - *Given this protein sequence, can you identify any common protein motifs or domains that it may contain?* MANTKYIFITGGVVSSLGKGIAAASIGALLESRGLSVSLIKVDPYINVDPGTMSPFQHGEVFVTEDGTETDLDLGHYERFVRFKASKKNNFTAGKVYETVIRNERKGNYLGGTVQVIPHITNEIKKRIKKGGQNKDIAIVEVGGTVGDIESQPFVEALRQMALELPNSSWAFVHLTLVPFINASGELKTKPTQHSVKELRSLGISPDVLVCRSEQELPKDEKNKIALFCSVPAKSVISMHDVDTVYSIPILLNKQKVDDTILKKLNLKIKKPNLNDWKRVVKAKLLPEKEVNVSFVGKYTELKDSYKSINEALEHAGIQNKAKVNINFVEAEQITSQNVRKVLKKSDAILVPGGFGERGIEGMILACKYARENNVPYLGICLGMQIAIIEYARNVLKLKSANSTEFDSSTKFPVIGLITEWSDISGKKEKRTKNSDLGGTMRLGGQVCKLKKKSNSYKMYKKSEIIERHRHRYEVNPNYKDKMIEQGLDVVGTSIDGKLVEMIELPSHKWFLACQFHPEFTSNPRDGHPIFNSYIKSTITK ``` Our predictive analysis of the given protein sequence reveals possible domains or motifs. These include: Glutamine amidotransferase, CTP synthase N-terminal domains. ``` </details> <h3 id="2-3"> 🥼 2.3 Biomolecule text</h3> <details> <summary><b>Chemical entity recognition</b></summary> - *Find and list all the instances of the chemical entities in the following content:* "Both the control and caramiphen groups with double cannulas had significantly shorter latencies to seizure onset than the corresponding groups with single cannula." ``` caramiphen ``` </details> <details> <summary><b>Chemical-disease interaction extraction</b></summary> - *You are provided with a set of clinical trial summaries. Extract the chemical-disease relations from the summaries and present your findings in the format of (Subject, Object):* "Eating disorders and the associated behavioural problems and drug abuse are uncommon in pregnancy. When they do occur they are often unrecognized because of denial but when significant may pose a risk to both the mother and her fetus. This case illustrates a number of problems that may be encountered in women with eating disorders in pregnancy, including prolonged and recurrent metabolic disturbances and diuretic abuse. In particular it illustrates the derangements of thyroid function seen in pregnant women with eating disorders and reminds us that when a cause for thyrotoxicosis remains obscure, thyroxine abuse should be considered and explored." ``` (thyroxine, thyrotoxicosis) ``` </details> <details> <summary><b>Chemical-protein interaction extraction</b></summary> - *Your task is to extract the subject and object entities from the provided text and establish the corresponding relation triples. Please format your answers as (Subject, Relation, Object):* "Opioid receptors are the membrane proteins that mediate the pain-relieving effect of opioid drugs, such as morphine and fentanyl as well as endogenous opioid peptides enkephalins and endorphins. Using cDNAs for the mu and the kappa opioid receptors, we mapped the chromosomal locations of their genes in mouse. Multilocus cross analysis located the mu receptor gene Oprm on Chr 10 and the kappa receptor gene Oprk1 on Chr 1. Both genes are near centromere, with no markers more centromeric. These data indicate that the two opioid receptors are different gene products, ruling out the possibility that they may be differential splicing products from the same gene." ``` (morphine, regulator, Opioid receptors), (fentanyl, regulator, Opioid receptors) ``` </details> <details> <summary><b>Multiple-choice question</b></summary> - *Which of the following conditions does not show multifactorial inheritance? What of the following is the right choice?* (A) Pyloric stenosis (B) Schizophrenia (C) Spina bifida (neural tube defects) (D) Marfan syndrome ``` The final answer is (D). ``` </details> <details> <summary><b>True or False question</b></summary> - *Does feeding tube insertion and its timing improve survival?* ``` No, neither insertion of PEG tubes nor timing of insertion affect survival. ``` </details> <details> <summary><b>Open question</b></summary> - *What virus causes chronic disease?* ``` Hepatitis C virus (HCV). ``` </details> <h2 id="3">3. Demo</h2>  <h3 id="3-1"> 🤗 3.1 Model Weight Release</h3> We release the model weights on Hugging Face at: - 🔬 *Molecule-oriented instructions:* [zjunlp/llama-molinst-molecule-7b](https://huggingface.co/zjunlp/llama-molinst-molecule-7b) - 🧬 *Protein-oriented instructions:* [zjunlp/llama-molinst-protein-7b](https://huggingface.co/zjunlp/llama-molinst-protein-7b) - 🥼 *Biomolecular text instructions:* [zjunlp/llama-molinst-biotext-7b](https://huggingface.co/zjunlp/llama-molinst-biotext-7b) <h3 id="3-2"> 📝 3.2 Model Usage Guide</h3> For this part, please refer to our [repository](https://github.com/zjunlp/Mol-Instructions). We have provided a web version demo based on [Gradio](https://gradio.app). To use it, you first need to download this repository: ```shell >> git clone https://github.com/zjunlp/Mol-Instruction >> cd demo ``` Step 1, install Gradio by running：`pip install gradio`. Step 2, specify the parameters in the [generate.sh](https://github.com/zjunlp/Mol-Instructions/blob/main/demo/generate.sh) file. ```shell >> CUDA_VISIBLE_DEVICES=0 python generate.py \ --CLI False\ --protein False\ --load_8bit \ --base_model $BASE_MODEL_PATH \ --share_gradio True\ --lora_weights $FINETUNED_MODEL_PATH \ ``` For models fine-tuned on *molecule-oriented* and *biomolecular text* instructions, please set `$FINETUNED_MODEL_PATH` to `'zjunlp/llama-molinst-molecule-7b'` or `'zjunlp/llama-molinst-biotext-7b'`. For the model fine-tuned on *protein-oriented* instructions, you need to perform additional steps as described in [this folder](https://github.com/zjunlp/Mol-Instructions/tree/main/demo). Step 3, run the [generate.sh](https://github.com/zjunlp/Mol-Instructions/blob/main/demo/generate.sh) file in the repository： ```shell >> sh generate.sh ``` We offer two methods: the first one is command-line interaction, and the second one is web-based interaction, which provides greater flexibility. 1. Use the following command to enter **web-based interaction**: ```shell >> python generate.py ``` The program will run a web server and output an address. Open the output address in a browser to use it. 2. Use the following command to enter **command-line interaction**: ```shell >> python generate.py --CLI True ``` The disadvantage is the inability to dynamically change decoding parameters. <h3 id="3-3"> 💡 3.3 FAQ</h3> - *Question:* What action should be taken if the model encounters `<unk>` and subsequently repeats the input during decoding? *Answer:* Consider reducing the value of the `max tokens`. - *Question:* What should I do if the model encounters � during decoding? *Answer:* If this symbol emerges in the middle of the decoded sentence, we recommend changing the input. If it shows up at the end of the sentence, you can tackle this issue by extending the output length. - *Question:* Why do I receive varied results despite using identical decoding parameters? *Answer:* This might occur if you have enabled `do_sample=True`. Another factor could be the order in which tasks are executed. A useful approach would be to use a for loop to generate multiple outputs with the same decoding parameters, enabling you to note the variance in each output. - *Question:* What could be the reason for subpar answer quality? *Answer:* Modifying the decoding parameters could help in improving the quality of the extraction or the answer. <h2 id="4">4. Notices</h2> <h3 id="4-1"> 🚨 4.1. Usage and License</h3> Please note that all data and model weights of **Mol-Instructions** is exclusively licensed for research purposes. The accompanying dataset is licensed under CC BY 4.0, which permits solely non-commercial usage. We emphatically urge all users to adhere to the highest ethical standards when using our dataset, including maintaining fairness, transparency, and responsibility in their research. Any usage of the dataset that may lead to harm or pose a detriment to society is strictly **forbidden**. In terms of dataset maintenance, we pledge our commitment to provide necessary upkeep. This will ensure the continued relevance and usability of the dataset in light of evolving research landscapes. This commitment encompasses regular updates, error checks, and amendments in accordance with field advancements and user feedback. <h3 id="4-2"> ❗️ 4.2. Limitations</h3> The current state of the model, obtained via instruction tuning, is a preliminary demonstration. Its capacity to handle real-world, production-grade tasks remains limited. Moreover, there is a vast reservoir of rich instruction data that remains to be collected and exploited. <h2 id="5">5. About</h2> <h3 id="5-1"> 📚 5.1 References</h3> If you use our repository, please cite the following related paper: ``` @article{molinst, title={Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models}, author={Fang, Yin and Liang, Xiaozhuan and Zhang, Ningyu and Liu, Kangwei and Huang, Rui and Chen, Zhuo and Fan, Xiaohui and Chen, Huajun}, journal={arXiv preprint arXiv:2306.08018}, year={2023} } ``` <h3 id="5-2"> 🫱🏻‍🫲 5.2 Acknowledgements</h3> We appreciate [LLaMA](https://github.com/facebookresearch/llama), [Huggingface Transformers Llama](https://github.com/huggingface/transformers/tree/main/src/transformers/models/llama), [Alpaca](https://crfm.stanford.edu/2023/03/13/alpaca.html), [Alpaca-LoRA](https://github.com/tloen/alpaca-lora), [Chatbot Service](https://github.com/deep-diver/LLM-As-Chatbot) and many other related works for their open-source contributions.

Hello-SimpleAI/HC3

Hello-SimpleAI

--- task_categories: - text-classification - question-answering - sentence-similarity - zero-shot-classification language: - en - zh tags: - ChatGPT - SimpleAI - Detection - OOD size_categories: - 10K<n<100K license: cc-by-sa-4.0 --- # Human ChatGPT Comparison Corpus (HC3) We propose the first human-ChatGPT comparison corpus, named **HC3** dataset. This dataset is introduced in our paper: - Paper: [***How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection***](https://arxiv.org/abs/2301.07597) Code, models and analysis are available on our GitHub: - GitHub: [**Chatgpt-Comparison-Detection project** 🔬](https://github.com/Hello-SimpleAI/chatgpt-comparison-detection) # Dataset Copyright If the source datasets used in this corpus has a specific license which is stricter than CC-BY-SA, our products follow the same. If not, they follow CC-BY-SA license. See [dataset copyright](https://github.com/Hello-SimpleAI/chatgpt-comparison-detection#dataset-copyright). # Citation Checkout this papaer [arxiv: 2301.07597](https://arxiv.org/abs/2301.07597) ``` @article{guo-etal-2023-hc3, title = "How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection", author = "Guo, Biyang and Zhang, Xin and Wang, Ziyuan and Jiang, Minqi and Nie, Jinran and Ding, Yuxuan and Yue, Jianwei and Wu, Yupeng", journal={arXiv preprint arxiv:2301.07597} year = "2023", } ```

mkshing/xlsum_ja

mkshing

--- license: cc-by-nc-sa-4.0 task_categories: - summarization - text-classification language: - ja --- This is the filtered Japanese subset of [XL-Sum](https://huggingface.co/datasets/csebuetnlp/xlsum) followed by [PaLM 2](https://arxiv.org/abs/2305.10403) **filters** - 15-gram overlap \* code: https://gist.github.com/mkshing/d6371cbfdd50d4f352cee247fd4dd86a **number of examples** - train: 4215 (before: 7113) - validation: 758 (before: 889) - test: 766 (before: 889)

Amod/mental_health_counseling_conversations

Amod

--- annotations_creators: - no-annotation language_creators: - found language: - en license: openrail multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - conversational - text-generation - question-answering task_ids: - sentiment-classification - language-modeling - open-domain-qa --- # Amod/mental_health_counseling_conversations ## 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-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** Bertagnolli, Nicolas (2020). Counsel chat: Bootstrapping high-quality therapy data. Towards Data Science. https://towardsdatascience.com/counsel-chat - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset is a collection of questions and answers sourced from two online counseling and therapy platforms. The questions cover a wide range of mental health topics, and the answers are provided by qualified psychologists. The dataset is intended to be used for fine-tuning language models to improve their ability to provide mental health advice. ### Supported Tasks and Leaderboards The dataset supports the task of text generation, particularly for generating advice or suggestions in response to a mental health-related question. ### Languages The text in the dataset is in English. ## Dataset Structure ### Data Instances A data instance includes a 'Context' and a 'Response'. 'Context' contains the question asked by a user, and 'Response' contains the corresponding answer provided by a psychologist. ### Data Fields - 'Context': a string containing the question asked by a user - 'Response': a string containing the corresponding answer provided by a psychologist ### Data Splits The dataset has no predefined splits. Users can create their own splits as needed. ## Dataset Creation ### Curation Rationale This dataset was created to aid in the development of AI models that can provide mental health advice or guidance. The raw data was meticulously cleaned to only include the conversations. ### Source Data The data was sourced from two online counseling and therapy platforms. The raw data can be found [here](https://github.com/nbertagnolli/counsel-chat/tree/master/data). ### Annotations The dataset does not contain any additional annotations. ### Personal and Sensitive Information The dataset may contain sensitive information related to mental health. All data was anonymized and no personally identifiable information is included.

approximatelabs/tablib-v1-full

approximatelabs

--- license: other pretty_name: TabLib size_categories: - 100M<n<1B extra_gated_prompt: >- Accessing the full TabLib dataset requires permission from Approximate Labs. To request access, please provide the following details and Approximate Labs will process your request as soon as possible. extra_gated_fields: Full Name: text Email: text Country: text Organization: text What do you intend to do with this dataset?: text I agree to abide by the license requirements of the data contained in TabLib: checkbox --- [](https://discord.gg/kW9nBQErGe) <img src="https://approximatelabs.com/tablib.png" width="800" /> # TabLib A minimally-preprocessed dataset of 627M tables (69 TiB) extracted from HTML, PDF, CSV, TSV, Excel, and SQLite files from GitHub and Common Crawl. This includes 867B tokens of "context metadata": each table includes provenance information and table context such as filename, text before/after, HTML metadata, etc. A smaller 0.1% sample of this dataset can be found [here](https://huggingface.co/datasets/approximatelabs/tablib-v1-sample). For more information, read the [paper](https://arxiv.org/abs/2310.07875) & [announcement blog](https://approximatelabs.com/blog/tablib). # Dataset Details ## Sources * **GitHub**: nearly all public GitHub repositories * **Common Crawl**: the `CC-MAIN-2023-23` crawl ## Reading Tables Tables are stored as serialized Arrow bytes in the `arrow_bytes` column. To read these, you will need to deserialize the bytes: ```python import datasets import pyarrow as pa # load a single file of the dataset ds = datasets.load_dataset( 'approximatelabs/tablib-v1-full', data_files='tablib/job=github_000005/batch=000001/part=000001/manifest.parquet', token='...', ) df = ds['train'].to_pandas() tables = [pa.RecordBatchStreamReader(b).read_all() for b in df['arrow_bytes']] ``` ## Licensing This dataset is intended for research use only. For specific licensing information, refer to the license of the specific datum being used. # Contact If you have any questions, comments, or concerns about licensing, pii, etc. please contact using [this form](https://forms.gle/C74VTWP7L78QDVR67). # Approximate Labs TabLib is a project from Approximate Labs. Find us on [Twitter](https://twitter.com/approximatelabs), [Github](https://github.com/approximatelabs), [Linkedin](https://www.linkedin.com/company/approximate-labs), and [Discord](https://discord.gg/kW9nBQErGe). # Citations If you use TabLib for any of your research, please cite the TabLib paper: ``` @misc{eggert2023tablib, title={TabLib: A Dataset of 627M Tables with Context}, author={Gus Eggert and Kevin Huo and Mike Biven and Justin Waugh}, year={2023}, eprint={2310.07875}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

THUDM/humaneval-x

THUDM

--- annotations_creators: [] language_creators: - crowdsourced - expert-generated language: - code license: - apache-2.0 multilinguality: - multilingual size_categories: - unknown source_datasets: [] task_categories: - text-generation task_ids: - language-modeling pretty_name: HumanEval-X --- # HumanEval-X ## Dataset Description [HumanEval-X](https://github.com/THUDM/CodeGeeX) is a benchmark for evaluating the multilingual ability of code generative models. It consists of 820 high-quality human-crafted data samples (each with test cases) in Python, C++, Java, JavaScript, and Go, and can be used for various tasks, such as code generation and translation. ## Languages The dataset contains coding problems in 5 programming languages: Python, C++, Java, JavaScript, and Go. ## Dataset Structure To load the dataset you need to specify a subset among the 5 exiting languages `[python, cpp, go, java, js]`. By default `python` is loaded. ```python from datasets import load_dataset load_dataset("THUDM/humaneval-x", "js") DatasetDict({ test: Dataset({ features: ['task_id', 'prompt', 'declaration', 'canonical_solution', 'test', 'example_test'], num_rows: 164 }) }) ``` ```python next(iter(data["test"])) {'task_id': 'JavaScript/0', 'prompt': '/* Check if in given list of numbers, are any two numbers closer to each other than\n given threshold.\n >>> hasCloseElements([1.0, 2.0, 3.0], 0.5)\n false\n >>> hasCloseElements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n true\n */\nconst hasCloseElements = (numbers, threshold) => {\n', 'declaration': '\nconst hasCloseElements = (numbers, threshold) => {\n', 'canonical_solution': ' for (let i = 0; i < numbers.length; i++) {\n for (let j = 0; j < numbers.length; j++) {\n if (i != j) {\n let distance = Math.abs(numbers[i] - numbers[j]);\n if (distance < threshold) {\n return true;\n }\n }\n }\n }\n return false;\n}\n\n', 'test': 'const testHasCloseElements = () => {\n console.assert(hasCloseElements([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.3) === true)\n console.assert(\n hasCloseElements([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.05) === false\n )\n console.assert(hasCloseElements([1.0, 2.0, 5.9, 4.0, 5.0], 0.95) === true)\n console.assert(hasCloseElements([1.0, 2.0, 5.9, 4.0, 5.0], 0.8) === false)\n console.assert(hasCloseElements([1.0, 2.0, 3.0, 4.0, 5.0, 2.0], 0.1) === true)\n console.assert(hasCloseElements([1.1, 2.2, 3.1, 4.1, 5.1], 1.0) === true)\n console.assert(hasCloseElements([1.1, 2.2, 3.1, 4.1, 5.1], 0.5) === false)\n}\n\ntestHasCloseElements()\n', 'example_test': 'const testHasCloseElements = () => {\n console.assert(hasCloseElements([1.0, 2.0, 3.0], 0.5) === false)\n console.assert(\n hasCloseElements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3) === true\n )\n}\ntestHasCloseElements()\n'} ``` ## Data Fields * ``task_id``: indicates the target language and ID of the problem. Language is one of ["Python", "Java", "JavaScript", "CPP", "Go"]. * ``prompt``: the function declaration and docstring, used for code generation. * ``declaration``: only the function declaration, used for code translation. * ``canonical_solution``: human-crafted example solutions. * ``test``: hidden test samples, used for evaluation. * ``example_test``: public test samples (appeared in prompt), used for evaluation. ## Data Splits Each subset has one split: test. ## Citation Information Refer to https://github.com/THUDM/CodeGeeX.

wiki_bio

--- annotations_creators: - found language_creators: - found language: - en license: - cc-by-sa-3.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - table-to-text task_ids: [] paperswithcode_id: wikibio pretty_name: WikiBio dataset_info: features: - name: input_text struct: - name: table sequence: - name: column_header dtype: string - name: row_number dtype: int16 - name: content dtype: string - name: context dtype: string - name: target_text dtype: string splits: - name: train num_bytes: 619269257 num_examples: 582659 - name: test num_bytes: 77264695 num_examples: 72831 - name: val num_bytes: 77335069 num_examples: 72831 download_size: 333998704 dataset_size: 773869021 --- # 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 - **Repository:** https://github.com/DavidGrangier/wikipedia-biography-dataset - **Paper:** https://arxiv.org/pdf/1603.07771.pdf - **GitHub:** https://github.com/DavidGrangier/wikipedia-biography-dataset ### Dataset Summary This Dataset contains 728321 biographies extracted from Wikipedia containing the first paragraph of the biography and the tabular infobox. ### Supported Tasks and Leaderboards The main purpose of this dataset is developing text generation models. ### Languages English. ## Dataset Structure ### Data Instances More Information Needed ### Data Fields The structure of a single sample is the following: ```json { "input_text":{ "context":"pope michael iii of alexandria\n", "table":{ "column_header":[ "type", "ended", "death_date", "title", "enthroned", "name", "buried", "religion", "predecessor", "nationality", "article_title", "feast_day", "birth_place", "residence", "successor" ], "content":[ "pope", "16 march 907", "16 march 907", "56th of st. mark pope of alexandria & patriarch of the see", "25 april 880", "michael iii of alexandria", "monastery of saint macarius the great", "coptic orthodox christian", "shenouda i", "egyptian", "pope michael iii of alexandria\n", "16 -rrb- march -lrb- 20 baramhat in the coptic calendar", "egypt", "saint mark 's church", "gabriel i" ], "row_number":[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] } }, "target_text":"pope michael iii of alexandria -lrb- also known as khail iii -rrb- was the coptic pope of alexandria and patriarch of the see of st. mark -lrb- 880 -- 907 -rrb- .\nin 882 , the governor of egypt , ahmad ibn tulun , forced khail to pay heavy contributions , forcing him to sell a church and some attached properties to the local jewish community .\nthis building was at one time believed to have later become the site of the cairo geniza .\n" } ``` where, in the `"table"` field, all the information of the Wikpedia infobox is stored (the header of the infobox is stored in `"column_header"` and the information in the `"content"` field). ### Data Splits - Train: 582659 samples. - Test: 72831 samples. - Validation: 72831 samples. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data This dataset was announced in the paper <em>Neural Text Generation from Structured Data with Application to the Biography Domain</em> [(arxiv link)](https://arxiv.org/pdf/1603.07771.pdf) and is stored in [this](https://github.com/DavidGrangier/wikipedia-biography-dataset) repo (owned by DavidGrangier). #### 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 dataset is ditributed under Creative Comons CC BY-SA 3.0 License. ### Citation Information For refering the original paper in BibTex format: ``` @article{DBLP:journals/corr/LebretGA16, author = {R{\'{e}}mi Lebret and David Grangier and Michael Auli}, title = {Generating Text from Structured Data with Application to the Biography Domain}, journal = {CoRR}, volume = {abs/1603.07771}, year = {2016}, url = {http://arxiv.org/abs/1603.07771}, archivePrefix = {arXiv}, eprint = {1603.07771}, timestamp = {Mon, 13 Aug 2018 16:48:30 +0200}, biburl = {https://dblp.org/rec/journals/corr/LebretGA16.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` ### Contributions Thanks to [@alejandrocros](https://github.com/alejandrocros) for adding this dataset.

copenlu/scientific-exaggeration-detection

copenlu

--- annotations_creators: - expert-generated language: - en language_creators: - found license: - gpl-3.0 multilinguality: - monolingual paperswithcode_id: semi-supervised-exaggeration-detection-of pretty_name: Scientific Exaggeration Detection size_categories: - n<1K source_datasets: [] tags: - scientific text - scholarly text - inference - fact checking - misinformation task_categories: - text-classification task_ids: - natural-language-inference - multi-input-text-classification --- # Dataset Card for Scientific Exaggeration Detection ## Dataset Description - **Homepage:** https://github.com/copenlu/scientific-exaggeration-detection - **Repository:** https://github.com/copenlu/scientific-exaggeration-detection - **Paper:** https://aclanthology.org/2021.emnlp-main.845.pdf ### Dataset Summary Public trust in science depends on honest and factual communication of scientific papers. However, recent studies have demonstrated a tendency of news media to misrepresent scientific papers by exaggerating their findings. Given this, we present a formalization of and study into the problem of exaggeration detection in science communication. While there are an abundance of scientific papers and popular media articles written about them, very rarely do the articles include a direct link to the original paper, making data collection challenging. We address this by curating a set of labeled press release/abstract pairs from existing expert annotated studies on exaggeration in press releases of scientific papers suitable for benchmarking the performance of machine learning models on the task. Using limited data from this and previous studies on exaggeration detection in science, we introduce MT-PET, a multi-task version of Pattern Exploiting Training (PET), which leverages knowledge from complementary cloze-style QA tasks to improve few-shot learning. We demonstrate that MT-PET outperforms PET and supervised learning both when data is limited, as well as when there is an abundance of data for the main task. ## Dataset Structure The training and test data are derived from the InSciOut studies from [Sumner et al. 2014](https://www.bmj.com/content/349/bmj.g7015) and [Bratton et al. 2019](https://pubmed.ncbi.nlm.nih.gov/31728413/#:~:text=Results%3A%20We%20found%20that%20the,inference%20from%20non%2Dhuman%20studies.). The splits have the following fields: ``` original_file_id: The ID of the original spreadsheet in the Sumner/Bratton data where the annotations are derived from press_release_conclusion: The conclusion sentence from the press release press_release_strength: The strength label for the press release abstract_conclusion: The conclusion sentence from the abstract abstract_strength: The strength label for the abstract exaggeration_label: The final exaggeration label ``` The exaggeration label is one of `same`, `exaggerates`, or `downplays`. The strength label is one of the following: ``` 0: Statement of no relationship 1: Statement of correlation 2: Conditional statement of causation 3: Statement of causation ``` ## Dataset Creation See section 4 of the [paper](https://aclanthology.org/2021.emnlp-main.845.pdf) for details on how the dataset was curated. The original InSciOut data can be found [here](https://figshare.com/articles/dataset/InSciOut/903704) ## Citation ``` @inproceedings{wright2021exaggeration, title={{Semi-Supervised Exaggeration Detection of Health Science Press Releases}}, author={Dustin Wright and Isabelle Augenstein}, booktitle = {Proceedings of EMNLP}, publisher = {Association for Computational Linguistics}, year = 2021 } ``` Thanks to [@dwright37](https://github.com/dwright37) for adding this dataset.

commaai/commavq

commaai

--- license: mit size_categories: - 100K<n<1M --- # commaVQ commaVQ is a dataset of 100,000 heavily compressed driving videos for Machine Learning research. A heavily compressed driving video like this is useful to experiment with GPT-like video prediction models. This repo includes an encoder/decoder and an example of a video prediction model. Examples and trained models can be found here: https://github.com/commaai/commavq # Overview A VQ-VAE [1,2] was used to heavily compress each frame into 128 "tokens" of 10 bits each. Each entry of the dataset is a "segment" of compressed driving video, i.e. 1min of frames at 20 FPS. Each file is of shape 1200x8x16 and saved as int16. Note that the compressor is extremely lossy on purpose. It makes the dataset smaller and easy to play with (train GPT with large context size, fast autoregressive generation, etc.). We might extend the dataset to a less lossy version when we see fit. <video title="source" controls> <source src="https://github.com/commaai/commavq/assets/29985433/91894bf7-592b-4204-b3f2-3e805984045c" type="video/mp4"> </video> <video title="compressed" controls> <source src="https://github.com/commaai/commavq/assets/29985433/3a799ac8-781e-461c-bf14-c15cea42b985" type="video/mp4"> </video> <video title="imagined" controls> <source src="https://github.com/commaai/commavq/assets/29985433/f6f7699b-b6cb-4f9c-80c9-8e00d75fbfae" type="video/mp4"> </video> # References [1] Van Den Oord, Aaron, and Oriol Vinyals. "Neural discrete representation learning." Advances in neural information processing systems 30 (2017). [2] Esser, Patrick, Robin Rombach, and Bjorn Ommer. "Taming transformers for high-resolution image synthesis." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021.

sms_spam

--- annotations_creators: - crowdsourced - found language_creators: - crowdsourced - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - extended|other-nus-sms-corpus task_categories: - text-classification task_ids: - intent-classification paperswithcode_id: sms-spam-collection-data-set pretty_name: SMS Spam Collection Data Set dataset_info: features: - name: sms dtype: string - name: label dtype: class_label: names: '0': ham '1': spam config_name: plain_text splits: - name: train num_bytes: 521756 num_examples: 5574 download_size: 203415 dataset_size: 521756 train-eval-index: - config: plain_text task: text-classification task_id: binary_classification splits: train_split: train col_mapping: sms: 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 --- # 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://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection - **Repository:** - **Paper:** Almeida, T.A., Gomez Hidalgo, J.M., Yamakami, A. Contributions to the study of SMS Spam Filtering: New Collection and Results. Proceedings of the 2011 ACM Symposium on Document Engineering (ACM DOCENG'11), Mountain View, CA, USA, 2011. - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The SMS Spam Collection v.1 is a public set of SMS labeled messages that have been collected for mobile phone spam research. It has one collection composed by 5,574 English, real and non-enconded messages, tagged according being legitimate (ham) or spam. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - sms: the sms message - label: indicating if the sms message is ham or spam, ham means it is not spam ### 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{Almeida2011SpamFiltering, title={Contributions to the Study of SMS Spam Filtering: New Collection and Results}, author={Tiago A. Almeida and Jose Maria Gomez Hidalgo and Akebo Yamakami}, year={2011}, booktitle = "Proceedings of the 2011 ACM Symposium on Document Engineering (DOCENG'11)", } ### Contributions Thanks to [@czabo](https://github.com/czabo) for adding this dataset.

crows_pairs

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - text-scoring paperswithcode_id: crows-pairs pretty_name: CrowS-Pairs tags: - bias-evaluation dataset_info: features: - name: id dtype: int32 - name: sent_more dtype: string - name: sent_less dtype: string - name: stereo_antistereo dtype: class_label: names: '0': stereo '1': antistereo - name: bias_type dtype: class_label: names: '0': race-color '1': socioeconomic '2': gender '3': disability '4': nationality '5': sexual-orientation '6': physical-appearance '7': religion '8': age - name: annotations sequence: sequence: class_label: names: '0': race-color '1': socioeconomic '2': gender '3': disability '4': nationality '5': sexual-orientation '6': physical-appearance '7': religion '8': age - name: anon_writer dtype: string - name: anon_annotators sequence: string config_name: crows_pairs splits: - name: test num_bytes: 419976 num_examples: 1508 download_size: 437764 dataset_size: 419976 --- # Dataset Card for CrowS-Pairs ## 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:** [Add homepage URL here if available (unless it's a GitHub repository)]() - **Repository:** https://github.com/nyu-mll/crows-pairs - **Paper:** https://aclanthology.org/2020.emnlp-main.154 - **Leaderboard:** [If the dataset supports an active leaderboard, add link here]() - **Point of Contact:** [If known, name and email of at least one person the reader can contact for questions about the dataset.]() ### 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 CrowS-Pairs is licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-sa/4.0/). It is created using prompts taken from the [ROCStories corpora](https://cs.rochester.edu/nlp/rocstories/) and the fiction part of [MNLI](https://cims.nyu.edu/~sbowman/multinli/). Please refer to their papers for more details. ### Citation Information ``` @inproceedings{nangia-etal-2020-crows, title = "{C}row{S}-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models", author = "Nangia, Nikita and Vania, Clara and Bhalerao, Rasika and Bowman, Samuel R.", 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", url = "https://aclanthology.org/2020.emnlp-main.154", doi = "10.18653/v1/2020.emnlp-main.154", pages = "1953--1967", } ``` ### Contributions Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.

opus_gnome

--- 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 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 license: - unknown multilinguality: - multilingual size_categories: - 10K<n<100K - 1K<n<10K - n<1K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: null pretty_name: OpusGnome 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 config_names: - ar-bal - bg-csb - ca-en_GB - cs-eo - cs-tk - da-vi - de-ha - de-tt - el-sk - en_GB-my --- # 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.

BeIR/trec-covid

BeIR

--- annotations_creators: [] language_creators: [] language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual paperswithcode_id: beir pretty_name: BEIR Benchmark size_categories: msmarco: - 1M<n<10M trec-covid: - 100k<n<1M nfcorpus: - 1K<n<10K nq: - 1M<n<10M hotpotqa: - 1M<n<10M fiqa: - 10K<n<100K arguana: - 1K<n<10K touche-2020: - 100K<n<1M cqadupstack: - 100K<n<1M quora: - 100K<n<1M dbpedia: - 1M<n<10M scidocs: - 10K<n<100K fever: - 1M<n<10M climate-fever: - 1M<n<10M scifact: - 1K<n<10K source_datasets: [] task_categories: - text-retrieval - zero-shot-retrieval - information-retrieval - zero-shot-information-retrieval task_ids: - passage-retrieval - entity-linking-retrieval - fact-checking-retrieval - tweet-retrieval - citation-prediction-retrieval - duplication-question-retrieval - argument-retrieval - news-retrieval - biomedical-information-retrieval - question-answering-retrieval --- # Dataset Card for BEIR Benchmark ## 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/UKPLab/beir - **Repository:** https://github.com/UKPLab/beir - **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ - **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns - **Point of Contact:** nandan.thakur@uwaterloo.ca ### Dataset Summary BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks: - Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact) - Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/) - Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) - News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html) - Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data) - Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) - Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs) - Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html) - Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/) All these datasets have been preprocessed and can be used for your experiments. ```python ``` ### Supported Tasks and Leaderboards The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia. The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/). ### Languages All tasks are in English (`en`). ## Dataset Structure All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format: - `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}` - `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}` - `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1` ### Data Instances A high level example of any beir dataset: ```python corpus = { "doc1" : { "title": "Albert Einstein", "text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \ one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \ its influence on the philosophy of science. He is best known to the general public for his mass–energy \ equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \ Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \ of the photoelectric effect', a pivotal step in the development of quantum theory." }, "doc2" : { "title": "", # Keep title an empty string if not present "text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \ malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\ with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)." }, } queries = { "q1" : "Who developed the mass-energy equivalence formula?", "q2" : "Which beer is brewed with a large proportion of wheat?" } qrels = { "q1" : {"doc1": 1}, "q2" : {"doc2": 1}, } ``` ### Data Fields Examples from all configurations have the following features: ### Corpus - `corpus`: a `dict` feature representing the document title and passage text, made up of: - `_id`: a `string` feature representing the unique document id - `title`: a `string` feature, denoting the title of the document. - `text`: a `string` feature, denoting the text of the document. ### Queries - `queries`: a `dict` feature representing the query, made up of: - `_id`: a `string` feature representing the unique query id - `text`: a `string` feature, denoting the text of the query. ### Qrels - `qrels`: a `dict` feature representing the query document relevance judgements, made up of: - `_id`: a `string` feature representing the query id - `_id`: a `string` feature, denoting the document id. - `score`: a `int32` feature, denoting the relevance judgement between query and document. ### Data Splits | Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 | | -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:| | MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` | | TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` | | NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` | | BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) | | NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` | | HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` | | FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` | | Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) | | TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) | | ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` | | Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` | | CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` | | Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` | | DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` | | SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` | | FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` | | Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` | | SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` | | Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) | ## 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 Cite as: ``` @inproceedings{ thakur2021beir, title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models}, author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych}, booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)}, year={2021}, url={https://openreview.net/forum?id=wCu6T5xFjeJ} } ``` ### Contributions Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.

opus_rf

--- 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: [] paperswithcode_id: null pretty_name: OpusRf 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 config_names: - de-en - de-es - de-fr - de-sv - en-es - en-fr - en-sv - es-fr - es-sv - fr-sv --- # 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.

pcuenq/lsun-bedrooms

pcuenq

--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 4450242498.020249 num_examples: 287968 - name: test num_bytes: 234247797.33875093 num_examples: 15157 download_size: 4756942293 dataset_size: 4684490295.359 license: mit --- # Dataset Card for "lsun-bedrooms" This is a 20% sample of the bedrooms category in [`LSUN`](https://github.com/fyu/lsun), uploaded as a dataset for convenience. The license for _this compilation only_ is MIT. The data retains the same license as the original dataset. This is (roughly) the code that was used to upload this dataset: ```Python import os import shutil from miniai.imports import * from miniai.diffusion import * from datasets import load_dataset path_data = Path('data') path_data.mkdir(exist_ok=True) path = path_data/'bedroom' url = 'https://s3.amazonaws.com/fast-ai-imageclas/bedroom.tgz' if not path.exists(): path_zip = fc.urlsave(url, path_data) shutil.unpack_archive('data/bedroom.tgz', 'data') dataset = load_dataset("imagefolder", data_dir="data/bedroom") dataset = dataset.remove_columns('label') dataset = dataset['train'].train_test_split(test_size=0.05) dataset.push_to_hub("pcuenq/lsun-bedrooms") ```

BeIR/scifact

BeIR

--- annotations_creators: [] language_creators: [] language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual paperswithcode_id: beir pretty_name: BEIR Benchmark size_categories: msmarco: - 1M<n<10M trec-covid: - 100k<n<1M nfcorpus: - 1K<n<10K nq: - 1M<n<10M hotpotqa: - 1M<n<10M fiqa: - 10K<n<100K arguana: - 1K<n<10K touche-2020: - 100K<n<1M cqadupstack: - 100K<n<1M quora: - 100K<n<1M dbpedia: - 1M<n<10M scidocs: - 10K<n<100K fever: - 1M<n<10M climate-fever: - 1M<n<10M scifact: - 1K<n<10K source_datasets: [] task_categories: - text-retrieval - zero-shot-retrieval - information-retrieval - zero-shot-information-retrieval task_ids: - passage-retrieval - entity-linking-retrieval - fact-checking-retrieval - tweet-retrieval - citation-prediction-retrieval - duplication-question-retrieval - argument-retrieval - news-retrieval - biomedical-information-retrieval - question-answering-retrieval --- # Dataset Card for BEIR Benchmark ## 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/UKPLab/beir - **Repository:** https://github.com/UKPLab/beir - **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ - **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns - **Point of Contact:** nandan.thakur@uwaterloo.ca ### Dataset Summary BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks: - Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact) - Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/) - Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) - News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html) - Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data) - Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) - Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs) - Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html) - Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/) All these datasets have been preprocessed and can be used for your experiments. ```python ``` ### Supported Tasks and Leaderboards The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia. The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/). ### Languages All tasks are in English (`en`). ## Dataset Structure All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format: - `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}` - `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}` - `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1` ### Data Instances A high level example of any beir dataset: ```python corpus = { "doc1" : { "title": "Albert Einstein", "text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \ one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \ its influence on the philosophy of science. He is best known to the general public for his mass–energy \ equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \ Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \ of the photoelectric effect', a pivotal step in the development of quantum theory." }, "doc2" : { "title": "", # Keep title an empty string if not present "text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \ malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\ with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)." }, } queries = { "q1" : "Who developed the mass-energy equivalence formula?", "q2" : "Which beer is brewed with a large proportion of wheat?" } qrels = { "q1" : {"doc1": 1}, "q2" : {"doc2": 1}, } ``` ### Data Fields Examples from all configurations have the following features: ### Corpus - `corpus`: a `dict` feature representing the document title and passage text, made up of: - `_id`: a `string` feature representing the unique document id - `title`: a `string` feature, denoting the title of the document. - `text`: a `string` feature, denoting the text of the document. ### Queries - `queries`: a `dict` feature representing the query, made up of: - `_id`: a `string` feature representing the unique query id - `text`: a `string` feature, denoting the text of the query. ### Qrels - `qrels`: a `dict` feature representing the query document relevance judgements, made up of: - `_id`: a `string` feature representing the query id - `_id`: a `string` feature, denoting the document id. - `score`: a `int32` feature, denoting the relevance judgement between query and document. ### Data Splits | Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 | | -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:| | MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` | | TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` | | NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` | | BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) | | NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` | | HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` | | FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` | | Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) | | TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) | | ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` | | Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` | | CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` | | Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` | | DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` | | SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` | | FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` | | Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` | | SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` | | Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) | ## 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 Cite as: ``` @inproceedings{ thakur2021beir, title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models}, author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych}, booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)}, year={2021}, url={https://openreview.net/forum?id=wCu6T5xFjeJ} } ``` ### Contributions Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.

hippocrates/clinical_train

hippocrates

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* dataset_info: features: - name: id dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string - name: text dtype: string splits: - name: train num_bytes: 52143984 num_examples: 10000 - name: valid num_bytes: 46961044 num_examples: 8164 download_size: 42209790 dataset_size: 99105028 --- # Dataset Card for "clinical_train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

mteb/medrxiv-clustering-s2s

mteb

--- language: - en ---

opus_ubuntu

--- 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 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 license: - bsd-3-clause multilinguality: - multilingual size_categories: - 10K<n<100K - 1K<n<10K - n<1K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: null pretty_name: Opus Ubuntu 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 config_names: - as-bs - az-cs - bg-de - bn-ga - br-es_PR - br-hi - br-la - br-uz - br-yi - bs-szl --- # 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.

e2e_nlg

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text2text-generation task_ids: [] paperswithcode_id: e2e pretty_name: End-to-End NLG Challenge tags: - meaning-representation-to-text dataset_info: features: - name: meaning_representation dtype: string - name: human_reference dtype: string splits: - name: train num_bytes: 9435824 num_examples: 42061 - name: validation num_bytes: 1171723 num_examples: 4672 - name: test num_bytes: 1320205 num_examples: 4693 download_size: 11812316 dataset_size: 11927752 --- # Dataset Card for End-to-End NLG Challenge ## 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](http://www.macs.hw.ac.uk/InteractionLab/E2E/) - **Repository:** [repository](https://github.com/tuetschek/e2e-dataset/) - **Paper:** [paper](https://arxiv.org/abs/1706.09254) - **Leaderboard:** [leaderboard](http://www.macs.hw.ac.uk/InteractionLab/E2E/) ### Dataset Summary The E2E dataset is used for training end-to-end, data-driven natural language generation systems in the restaurant domain, which is ten times bigger than existing, frequently used datasets in this area. The E2E dataset poses new challenges: (1) its human reference texts show more lexical richness and syntactic variation, including discourse phenomena; (2) generating from this set requires content selection. As such, learning from this dataset promises more natural, varied and less template-like system utterances. E2E is released in the following paper where you can find more details and baseline results: https://arxiv.org/abs/1706.09254 ### Supported Tasks and Leaderboards - `text2text-generation-other-meaning-representation-to-text`: The dataset can be used to train a model to generate descriptions in the restaurant domain from meaning representations, which consists in taking as input some data about a restaurant and generate a sentence in natural language that presents the different aspects of the data about the restaurant.. Success on this task is typically measured by achieving a *high* [BLEU](https://huggingface.co/metrics/bleu), [NIST](https://huggingface.co/metrics/nist), [METEOR](https://huggingface.co/metrics/meteor), [Rouge-L](https://huggingface.co/metrics/rouge), [CIDEr](https://huggingface.co/metrics/cider). The TGen model (Dusek and Jurcıcek, 2016a) was used a baseline, had the following scores: | | BLEU | NIST | METEOR | ROUGE_L | CIDEr | | -------- | ------ | ------ | ------ | ------- | ------ | | BASELINE | 0.6593 | 8.6094 | 0.4483 | 0.6850 | 2.2338 | This task has an inactive leaderboard which can be found [here](http://www.macs.hw.ac.uk/InteractionLab/E2E/) and ranks models based on the metrics above. ### Languages The dataset is in english (en). ## Dataset Structure ### Data Instances Example of one instance: ``` {'human_reference': 'The Vaults pub near Café Adriatic has a 5 star rating. Prices start at £30.', 'meaning_representation': 'name[The Vaults], eatType[pub], priceRange[more than £30], customer rating[5 out of 5], near[Café Adriatic]'} ``` ### Data Fields - `human_reference`: string, the text is natural language that describes the different characteristics in the meaning representation - `meaning_representation`: list of slots and values to generate a description from Each MR consists of 3–8 attributes (slots), such as name, food or area, and their values. ### Data Splits The dataset is split into training, validation and testing sets (in a 76.5-8.5-15 ratio), keeping a similar distribution of MR and reference text lengths and ensuring that MRs in different sets are distinct. | | train | validation | test | | ----- |-------:|------------:|------:| | N. Instances | 42061 | 4672 | 4693 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization The data was collected using the CrowdFlower platform and quality-controlled following Novikova et al. (2016). #### Who are the source language producers? [More Information Needed] ### Annotations Following Novikova et al. (2016), the E2E data was collected using pictures as stimuli, which was shown to elicit significantly more natural, more informative, and better phrased human references than textual MRs. #### 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 ``` @article{dusek.etal2020:csl, title = {Evaluating the {{State}}-of-the-{{Art}} of {{End}}-to-{{End Natural Language Generation}}: {{The E2E NLG Challenge}}}, author = {Du{\v{s}}ek, Ond\v{r}ej and Novikova, Jekaterina and Rieser, Verena}, year = {2020}, month = jan, volume = {59}, pages = {123--156}, doi = {10.1016/j.csl.2019.06.009}, archivePrefix = {arXiv}, eprint = {1901.11528}, eprinttype = {arxiv}, journal = {Computer Speech \& Language} ``` ### Contributions Thanks to [@lhoestq](https://github.com/lhoestq) for adding this dataset.

opus_dgt

--- 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: [] paperswithcode_id: null pretty_name: OpusDgt dataset_info: - config_name: bg-ga features: - name: id dtype: string - name: translation dtype: translation: languages: - bg - ga splits: - name: train num_bytes: 82972428 num_examples: 179142 download_size: 15935979 dataset_size: 82972428 - config_name: bg-hr features: - name: id dtype: string - name: translation dtype: translation: languages: - bg - hr splits: - name: train num_bytes: 239828651 num_examples: 701572 download_size: 46804111 dataset_size: 239828651 - config_name: bg-sh features: - name: id dtype: string - name: translation dtype: translation: languages: - bg - sh splits: - name: train num_bytes: 498884905 num_examples: 1488507 download_size: 97402723 dataset_size: 498884905 - config_name: fi-ga features: - name: id dtype: string - name: translation dtype: translation: languages: - fi - ga splits: - name: train num_bytes: 61313136 num_examples: 178619 download_size: 14385114 dataset_size: 61313136 - config_name: es-ga features: - name: id dtype: string - name: translation dtype: translation: languages: - es - ga splits: - name: train num_bytes: 63115666 num_examples: 178696 download_size: 14447359 dataset_size: 63115666 - config_name: ga-sh features: - name: id dtype: string - name: translation dtype: translation: languages: - ga - sh splits: - name: train num_bytes: 28666585 num_examples: 91613 download_size: 6963357 dataset_size: 28666585 - config_name: hr-sk features: - name: id dtype: string - name: translation dtype: translation: languages: - hr - sk splits: - name: train num_bytes: 170718371 num_examples: 689263 download_size: 42579941 dataset_size: 170718371 - config_name: mt-sh features: - name: id dtype: string - name: translation dtype: translation: languages: - mt - sh splits: - name: train num_bytes: 368562443 num_examples: 1450424 download_size: 88598048 dataset_size: 368562443 - config_name: hr-sv features: - name: id dtype: string - name: translation dtype: translation: languages: - hr - sv splits: - name: train num_bytes: 171858392 num_examples: 696334 download_size: 41410203 dataset_size: 171858392 - config_name: ga-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - ga - nl splits: - name: train num_bytes: 59065574 num_examples: 170644 download_size: 13730934 dataset_size: 59065574 config_names: - bg-ga - bg-hr - bg-sh - es-ga - fi-ga - ga-nl - ga-sh - hr-sk - hr-sv - mt-sh --- # 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.

wmt20_mlqe_task1

--- pretty_name: WMT20 - MultiLingual Quality Estimation (MLQE) Task1 annotations_creators: - expert-generated - machine-generated language_creators: - found language: - de - en - et - ne - ro - ru - si - zh license: - unknown multilinguality: - translation size_categories: - 1K<n<10K source_datasets: - extended|reddit - extended|wikipedia task_categories: - translation task_ids: [] paperswithcode_id: null dataset_info: - config_name: en-de features: - name: segid dtype: int32 - name: translation dtype: translation: languages: - en - de - name: scores sequence: float32 - name: mean dtype: float32 - name: z_scores sequence: float32 - name: z_mean dtype: float32 - name: model_score dtype: float32 - name: doc_id dtype: string - name: nmt_output dtype: string - name: word_probas sequence: float32 splits: - name: train num_bytes: 3492361 num_examples: 7000 - name: test num_bytes: 524159 num_examples: 1000 - name: validation num_bytes: 522576 num_examples: 1000 download_size: 2343124 dataset_size: 4539096 - config_name: en-zh features: - name: segid dtype: int32 - name: translation dtype: translation: languages: - en - zh - name: scores sequence: float32 - name: mean dtype: float32 - name: z_scores sequence: float32 - name: z_mean dtype: float32 - name: model_score dtype: float32 - name: doc_id dtype: string - name: nmt_output dtype: string - name: word_probas sequence: float32 splits: - name: train num_bytes: 3286433 num_examples: 7000 - name: test num_bytes: 494994 num_examples: 1000 - name: validation num_bytes: 488477 num_examples: 1000 download_size: 2557445 dataset_size: 4269904 - config_name: et-en features: - name: segid dtype: int32 - name: translation dtype: translation: languages: - et - en - name: scores sequence: float32 - name: mean dtype: float32 - name: z_scores sequence: float32 - name: z_mean dtype: float32 - name: model_score dtype: float32 - name: doc_id dtype: string - name: nmt_output dtype: string - name: word_probas sequence: float32 splits: - name: train num_bytes: 3483669 num_examples: 7000 - name: test num_bytes: 527372 num_examples: 1000 - name: validation num_bytes: 531499 num_examples: 1000 download_size: 2320067 dataset_size: 4542540 - config_name: ne-en features: - name: segid dtype: int32 - name: translation dtype: translation: languages: - ne - en - name: scores sequence: float32 - name: mean dtype: float32 - name: z_scores sequence: float32 - name: z_mean dtype: float32 - name: model_score dtype: float32 - name: doc_id dtype: string - name: nmt_output dtype: string - name: word_probas sequence: float32 splits: - name: train num_bytes: 5313197 num_examples: 7000 - name: test num_bytes: 767637 num_examples: 1000 - name: validation num_bytes: 784784 num_examples: 1000 download_size: 2913901 dataset_size: 6865618 - config_name: ro-en features: - name: segid dtype: int32 - name: translation dtype: translation: languages: - ro - en - name: scores sequence: float32 - name: mean dtype: float32 - name: z_scores sequence: float32 - name: z_mean dtype: float32 - name: model_score dtype: float32 - name: doc_id dtype: string - name: nmt_output dtype: string - name: word_probas sequence: float32 splits: - name: train num_bytes: 3360498 num_examples: 7000 - name: test num_bytes: 500700 num_examples: 1000 - name: validation num_bytes: 507646 num_examples: 1000 download_size: 2222103 dataset_size: 4368844 - config_name: si-en features: - name: segid dtype: int32 - name: translation dtype: translation: languages: - si - en - name: scores sequence: float32 - name: mean dtype: float32 - name: z_scores sequence: float32 - name: z_mean dtype: float32 - name: model_score dtype: float32 - name: doc_id dtype: string - name: nmt_output dtype: string - name: word_probas sequence: float32 splits: - name: train num_bytes: 5135077 num_examples: 7000 - name: test num_bytes: 763730 num_examples: 1000 - name: validation num_bytes: 758271 num_examples: 1000 download_size: 2841894 dataset_size: 6657078 - config_name: ru-en features: - name: segid dtype: int32 - name: translation dtype: translation: languages: - ru - en - name: scores sequence: float32 - name: mean dtype: float32 - name: z_scores sequence: float32 - name: z_mean dtype: float32 - name: model_score dtype: float32 - name: doc_id dtype: string - name: nmt_output dtype: string - name: word_probas sequence: float32 splits: - name: train num_bytes: 3520776 num_examples: 7000 - name: test num_bytes: 478531 num_examples: 1000 - name: validation num_bytes: 499601 num_examples: 1000 download_size: 2123684 dataset_size: 4498908 config_names: - en-de - en-zh - et-en - ne-en - ro-en - ru-en - si-en --- # Dataset Card for WMT20 - MultiLingual Quality Estimation (MLQE) Task1 ## 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:** [WMT20 Quality Estimation Shared Task](http://www.statmt.org/wmt20/quality-estimation-task.html) - **Repository:** [Github repository](https://github.com/facebookresearch/mlqe/) - **Paper:** *Not available* ### Dataset Summary From the homepage: *This shared task (part of WMT20) will build on its previous editions to further examine automatic methods for estimating the quality of neural machine translation output at run-time, without relying on reference translations. As in previous years, we cover estimation at various levels. Important elements introduced this year include: a new task where sentences are annotated with Direct Assessment (DA) scores instead of labels based on post-editing; a new multilingual sentence-level dataset mainly from Wikipedia articles, where the source articles can be retrieved for document-wide context; the availability of NMT models to explore system-internal information for the task.* *Task 1 uses Wikipedia data for 6 language pairs that includes high-resource English--German (En-De) and English--Chinese (En-Zh), medium-resource Romanian--English (Ro-En) and Estonian--English (Et-En), and low-resource Sinhalese--English (Si-En) and Nepalese--English (Ne-En), as well as a dataset with a combination of Wikipedia articles and Reddit articles for Russian-English (En-Ru). The datasets were collected by translating sentences sampled from source language articles using state-of-the-art NMT models built using the fairseq toolkit and annotated with Direct Assessment (DA) scores by professional translators. Each sentence was annotated following the FLORES setup, which presents a form of DA, where at least three professional translators rate each sentence from 0-100 according to the perceived translation quality. DA scores are standardised using the z-score by rater. Participating systems are required to score sentences according to z-standardised DA scores.* ### Supported Tasks and Leaderboards From the homepage: *Sentence-level submissions will be evaluated in terms of the Pearson's correlation metric for the DA prediction agains human DA (z-standardised mean DA score, i.e. z_mean). These are the [official evaluation scripts](https://github.com/sheffieldnlp/qe-eval-scripts). The evaluation will focus on multilingual systems, i.e. systems that are able to provide predictions for all languages in the Wikipedia domain. Therefore, average Pearson correlation across all these languages will be used to rank QE systems. We will also evaluate QE systems on a per-language basis for those interested in particular languages.* ### Languages Eight languages are represented in this dataset: - English (`en`) - German (`de`) - Romanian (`ro`) - Estonian (`et`) - Nepalese (`ne`) - Sinhala (`si`) - Russian (`ru`) ## Dataset Structure ### Data Instances An example looks like this: ``` { 'segid': 123, 'translation': { 'en': 'José Ortega y Gasset visited Husserl at Freiburg in 1934.', 'de': '1934 besuchte José Ortega y Gasset Husserl in Freiburg.', }, 'scores': [100.0, 100.0, 100.0], 'mean': 100.0, 'z_scores': [0.9553316831588745, 1.552362322807312, 0.850531816482544], 'z_mean': 1.1194086074829102, 'model_score': -0.10244649648666382, 'doc_id': 'Edmund Husserl', 'nmt_output': '1934 besuchte José Ort@@ ega y G@@ asset Hus@@ ser@@ l in Freiburg .', 'word_probas': [-0.4458000063896179, -0.2745000123977661, -0.07199999690055847, -0.002300000051036477, -0.005900000222027302, -0.14579999446868896, -0.07500000298023224, -0.012400000356137753, -0.026900000870227814, -0.036400001496076584, -0.05299999937415123, -0.14990000426769257, -0.012400000356137753, -0.1145000010728836, -0.10999999940395355], } ``` ### Data Fields - `segid`: segment id. - `original`: original sentence. - `translation`: Dictionary with pairs (source,target). - src_lg: sequence of text in source language. - tgt_lg: sequence of text in target language. - `scores`: list of DA scores by all annotators - the number of annotators may vary. [] if N/A (only for `ru-en/test`). - `mean`: average of DA scores. -10_000 if N/A (only for `ru-en/test`). - `z_scores`: list of z-standardized DA scores. [] if N/A (only for `ru-en/test`). - `z_mean`: average of z-standardized DA scores. -10_000 if N/A (only for `ru-en/test`). - `model_score`: NMT model score for sentence. -10_000 if N/A (only for `ru-en/test`). - `doc_id`: the name of the article where each original segment came from. - `nmt_output`: the actual output of the NMT model before any post-processing, corresponding to the log-probas in `word_probas` (the token is not printed, so the number of log-probabilities equals the number of tokens plus 1). - `word_probas`: log-probabilities from the NMT model for each decoded token including the token. ### Data Splits There are 7 configurations in this dataset (one for each available language pair). Each configuration is composed of 7K examples for training, 1K for validation and 1K for test. ## Dataset Creation ### Curation Rationale The original text is extracted from Wikipedia, Russian Reddit and Russian WikiQuotes. Translations are obtained using state-of-the-art NMT models built using the [fairseq toolkit](https://github.com/pytorch/fairseq) and annotated with Direct Assesment scores by professional translators. ### 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 Unknown ### Citation Information ``` Not available. ``` ### Contributions Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset.

natural_questions

--- annotations_creators: - no-annotation language_creators: - crowdsourced language: - en license: - cc-by-sa-3.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - question-answering task_ids: - open-domain-qa paperswithcode_id: natural-questions pretty_name: Natural Questions dataset_info: features: - name: id dtype: string - name: document struct: - name: title dtype: string - name: url dtype: string - name: html dtype: string - name: tokens sequence: - name: token dtype: string - name: is_html dtype: bool - name: question struct: - name: text dtype: string - name: tokens sequence: string - name: annotations sequence: - name: id dtype: string - name: long_answer struct: - name: start_token dtype: int64 - name: end_token dtype: int64 - name: start_byte dtype: int64 - name: end_byte dtype: int64 - name: short_answers sequence: - name: start_token dtype: int64 - name: end_token dtype: int64 - name: start_byte dtype: int64 - name: end_byte dtype: int64 - name: text dtype: string - name: yes_no_answer dtype: class_label: names: '0': 'NO' '1': 'YES' - name: long_answer_candidates sequence: - name: start_token dtype: int64 - name: end_token dtype: int64 - name: start_byte dtype: int64 - name: end_byte dtype: int64 - name: top_label dtype: bool splits: - name: train num_bytes: 97445142568 num_examples: 307373 - name: validation num_bytes: 2353975312 num_examples: 7830 download_size: 45069199013 dataset_size: 99799117880 --- # Dataset Card for Natural Questions ## 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://ai.google.com/research/NaturalQuestions/dataset](https://ai.google.com/research/NaturalQuestions/dataset) - **Repository:** [https://github.com/google-research-datasets/natural-questions](https://github.com/google-research-datasets/natural-questions) - **Paper:** [https://research.google/pubs/pub47761/](https://research.google/pubs/pub47761/) - **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:** 45.07 GB - **Size of the generated dataset:** 99.80 GB - **Total amount of disk used:** 144.87 GB ### Dataset Summary The NQ corpus contains questions from real users, and it requires QA systems to read and comprehend an entire Wikipedia article that may or may not contain the answer to the question. The inclusion of real user questions, and the requirement that solutions should read an entire page to find the answer, cause NQ to be a more realistic and challenging task than prior QA datasets. ### Supported Tasks and Leaderboards [https://ai.google.com/research/NaturalQuestions](https://ai.google.com/research/NaturalQuestions) ### Languages en ## Dataset Structure ### Data Instances - **Size of downloaded dataset files:** 45.07 GB - **Size of the generated dataset:** 99.80 GB - **Total amount of disk used:** 144.87 GB An example of 'train' looks as follows. This is a toy example. ``` { "id": "797803103760793766", "document": { "title": "Google", "url": "http://www.wikipedia.org/Google", "html": "<html><body><h1>Google Inc.</h1><p>Google was founded in 1998 By:<ul><li>Larry</li><li>Sergey</li></ul></p></body></html>", "tokens":[ {"token": "<h1>", "start_byte": 12, "end_byte": 16, "is_html": True}, {"token": "Google", "start_byte": 16, "end_byte": 22, "is_html": False}, {"token": "inc", "start_byte": 23, "end_byte": 26, "is_html": False}, {"token": ".", "start_byte": 26, "end_byte": 27, "is_html": False}, {"token": "</h1>", "start_byte": 27, "end_byte": 32, "is_html": True}, {"token": "<p>", "start_byte": 32, "end_byte": 35, "is_html": True}, {"token": "Google", "start_byte": 35, "end_byte": 41, "is_html": False}, {"token": "was", "start_byte": 42, "end_byte": 45, "is_html": False}, {"token": "founded", "start_byte": 46, "end_byte": 53, "is_html": False}, {"token": "in", "start_byte": 54, "end_byte": 56, "is_html": False}, {"token": "1998", "start_byte": 57, "end_byte": 61, "is_html": False}, {"token": "by", "start_byte": 62, "end_byte": 64, "is_html": False}, {"token": ":", "start_byte": 64, "end_byte": 65, "is_html": False}, {"token": "<ul>", "start_byte": 65, "end_byte": 69, "is_html": True}, {"token": "<li>", "start_byte": 69, "end_byte": 73, "is_html": True}, {"token": "Larry", "start_byte": 73, "end_byte": 78, "is_html": False}, {"token": "</li>", "start_byte": 78, "end_byte": 83, "is_html": True}, {"token": "<li>", "start_byte": 83, "end_byte": 87, "is_html": True}, {"token": "Sergey", "start_byte": 87, "end_byte": 92, "is_html": False}, {"token": "</li>", "start_byte": 92, "end_byte": 97, "is_html": True}, {"token": "</ul>", "start_byte": 97, "end_byte": 102, "is_html": True}, {"token": "</p>", "start_byte": 102, "end_byte": 106, "is_html": True} ], }, "question" :{ "text": "who founded google", "tokens": ["who", "founded", "google"] }, "long_answer_candidates": [ {"start_byte": 32, "end_byte": 106, "start_token": 5, "end_token": 22, "top_level": True}, {"start_byte": 65, "end_byte": 102, "start_token": 13, "end_token": 21, "top_level": False}, {"start_byte": 69, "end_byte": 83, "start_token": 14, "end_token": 17, "top_level": False}, {"start_byte": 83, "end_byte": 92, "start_token": 17, "end_token": 20 , "top_level": False} ], "annotations": [{ "id": "6782080525527814293", "long_answer": {"start_byte": 32, "end_byte": 106, "start_token": 5, "end_token": 22, "candidate_index": 0}, "short_answers": [ {"start_byte": 73, "end_byte": 78, "start_token": 15, "end_token": 16, "text": "Larry"}, {"start_byte": 87, "end_byte": 92, "start_token": 18, "end_token": 19, "text": "Sergey"} ], "yes_no_answer": -1 }] } ``` ### Data Fields The data fields are the same among all splits. #### default - `id`: a `string` feature. - `document` a dictionary feature containing: - `title`: a `string` feature. - `url`: a `string` feature. - `html`: a `string` feature. - `tokens`: a dictionary feature containing: - `token`: a `string` feature. - `is_html`: a `bool` feature. - `start_byte`: a `int64` feature. - `end_byte`: a `int64` feature. - `question`: a dictionary feature containing: - `text`: a `string` feature. - `tokens`: a `list` of `string` features. - `long_answer_candidates`: a dictionary feature containing: - `start_token`: a `int64` feature. - `end_token`: a `int64` feature. - `start_byte`: a `int64` feature. - `end_byte`: a `int64` feature. - `top_level`: a `bool` feature. - `annotations`: a dictionary feature containing: - `id`: a `string` feature. - `long_answers`: a dictionary feature containing: - `start_token`: a `int64` feature. - `end_token`: a `int64` feature. - `start_byte`: a `int64` feature. - `end_byte`: a `int64` feature. - `candidate_index`: a `int64` feature. - `short_answers`: a dictionary feature containing: - `start_token`: a `int64` feature. - `end_token`: a `int64` feature. - `start_byte`: a `int64` feature. - `end_byte`: a `int64` feature. - `text`: a `string` feature. - `yes_no_answer`: a classification label, with possible values including `NO` (0), `YES` (1). ### Data Splits | name | train | validation | |---------|-------:|-----------:| | default | 307373 | 7830 | | dev | N/A | 7830 | ## 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 Attribution-ShareAlike 3.0 Unported](https://creativecommons.org/licenses/by-sa/3.0/). ### Citation Information ``` @article{47761, title = {Natural Questions: a Benchmark for Question Answering Research}, author = {Tom Kwiatkowski and Jennimaria Palomaki and Olivia Redfield and Michael Collins and Ankur Parikh and Chris Alberti and Danielle Epstein and Illia Polosukhin and Matthew Kelcey and Jacob Devlin and Kenton Lee and Kristina N. Toutanova and Llion Jones and Ming-Wei Chang and Andrew Dai and Jakob Uszkoreit and Quoc Le and Slav Petrov}, year = {2019}, journal = {Transactions of the Association of Computational Linguistics} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

ai4bharat/IN22-Gen

ai4bharat

--- language: - as - bn - brx - doi - en - gom - gu - hi - kn - ks - mai - ml - mr - mni - ne - or - pa - sa - sat - sd - ta - te - ur language_details: >- asm_Beng, ben_Beng, brx_Deva, doi_Deva, eng_Latn, gom_Deva, guj_Gujr, hin_Deva, kan_Knda, kas_Arab, mai_Deva, mal_Mlym, mar_Deva, mni_Mtei, npi_Deva, ory_Orya, pan_Guru, san_Deva, sat_Olck, snd_Deva, tam_Taml, tel_Telu, urd_Arab license: cc-by-4.0 language_creators: - expert-generated multilinguality: - multilingual - translation pretty_name: in22-gen size_categories: - 1K<n<10K task_categories: - translation --- # IN22-Gen IN22 is a newly created comprehensive benchmark for evaluating machine translation performance in multi-domain, n-way parallel contexts across 22 Indic languages. IN22-Gen is a general-purpose multi-domain evaluation subset of IN22. It has been created from two sources: Wikipedia and Web Sources offering diverse content spanning news, entertainment, culture, legal, and India-centric topics. The evaluation subset consists of 1024 sentences translated across 22 Indic languages enabling evaluation of MT systems across 506 directions. Here is the domain and source distribution of our IN22-Gen evaluation subset. <table style="width: 40%"> <tr> <td>domain</td> <td>web sources</td> <td>wikipedia</td> </tr> <tr> <td>culture</td> <td>40</td> <td>40</td> </tr> <tr> <td>economy</td> <td>40</td> <td>40</td> </tr> <tr> <td>education</td> <td>40</td> <td>40</td> </tr> <tr> <td>entertainment</td> <td>40</td> <td>40</td> </tr> <tr> <td>geography</td> <td>40</td> <td>40</td> </tr> <tr> <td>governments</td> <td>40</td> <td>40</td> </tr> <tr> <td>health</td> <td>40</td> <td>40</td> </tr> <tr> <td>industry</td> <td>40</td> <td>40</td> </tr> <tr> <td>legal</td> <td>40</td> <td>40</td> </tr> <tr> <td>news</td> <td>32</td> <td>32</td> </tr> <tr> <td>religion</td> <td>40</td> <td>40</td> </tr> <tr> <td>sports</td> <td>40</td> <td>40</td> </tr> <tr> <td>tourism</td> <td>40</td> <td>40</td> </tr> <tr> <td>total</td> <td>512</td> <td>512</td> </tr> </table> Please refer to the `Appendix E: Dataset Card` of the [preprint](https://arxiv.org/abs/2305.16307) on detailed description of dataset curation, annotation and quality control process. ### Dataset Structure #### Dataset Fields - `id`: Row number for the data entry, starting at 1. - `context`: Context window of 3 sentences, typically includes one sentence before and after the candidate sentence. - `source`: The source from which the candidate sentence is considered. - `url`: The URL for the English article from which the sentence was extracted. Only available for candidate sentences sourced from Wikipedia - `domain`: The domain of the sentence. - `num_words`: The number of words in the candidate sentence. - `bucket`: Classification of the candidate sentence as per predefined bucket categories. - `sentence`: The full sentence in the specific language (may have _lang for pairings) #### Data Instances A sample from the `gen` split for the English language (`eng_Latn` config) is provided below. All configurations have the same structure, and all sentences are aligned across configurations and splits. ```python { "id": 1, "context": "A uniform is often viewed as projecting a positive image of an organisation. Maintaining personal hygiene is also an important aspect of personal appearance and dressing. An appearance is a bunch of attributes related with the service person, like their shoes, clothes, tie, jewellery, hairstyle, make-up, watch, cosmetics, perfume, etc.", "source": "web", "url": "", "domain": "culture", "num_words": 24, "bucket": "18 - 25", "sentence": "An appearance is a bunch of attributes related to the service person, like their shoes, clothes, tie, jewellery, hairstyle, make-up, watch, cosmetics, perfume, etc." } ``` When using a hyphenated pairing or using the `all` function, data will be presented as follows: ```python { "id": 1, "context": "A uniform is often viewed as projecting a positive image of an organisation. Maintaining personal hygiene is also an important aspect of personal appearance and dressing. An appearance is a bunch of attributes related with the service person, like their shoes, clothes, tie, jewellery, hairstyle, make-up, watch, cosmetics, perfume, etc.", "source": "web", "url": "", "domain": "culture", "num_words": 24, "bucket": "18 - 25", "sentence_eng_Latn": "An appearance is a bunch of attributes related to the service person, like their shoes, clothes, tie, jewellery, hairstyle, make-up, watch, cosmetics, perfume, etc.", "sentence_hin_Deva": "सेवा संबंधी लोगों के लिए भेष कई गुणों का संयोजन है, जैसे कि उनके जूते, कपड़े, टाई, आभूषण, केश शैली, मेक-अप, घड़ी, कॉस्मेटिक, इत्र, आदि।" } ``` ### Usage Instructions ```python from datasets import load_dataset # download and load all the pairs dataset = load_dataset("ai4bharat/IN22-Gen", "all") # download and load specific pairs dataset = load_dataset("ai4bharat/IN22-Gen", "eng_Latn-hin_Deva") ``` ### Languages Covered <table style="width: 40%"> <tr> <td>Assamese (asm_Beng)</td> <td>Kashmiri (Arabic) (kas_Arab)</td> <td>Punjabi (pan_Guru)</td> </tr> <tr> <td>Bengali (ben_Beng)</td> <td>Kashmiri (Devanagari) (kas_Deva)</td> <td>Sanskrit (san_Deva)</td> </tr> <tr> <td>Bodo (brx_Deva)</td> <td>Maithili (mai_Deva)</td> <td>Santali (sat_Olck)</td> </tr> <tr> <td>Dogri (doi_Deva)</td> <td>Malayalam (mal_Mlym)</td> <td>Sindhi (Arabic) (snd_Arab)</td> </tr> <tr> <td>English (eng_Latn)</td> <td>Marathi (mar_Deva)</td> <td>Sindhi (Devanagari) (snd_Deva)</td> </tr> <tr> <td>Konkani (gom_Deva)</td> <td>Manipuri (Bengali) (mni_Beng)</td> <td>Tamil (tam_Taml)</td> </tr> <tr> <td>Gujarati (guj_Gujr)</td> <td>Manipuri (Meitei) (mni_Mtei)</td> <td>Telugu (tel_Telu)</td> </tr> <tr> <td>Hindi (hin_Deva)</td> <td>Nepali (npi_Deva)</td> <td>Urdu (urd_Arab)</td> </tr> <tr> <td>Kannada (kan_Knda)</td> <td>Odia (ory_Orya)</td> </tr> </table> ### Citation If you consider using our work then please cite using: ``` @article{ai4bharat2023indictrans2, title = {IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages}, author = {AI4Bharat and Jay Gala and Pranjal A. Chitale and Raghavan AK and Sumanth Doddapaneni and Varun Gumma and Aswanth Kumar and Janki Nawale and Anupama Sujatha and Ratish Puduppully and Vivek Raghavan and Pratyush Kumar and Mitesh M. Khapra and Raj Dabre and Anoop Kunchukuttan}, year = {2023}, journal = {arXiv preprint arXiv: 2305.16307} } ```

castorini/afriberta-corpus

castorini

--- language: - om - am - rw - rn - ha - ig - pcm - so - sw - ti - yo - multilingual license: apache-2.0 task_categories: - text-generation task_ids: - language-modeling --- # Dataset Card for AfriBERTa's Corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Loading Dataset](#loading-dataset) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Discussion of Biases](#discussion-of-biases) - [Additional Information](#additional-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description ### Dataset Summary This is the corpus on which AfriBERTa was trained on. The dataset is mostly from the BBC news website, but some languages also have data from Common Crawl. - **Homepage:** https://github.com/keleog/afriberta - **Models:** - https://huggingface.co/castorini/afriberta_small - https://huggingface.co/castorini/afriberta_base - https://huggingface.co/castorini/afriberta_large - **Paper:** https://aclanthology.org/2021.mrl-1.11/ - **Point of Contact:** kelechi.ogueji@uwaterloo.ca ### Supported Tasks and Leaderboards The AfriBERTa corpus was mostly intended to pre-train language models. ### Languages ``` afaanoromoo amharic gahuza hausa igbo pidgin somali swahili tigrinya yoruba ``` ### Loading Dataset An example to load the train split of the Somali corpus: ``` dataset = load_dataset("castorini/afriberta-corpus", "somali", split="train") ``` An example to load the test split of the Pidgin corpus: ``` dataset = load_dataset("castorini/afriberta-corpus", "pidgin", split="test") ``` ## Dataset Structure ### Data Instances Each data point is a line of text. An example from the `igbo` dataset: ``` {"id": "6", "text": "Ngwá ọrụ na-echebe ma na-ebuli gị na kọmputa."} ``` ### Data Fields The data fields are: - id: id of the example - text: content as a string ### Data Splits Each language has a train and test split, with varying sizes. ## Considerations for Using the Data ### Discussion of Biases Since majority of the data is obtained from the BBC's news website, models trained on this dataset are likely going to be biased towards the news domain. Also, since some of the data is obtained from Common Crawl, care should be taken (especially for text generation models) since personal and sensitive information might be present. ## Additional Information ### Citation Information ``` @inproceedings{ogueji-etal-2021-small, title = "Small Data? No Problem! Exploring the Viability of Pretrained Multilingual Language Models for Low-resourced Languages", author = "Ogueji, Kelechi and Zhu, Yuxin and Lin, Jimmy", booktitle = "Proceedings of the 1st Workshop on Multilingual Representation Learning", month = nov, year = "2021", address = "Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.mrl-1.11", pages = "116--126", } ``` ### Contributions Thanks to [Kelechi Ogueji](https://github.com/keleog) for adding this dataset.

wmt18

--- annotations_creators: - no-annotation language_creators: - found language: - cs - de - en - et - fi - kk - ru - tr - zh license: - unknown multilinguality: - translation size_categories: - 10M<n<100M source_datasets: - extended|europarl_bilingual - extended|news_commentary - extended|opus_paracrawl - extended|setimes - extended|un_multi task_categories: - translation task_ids: [] pretty_name: WMT18 paperswithcode_id: wmt-2018 dataset_info: - config_name: cs-en features: - name: translation dtype: translation: languages: - cs - en splits: - name: train num_bytes: 1461016186 num_examples: 11046024 - name: validation num_bytes: 674430 num_examples: 3005 - name: test num_bytes: 696229 num_examples: 2983 download_size: 2030359086 dataset_size: 1462386845 - config_name: de-en features: - name: translation dtype: translation: languages: - de - en splits: - name: train num_bytes: 8187552108 num_examples: 42271874 - name: validation num_bytes: 729519 num_examples: 3004 - name: test num_bytes: 757649 num_examples: 2998 download_size: 3808612335 dataset_size: 8189039276 - config_name: et-en features: - name: translation dtype: translation: languages: - et - en splits: - name: train num_bytes: 647992667 num_examples: 2175873 - name: validation num_bytes: 459398 num_examples: 2000 - name: test num_bytes: 489394 num_examples: 2000 download_size: 524534404 dataset_size: 648941459 - config_name: fi-en features: - name: translation dtype: translation: languages: - fi - en splits: - name: train num_bytes: 857171881 num_examples: 3280600 - name: validation num_bytes: 1388828 num_examples: 6004 - name: test num_bytes: 691841 num_examples: 3000 download_size: 491874780 dataset_size: 859252550 - config_name: kk-en features: - name: translation dtype: translation: languages: - kk - en splits: - name: train - name: validation - name: test download_size: 0 dataset_size: 0 - config_name: ru-en features: - name: translation dtype: translation: languages: - ru - en splits: - name: train num_bytes: 13665367647 num_examples: 36858512 - name: validation num_bytes: 1040195 num_examples: 3001 - name: test num_bytes: 1085596 num_examples: 3000 download_size: 4195144356 dataset_size: 13667493438 - config_name: tr-en features: - name: translation dtype: translation: languages: - tr - en splits: - name: train num_bytes: 60416617 num_examples: 205756 - name: validation num_bytes: 752773 num_examples: 3007 - name: test num_bytes: 770313 num_examples: 3000 download_size: 62263061 dataset_size: 61939703 - config_name: zh-en features: - name: translation dtype: translation: languages: - zh - en splits: - name: train num_bytes: 5536169801 num_examples: 25160346 - name: validation num_bytes: 540347 num_examples: 2001 - name: test num_bytes: 1107522 num_examples: 3981 download_size: 2259428767 dataset_size: 5537817670 --- # Dataset Card for "wmt18" ## 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.statmt.org/wmt18/translation-task.html](http://www.statmt.org/wmt18/translation-task.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:** 2.03 GB - **Size of the generated dataset:** 1.46 GB - **Total amount of disk used:** 3.49 GB ### Dataset Summary <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"> <p><b>Warning:</b> There are issues with the Common Crawl corpus data (<a href="https://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz">training-parallel-commoncrawl.tgz</a>):</p> <ul> <li>Non-English files contain many English sentences.</li> <li>Their "parallel" sentences in English are not aligned: they are uncorrelated with their counterpart.</li> </ul> <p>We have contacted the WMT organizers.</p> </div> Translation dataset based on the data from statmt.org. Versions exist for different years using a combination of data sources. The base `wmt` allows you to create a custom dataset by choosing your own data/language pair. This can be done as follows: ```python from datasets import inspect_dataset, load_dataset_builder inspect_dataset("wmt18", "path/to/scripts") builder = load_dataset_builder( "path/to/scripts/wmt_utils.py", language_pair=("fr", "de"), subsets={ datasets.Split.TRAIN: ["commoncrawl_frde"], datasets.Split.VALIDATION: ["euelections_dev2019"], }, ) # Standard version builder.download_and_prepare() ds = builder.as_dataset() # Streamable version ds = builder.as_streaming_dataset() ``` ### 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 #### cs-en - **Size of downloaded dataset files:** 2.03 GB - **Size of the generated dataset:** 1.46 GB - **Total amount of disk used:** 3.49 GB An example of 'validation' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### cs-en - `translation`: a multilingual `string` variable, with possible languages including `cs`, `en`. ### Data Splits |name | train |validation|test| |-----|-------:|---------:|---:| |cs-en|11046024| 3005|2983| ## 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{bojar-EtAl:2018:WMT1, author = {Bojar, Ond {r}ej and Federmann, Christian and Fishel, Mark and Graham, Yvette and Haddow, Barry and Huck, Matthias and Koehn, Philipp and Monz, Christof}, title = {Findings of the 2018 Conference on Machine Translation (WMT18)}, booktitle = {Proceedings of the Third Conference on Machine Translation, Volume 2: Shared Task Papers}, month = {October}, year = {2018}, address = {Belgium, Brussels}, publisher = {Association for Computational Linguistics}, pages = {272--307}, url = {http://www.aclweb.org/anthology/W18-6401} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

DeveloperOats/DBPedia_Classes

DeveloperOats

--- annotations_creators: [] language: - en language_creators: [] license: - cc0-1.0 multilinguality: - monolingual pretty_name: 'DBpedia' size_categories: - 1M<n<10M source_datasets: [] tags: [] task_categories: - text-classification task_ids: - topic-classification --- About Dataset DBpedia (from "DB" for "database") is a project aiming to extract structured content from the information created in Wikipedia. This is an extract of the data (after cleaning, kernel included) that provides taxonomic, hierarchical categories ("classes") for 342,782 wikipedia articles. There are 3 levels, with 9, 70 and 219 classes respectively. A version of this dataset is a popular baseline for NLP/text classification tasks. This version of the dataset is much tougher, especially if the L2/L3 levels are used as the targets. This is an excellent benchmark for hierarchical multiclass/multilabel text classification. Some example approaches are included as code snippets. Content DBPedia dataset with multiple levels of hierarchy/classes, as a multiclass dataset. Original DBPedia ontology (triplets data): https://wiki.dbpedia.org/develop/datasets Listing of the class tree/taxonomy: http://mappings.dbpedia.org/server/ontology/classes/ Acknowledgements Thanks to the Wikimedia foundation for creating Wikipedia, DBPedia and associated open-data goodness! Thanks to my colleagues at Sparkbeyond (https://www.sparkbeyond.com) for pointing me towards the taxonomical version of this dataset (as opposed to the classic 14 class version) Inspiration Try different NLP models. See also https://www.kaggle.com/datasets/danofer/dbpedia-classes Compare to the SOTA in Text Classification on DBpedia - https://paperswithcode.com/sota/text-classification-on-dbpedia

mteb/scidocs-reranking

mteb

--- language: - en ---

wiqa

--- language: - en paperswithcode_id: wiqa pretty_name: What-If Question Answering dataset_info: features: - name: question_stem dtype: string - name: question_para_step sequence: string - name: answer_label dtype: string - name: answer_label_as_choice dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: metadata_question_id dtype: string - name: metadata_graph_id dtype: string - name: metadata_para_id dtype: string - name: metadata_question_type dtype: string - name: metadata_path_len dtype: int32 splits: - name: train num_bytes: 17089298 num_examples: 29808 - name: test num_bytes: 1532223 num_examples: 3003 - name: validation num_bytes: 3779584 num_examples: 6894 download_size: 5247733 dataset_size: 22401105 --- # Dataset Card for "wiqa" ## 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/wiqa](https://allenai.org/data/wiqa) - **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:** 5.24 MB - **Size of the generated dataset:** 22.40 MB - **Total amount of disk used:** 27.65 MB ### Dataset Summary The WIQA dataset V1 has 39705 questions containing a perturbation and a possible effect in the context of a paragraph. The dataset is split into 29808 train questions, 6894 dev questions and 3003 test 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:** 5.24 MB - **Size of the generated dataset:** 22.40 MB - **Total amount of disk used:** 27.65 MB An example of 'validation' looks as follows. ``` { "answer_label": "more", "answer_label_as_choice": "A", "choices": { "label": ["A", "B", "C"], "text": ["more", "less", "no effect"] }, "metadata_graph_id": "481", "metadata_para_id": "528", "metadata_path_len": 3, "metadata_question_id": "influence_graph:528:481:77#0", "metadata_question_type": "INPARA_EFFECT", "question_para_step": ["A male and female rabbit mate", "The female rabbit becomes pregnant", "Baby rabbits form inside of the mother rabbit", "The female rabbit gives birth to a litter", "The newborn rabbits grow up to become adults", "The adult rabbits find mates."], "question_stem": "suppose the female is sterile happens, how will it affect LESS rabbits." } ``` ### Data Fields The data fields are the same among all splits. #### default - `question_stem`: a `string` feature. - `question_para_step`: a `list` of `string` features. - `answer_label`: a `string` feature. - `answer_label_as_choice`: a `string` feature. - `choices`: a dictionary feature containing: - `text`: a `string` feature. - `label`: a `string` feature. - `metadata_question_id`: a `string` feature. - `metadata_graph_id`: a `string` feature. - `metadata_para_id`: a `string` feature. - `metadata_question_type`: a `string` feature. - `metadata_path_len`: a `int32` feature. ### Data Splits | name |train|validation|test| |-------|----:|---------:|---:| |default|29808| 6894|3003| ## 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{wiqa, author = {Niket Tandon and Bhavana Dalvi Mishra and Keisuke Sakaguchi and Antoine Bosselut and Peter Clark} title = {WIQA: A dataset for "What if..." reasoning over procedural text}, journal = {arXiv:1909.04739v1}, year = {2019}, } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

ccdv/pubmed-summarization

ccdv

--- language: - en multilinguality: - monolingual size_categories: - 100K<n<1M task_categories: - summarization - text-generation task_ids: [] tags: - conditional-text-generation --- # PubMed dataset for summarization Dataset for summarization of long documents.\ Adapted from this [repo](https://github.com/armancohan/long-summarization).\ Note that original data are pre-tokenized so this dataset returns " ".join(text) and add "\n" for paragraphs. \ This dataset is compatible with the [`run_summarization.py`](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization) script from Transformers if you add this line to the `summarization_name_mapping` variable: ```python "ccdv/pubmed-summarization": ("article", "abstract") ``` ### Data Fields - `id`: paper id - `article`: a string containing the body of the paper - `abstract`: a string containing the abstract of the paper ### Data Splits This dataset has 3 splits: _train_, _validation_, and _test_. \ Token counts are white space based. | Dataset Split | Number of Instances | Avg. tokens | | ------------- | --------------------|:----------------------| | Train | 119,924 | 3043 / 215 | | Validation | 6,633 | 3111 / 216 | | Test | 6,658 | 3092 / 219 | # Cite original article ``` @inproceedings{cohan-etal-2018-discourse, title = "A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents", author = "Cohan, Arman and Dernoncourt, Franck and Kim, Doo Soon and Bui, Trung and Kim, Seokhwan and Chang, Walter and Goharian, Nazli", booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)", month = jun, year = "2018", address = "New Orleans, Louisiana", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/N18-2097", doi = "10.18653/v1/N18-2097", pages = "615--621", abstract = "Neural abstractive summarization models have led to promising results in summarizing relatively short documents. We propose the first model for abstractive summarization of single, longer-form documents (e.g., research papers). Our approach consists of a new hierarchical encoder that models the discourse structure of a document, and an attentive discourse-aware decoder to generate the summary. Empirical results on two large-scale datasets of scientific papers show that our model significantly outperforms state-of-the-art models.", } ```

vwxyzjn/summarize_from_feedback_oai_preprocessing_pythia-160m_48

vwxyzjn

--- dataset_info: features: - name: info struct: - name: id dtype: string - name: post dtype: string - name: title dtype: string - name: subreddit dtype: string - name: site dtype: string - name: article dtype: string - name: summaries list: - name: text dtype: string - name: policy dtype: string - name: note dtype: string - name: choice dtype: int32 - name: worker dtype: string - name: batch dtype: string - name: split dtype: string - name: extra struct: - name: confidence dtype: int32 - name: query_token sequence: int64 - name: query dtype: string - name: response0 dtype: string - name: response0_token sequence: int64 - name: response0_token_len dtype: int64 - name: response1 dtype: string - name: response1_token sequence: int64 - name: response1_token_len dtype: int64 - name: response0_policy dtype: string - name: response1_policy dtype: string - name: policies dtype: string splits: - name: train num_bytes: 790484659 num_examples: 92858 - name: validation num_bytes: 743124619 num_examples: 86086 download_size: 125328309 dataset_size: 1533609278 --- # Dataset Card for "summarize_from_feedback_oai_preprocessing_pythia-160m_48" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

opus_paracrawl

--- 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: [] paperswithcode_id: null pretty_name: OpusParaCrawl 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 config_names: - de-pl - el-en - en-ha - en-ig - en-km - en-so - en-sw - en-tl - es-gl - fr-nl --- # 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.

scikit-learn/imdb

scikit-learn

--- license: other --- This is the sentiment analysis dataset based on IMDB reviews initially released by Stanford University. ``` This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well. Raw text and already processed bag of words formats are provided. See the README file contained in the release for more details. ``` [Here](http://ai.stanford.edu/~amaas/data/sentiment/) is the redirection. ``` @InProceedings{maas-EtAl:2011:ACL-HLT2011, author = {Maas, Andrew L. and Daly, Raymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y. and Potts, Christopher}, title = {Learning Word Vectors for Sentiment Analysis}, booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies}, month = {June}, year = {2011}, address = {Portland, Oregon, USA}, publisher = {Association for Computational Linguistics}, pages = {142--150}, url = {http://www.aclweb.org/anthology/P11-1015} } ```

wmt17

--- annotations_creators: - no-annotation language_creators: - found language: - cs - de - en - fi - lv - ru - tr - zh license: - unknown multilinguality: - translation size_categories: - 10M<n<100M source_datasets: - extended|europarl_bilingual - extended|news_commentary - extended|setimes - extended|un_multi task_categories: - translation task_ids: [] pretty_name: WMT17 paperswithcode_id: null dataset_info: - config_name: cs-en features: - name: translation dtype: translation: languages: - cs - en splits: - name: train num_bytes: 300698431 num_examples: 1018291 - name: validation num_bytes: 707870 num_examples: 2999 - name: test num_bytes: 674430 num_examples: 3005 download_size: 1784240523 dataset_size: 302080731 - config_name: de-en features: - name: translation dtype: translation: languages: - de - en splits: - name: train num_bytes: 1715537443 num_examples: 5906184 - name: validation num_bytes: 735516 num_examples: 2999 - name: test num_bytes: 729519 num_examples: 3004 download_size: 1945382236 dataset_size: 1717002478 - config_name: fi-en features: - name: translation dtype: translation: languages: - fi - en splits: - name: train num_bytes: 743856525 num_examples: 2656542 - name: validation num_bytes: 1410515 num_examples: 6000 - name: test num_bytes: 1388828 num_examples: 6004 download_size: 434531933 dataset_size: 746655868 - config_name: lv-en features: - name: translation dtype: translation: languages: - lv - en splits: - name: train num_bytes: 517419100 num_examples: 3567528 - name: validation num_bytes: 544604 num_examples: 2003 - name: test num_bytes: 530474 num_examples: 2001 download_size: 169634544 dataset_size: 518494178 - config_name: ru-en features: - name: translation dtype: translation: languages: - ru - en splits: - name: train num_bytes: 11000075522 num_examples: 24782720 - name: validation num_bytes: 1050677 num_examples: 2998 - name: test num_bytes: 1040195 num_examples: 3001 download_size: 3582640660 dataset_size: 11002166394 - config_name: tr-en features: - name: translation dtype: translation: languages: - tr - en splits: - name: train num_bytes: 60416617 num_examples: 205756 - name: validation num_bytes: 732436 num_examples: 3000 - name: test num_bytes: 752773 num_examples: 3007 download_size: 62263061 dataset_size: 61901826 - config_name: zh-en features: - name: translation dtype: translation: languages: - zh - en splits: - name: train num_bytes: 5529286149 num_examples: 25134743 - name: validation num_bytes: 589591 num_examples: 2002 - name: test num_bytes: 540347 num_examples: 2001 download_size: 2314906945 dataset_size: 5530416087 --- # Dataset Card for "wmt17" ## 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.statmt.org/wmt17/translation-task.html](http://www.statmt.org/wmt17/translation-task.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:** 1.78 GB - **Size of the generated dataset:** 302.09 MB - **Total amount of disk used:** 2.09 GB ### Dataset Summary <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"> <p><b>Warning:</b> There are issues with the Common Crawl corpus data (<a href="https://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz">training-parallel-commoncrawl.tgz</a>):</p> <ul> <li>Non-English files contain many English sentences.</li> <li>Their "parallel" sentences in English are not aligned: they are uncorrelated with their counterpart.</li> </ul> <p>We have contacted the WMT organizers.</p> </div> Translation dataset based on the data from statmt.org. Versions exist for different years using a combination of data sources. The base `wmt` allows you to create a custom dataset by choosing your own data/language pair. This can be done as follows: ```python from datasets import inspect_dataset, load_dataset_builder inspect_dataset("wmt17", "path/to/scripts") builder = load_dataset_builder( "path/to/scripts/wmt_utils.py", language_pair=("fr", "de"), subsets={ datasets.Split.TRAIN: ["commoncrawl_frde"], datasets.Split.VALIDATION: ["euelections_dev2019"], }, ) # Standard version builder.download_and_prepare() ds = builder.as_dataset() # Streamable version ds = builder.as_streaming_dataset() ``` ### 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 #### cs-en - **Size of downloaded dataset files:** 1.78 GB - **Size of the generated dataset:** 302.09 MB - **Total amount of disk used:** 2.09 GB An example of 'train' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### cs-en - `translation`: a multilingual `string` variable, with possible languages including `cs`, `en`. ### Data Splits |name | train |validation|test| |-----|------:|---------:|---:| |cs-en|1018291| 2999|3005| ## 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{bojar-EtAl:2017:WMT1, author = {Bojar, Ond {r}ej and Chatterjee, Rajen and Federmann, Christian and Graham, Yvette and Haddow, Barry and Huang, Shujian and Huck, Matthias and Koehn, Philipp and Liu, Qun and Logacheva, Varvara and Monz, Christof and Negri, Matteo and Post, Matt and Rubino, Raphael and Specia, Lucia and Turchi, Marco}, title = {Findings of the 2017 Conference on Machine Translation (WMT17)}, booktitle = {Proceedings of the Second Conference on Machine Translation, Volume 2: Shared Task Papers}, month = {September}, year = {2017}, address = {Copenhagen, Denmark}, publisher = {Association for Computational Linguistics}, pages = {169--214}, url = {http://www.aclweb.org/anthology/W17-4717} } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

sentiment140

--- language: - en paperswithcode_id: sentiment140 pretty_name: Sentiment140 dataset_info: config_name: sentiment140 features: - name: text dtype: string - name: date dtype: string - name: user dtype: string - name: sentiment dtype: int32 - name: query dtype: string splits: - name: train num_bytes: 224542690 num_examples: 1600000 - name: test num_bytes: 72971 num_examples: 498 download_size: 81363704 dataset_size: 224615661 train-eval-index: - config: sentiment140 task: text-classification task_id: multi_class_classification splits: train_split: train eval_split: test col_mapping: text: text sentiment: 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 --- # Dataset Card for "sentiment140" ## 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://help.sentiment140.com/home](http://help.sentiment140.com/home) - **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:** 81.36 MB - **Size of the generated dataset:** 225.82 MB - **Total amount of disk used:** 307.18 MB ### Dataset Summary Sentiment140 consists of Twitter messages with emoticons, which are used as noisy labels for sentiment classification. For more detailed information please refer to the paper. ### 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 #### sentiment140 - **Size of downloaded dataset files:** 81.36 MB - **Size of the generated dataset:** 225.82 MB - **Total amount of disk used:** 307.18 MB An example of 'train' looks as follows. ``` { "date": "23-04-2010", "query": "NO_QUERY", "sentiment": 3, "text": "train message", "user": "train user" } ``` ### Data Fields The data fields are the same among all splits. #### sentiment140 - `text`: a `string` feature. - `date`: a `string` feature. - `user`: a `string` feature. - `sentiment`: a `int32` feature. - `query`: a `string` feature. ### Data Splits | name | train |test| |------------|------:|---:| |sentiment140|1600000| 498| ## 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{go2009twitter, title={Twitter sentiment classification using distant supervision}, author={Go, Alec and Bhayani, Richa and Huang, Lei}, journal={CS224N project report, Stanford}, volume={1}, number={12}, pages={2009}, year={2009} } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

mteb/stackoverflowdupquestions-reranking

mteb

--- language: - en ---

jon-tow/okapi_truthfulqa

jon-tow

--- language: - ar - bn - ca - da - de - es - eu - fr - gu - hi - hr - hu - hy - id - it - kn - ml - mr - ne - nl - pt - ro - ru - sk - sr - sv - ta - te - uk - vi license: cc-by-nc-4.0 --- # okapi_truthfulqa <!-- Provide a quick summary of the dataset. --> Multilingual translation of [TruthfulQA](https://arxiv.org/abs/2109.07958). ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> TruthfulQA is a benchmark to measure whether a language model is truthful in generating answers to questions. The benchmark comprises 817 questions that span 38 categories, including health, law, finance and politics. Questions are crafted so that some humans would answer falsely due to a false belief or misconception. To perform well, models must avoid generating false answers learned from imitating human texts. - **Curated by:** Dac Lai, Viet and Van Nguyen, Chien and Ngo, Nghia Trung and Nguyen, Thuat and Dernoncourt, Franck and Rossi, Ryan A and Nguyen, Thien Huu - **License:** The datasets are CC BY NC 4.0 (allowing only non-commercial use). ### Dataset Sources <!-- Provide the basic links for the dataset. --> - **Repository:** http://nlp.uoregon.edu/download/okapi-eval/datasets/ - **Paper:** Okapi ([Lai et al., 2023](https://arxiv.org/abs/2307.16039)) ## Citation <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> ```bibtex @article{dac2023okapi, title={Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback}, author={Dac Lai, Viet and Van Nguyen, Chien and Ngo, Nghia Trung and Nguyen, Thuat and Dernoncourt, Franck and Rossi, Ryan A and Nguyen, Thien Huu}, journal={arXiv e-prints}, pages={arXiv--2307}, year={2023} } ``` ```bibtex @misc{lin2021truthfulqa, title={TruthfulQA: Measuring How Models Mimic Human Falsehoods}, author={Stephanie Lin and Jacob Hilton and Owain Evans}, year={2021}, eprint={2109.07958}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

deepset/germanquad

deepset

--- thumbnail: >- https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg language: - de multilinguality: - monolingual source_datasets: - original task_categories: - question-answering - text-retrieval task_ids: - extractive-qa - closed-domain-qa - open-domain-qa train-eval-index: - config: plain_text task: question-answering task_id: extractive_question_answering splits: train_split: train eval_split: test col_mapping: context: context question: question answers.text: answers.text answers.answer_start: answers.answer_start license: cc-by-4.0 ---  # Dataset Card for germanquad ## 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) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Citation Information](#citation-information) ## Dataset Description - **Homepage:** https://deepset.ai/germanquad - **Repository:** https://github.com/deepset-ai/haystack - **Paper:** https://arxiv.org/abs/2104.12741 ### Dataset Summary In order to raise the bar for non-English QA, we are releasing a high-quality, human-labeled German QA dataset consisting of 13 722 questions, incl. a three-way annotated test set. The creation of GermanQuAD is inspired by insights from existing datasets as well as our labeling experience from several industry projects. We combine the strengths of SQuAD, such as high out-of-domain performance, with self-sufficient questions that contain all relevant information for open-domain QA as in the NaturalQuestions dataset. Our training and test datasets do not overlap like other popular datasets and include complex questions that cannot be answered with a single entity or only a few words. ### Supported Tasks and Leaderboards - `extractive-qa`, `closed-domain-qa`, `open-domain-qa`, `text-retrieval`: This dataset is intended to be used for `open-domain-qa`, but can also be used for information retrieval tasks. ### Languages The sentences in the dataset are in German (de). ## Dataset Structure ### Data Instances A sample from the training set is provided below: ``` { "paragraphs": [ { "qas": [ { "question": "Von welchem Gesetzt stammt das Amerikanische ab? ", "id": 51870, "answers": [ { "answer_id": 53778, "document_id": 43958, "question_id": 51870, "text": "britischen Common Laws", "answer_start": 146, "answer_category": "SHORT" } ], "is_impossible": false } ], "context": "Recht_der_Vereinigten_Staaten\ \ === Amerikanisches Common Law ===\ Obwohl die Vereinigten Staaten wie auch viele Staaten des Commonwealth Erben des britischen Common Laws sind, setzt sich das amerikanische Recht bedeutend davon ab. Dies rührt größtenteils von dem langen Zeitraum her, in dem sich das amerikanische Recht unabhängig vom Britischen entwickelt hat. Entsprechend schauen die Gerichte in den Vereinigten Staaten bei der Analyse von eventuell zutreffenden britischen Rechtsprinzipien im Common Law gewöhnlich nur bis ins frühe 19. Jahrhundert.\ Während es in den Commonwealth-Staaten üblich ist, dass Gerichte sich Entscheidungen und Prinzipien aus anderen Commonwealth-Staaten importieren, ist das in der amerikanischen Rechtsprechung selten. Ausnahmen bestehen hier nur, wenn sich überhaupt keine relevanten amerikanischen Fälle finden lassen, die Fakten nahezu identisch sind und die Begründung außerordentlich überzeugend ist. Frühe amerikanische Entscheidungen zitierten oft britische Fälle, solche Zitate verschwanden aber während des 19. Jahrhunderts, als die Gerichte eindeutig amerikanische Lösungen zu lokalen Konflikten fanden. In der aktuellen Rechtsprechung beziehen sich fast alle Zitate auf amerikanische Fälle.\ Einige Anhänger des Originalismus und der strikten Gesetzestextauslegung (''strict constructionism''), wie zum Beispiel der verstorbene Bundesrichter am Obersten Gerichtshof, Antonin Scalia, vertreten die Meinung, dass amerikanische Gerichte ''nie'' ausländische Fälle überprüfen sollten, die nach dem Unabhängigkeitskrieg entschieden wurden, unabhängig davon, ob die Argumentation überzeugend ist oder nicht. Die einzige Ausnahme wird hier in Fällen gesehen, die durch die Vereinigten Staaten ratifizierte völkerrechtliche Verträge betreffen. Andere Richter, wie zum Beispiel Anthony Kennedy und Stephen Breyer vertreten eine andere Ansicht und benutzen ausländische Rechtsprechung, sofern ihre Argumentation für sie überzeugend, nützlich oder hilfreich ist.", "document_id": 43958 } ] }, ``` ### Data Fields - `id`: 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 The dataset is split into a one-way annotated training set and a three-way annotated test set of German Wikipedia passages (paragraphs). Each passage is from a different article. | |passages|questions|answers| |----------|----:|---------:|---------:| |train|2540| 11518|11518| |test|474| 2204|6536| ## Additional Information ### Dataset Curators The dataset was initially created by Timo Möller, Julian Risch, Malte Pietsch, Julian Gutsch, Tom Hersperger, Luise Köhler, Iuliia Mozhina, and Justus Peter, during work done at deepset.ai ### Citation Information ``` @misc{möller2021germanquad, title={GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrieval}, author={Timo Möller and Julian Risch and Malte Pietsch}, year={2021}, eprint={2104.12741}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

mozilla-foundation/common_voice_9_0

mozilla-foundation

--- annotations_creators: - crowdsourced language_creators: - crowdsourced license: - cc0-1.0 multilinguality: - multilingual size_categories: ab: - 10K<n<100K ar: - 100K<n<1M as: - n<1K az: - n<1K ba: - 100K<n<1M bas: - 1K<n<10K be: - 100K<n<1M bg: - 1K<n<10K bn: - 100K<n<1M br: - 10K<n<100K ca: - 1M<n<10M ckb: - 10K<n<100K cnh: - 1K<n<10K cs: - 10K<n<100K cv: - 10K<n<100K cy: - 100K<n<1M da: - 1K<n<10K de: - 100K<n<1M dv: - 10K<n<100K el: - 10K<n<100K en: - 1M<n<10M eo: - 1M<n<10M es: - 100K<n<1M et: - 10K<n<100K eu: - 100K<n<1M fa: - 100K<n<1M fi: - 10K<n<100K fr: - 100K<n<1M fy-NL: - 10K<n<100K ga-IE: - 1K<n<10K gl: - 10K<n<100K gn: - 1K<n<10K ha: - 1K<n<10K hi: - 10K<n<100K hsb: - 1K<n<10K hu: - 10K<n<100K hy-AM: - 1K<n<10K ia: - 10K<n<100K id: - 10K<n<100K ig: - 1K<n<10K it: - 100K<n<1M ja: - 10K<n<100K ka: - 1K<n<10K kab: - 100K<n<1M kk: - 1K<n<10K kmr: - 10K<n<100K ky: - 10K<n<100K lg: - 100K<n<1M lt: - 10K<n<100K lv: - 1K<n<10K mdf: - n<1K mhr: - 10K<n<100K mk: - n<1K ml: - 1K<n<10K mn: - 10K<n<100K mr: - 10K<n<100K mt: - 10K<n<100K myv: - 1K<n<10K nan-tw: - 1K<n<10K nl: - 10K<n<100K nn-NO: - n<1K or: - 1K<n<10K pa-IN: - 1K<n<10K pl: - 100K<n<1M pt: - 100K<n<1M rm-sursilv: - 1K<n<10K rm-vallader: - 1K<n<10K ro: - 10K<n<100K ru: - 100K<n<1M rw: - 1M<n<10M sah: - 1K<n<10K sat: - n<1K sk: - 10K<n<100K sl: - 10K<n<100K sr: - 1K<n<10K sv-SE: - 10K<n<100K sw: - 100K<n<1M ta: - 100K<n<1M th: - 100K<n<1M tig: - n<1K tok: - 1K<n<10K tr: - 10K<n<100K tt: - 10K<n<100K ug: - 10K<n<100K uk: - 10K<n<100K ur: - 10K<n<100K uz: - 100K<n<1M vi: - 10K<n<100K vot: - n<1K yue: - 10K<n<100K zh-CN: - 10K<n<100K zh-HK: - 100K<n<1M zh-TW: - 100K<n<1M source_datasets: - extended|common_voice paperswithcode_id: common-voice pretty_name: Common Voice Corpus 9.0 language_bcp47: - ab - ar - as - az - ba - bas - be - bg - bn - br - ca - ckb - cnh - cs - cv - cy - da - de - dv - el - en - eo - es - et - eu - fa - fi - fr - fy-NL - ga-IE - gl - gn - ha - hi - hsb - hu - hy-AM - ia - id - ig - it - ja - ka - kab - kk - kmr - ky - lg - lt - lv - mdf - mhr - mk - ml - mn - mr - mt - myv - nan-tw - nl - nn-NO - or - pa-IN - pl - pt - rm-sursilv - rm-vallader - ro - ru - rw - sah - sat - sk - sl - sr - sv-SE - sw - ta - th - tig - tok - tr - tt - ug - uk - ur - uz - vi - vot - yue - zh-CN - zh-HK - zh-TW extra_gated_prompt: By clicking on “Access repository” below, you also agree to not attempt to determine the identity of speakers in the Common Voice dataset. task_categories: - automatic-speech-recognition --- # Dataset Card for Common Voice Corpus 9.0 ## 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://commonvoice.mozilla.org/en/datasets - **Repository:** https://github.com/common-voice/common-voice - **Paper:** https://arxiv.org/abs/1912.06670 - **Leaderboard:** https://paperswithcode.com/dataset/common-voice - **Point of Contact:** [Anton Lozhkov](mailto:anton@huggingface.co) ### Dataset Summary The Common Voice dataset consists of a unique MP3 and corresponding text file. Many of the 20217 recorded hours in the dataset also include demographic metadata like age, sex, and accent that can help improve the accuracy of speech recognition engines. The dataset currently consists of 14973 validated hours in 93 languages, but more voices and languages are always added. Take a look at the [Languages](https://commonvoice.mozilla.org/en/languages) page to request a language or start contributing. ### Supported Tasks and Leaderboards The results for models trained on the Common Voice datasets are available via the [🤗 Speech Bench](https://huggingface.co/spaces/huggingface/hf-speech-bench) ### Languages ``` Abkhaz, Arabic, Armenian, Assamese, Azerbaijani, Basaa, Bashkir, Basque, Belarusian, Bengali, Breton, Bulgarian, Cantonese, Catalan, Central Kurdish, Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Chuvash, Czech, Danish, Dhivehi, Dutch, English, Erzya, Esperanto, Estonian, Finnish, French, Frisian, Galician, Georgian, German, Greek, Guarani, Hakha Chin, Hausa, Hindi, Hungarian, Igbo, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kazakh, Kinyarwanda, Kurmanji Kurdish, Kyrgyz, Latvian, Lithuanian, Luganda, Macedonian, Malayalam, Maltese, Marathi, Meadow Mari, Moksha, Mongolian, Norwegian Nynorsk, Odia, Persian, Polish, Portuguese, Punjabi, Romanian, Romansh Sursilvan, Romansh Vallader, Russian, Sakha, Santali (Ol Chiki), Serbian, Slovak, Slovenian, Sorbian, Upper, Spanish, Swahili, Swedish, Taiwanese (Minnan), Tamil, Tatar, Thai, Tigre, Toki Pona, Turkish, Ukrainian, Urdu, Uyghur, Uzbek, Vietnamese, Votic, Welsh ``` ## Dataset Structure ### Data Instances A typical data point comprises the `path` to the audio file and its `sentence`. Additional fields include `accent`, `age`, `client_id`, `up_votes`, `down_votes`, `gender`, `locale` and `segment`. ```python { 'client_id': 'd59478fbc1ee646a28a3c652a119379939123784d99131b865a89f8b21c81f69276c48bd574b81267d9d1a77b83b43e6d475a6cfc79c232ddbca946ae9c7afc5', 'path': 'et/clips/common_voice_et_18318995.mp3', 'audio': { 'path': 'et/clips/common_voice_et_18318995.mp3', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 48000 }, 'sentence': 'Tasub kokku saada inimestega, keda tunned juba ammust ajast saati.', 'up_votes': 2, 'down_votes': 0, 'age': 'twenties', 'gender': 'male', 'accent': '', 'locale': 'et', 'segment': '' } ``` ### Data Fields `client_id` (`string`): An id for which client (voice) made the recording `path` (`string`): The path to the audio file `audio` (`dict`): 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` (`string`): The sentence the user was prompted to speak `up_votes` (`int64`): How many upvotes the audio file has received from reviewers `down_votes` (`int64`): How many downvotes the audio file has received from reviewers `age` (`string`): The age of the speaker (e.g. `teens`, `twenties`, `fifties`) `gender` (`string`): The gender of the speaker `accent` (`string`): Accent of the speaker `locale` (`string`): The locale of the speaker `segment` (`string`): Usually an empty field ### Data Splits The speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other. The validated data is data that has been validated with reviewers and received upvotes that the data is of high quality. The invalidated data is data has been invalidated by reviewers and received downvotes indicating that the data is of low quality. The reported data is data that has been reported, for different reasons. The other data is data that has not yet been reviewed. The dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train. ## Data Preprocessing Recommended by Hugging Face The following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. Many examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_. In addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, **almost all** sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation. ```python from datasets import load_dataset ds = load_dataset("mozilla-foundation/common_voice_9_0", "en", use_auth_token=True) def prepare_dataset(batch): """Function to preprocess the dataset with the .map method""" transcription = batch["sentence"] if transcription.startswith('"') and transcription.endswith('"'): # we can remove trailing quotation marks as they do not affect the transcription transcription = transcription[1:-1] if transcription[-1] not in [".", "?", "!"]: # append a full-stop to sentences that do not end in punctuation transcription = transcription + "." batch["sentence"] = transcription return batch ds = ds.map(prepare_dataset, desc="preprocess dataset") ``` ## 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 the Common Voice dataset. ## Considerations for Using the Data ### Social Impact of Dataset The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset. ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Public Domain, [CC-0](https://creativecommons.org/share-your-work/public-domain/cc0/) ### Citation Information ``` @inproceedings{commonvoice:2020, author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.}, title = {Common Voice: A Massively-Multilingual Speech Corpus}, booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)}, pages = {4211--4215}, year = 2020 } ```

open-llm-leaderboard/details_Charlie911__vicuna-7b-v1.5-lora-mctaco

open-llm-leaderboard

--- pretty_name: Evaluation run of Charlie911/vicuna-7b-v1.5-lora-mctaco dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Charlie911/vicuna-7b-v1.5-lora-mctaco](https://huggingface.co/Charlie911/vicuna-7b-v1.5-lora-mctaco)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Charlie911__vicuna-7b-v1.5-lora-mctaco\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-17T20:27:23.554125](https://huggingface.co/datasets/open-llm-leaderboard/details_Charlie911__vicuna-7b-v1.5-lora-mctaco/blob/main/results_2023-09-17T20-27-23.554125.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.2488464765100671,\n\ \ \"em_stderr\": 0.004427614016278926,\n \"f1\": 0.28849937080536914,\n\ \ \"f1_stderr\": 0.00442953185165108,\n \"acc\": 0.372010258662628,\n\ \ \"acc_stderr\": 0.00929094831305589\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.2488464765100671,\n \"em_stderr\": 0.004427614016278926,\n\ \ \"f1\": 0.28849937080536914,\n \"f1_stderr\": 0.00442953185165108\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.04473085670962851,\n \ \ \"acc_stderr\": 0.005693886131407047\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.6992896606156275,\n \"acc_stderr\": 0.012888010494704732\n\ \ }\n}\n```" repo_url: https://huggingface.co/Charlie911/vicuna-7b-v1.5-lora-mctaco leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|arc:challenge|25_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|arc:challenge|25_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-09-01T09:03:24.370765.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_17T20_27_23.554125 path: - '**/details_harness|drop|3_2023-09-17T20-27-23.554125.parquet' - split: latest path: - '**/details_harness|drop|3_2023-09-17T20-27-23.554125.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_17T20_27_23.554125 path: - '**/details_harness|gsm8k|5_2023-09-17T20-27-23.554125.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-09-17T20-27-23.554125.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hellaswag|10_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hellaswag|10_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-01T09:00:53.100273.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-01T09:03:24.370765.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-management|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-management|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-virology|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-virology|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-01T09:03:24.370765.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_01T09_00_53.100273 path: - '**/details_harness|truthfulqa:mc|0_2023-09-01T09:00:53.100273.parquet' - split: 2023_09_01T09_03_24.370765 path: - '**/details_harness|truthfulqa:mc|0_2023-09-01T09:03:24.370765.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-09-01T09:03:24.370765.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_17T20_27_23.554125 path: - '**/details_harness|winogrande|5_2023-09-17T20-27-23.554125.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-09-17T20-27-23.554125.parquet' - config_name: results data_files: - split: 2023_09_01T09_00_53.100273 path: - results_2023-09-01T09:00:53.100273.parquet - split: 2023_09_01T09_03_24.370765 path: - results_2023-09-01T09:03:24.370765.parquet - split: 2023_09_17T20_27_23.554125 path: - results_2023-09-17T20-27-23.554125.parquet - split: latest path: - results_2023-09-17T20-27-23.554125.parquet --- # Dataset Card for Evaluation run of Charlie911/vicuna-7b-v1.5-lora-mctaco ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/Charlie911/vicuna-7b-v1.5-lora-mctaco - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [Charlie911/vicuna-7b-v1.5-lora-mctaco](https://huggingface.co/Charlie911/vicuna-7b-v1.5-lora-mctaco) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Charlie911__vicuna-7b-v1.5-lora-mctaco", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-17T20:27:23.554125](https://huggingface.co/datasets/open-llm-leaderboard/details_Charlie911__vicuna-7b-v1.5-lora-mctaco/blob/main/results_2023-09-17T20-27-23.554125.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.2488464765100671, "em_stderr": 0.004427614016278926, "f1": 0.28849937080536914, "f1_stderr": 0.00442953185165108, "acc": 0.372010258662628, "acc_stderr": 0.00929094831305589 }, "harness|drop|3": { "em": 0.2488464765100671, "em_stderr": 0.004427614016278926, "f1": 0.28849937080536914, "f1_stderr": 0.00442953185165108 }, "harness|gsm8k|5": { "acc": 0.04473085670962851, "acc_stderr": 0.005693886131407047 }, "harness|winogrande|5": { "acc": 0.6992896606156275, "acc_stderr": 0.012888010494704732 } } ``` ### 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 [More Information Needed]

dell-research-harvard/AmericanStories

dell-research-harvard

--- license: cc-by-4.0 task_categories: - text-classification - text-generation - text-retrieval - summarization - question-answering language: - en tags: - social science - economics - news - newspaper - large language modeling - nlp - lam pretty_name: AmericanStories size_categories: - 100M<n<1B --- # Dataset Card for the American Stories dataset ## Dataset Description - **Homepage:** Coming Soon - **Repository:** https://github.com/dell-research-harvard/AmericanStories - **Paper:** Coming Soon =- **Point of Contact:** melissa.dell@gmail.com ### Dataset Summary The American Stories dataset is a collection of full article texts extracted from historical U.S. newspaper images. It includes nearly 20 million scans from the public domain Chronicling America collection maintained by the Library of Congress. The dataset is designed to address the challenges posed by complex layouts and low OCR quality in existing newspaper datasets. It was created using a novel deep learning pipeline that incorporates layout detection, legibility classification, custom OCR, and the association of article texts spanning multiple bounding boxes. It employs efficient architectures specifically designed for mobile phones to ensure high scalability. The dataset offers high-quality data that can be utilized for various purposes. It can be used to pre-train large language models and improve their understanding of historical English and world knowledge. The dataset can also be integrated into retrieval-augmented language models, making historical information more accessible, including interpretations of political events and details about people's ancestors. Additionally, the structured article texts in the dataset enable the use of transformer-based methods for applications such as detecting reproduced content. This significantly enhances accuracy compared to relying solely on existing OCR techniques. The American Stories dataset serves as an invaluable resource for developing multimodal layout analysis models and other multimodal applications. Its vast size and silver quality make it ideal for innovation and research in this domain. ### Languages English (en) ## Dataset Structure The raw data on this repo contains compressed chunks of newspaper scans for each year. Each scan has its own JSON file named as the {scan_id}.json. The data loading script takes care of the downloading, extraction, and parsing to outputs of two kinds : + Article-Level Output: The unit of the Dataset Dict is an associated article + Scan Level Output: The unit of the Dataset Dict is an entire scan with all the raw unparsed data ### Data Instances Here are some examples of what the output looks like. #### Article level ``` { 'article_id': '1_1870-01-01_p1_sn82014899_00211105483_1870010101_0773', 'newspaper_name': 'The weekly Arizona miner.', 'edition': '01', 'date': '1870-01-01', 'page': 'p1', 'headline': '', 'byline': '', 'article': 'PREyors 10 leaving San Francisco for Wash ington City, our Governor, A. r. K. Saford. called upon Generals Thomas and Ord and nt the carrying out of what (truncated)' } ``` #### Scan level ``` {'raw_data_string': '{"lccn": {"title": "The Massachusetts spy, or, Thomas\'s Boston journal.", "geonames_ids": ["4930956"],....other_keys:values} ``` ### Data Fields #### Article Level + "article_id": Unique Id for an associated article + "newspaper_name": Newspaper Name + "edition": Edition number + "date": Date of publication + "page": Page number + "headline": Headline Text + "byline": Byline Text + "article": Article Text #### Scan Level "raw_data_string": Unparsed scan-level data that contains scan metadata from Library of Congress, all content regions with their bounding boxes, OCR text and legibility classification ### Data Splits There are no train, test or val splits. Since the dataset has a massive number of units (articles or newspaper scans), we have split the data by year. Once the dataset is loaded, instead of the usual way of accessing a split as dataset["train"], specific years can be accessed using the syntax dataset["year"] where year can be any year between 1774-1963 as long as there is at least one scan for the year. The data loading script provides options to download both a subset of years and all years at a time. ### Accessing the Data There are 4 config options that can be used to access the data depending upon the use-case. ``` from datasets import load_dataset # Download data for the year 1809 at the associated article level (Default) dataset = load_dataset("dell-research-harvard/AmericanStories", "subset_years", year_list=["1809", "1810"] ) # Download and process data for all years at the article level dataset = load_dataset("dell-research-harvard/AmericanStories", "all_years" ) # Download and process data for 1809 at the scan level dataset = load_dataset("dell-research-harvard/AmericanStories", "subset_years_content_regions", year_list=["1809"] ) # Download ad process data for all years at the scan level dataset = load_dataset("dell-research-harvard/AmericanStories", "all_years_content_regions") ``` ## Dataset Creation ### Curation Rationale The dataset was created to provide researchers with a large, high-quality corpus of structured and transcribed newspaper article texts from historical local American newspapers. These texts provide a massive repository of information about topics ranging from political polarization to the construction of national and cultural identities to the minutiae of the daily lives of people's ancestors. The dataset will be useful to a wide variety of researchers including historians, other social scientists, and NLP practitioners. ### Source Data #### Initial Data Collection and Normalization The dataset is drawn entirely from image scans in the public domain that are freely available for download from the Library of Congress's website. We processed all images as described in the associated paper. #### Who are the source language producers? The source language was produced by people - by newspaper editors, columnists, and other sources. ### Annotations #### Annotation process Not Applicable #### Who are the annotators? Not Applicable ### Personal and Sensitive Information Not Applicable ## Considerations for Using the Data ### Social Impact of Dataset This dataset provides high-quality data that could be used for pre-training a large language model to achieve better understanding of historical English and historical world knowledge. The dataset could also be added to the external database of a retrieval-augmented language model to make historical information - ranging from interpretations of political events to minutiae about the lives of people's ancestors - more widely accessible. Furthermore, structured article texts that it provides can facilitate using transformer-based methods for popular applications like detection of reproduced content, significantly improving accuracy relative to using the existing OCR. It can also be used for innovating multimodal layout analysis models and other multimodal applications. ### Discussion of Biases This dataset contains unfiltered content composed by newspaper editors, columnists, and other sources. In addition to other potentially harmful content, the corpus may contain factual errors and intentional misrepresentations of news events. All content should be viewed as individuals' opinions and not as a purely factual account of events of the day. ## Additional Information ### Dataset Curators Melissa Dell (Harvard), Jacob Carlson (Harvard), Tom Bryan (Harvard) , Emily Silcock (Harvard), Abhishek Arora (Harvard), Zejiang Shen (MIT), Luca D'Amico-Wong (Harvard), Quan Le (Princeton), Pablo Querubin (NYU), Leander Heldring (Kellog School of Business) ### Licensing Information The dataset has a CC-BY 4.0 license ### Citation Information Coming Soon ### Contributions Coming Soon

igbo_monolingual

--- annotations_creators: - found language_creators: - found language: - ig license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K - n<1K source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: null pretty_name: Igbo Monolingual Dataset dataset_info: - config_name: eze_goes_to_school features: - name: format dtype: string - name: title dtype: string - name: chapters sequence: - name: title dtype: string - name: content dtype: string splits: - name: train num_bytes: 128309 num_examples: 1 download_size: 8260947 dataset_size: 128309 - config_name: bbc-igbo features: - name: source dtype: string - name: title dtype: string - name: description dtype: string - name: date dtype: string - name: headline dtype: string - name: content dtype: string - name: tags sequence: string splits: - name: train num_bytes: 3488908 num_examples: 1297 download_size: 8260947 dataset_size: 3488908 - config_name: igbo-radio features: - name: source dtype: string - name: headline dtype: string - name: author dtype: string - name: date dtype: string - name: description dtype: string - name: content dtype: string splits: - name: train num_bytes: 1129644 num_examples: 440 download_size: 8260947 dataset_size: 1129644 - config_name: jw-ot-igbo features: - name: format dtype: string - name: title dtype: string - name: chapters sequence: - name: title dtype: string - name: content dtype: string splits: - name: train num_bytes: 3489314 num_examples: 39 download_size: 8260947 dataset_size: 3489314 - config_name: jw-nt-igbo features: - name: format dtype: string - name: title dtype: string - name: chapters sequence: - name: title dtype: string - name: content dtype: string splits: - name: train num_bytes: 1228779 num_examples: 27 download_size: 8260947 dataset_size: 1228779 - config_name: jw-books features: - name: title dtype: string - name: content dtype: string - name: format dtype: string - name: date dtype: string splits: - name: train num_bytes: 9456342 num_examples: 48 download_size: 8260947 dataset_size: 9456342 - config_name: jw-teta features: - name: title dtype: string - name: content dtype: string - name: format dtype: string - name: date dtype: string splits: - name: train num_bytes: 991111 num_examples: 37 download_size: 8260947 dataset_size: 991111 - config_name: jw-ulo_nche features: - name: title dtype: string - name: content dtype: string - name: format dtype: string - name: date dtype: string splits: - name: train num_bytes: 1952360 num_examples: 55 download_size: 8260947 dataset_size: 1952360 - config_name: jw-ulo_nche_naamu features: - name: title dtype: string - name: content dtype: string - name: format dtype: string - name: date dtype: string splits: - name: train num_bytes: 7248017 num_examples: 88 download_size: 8260947 dataset_size: 7248017 config_names: - bbc-igbo - eze_goes_to_school - igbo-radio - jw-books - jw-nt-igbo - jw-ot-igbo - jw-teta - jw-ulo_nche - jw-ulo_nche_naamu --- # Dataset Card for Igbo Monolingual 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:** https://github.com/IgnatiusEzeani/IGBONLP/tree/master/ig_monoling - **Repository:** https://github.com/IgnatiusEzeani/IGBONLP/tree/master/ig_monoling - **Paper:** https://arxiv.org/abs/2004.00648 ### Dataset Summary A dataset is a collection of Monolingual Igbo sentences. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Igbo (ig) ## Dataset Structure ### Data Instances Here is an example from the bb-igbo config: ``` {'content': 'Ike Ekweremmadụ\n\nIke ịda jụụ otụ nkeji banyere oke ogbugbu na-eme n\'ala Naijiria agwụla Ekweremmadụ\n\nOsote onye-isi ndị ome-iwu Naịjirịa bụ Ike Ekweremadu ekwuola na ike agwụla ndị Sịnatị iji otu nkeji darajụụ akwanyere ndị egburu n\'ime oke ọgbaghara dị na Naịjirịa oge ọ bula.\n\nEkweremadu katọrọ mwakpọ na ogbugbu ndị Naịjirịa aka ha dị ọcha nke ndị Fulani na-achị ehi mere, kwuo na ike agwụla ndị ome- iwu ịkwanyere ha ugwu n\'otu nkeji\'\n\nCheta n\'otu ịzụka gara-aga ka emere akwam ozu mmadụ ruru iri asaa egburu na Local Gọọmenti Logo na Guma nke Benue Steeti, e be ihe kariri mmadụ iri ise ka akụkọ kwuru n\'egburu na Taraba Steeti.\n\nEkweremadu gosiri iwe gbasara ogbugbu ndị mmadụ na nzukọ ndị ome-iwu n\'ụbọchị taa, kwuo na Naịjirịa ga-ebu ụzọ nwe udo na nchekwa, tupu e kwuowa okwu iwulite obodo.\n\nỌ sịrị: "Ndị ome-iwu abụghị sọ ọsọ ndị ihe a metụtara, kama ndị Naịjirịa niile.\n\n\'Ike agwụla anyị iji otu nkeji dị jụụ maka nkwanye ugwu. Ihe anyị chọrọ bụ udo na nchekwa tupu echewa echịchị nwuli obodo."', 'date': '2018-01-19T17:07:38Z', 'description': "N'ihi oke ogbugbu ndị mmadụ na Naịjirịa gbagburu gburu, osota onyeisi ndị ome-iwu Naịjirịa bụ Ike Ekweremadu ekwuola na ihe Naịjiria chọrọ bụ nchekwa tara ọchịchị, tupu ekwuwa okwu ihe ọzọ.", 'headline': 'Ekweremadu: Ike agwụla ndị ụlọ ome iwu', 'source': 'https://www.bbc.com/igbo/42712250', 'tags': [], 'title': 'Ekweremadu: Ike agwụla ndị ụlọ ome iwu'} ``` ### Data Fields For config 'eze_goes_to_school': - format, title, chapters For config 'bbc-igbo' : - source, title, description, date (Missing date values replaced with empty strings), headline, content, tags (Missing tags replaced with empty list) For config 'igbo-radio': - source, headline, author, date, description, content For config 'jw-ot-igbo': - format, title, chapters For config 'jw-nt-igbo': - format, title, chapters For config 'jw-books': - title, content, format, date (Missing date values replaced with empty strings) For config 'jw-teta': - title, content, format, date (Missing date values replaced with empty strings) For config 'jw-ulo_nche': - title, content, format, date (Missing date values replaced with empty strings) For config 'jw-ulo_nche_naamu': - title, content, format, date (Missing date values replaced with empty strings) ### Data Splits | bbc-igbo | eze_goes_to_school |igbo-radio| jw-books|jw-nt-igbo| jw-ot-igbo | jw-teta |jw-ulo_nche |jw-ulo_nche_naamu | ------------- |:-------------:|:-------------:|:-------------:|:-------------:|:-------------:|:-------------:|:-------------:|:-------------:| | 1297 | 1 | 440 | 48 | 27 | 39 | 37 | 55 | 88 ## 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 @misc{ezeani2020igboenglish, title={Igbo-English Machine Translation: An Evaluation Benchmark}, author={Ignatius Ezeani and Paul Rayson and Ikechukwu Onyenwe and Chinedu Uchechukwu and Mark Hepple}, year={2020}, eprint={2004.00648}, archivePrefix={arXiv}, primaryClass={cs.CL} } ### Contributions Thanks to [@purvimisal](https://github.com/purvimisal) for adding this dataset.

liwu/MNBVC

liwu

--- annotations_creators: - other language: - zh language_creators: - other license: - mit multilinguality: - monolingual pretty_name: MNBVC size_categories: - unknown source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling --- # Dataset Card for MNBVC ## Table of Contents - [Dataset Card for MNBVC](#dataset-card-for-mnbvc) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [数据集介绍](#数据集介绍) - [数据子集](#数据子集) - [数据格式](#数据格式) - [文本数据](#文本数据) - [问答数据](#问答数据) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://mnbvc.253874.net/ - **Repository:** https://github.com/esbatmop/MNBVC - **Paper:** N/A - **Leaderboard:** N/A - **Point of Contact:** N/A ### 数据集介绍 中文互联网上最古老最神秘(没有之一)的里屋社区于2023.1.1庄重宣布: 在英明神武的里屋管子带领下，决心发挥社区所长(哪都长)，帮助开源社区长期更新一份最大的中文互联网语料集。 Huggingface上的MNBVC数据集在逐渐更新中，请到[https://github.com/esbatmop/MNBVC](https://github.com/esbatmop/MNBVC) 获取未完成清洗的更多数据。 可以使用如下脚本加载： ```python from datasets import load_dataset dataset = load_dataset("liwu/MNBVC", 'law_judgement', split='train', streaming=True) next(iter(dataset)) # get the first line ``` ## 数据子集 MNBVC数据集包含数个子集： - `law_judgement`: 来自法律文书的文本。 - `gov_xuexiqiangguo`: 来自学习强国的文本。 - `gov_report`: 来自政府工作报告的文本。 - `co_ann_report`: 企业年报文本。 - `code_metadata`: 代码元数据。 - `qa_zhihu`: 来自知乎的问答数据。 - `qa_wikihow`: 来自wikihow的问答数据。 - `qa_mfa`: 外交部问答数据。 - `news_peoples_daily`: 来自人民日报的文本数据。 - `wikipedia`: 来自维基百科的文本数据。 - `qa_stackexchange`: 来自StackExchange的问答数据。 - `qa_chatgpt`: 使用ChatGPT构造的问答语料，感谢[genggui001](https://github.com/genggui001)贡献语料。 - `math_qa`: 和数学领域有关的问答数据。 - `math_chat`: 和数学领域有关的对话数据数据，可以提升模型Chain of Thought的能力。 - `crawler_oscar`: 从CommonCrawl中清洗出来的通用文本数据。 ## 数据格式 目前MNBVC数据集包含如下几类数据： - 通用文本 - 问答语料 - 代码语料 - 多轮对话 - 论坛语料 - 平行语料 可以在[MNBVC的wiki页面](https://wiki.mnbvc.org/doku.php/%E7%8E%B0%E6%9C%89%E8%AF%AD%E6%96%99%E6%A0%BC%E5%BC%8F)上查看这几类数据的具体格式。 项目早期所上传的数据使用如下格式，以后这一格式会被废弃，相应数据也会重新上传： ```json { "text": datasets.Value("string"), "meta": datasets.Value("string") } ``` ### Contributions Thanks to the [Liwu community](http://mnbvc.253874.net/) for constructing this dataset. Thanks to [silver](https://github.com/silverriver) and [jiaming](https://huggingface.co/Yjiaming) for adding and uploading this dataset to Huggingface.

cfq

--- annotations_creators: - no-annotation language_creators: - expert-generated language: - en license: - cc-by-4.0 multilinguality: - monolingual pretty_name: Compositional Freebase Questions size_categories: - 100K<n<1M source_datasets: - original task_categories: - question-answering - other task_ids: - open-domain-qa - closed-domain-qa paperswithcode_id: cfq tags: - compositionality dataset_info: - config_name: mcd1 features: - name: question dtype: string - name: query dtype: string splits: - name: train num_bytes: 37408806 num_examples: 95743 - name: test num_bytes: 5446503 num_examples: 11968 download_size: 267599061 dataset_size: 42855309 - config_name: mcd2 features: - name: question dtype: string - name: query dtype: string splits: - name: train num_bytes: 39424657 num_examples: 95743 - name: test num_bytes: 5314019 num_examples: 11968 download_size: 267599061 dataset_size: 44738676 - config_name: mcd3 features: - name: question dtype: string - name: query dtype: string splits: - name: train num_bytes: 38316345 num_examples: 95743 - name: test num_bytes: 5244503 num_examples: 11968 download_size: 267599061 dataset_size: 43560848 - config_name: question_complexity_split features: - name: question dtype: string - name: query dtype: string splits: - name: train num_bytes: 39989433 num_examples: 98999 - name: test num_bytes: 5781561 num_examples: 10340 download_size: 267599061 dataset_size: 45770994 - config_name: question_pattern_split features: - name: question dtype: string - name: query dtype: string splits: - name: train num_bytes: 41217350 num_examples: 95654 - name: test num_bytes: 5179936 num_examples: 11909 download_size: 267599061 dataset_size: 46397286 - config_name: query_complexity_split features: - name: question dtype: string - name: query dtype: string splits: - name: train num_bytes: 40270175 num_examples: 100654 - name: test num_bytes: 5634924 num_examples: 9512 download_size: 267599061 dataset_size: 45905099 - config_name: query_pattern_split features: - name: question dtype: string - name: query dtype: string splits: - name: train num_bytes: 40811284 num_examples: 94600 - name: test num_bytes: 5268358 num_examples: 12589 download_size: 267599061 dataset_size: 46079642 - config_name: random_split features: - name: question dtype: string - name: query dtype: string splits: - name: train num_bytes: 41279218 num_examples: 95744 - name: test num_bytes: 5164923 num_examples: 11967 download_size: 267599061 dataset_size: 46444141 --- # Dataset Card for "cfq" ## 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/google-research/google-research/tree/master/cfq](https://github.com/google-research/google-research/tree/master/cfq) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** https://arxiv.org/abs/1912.09713 - **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:** 2.14 GB - **Size of the generated dataset:** 362.07 MB - **Total amount of disk used:** 2.50 GB ### Dataset Summary The Compositional Freebase Questions (CFQ) is a dataset that is specifically designed to measure compositional generalization. CFQ is a simple yet realistic, large dataset of natural language questions and answers that also provides for each question a corresponding SPARQL query against the Freebase knowledge base. This means that CFQ can also be used for semantic parsing. ### 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 #### mcd1 - **Size of downloaded dataset files:** 267.60 MB - **Size of the generated dataset:** 42.90 MB - **Total amount of disk used:** 310.49 MB An example of 'train' looks as follows. ``` { 'query': 'SELECT count(*) WHERE {\n?x0 a ns:people.person .\n?x0 ns:influence.influence_node.influenced M1 .\n?x0 ns:influence.influence_node.influenced M2 .\n?x0 ns:people.person.spouse_s/ns:people.marriage.spouse|ns:fictional_universe.fictional_character.married_to/ns:fictional_universe.marriage_of_fictional_characters.spouses ?x1 .\n?x1 a ns:film.cinematographer .\nFILTER ( ?x0 != ?x1 )\n}', 'question': 'Did a person marry a cinematographer , influence M1 , and influence M2' } ``` #### mcd2 - **Size of downloaded dataset files:** 267.60 MB - **Size of the generated dataset:** 44.77 MB - **Total amount of disk used:** 312.38 MB An example of 'train' looks as follows. ``` { 'query': 'SELECT count(*) WHERE {\n?x0 ns:people.person.parents|ns:fictional_universe.fictional_character.parents|ns:organization.organization.parent/ns:organization.organization_relationship.parent ?x1 .\n?x1 a ns:people.person .\nM1 ns:business.employer.employees/ns:business.employment_tenure.person ?x0 .\nM1 ns:business.employer.employees/ns:business.employment_tenure.person M2 .\nM1 ns:business.employer.employees/ns:business.employment_tenure.person M3 .\nM1 ns:business.employer.employees/ns:business.employment_tenure.person M4 .\nM5 ns:business.employer.employees/ns:business.employment_tenure.person ?x0 .\nM5 ns:business.employer.employees/ns:business.employment_tenure.person M2 .\nM5 ns:business.employer.employees/ns:business.employment_tenure.person M3 .\nM5 ns:business.employer.employees/ns:business.employment_tenure.person M4\n}', 'question': "Did M1 and M5 employ M2 , M3 , and M4 and employ a person 's child" } ``` #### mcd3 - **Size of downloaded dataset files:** 267.60 MB - **Size of the generated dataset:** 43.60 MB - **Total amount of disk used:** 311.20 MB An example of 'train' looks as follows. ``` { "query": "SELECT /producer M0 . /director M0 . ", "question": "Who produced and directed M0?" } ``` #### query_complexity_split - **Size of downloaded dataset files:** 267.60 MB - **Size of the generated dataset:** 45.95 MB - **Total amount of disk used:** 313.55 MB An example of 'train' looks as follows. ``` { "query": "SELECT /producer M0 . /director M0 . ", "question": "Who produced and directed M0?" } ``` #### query_pattern_split - **Size of downloaded dataset files:** 267.60 MB - **Size of the generated dataset:** 46.12 MB - **Total amount of disk used:** 313.72 MB An example of 'train' looks as follows. ``` { "query": "SELECT /producer M0 . /director M0 . ", "question": "Who produced and directed M0?" } ``` ### Data Fields The data fields are the same among all splits and configurations: - `question`: a `string` feature. - `query`: a `string` feature. ### Data Splits | name | train | test | |---------------------------|-------:|------:| | mcd1 | 95743 | 11968 | | mcd2 | 95743 | 11968 | | mcd3 | 95743 | 11968 | | query_complexity_split | 100654 | 9512 | | query_pattern_split | 94600 | 12589 | | question_complexity_split | 98999 | 10340 | | question_pattern_split | 95654 | 11909 | | random_split | 95744 | 11967 | ## 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{Keysers2020, title={Measuring Compositional Generalization: A Comprehensive Method on Realistic Data}, author={Daniel Keysers and Nathanael Sch"{a}rli and Nathan Scales and Hylke Buisman and Daniel Furrer and Sergii Kashubin and Nikola Momchev and Danila Sinopalnikov and Lukasz Stafiniak and Tibor Tihon and Dmitry Tsarkov and Xiao Wang and Marc van Zee and Olivier Bousquet}, booktitle={ICLR}, year={2020}, url={https://arxiv.org/abs/1912.09713.pdf}, } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@brainshawn](https://github.com/brainshawn) for adding this dataset.

Multimodal-Fatima/OK-VQA_train

Multimodal-Fatima

--- dataset_info: features: - name: image dtype: image - name: question_type dtype: string - name: confidence dtype: int32 - name: answers sequence: string - name: answers_original list: - name: answer dtype: string - name: raw_answer dtype: string - name: answer_confidence dtype: string - name: answer_id dtype: int64 - name: id_image dtype: int64 - name: answer_type dtype: string - name: question_id dtype: int64 - name: question dtype: string - name: id dtype: int64 - name: clip_tags_ViT_L_14 sequence: string - name: clip_tags_LAION_ViT_H_14_2B sequence: string - name: blip_caption_beam_5 dtype: string - name: LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14 sequence: string - name: LLM_Description_gpt3_downstream_tasks_visual_genome_LAION-ViT-H-14-2B sequence: string - name: DETA_detections_deta_swin_large_o365_coco_classes list: - name: attribute dtype: string - name: box sequence: float32 - name: label dtype: string - name: location dtype: string - name: ratio dtype: float32 - name: size dtype: string - name: tag dtype: string - name: DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random list: - name: attribute dtype: string - name: box sequence: float64 - name: captions_module sequence: string - name: captions_module_filter sequence: string - name: label dtype: string - name: location dtype: string - name: ratio dtype: float64 - name: size dtype: string - name: tag dtype: string splits: - name: train num_bytes: 1686555802.0 num_examples: 9009 download_size: 1572400067 dataset_size: 1686555802.0 --- # Dataset Card for "OK-VQA_train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

hatexplain

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: [] paperswithcode_id: hatexplain pretty_name: hatexplain tags: - hate-speech-detection dataset_info: features: - name: id dtype: string - name: annotators sequence: - name: label dtype: class_label: names: '0': hatespeech '1': normal '2': offensive - name: annotator_id dtype: int32 - name: target sequence: string - name: rationales sequence: sequence: int32 - name: post_tokens sequence: string config_name: plain_text splits: - name: train num_bytes: 7114730 num_examples: 15383 - name: validation num_bytes: 884940 num_examples: 1922 - name: test num_bytes: 884784 num_examples: 1924 download_size: 12848091 dataset_size: 8884454 --- # Dataset Card for hatexplain ## 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:** [Needs More Information] - **Repository:** https://github.com/punyajoy/HateXplain/ - **Paper:** https://arxiv.org/abs/2012.10289 - **Leaderboard:** [Needs More Information] - **Point of Contact:** Punyajoy Saha (punyajoys@iitkgp.ac.in) ### Dataset Summary Hatexplain is the first benchmark hate speech dataset covering multiple aspects of the issue. Each post in the dataset is annotated from three different perspectives: the basic, commonly used 3-class classification (i.e., hate, offensive or normal), the target community (i.e., the community that has been the victim of hate speech/offensive speech in the post), and the rationales, i.e., the portions of the post on which their labeling decision (as hate, offensive or normal) is based. WARNING: This dataset contains content that are offensive and/or hateful in nature. ### Supported Tasks and Leaderboards [Needs More Information] ### Languages The language supported is English. ## Dataset Structure ### Data Instances Sample Entry: ``` { "id": "24198545_gab", "annotators": [ { "label": 0, # hatespeech "annotator_id": 4, "target": ["African"] }, { "label": 0, # hatespeech "annotator_id": 3, "target": ["African"] }, { "label": 2, # offensive "annotator_id": 5, "target": ["African"] } ], "rationales":[ [0,0,0,0,0,0,0,0,1,0,0,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0], [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], [0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] ], "post_tokens": ["and","this","is","why","i","end","up","with","nigger","trainee","doctors","who","can","not","speak","properly","lack","basic","knowledge","of","biology","it","truly","scary","if","the","public","only","knew"] } } ``` ### Data Fields :small_blue_diamond:post_id : Unique id for each post<br/> :small_blue_diamond:annotators : The list of annotations from each annotator<br/> :small_blue_diamond:annotators[label] : The label assigned by the annotator to this post. Possible values: `hatespeech` (0), `normal` (1) or `offensive` (2)<br/> :small_blue_diamond:annotators[annotator_id] : The unique Id assigned to each annotator<br/> :small_blue_diamond:annotators[target] : A list of target community present in the post<br/> :small_blue_diamond:rationales : A list of rationales selected by annotators. Each rationales represents a list with values 0 or 1. A value of 1 means that the token is part of the rationale selected by the annotator. To get the particular token, we can use the same index position in "post_tokens"<br/> :small_blue_diamond:post_tokens : The list of tokens representing the post which was annotated<br/> ### Data Splits [Post_id_divisions](https://github.com/hate-alert/HateXplain/blob/master/Data/post_id_divisions.json) has a dictionary having train, valid and test post ids that are used to divide the dataset into train, val and test set in the ratio of 8:1:1. ## Dataset Creation ### Curation Rationale The existing hate speech datasets do not provide human rationale which could justify the human reasoning behind their annotation process. This dataset allows researchers to move a step in this direction. The dataset provides token-level annotatoins for the annotation decision. ### Source Data We collected the data from Twitter and Gab. #### Initial Data Collection and Normalization We combined the lexicon set provided by [Davidson 2017](https://arxiv.org/abs/1703.04009), [Ousidhoum 2019](https://arxiv.org/abs/1908.11049), and [Mathew 2019](https://arxiv.org/abs/1812.01693) to generate a single lexicon. We do not consider reposts and remove duplicates. We also ensure that the posts do not contain links, pictures, or videos as they indicate additional information that mightnot be available to the annotators. However, we do not exclude the emojis from the text as they might carry importantinformation for the hate and offensive speech labeling task. #### Who are the source language producers? The dataset is human generated using Amazon Mechanical Turk (AMT). ### Annotations #### Annotation process Each post in our dataset contains three types of annotations. First, whether the text is a hate speech, offensive speech, or normal. Second, the target communities in the text. Third, if the text is considered as hate speech, or offensive by majority of the annotators, we further ask the annotators to annotate parts of the text, which are words orphrases that could be a potential reason for the given annotation. Before starting the annotation task, workers are explicitly warned that the annotation task displays some hateful or offensive content. We prepare instructions for workers that clearly explain the goal of the annotation task, how to annotate spans and also include a definition for each category. We provide multiple examples with classification, target community and span annotations to help the annotators understand the task. #### Who are the annotators? To ensure high quality dataset, we use built-in MTurk qualification requirements, namely the HITApproval Rate(95%) for all Requesters’ HITs and the Number of HITs Approved(5,000) requirements. Pilot annotation: In the pilot task, each annotator was provided with 20 posts and they were required to do the hate/offensive speech classification as well as identify the target community (if any). In order to have a clear understanding of the task, they were provided with multiple examples along with explanations for the labelling process. The main purpose of the pilot task was to shortlist those annotators who were able to do the classification accurately. We also collected feedback from annotators to improve the main annotation task. A total of 621 annotators took part in the pilot task. Out of these, 253 were selected for the main task. Main annotation: After the pilot annotation, once we had ascertained the quality of the annotators, we started with the main annotation task. In each round, we would select a batch of around 200 posts. Each post was annotated by three annotators, then majority voting was applied to decide the final label. The final dataset is composed of 9,055 posts from Twitter and 11,093 posts from Gab. The Krippendorff's alpha for the inter-annotator agreement is 0.46 which is higher than other hate speech datasets. ### Personal and Sensitive Information The posts were anonymized by replacing the usernames with <user> token. ## Considerations for Using the Data ### Social Impact of Dataset The dataset could prove beneficial to develop models which are more explainable and less biased. ### Discussion of Biases [Needs More Information] ### Other Known Limitations The dataset has some limitations. First is the lack of external context. The dataset lacks any external context such as profile bio, user gender, history of posts etc., which might be helpful in the classification task. Another issue is the focus on English language and lack of multilingual hate speech. ## Additional Information ### Dataset Curators Binny Mathew - IIT Kharagpur, India Punyajoy Saha - IIT Kharagpur, India Seid Muhie Yimam - Universit ̈at Hamburg, Germany Chris Biemann - Universit ̈at Hamburg, Germany Pawan Goyal - IIT Kharagpur, India Animesh Mukherjee - IIT Kharagpur, India ### Licensing Information MIT License ### Citation Information ```bibtex @article{mathew2020hatexplain, title={HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection}, author={Binny Mathew and Punyajoy Saha and Seid Muhie Yimam and Chris Biemann and Pawan Goyal and Animesh Mukherjee}, year={2021}, conference={AAAI conference on artificial intelligence} } ### Contributions Thanks to [@kushal2000](https://github.com/kushal2000) for adding this dataset.

alkzar90/CC6204-Hackaton-Cub-Dataset

alkzar90

--- language: - en license: - apache-2.0 pretty_name: CC6204-Hackaton-CUB200 size_categories: - 10K<n<15K source_datasets: - extended|other paperswithcode_id: cub-200-2011 task_categories: - image-classification - text-classification task_ids: - multi-class-image-classification --- ## Dataset Description - **Homepage:** [CUB 200 2011](http://www.vision.caltech.edu/datasets/cub_200_2011/) - **Repository:** [Caltech Vision Lab](http://www.vision.caltech.edu/datasets/cub_200_2011/) - **Paper:** [The Caltech-UCSD Birds-200-2011 Dataset](https://authors.library.caltech.edu/27452/1/CUB_200_2011.pdf) - **Leaderboard:** [Paperswithcode](https://paperswithcode.com/dataset/cub-200-2011) - **Point of Contact:** [Catherine Wah](https://scholar.google.com/citations?user=rCDdLUsAAAAJ&hl=en) # CC6204: Hackaton Deep Learning 2022 **Nota:** esta fue un actividad del curso CC6204: Deep Learning, Universidad de Chile, año 2022. Dictado por el profesor Iván Sipiran, material del curso [aquí](https://github.com/ivansipiran/CC6204-Deep-Learning). En esta actividad intentaremos resolver un problema de clasificación multimodal. En un problema de clasificación multimodal, cada pieza de información viene en diferentes representaciones (imágenes, texto, audios, etc) y la idea es determinar cómo usar esos datos para un problema de clasificación. En este caso trabajaremos con un dataset que contiene datos sobre especies de pájaros. ## Dataset ### Data Instances Una muestra del _dataset_ se encuentra a continuación: ``` {'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=334x500 at 0x7F59DE348AF0>, 'description': 'this bird has a short orange bill, white breast and body and white eyes.\na medium sized bird with a orange bill and a black crown and white eyes\nthis white-breasted bird has a short, squat, orange bill, a black head and wings, and small white eyes above a white stripe.\nthis bird has a white breast, a black head, a short red beak, and webbed feet.\nthis bird is white with black on its neck and has a long, pointy beak.\nthis bird has wings that are black and has a white belly\nthis bird has wings that are black and has a long bill\nthis is a medium sized bird, with a white belly, and a grey head and wings, with a short yellow bill.\nthis bird is white and gray in color, and has a bright orange beak.\nthis bird has a blunt orange beak with mostly black above the neck, the belly is solid white.\n', 'label': 6, 'file_name': 'Parakeet_Auklet_0048_795980.jpg'} ``` ### Data Fields Cada instancia de datos tiene los siguientes campos: - `image`: imagen RGB de un pájaro - `description`: texto con 10 descripciones del pájaro en la foto, cada descripción esta separado por un salto de linea (i.e. `\n`) - `label`: un número entero que representa el id de la especie a la que pertenece el pájaro <details> <summary>Id2String</summary> ```bash 1 001.Black_footed_Albatross 2 002.Laysan_Albatross 3 003.Sooty_Albatross 4 004.Groove_billed_Ani 5 005.Crested_Auklet 6 006.Least_Auklet 7 007.Parakeet_Auklet 8 008.Rhinoceros_Auklet 9 009.Brewer_Blackbird 10 010.Red_winged_Blackbird 11 011.Rusty_Blackbird 12 012.Yellow_headed_Blackbird 13 013.Bobolink 14 014.Indigo_Bunting 15 015.Lazuli_Bunting 16 016.Painted_Bunting 17 017.Cardinal 18 018.Spotted_Catbird 19 019.Gray_Catbird 20 020.Yellow_breasted_Chat 21 021.Eastern_Towhee 22 022.Chuck_will_Widow 23 023.Brandt_Cormorant 24 024.Red_faced_Cormorant 25 025.Pelagic_Cormorant 26 026.Bronzed_Cowbird 27 027.Shiny_Cowbird 28 028.Brown_Creeper 29 029.American_Crow 30 030.Fish_Crow 31 031.Black_billed_Cuckoo 32 032.Mangrove_Cuckoo 33 033.Yellow_billed_Cuckoo 34 034.Gray_crowned_Rosy_Finch 35 035.Purple_Finch 36 036.Northern_Flicker 37 037.Acadian_Flycatcher 38 038.Great_Crested_Flycatcher 39 039.Least_Flycatcher 40 040.Olive_sided_Flycatcher 41 041.Scissor_tailed_Flycatcher 42 042.Vermilion_Flycatcher 43 043.Yellow_bellied_Flycatcher 44 044.Frigatebird 45 045.Northern_Fulmar 46 046.Gadwall 47 047.American_Goldfinch 48 048.European_Goldfinch 49 049.Boat_tailed_Grackle 50 050.Eared_Grebe 51 051.Horned_Grebe 52 052.Pied_billed_Grebe 53 053.Western_Grebe 54 054.Blue_Grosbeak 55 055.Evening_Grosbeak 56 056.Pine_Grosbeak 57 057.Rose_breasted_Grosbeak 58 058.Pigeon_Guillemot 59 059.California_Gull 60 060.Glaucous_winged_Gull 61 061.Heermann_Gull 62 062.Herring_Gull 63 063.Ivory_Gull 64 064.Ring_billed_Gull 65 065.Slaty_backed_Gull 66 066.Western_Gull 67 067.Anna_Hummingbird 68 068.Ruby_throated_Hummingbird 69 069.Rufous_Hummingbird 70 070.Green_Violetear 71 071.Long_tailed_Jaeger 72 072.Pomarine_Jaeger 73 073.Blue_Jay 74 074.Florida_Jay 75 075.Green_Jay 76 076.Dark_eyed_Junco 77 077.Tropical_Kingbird 78 078.Gray_Kingbird 79 079.Belted_Kingfisher 80 080.Green_Kingfisher 81 081.Pied_Kingfisher 82 082.Ringed_Kingfisher 83 083.White_breasted_Kingfisher 84 084.Red_legged_Kittiwake 85 085.Horned_Lark 86 086.Pacific_Loon 87 087.Mallard 88 088.Western_Meadowlark 89 089.Hooded_Merganser 90 090.Red_breasted_Merganser 91 091.Mockingbird 92 092.Nighthawk 93 093.Clark_Nutcracker 94 094.White_breasted_Nuthatch 95 095.Baltimore_Oriole 96 096.Hooded_Oriole 97 097.Orchard_Oriole 98 098.Scott_Oriole 99 099.Ovenbird 100 100.Brown_Pelican 101 101.White_Pelican 102 102.Western_Wood_Pewee 103 103.Sayornis 104 104.American_Pipit 105 105.Whip_poor_Will 106 106.Horned_Puffin 107 107.Common_Raven 108 108.White_necked_Raven 109 109.American_Redstart 110 110.Geococcyx 111 111.Loggerhead_Shrike 112 112.Great_Grey_Shrike 113 113.Baird_Sparrow 114 114.Black_throated_Sparrow 115 115.Brewer_Sparrow 116 116.Chipping_Sparrow 117 117.Clay_colored_Sparrow 118 118.House_Sparrow 119 119.Field_Sparrow 120 120.Fox_Sparrow 121 121.Grasshopper_Sparrow 122 122.Harris_Sparrow 123 123.Henslow_Sparrow 124 124.Le_Conte_Sparrow 125 125.Lincoln_Sparrow 126 126.Nelson_Sharp_tailed_Sparrow 127 127.Savannah_Sparrow 128 128.Seaside_Sparrow 129 129.Song_Sparrow 130 130.Tree_Sparrow 131 131.Vesper_Sparrow 132 132.White_crowned_Sparrow 133 133.White_throated_Sparrow 134 134.Cape_Glossy_Starling 135 135.Bank_Swallow 136 136.Barn_Swallow 137 137.Cliff_Swallow 138 138.Tree_Swallow 139 139.Scarlet_Tanager 140 140.Summer_Tanager 141 141.Artic_Tern 142 142.Black_Tern 143 143.Caspian_Tern 144 144.Common_Tern 145 145.Elegant_Tern 146 146.Forsters_Tern 147 147.Least_Tern 148 148.Green_tailed_Towhee 149 149.Brown_Thrasher 150 150.Sage_Thrasher 151 151.Black_capped_Vireo 152 152.Blue_headed_Vireo 153 153.Philadelphia_Vireo 154 154.Red_eyed_Vireo 155 155.Warbling_Vireo 156 156.White_eyed_Vireo 157 157.Yellow_throated_Vireo 158 158.Bay_breasted_Warbler 159 159.Black_and_white_Warbler 160 160.Black_throated_Blue_Warbler 161 161.Blue_winged_Warbler 162 162.Canada_Warbler 163 163.Cape_May_Warbler 164 164.Cerulean_Warbler 165 165.Chestnut_sided_Warbler 166 166.Golden_winged_Warbler 167 167.Hooded_Warbler 168 168.Kentucky_Warbler 169 169.Magnolia_Warbler 170 170.Mourning_Warbler 171 171.Myrtle_Warbler 172 172.Nashville_Warbler 173 173.Orange_crowned_Warbler 174 174.Palm_Warbler 175 175.Pine_Warbler 176 176.Prairie_Warbler 177 177.Prothonotary_Warbler 178 178.Swainson_Warbler 179 179.Tennessee_Warbler 180 180.Wilson_Warbler 181 181.Worm_eating_Warbler 182 182.Yellow_Warbler 183 183.Northern_Waterthrush 184 184.Louisiana_Waterthrush 185 185.Bohemian_Waxwing 186 186.Cedar_Waxwing 187 187.American_Three_toed_Woodpecker 188 188.Pileated_Woodpecker 189 189.Red_bellied_Woodpecker 190 190.Red_cockaded_Woodpecker 191 191.Red_headed_Woodpecker 192 192.Downy_Woodpecker 193 193.Bewick_Wren 194 194.Cactus_Wren 195 195.Carolina_Wren 196 196.House_Wren 197 197.Marsh_Wren 198 198.Rock_Wren 199 199.Winter_Wren 200 200.Common_Yellowthroat ``` </details> - `file_name`: nombre del archivo que tiene la imagen ### Data Splits | |train| test| |------------------|----:|----:| |# de observaciones|5994 |5794 | ## Problema El problema consiste en entrenar un modelo que clasifique instancias del dataset CUB de la mejor manera posible. Algunas preguntas que podrían guiar nuestro desarrollo son: * Se podrá obtener un buen _performance_ de clasificación solo usando las imágenes del dataset? Este tipo de problema sería el clásico problema de clasificar imágenes. * Se podrá obtener un buen _performance_ de clasificación solo usando los textos del dataset? Este tipo de problema sería el clásico problema de clasificar texto. * Se podrá obtener un mejor _performance_ si combino la información en un modelo multimodal? Cómo construyo un modelo multimodal que reciba una imagen y un texto y clasifique la instancia con su respectiva especie? Hint: piense en cómo una red neuronal (la que sea) es simplemente una función que recibe un dato y genera una representación de alto nivel (vector característico) de ese dato. Una red CNN podría hacerse cargo de calcular la representación de una imagen y una red RNN podría hacerse cargo de calcular la representación del texto. Finalmente concateno ambas representaciones y entreno un MLP final que hace la clasificación. ## Experimentación Como el dataset es grande y los recursos de computación son muy limitados, una estrategia para hacer los experimentos es tomar una muestra más pequeña de datos para ir probando las ideas. Para esta estrategia, éstas son dos ideas válidas: * Tomar menos instancias por cada clase para el desarrollo y solo dejar el dataset final para hacer el entrenamiento final y la evaluación final con testing. * Tomar menos clases para el desarrollo inicial y solo dejar el dataset final para hacer el entrenamiento final y la evaluación final con testing. Ambas estrategias nos permiten lidiar con los recursos limitados que tenemos, pero cuáles son sus ventajas o desventajas? Si usas alguna de estas estrategias, puedes comentar este punto en tu desarrollo final. ## Métrica de Evaluación La métrica que se debe reportar es el accuracy en conjunto de test. ## Citation Information Sitio web del [_dataset_ CUB200](http://www.vision.caltech.edu/datasets/cub_200_2011/), y reporte técnico [aquí](https://authors.library.caltech.edu/27452/1/CUB_200_2011.pdf). ``` @techreport{WahCUB_200_2011, Title = The Caltech-UCSD Birds-200-2011 Dataset, Author = {Wah, C. and Branson, S. and Welinder, P. and Perona, P. and Belongie, S.}, Year = {2011} Institution = {California Institute of Technology}, Number = {CNS-TR-2011-001} } ``` ## Contributions Creación y adaptación del material de la actividad en un Hugging Face dataset por Cristóbal Alcázar.

nchlt

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - af - nr - nso - ss - tn - ts - ve - xh - zu license: - cc-by-2.5 multilinguality: - multilingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: NCHLT dataset_info: - config_name: af features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': OUT '1': B-PERS '2': I-PERS '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-MISC '8': I-MISC splits: - name: train num_bytes: 3955069 num_examples: 8961 download_size: 25748344 dataset_size: 3955069 - config_name: nr features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': OUT '1': B-PERS '2': I-PERS '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-MISC '8': I-MISC splits: - name: train num_bytes: 3188781 num_examples: 9334 download_size: 20040327 dataset_size: 3188781 - config_name: xh features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': OUT '1': B-PERS '2': I-PERS '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-MISC '8': I-MISC splits: - name: train num_bytes: 2365821 num_examples: 6283 download_size: 14513302 dataset_size: 2365821 - config_name: zu features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': OUT '1': B-PERS '2': I-PERS '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-MISC '8': I-MISC splits: - name: train num_bytes: 3951366 num_examples: 10955 download_size: 25097584 dataset_size: 3951366 - config_name: nso-sepedi features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': OUT '1': B-PERS '2': I-PERS '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-MISC '8': I-MISC splits: - name: train num_bytes: 3322296 num_examples: 7116 download_size: 22077376 dataset_size: 3322296 - config_name: nso-sesotho features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': OUT '1': B-PERS '2': I-PERS '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-MISC '8': I-MISC splits: - name: train num_bytes: 4427898 num_examples: 9471 download_size: 30421109 dataset_size: 4427898 - config_name: tn features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': OUT '1': B-PERS '2': I-PERS '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-MISC '8': I-MISC splits: - name: train num_bytes: 3812339 num_examples: 7943 download_size: 25905236 dataset_size: 3812339 - config_name: ss features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': OUT '1': B-PERS '2': I-PERS '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-MISC '8': I-MISC splits: - name: train num_bytes: 3431063 num_examples: 10797 download_size: 21882224 dataset_size: 3431063 - config_name: ve features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': OUT '1': B-PERS '2': I-PERS '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-MISC '8': I-MISC splits: - name: train num_bytes: 3941041 num_examples: 8477 download_size: 26382457 dataset_size: 3941041 - config_name: ts features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': OUT '1': B-PERS '2': I-PERS '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-MISC '8': I-MISC splits: - name: train num_bytes: 3941041 num_examples: 8477 download_size: 26382457 dataset_size: 3941041 --- # Dataset Card for NCHLT ## 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](https://repo.sadilar.org/handle/20.500.12185/7/discover?filtertype_0=database&filtertype_1=title&filter_relational_operator_1=contains&filter_relational_operator_0=equals&filter_1=&filter_0=Monolingual+Text+Corpora%3A+Annotated&filtertype=project&filter_relational_operator=equals&filter=NCHLT+Text+II) - **Repository:** []() - **Paper:** []() - **Leaderboard:** []() - **Point of Contact:** []() ### Dataset Summary The development of linguistic resources for use in natural language processingis of utmost importance for the continued growth of research anddevelopment in the field, especially for resource-scarce languages. In this paper we describe the process and challenges of simultaneouslydevelopingmultiple linguistic resources for ten of the official languages of South Africa. The project focussed on establishing a set of foundational resources that can foster further development of both resources and technologies for the NLP industry in South Africa. The development efforts during the project included creating monolingual unannotated corpora, of which a subset of the corpora for each language was annotated on token, orthographic, morphological and morphosyntactic layers. The annotated subsetsincludes both development and test setsand were used in the creation of five core-technologies, viz. atokeniser, sentenciser,lemmatiser, part of speech tagger and morphological decomposer for each language. We report on the quality of these tools for each language and provide some more context of the importance of the resources within the South African context. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure [More Information Needed] ### 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 Martin.Puttkammer@nwu.ac.za ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{eiselen2014developing, title={Developing Text Resources for Ten South African Languages.}, author={Eiselen, Roald and Puttkammer, Martin J}, booktitle={LREC}, pages={3698--3703}, year={2014} } ``` ### Contributions Thanks to [@Narsil](https://github.com/Narsil) for adding this dataset.

anyspeech/ucla_phonetic_corpus

anyspeech

--- dataset_info: features: - name: filename dtype: string - name: phones dtype: string - name: audio struct: - name: array sequence: float32 - name: sampling_rate dtype: int64 splits: - name: eus num_bytes: 3108551 num_examples: 47 - name: kub num_bytes: 1715709 num_examples: 29 - name: abk num_bytes: 4403000 num_examples: 54 - name: ace num_bytes: 2704786 num_examples: 39 - name: ady num_bytes: 10482658 num_examples: 124 - name: aeb num_bytes: 2833699 num_examples: 43 - name: afn num_bytes: 4851569 num_examples: 85 - name: afr num_bytes: 6692077 num_examples: 124 - name: agx num_bytes: 5937667 num_examples: 75 - name: ajp num_bytes: 3582911 num_examples: 51 - name: aka num_bytes: 2255575 num_examples: 40 - name: apc num_bytes: 11257587 num_examples: 157 - name: ape num_bytes: 4480181 num_examples: 70 - name: apw num_bytes: 4576388 num_examples: 62 - name: asm num_bytes: 6262493 num_examples: 86 - name: azb num_bytes: 4725581 num_examples: 60 - name: bam num_bytes: 4344032 num_examples: 69 - name: bem num_bytes: 1838480 num_examples: 26 - name: ben num_bytes: 2484081 num_examples: 40 - name: bfd num_bytes: 1792407 num_examples: 24 - name: bfq num_bytes: 2312935 num_examples: 34 - name: bhk num_bytes: 2261168 num_examples: 33 - name: bin num_bytes: 1596474 num_examples: 24 - name: brv num_bytes: 2927768 num_examples: 45 - name: bsq num_bytes: 1237379 num_examples: 24 - name: bwr num_bytes: 2562919 num_examples: 41 - name: cbv num_bytes: 4163303 num_examples: 63 - name: ces num_bytes: 2866267 num_examples: 42 - name: cha num_bytes: 1527287 num_examples: 24 - name: cji num_bytes: 3050715 num_examples: 45 - name: col num_bytes: 4068720 num_examples: 46 - name: cpn num_bytes: 3932592 num_examples: 63 - name: dag num_bytes: 1617536 num_examples: 23 - name: dan num_bytes: 5385298 num_examples: 87 - name: deg num_bytes: 2555446 num_examples: 39 - name: dyo num_bytes: 2136186 num_examples: 31 - name: efi num_bytes: 3350397 num_examples: 49 - name: ell num_bytes: 3481047 num_examples: 51 - name: ema num_bytes: 1713575 num_examples: 23 - name: ewe num_bytes: 2530156 num_examples: 38 - name: ffm num_bytes: 2261106 num_examples: 31 - name: fin num_bytes: 6433992 num_examples: 107 - name: fub num_bytes: 1490759 num_examples: 23 - name: gaa num_bytes: 1750241 num_examples: 28 - name: gla num_bytes: 1669576 num_examples: 27 - name: guj num_bytes: 3936456 num_examples: 60 - name: gwx num_bytes: 1387208 num_examples: 22 - name: hak num_bytes: 2480163 num_examples: 40 - name: hau num_bytes: 3942393 num_examples: 62 - name: haw num_bytes: 3254444 num_examples: 54 - name: heb num_bytes: 3544505 num_examples: 53 - name: hil num_bytes: 3170052 num_examples: 51 - name: hin num_bytes: 5300326 num_examples: 77 - name: hni num_bytes: 1427423 num_examples: 22 - name: hrv num_bytes: 4676073 num_examples: 74 - name: hun num_bytes: 7922854 num_examples: 124 - name: hye num_bytes: 6344958 num_examples: 81 - name: ibb num_bytes: 4057572 num_examples: 63 - name: ibo num_bytes: 3148749 num_examples: 48 - name: idu num_bytes: 3304523 num_examples: 48 - name: ilo num_bytes: 7581817 num_examples: 90 - name: isl num_bytes: 9679083 num_examples: 162 - name: its num_bytes: 1629008 num_examples: 22 - name: kan num_bytes: 5438898 num_examples: 86 - name: kea num_bytes: 3227702 num_examples: 54 - name: khm num_bytes: 4098080 num_examples: 70 - name: klu num_bytes: 4025430 num_examples: 75 - name: knn num_bytes: 4568917 num_examples: 82 - name: kri num_bytes: 1162442 num_examples: 22 - name: kye num_bytes: 1319998 num_examples: 23 - name: lad num_bytes: 3550365 num_examples: 59 - name: lar num_bytes: 1452546 num_examples: 25 - name: lav num_bytes: 4733523 num_examples: 68 - name: led num_bytes: 1327549 num_examples: 23 - name: lgq num_bytes: 1513947 num_examples: 24 - name: lit num_bytes: 10973034 num_examples: 134 - name: lkt num_bytes: 2718478 num_examples: 42 - name: lug num_bytes: 5087192 num_examples: 67 - name: mak num_bytes: 3951387 num_examples: 49 - name: mal num_bytes: 1484963 num_examples: 20 - name: mlt num_bytes: 6205176 num_examples: 93 - name: mya num_bytes: 6734121 num_examples: 116 - name: nan num_bytes: 4714799 num_examples: 76 - name: njm num_bytes: 2034534 num_examples: 34 - name: nld num_bytes: 5826824 num_examples: 91 - name: ozm num_bytes: 1974820 num_examples: 27 - name: pam num_bytes: 4014947 num_examples: 57 - name: pes num_bytes: 10911547 num_examples: 156 - name: prs num_bytes: 7895016 num_examples: 103 - name: run num_bytes: 3540544 num_examples: 46 - name: sbc num_bytes: 1778804 num_examples: 23 - name: tsw num_bytes: 1913455 num_examples: 27 - name: tzm num_bytes: 2457176 num_examples: 40 - name: wuu num_bytes: 3631436 num_examples: 71 - name: yue num_bytes: 7815231 num_examples: 127 download_size: 427484194 dataset_size: 368082762 --- # Dataset Card for "ucla_phonetic_corpus" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

TokenBender/code_instructions_122k_alpaca_style

TokenBender

--- license: apache-2.0 ---