Datasets:

librarian-bots

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dataset_cards_with_metadata_with_embeddings

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shlomihod/civil-comments-wilds

shlomihod

--- license: cc0-1.0 task_categories: - text-classification language: - en tags: - toxicity pretty_name: CivilComments WILDS size_categories: - 100K<n<1M --- # Dataset Card for CivilComments WILDS ## Dataset Description - **Homepage:** https://wilds.stanford.edu/datasets/#civilcomments - **Repository:** - **Paper:** https://arxiv.org/abs/2012.07421 | https://arxiv.org/abs/1903.04561 - **Leaderboard:** - **Point of Contact:** ### Dataset Summary  Automatic review of user-generated text—e.g., detecting toxic comments—is an important tool for moderating the sheer volume of text written on the Internet. Unfortunately, prior work has shown that such toxicity classifiers pick up on biases in the training data and spuriously associate toxicity with the mention of certain demographics ([Park et al., 2018](https://arxiv.org/abs/1808.07231); [Dixon et al., 2018](https://research.google/pubs/pub46743/)). These types of spurious correlations can significantly degrade model performance on particular subpopulations ([Sagawa et al.,2020](https://arxiv.org/abs/1911.08731)). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English ## 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 This dataset is in the public domain and is distributed under [CC0](https://creativecommons.org/publicdomain/zero/1.0/). ### Citation Information @inproceedings{wilds2021, title = {{WILDS}: A Benchmark of in-the-Wild Distribution Shifts}, author = {Pang Wei Koh and Shiori Sagawa and Henrik Marklund and Sang Michael Xie and Marvin Zhang and Akshay Balsubramani and Weihua Hu and Michihiro Yasunaga and Richard Lanas Phillips and Irena Gao and Tony Lee and Etienne David and Ian Stavness and Wei Guo and Berton A. Earnshaw and Imran S. Haque and Sara Beery and Jure Leskovec and Anshul Kundaje and Emma Pierson and Sergey Levine and Chelsea Finn and Percy Liang}, booktitle = {International Conference on Machine Learning (ICML)}, year = {2021} } @inproceedings{borkan2019nuanced, title={Nuanced metrics for measuring unintended bias with real data for text classification}, author={Borkan, Daniel and Dixon, Lucas and Sorensen, Jeffrey and Thain, Nithum and Vasserman, Lucy}, booktitle={Companion Proceedings of The 2019 World Wide Web Conference}, pages={491--500}, year={2019} } @article{DBLP:journals/corr/abs-2211-09110, author = {Percy Liang and Rishi Bommasani and Tony Lee and Dimitris Tsipras and Dilara Soylu and Michihiro Yasunaga and Yian Zhang and Deepak Narayanan and Yuhuai Wu and Ananya Kumar and Benjamin Newman and Binhang Yuan and Bobby Yan and Ce Zhang and Christian Cosgrove and Christopher D. Manning and Christopher R{\'{e}} and Diana Acosta{-}Navas and Drew A. Hudson and Eric Zelikman and Esin Durmus and Faisal Ladhak and Frieda Rong and Hongyu Ren and Huaxiu Yao and Jue Wang and Keshav Santhanam and Laurel J. Orr and Lucia Zheng and Mert Y{\"{u}}ksekg{\"{o}}n{\"{u}}l and Mirac Suzgun and Nathan Kim and Neel Guha and Niladri S. Chatterji and Omar Khattab and Peter Henderson and Qian Huang and Ryan Chi and Sang Michael Xie and Shibani Santurkar and Surya Ganguli and Tatsunori Hashimoto and Thomas Icard and Tianyi Zhang and Vishrav Chaudhary and William Wang and Xuechen Li and Yifan Mai and Yuhui Zhang and Yuta Koreeda}, title = {Holistic Evaluation of Language Models}, journal = {CoRR}, volume = {abs/2211.09110}, year = {2022}, url = {https://doi.org/10.48550/arXiv.2211.09110}, doi = {10.48550/arXiv.2211.09110}, eprinttype = {arXiv}, eprint = {2211.09110}, timestamp = {Wed, 23 Nov 2022 18:03:56 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2211-09110.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ### Contributions [More Information Needed]

ropes

--- pretty_name: ROPES annotations_creators: - crowdsourced language_creators: - crowdsourced - found language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended|wikipedia - original task_categories: - question-answering task_ids: - extractive-qa paperswithcode_id: ropes dataset_info: features: - name: id dtype: string - name: background dtype: string - name: situation dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string config_name: plain_text splits: - name: train num_bytes: 12231940 num_examples: 10924 - name: test num_bytes: 1928532 num_examples: 1710 - name: validation num_bytes: 1643498 num_examples: 1688 download_size: 3516917 dataset_size: 15803970 --- # Dataset Card for ROPES ## 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:** [ROPES dataset](https://allenai.org/data/ropes) - **Paper:** [Reasoning Over Paragraph Effects in Situations](https://arxiv.org/abs/1908.05852) - **Leaderboard:** [ROPES leaderboard](https://leaderboard.allenai.org/ropes) ### Dataset Summary ROPES (Reasoning Over Paragraph Effects in Situations) is a QA dataset which tests a system's ability to apply knowledge from a passage of text to a new situation. A system is presented a background passage containing a causal or qualitative relation(s) (e.g., "animal pollinators increase efficiency of fertilization in flowers"), a novel situation that uses this background, and questions that require reasoning about effects of the relationships in the background passage in the context of the situation. ### Supported Tasks and Leaderboards The reading comprehension task is framed as an extractive question answering problem. Models are evaluated by computing word-level F1 and exact match (EM) metrics, following common practice for recent reading comprehension datasets (e.g., SQuAD). ### Languages The text in the dataset is in English. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances Data closely follow the SQuAD v1.1 format. An example looks like this: ``` { "id": "2058517998", "background": "Cancer is a disease that causes cells to divide out of control. Normally, the body has systems that prevent cells from dividing out of control. But in the case of cancer, these systems fail. Cancer is usually caused by mutations. Mutations are random errors in genes. Mutations that lead to cancer usually happen to genes that control the cell cycle. Because of the mutations, abnormal cells divide uncontrollably. This often leads to the development of a tumor. A tumor is a mass of abnormal tissue. As a tumor grows, it may harm normal tissues around it. Anything that can cause cancer is called a carcinogen . Carcinogens may be pathogens, chemicals, or radiation.", "situation": "Jason recently learned that he has cancer. After hearing this news, he convinced his wife, Charlotte, to get checked out. After running several tests, the doctors determined Charlotte has no cancer, but she does have high blood pressure. Relieved at this news, Jason was now focused on battling his cancer and fighting as hard as he could to survive.", "question": "Whose cells are dividing more rapidly?", "answers": { "text": ["Jason"] }, } ``` ### Data Fields - `id`: identification - `background`: background passage - `situation`: the grounding situation - `question`: the question to answer - `answers`: the answer text which is a span from either the situation or the question. The text list always contain a single element. Note that the answers for the test set are hidden (and thus represented as an empty list). Predictions for the test set should be submitted to the leaderboard. ### Data Splits The dataset contains 14k QA pairs over 1.7K paragraphs, split between train (10k QAs), development (1.6k QAs) and a hidden test partition (1.7k QAs). ## Dataset Creation ### Curation Rationale From the original paper: *ROPES challenges reading comprehension models to handle more difficult phenomena: understanding the implications of a passage of text. ROPES is also particularly related to datasets focusing on "multi-hop reasoning", as by construction answering questions in ROPES requires connecting information from multiple parts of a given passage.* *We constructed ROPES by first collecting background passages from science textbooks and Wikipedia articles that describe causal relationships. We showed the collected paragraphs to crowd workers and asked them to write situations that involve the relationships found in the background passage, and questions that connect the situation and the background using the causal relationships. The answers are spans from either the situation or the question. The dataset consists of 14,322 questions from various domains, mostly in science and economics.* ### Source Data From the original paper: *We automatically scraped passages from science textbooks and Wikipedia that contained causal connectives eg. ”causes,” ”leads to,” and keywords that signal qualitative relations, e.g. ”increases,” ”decreases.”. We then manually filtered out the passages that do not have at least one relation. The passages can be categorized into physical science (49%), life science (45%), economics (5%) and other (1%). In total, we collected over 1,000 background passages.* #### Initial Data Collection and Normalization From the original paper: *We used Amazon Mechanical Turk (AMT) to generate the situations, questions, and answers. The AMT workers were given background passages and asked to write situations that involved the relation(s) in the background passage. The AMT workers then authored questions about the situation that required both the background and the situation to answer. In each human intelligence task (HIT), AMT workers are given 5 background passages to select from and are asked to create a total of 10 questions. To mitigate the potential for easy lexical shortcuts in the dataset, the workers were encouraged via instructions to write questions in minimal pairs, where a very small change in the question results in a different answer.* *Most questions are designed to have two sensible answer choices (eg. “more” vs. “less”).* To reduce annotator bias, training and evaluation sets are writter by different annotators. #### 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 The data is distributed under the [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license. ### Citation Information ``` @inproceedings{Lin2019ReasoningOP, title={Reasoning Over Paragraph Effects in Situations}, author={Kevin Lin and Oyvind Tafjord and Peter Clark and Matt Gardner}, booktitle={MRQA@EMNLP}, year={2019} } ``` ### Contributions Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset.

kd_conv

--- annotations_creators: - crowdsourced - machine-generated language_creators: - crowdsourced language: - zh license: - apache-2.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - dialogue-modeling paperswithcode_id: kdconv pretty_name: Knowledge-driven Conversation dataset_info: - config_name: travel_dialogues features: - name: messages sequence: - name: message dtype: string - name: attrs sequence: - name: attrname dtype: string - name: attrvalue dtype: string - name: name dtype: string - name: name dtype: string - name: domain dtype: string splits: - name: train num_bytes: 3241550 num_examples: 1200 - name: test num_bytes: 793883 num_examples: 150 - name: validation num_bytes: 617177 num_examples: 150 download_size: 11037768 dataset_size: 4652610 - config_name: travel_knowledge_base features: - name: head_entity dtype: string - name: kb_triplets sequence: sequence: string - name: domain dtype: string splits: - name: train num_bytes: 1517024 num_examples: 1154 download_size: 11037768 dataset_size: 1517024 - config_name: music_dialogues features: - name: messages sequence: - name: message dtype: string - name: attrs sequence: - name: attrname dtype: string - name: attrvalue dtype: string - name: name dtype: string - name: name dtype: string - name: domain dtype: string splits: - name: train num_bytes: 3006192 num_examples: 1200 - name: test num_bytes: 801012 num_examples: 150 - name: validation num_bytes: 633905 num_examples: 150 download_size: 11037768 dataset_size: 4441109 - config_name: music_knowledge_base features: - name: head_entity dtype: string - name: kb_triplets sequence: sequence: string - name: domain dtype: string splits: - name: train num_bytes: 5980643 num_examples: 4441 download_size: 11037768 dataset_size: 5980643 - config_name: film_dialogues features: - name: messages sequence: - name: message dtype: string - name: attrs sequence: - name: attrname dtype: string - name: attrvalue dtype: string - name: name dtype: string - name: name dtype: string - name: domain dtype: string splits: - name: train num_bytes: 4867659 num_examples: 1200 - name: test num_bytes: 956995 num_examples: 150 - name: validation num_bytes: 884232 num_examples: 150 download_size: 11037768 dataset_size: 6708886 - config_name: film_knowledge_base features: - name: head_entity dtype: string - name: kb_triplets sequence: sequence: string - name: domain dtype: string splits: - name: train num_bytes: 10500882 num_examples: 8090 download_size: 11037768 dataset_size: 10500882 - config_name: all_dialogues features: - name: messages sequence: - name: message dtype: string - name: attrs sequence: - name: attrname dtype: string - name: attrvalue dtype: string - name: name dtype: string - name: name dtype: string - name: domain dtype: string splits: - name: train num_bytes: 11115313 num_examples: 3600 - name: test num_bytes: 2551802 num_examples: 450 - name: validation num_bytes: 2135226 num_examples: 450 download_size: 11037768 dataset_size: 15802341 - config_name: all_knowledge_base features: - name: head_entity dtype: string - name: kb_triplets sequence: sequence: string - name: domain dtype: string splits: - name: train num_bytes: 17998529 num_examples: 13685 download_size: 11037768 dataset_size: 17998529 --- # Dataset Card for KdConv ## 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:** [Github](https://github.com/thu-coai/KdConv) - **Paper:** [{K}d{C}onv: A {C}hinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation](https://www.aclweb.org/anthology/2020.acl-main.635.pdf) ### Dataset Summary KdConv is a Chinese multi-domain Knowledge-driven Conversionsation dataset, grounding the topics in multi-turn conversations to knowledge graphs. KdConv contains 4.5K conversations from three domains (film, music, and travel), and 86K utterances with an average turn number of 19.0. These conversations contain in-depth discussions on related topics and natural transition between multiple topics, while the corpus can also used for exploration of transfer learning and domain adaptation. ### Supported Tasks and Leaderboards This dataset can be leveraged for dialogue modelling tasks involving multi-turn and Knowledge base setup. ### Languages This dataset has only Chinese Language. ## Dataset Structure ### Data Instances Each data instance is a multi-turn conversation between 2 people with annotated knowledge base data used while talking , e.g.: ``` { "messages": [ { "message": "对《我喜欢上你时的内心活动》这首歌有了解吗？" }, { "attrs": [ { "attrname": "Information", "attrvalue": "《我喜欢上你时的内心活动》是由韩寒填词，陈光荣作曲，陈绮贞演唱的歌曲，作为电影《喜欢你》的主题曲于2017年4月10日首发。2018年，该曲先后提名第37届香港电影金像奖最佳原创电影歌曲奖、第7届阿比鹿音乐奖流行单曲奖。", "name": "我喜欢上你时的内心活动" } ], "message": "有些了解，是电影《喜欢你》的主题曲。" }, ... { "attrs": [ { "attrname": "代表作品", "attrvalue": "旅行的意义", "name": "陈绮贞" }, { "attrname": "代表作品", "attrvalue": "时间的歌", "name": "陈绮贞" } ], "message": "我还知道《旅行的意义》与《时间的歌》，都算是她的代表作。" }, { "message": "好，有时间我找出来听听。" } ], "name": "我喜欢上你时的内心活动" } ``` The corresponding entries in Knowledge base is a dictionary with list of knowledge base triplets (head entity , relationship, tail entity), e.g.: ``` "忽然之间": [ [ "忽然之间", "Information", "《忽然之间》是歌手 莫文蔚演唱的歌曲，由 周耀辉， 李卓雄填词， 林健华谱曲，收录在莫文蔚1999年发行专辑《 就是莫文蔚》里。" ], [ "忽然之间", "谱曲", "林健华" ] ... ] ``` ### Data Fields Conversation data fields: - `name`: the starting topic (entity) of the conversation - `domain`: the domain this sample belongs to. Categorical value among `{travel, film, music}` - `messages`: list of all the turns in the dialogue. For each turn: - `message`: the utterance - `attrs`: list of knowledge graph triplets referred by the utterance. For each triplet: - `name`: the head entity - `attrname`: the relation - `attrvalue`: the tail entity Knowledge Base data fields: - `head_entity`: the head entity - `kb_triplets`: list of corresponding triplets - `domain`: the domain this sample belongs to. Categorical value among `{travel, film, music}` ### Data Splits The conversation dataset is split into a `train`, `validation`, and `test` split with the following sizes: | | train | validation | test | |--------|------:|-----------:|-----:| | travel | 1200 | 1200 | 1200 | | film | 1200 | 150 | 150 | | music | 1200 | 150 | 150 | | all | 3600 | 450 | 450 | The Knowledge base dataset is having only train split with following sizes: | | train | |--------|------:| | travel | 1154 | | film | 8090 | | music | 4441 | | all | 13685 | ## 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 Apache License 2.0 ### Citation Information ``` @inproceedings{zhou-etal-2020-kdconv, title = "{K}d{C}onv: A {C}hinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation", author = "Zhou, Hao and Zheng, Chujie and Huang, Kaili and Huang, Minlie and Zhu, Xiaoyan", 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.635", doi = "10.18653/v1/2020.acl-main.635", pages = "7098--7108", } ``` ### Contributions Thanks to [@pacman100](https://github.com/pacman100) for adding this dataset.

snips_built_in_intents

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - cc0-1.0 multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - text-classification task_ids: - intent-classification paperswithcode_id: snips pretty_name: SNIPS Natural Language Understanding benchmark dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': ComparePlaces '1': RequestRide '2': GetWeather '3': SearchPlace '4': GetPlaceDetails '5': ShareCurrentLocation '6': GetTrafficInformation '7': BookRestaurant '8': GetDirections '9': ShareETA splits: - name: train num_bytes: 19431 num_examples: 328 download_size: 9130264 dataset_size: 19431 train-eval-index: - config: default task: text-classification task_id: multi_class_classification train_split: train 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 --- # Dataset Card for Snips Built In Intents ## 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/sonos/nlu-benchmark/tree/master/2016-12-built-in-intents - **Repository:** https://github.com/sonos/nlu-benchmark/tree/master/2016-12-built-in-intents - **Paper:** https://arxiv.org/abs/1805.10190 - **Point of Contact:** The Snips team has joined Sonos in November 2019. These open datasets remain available and their access is now managed by the Sonos Voice Experience Team. Please email sve-research@sonos.com with any question. ### Dataset Summary Snips' built in intents dataset was initially used to compare different voice assistants and released as a public dataset hosted at https://github.com/sonos/nlu-benchmark in folder 2016-12-built-in-intents. The dataset contains 328 utterances over 10 intent classes. A related Medium post is https://medium.com/snips-ai/benchmarking-natural-language-understanding-systems-d35be6ce568d. ### Supported Tasks and Leaderboards There are no related shared tasks that we are aware of. ### Languages English ## Dataset Structure ### Data Instances The dataset contains 328 utterances over 10 intent classes. Each sample looks like: `{'label': 8, 'text': 'Transit directions to Barcelona Pizza.'}` ### Data Fields - `text`: The text utterance expressing some user intent. - `label`: The intent label of the piece of text utterance. ### Data Splits The source data is not split. ## Dataset Creation ### Curation Rationale The dataset was originally created to compare the performance of a number of voice assistants. However, the labelled utterances are useful for developing and benchmarking text chatbots as well. ### Source Data #### Initial Data Collection and Normalization It is not clear how the data was collected. From the Medium post: `The benchmark relies on a set of 328 queries built by the business team at Snips, and kept secret from data scientists and engineers throughout the development of the solution.` #### Who are the source language producers? Originally prepared by snips.ai. The Snips team has since joined Sonos in November 2019. These open datasets remain available and their access is now managed by the Sonos Voice Experience Team. Please email sve-research@sonos.com with any question. ### Annotations #### Annotation process It is not clear how the data was collected. From the Medium post: `The benchmark relies on a set of 328 queries built by the business team at Snips, and kept secret from data scientists and engineers throughout the development of the solution.` #### 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 Originally prepared by snips.ai. The Snips team has since joined Sonos in November 2019. These open datasets remain available and their access is now managed by the Sonos Voice Experience Team. Please email sve-research@sonos.com with any question. ### Licensing Information The source data is licensed under Creative Commons Zero v1.0 Universal. ### Citation Information Any publication based on these datasets must include a full citation to the following paper in which the results were published by the Snips Team: Coucke A. et al., "Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces." CoRR 2018, https://arxiv.org/abs/1805.10190 ### Contributions Thanks to [@bduvenhage](https://github.com/bduvenhage) for adding this dataset.

open-llm-leaderboard/details_Aspik101__trurl-2-7b-pl-instruct_unload

open-llm-leaderboard

--- pretty_name: Evaluation run of Aspik101/trurl-2-7b-pl-instruct_unload dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Aspik101/trurl-2-7b-pl-instruct_unload](https://huggingface.co/Aspik101/trurl-2-7b-pl-instruct_unload)\ \ 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 2 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_Aspik101__trurl-2-7b-pl-instruct_unload\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-22T23:43:45.355114](https://huggingface.co/datasets/open-llm-leaderboard/details_Aspik101__trurl-2-7b-pl-instruct_unload/blob/main/results_2023-09-22T23-43-45.355114.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\": 0.16096895973154363,\n\ \ \"em_stderr\": 0.0037635677120072437,\n \"f1\": 0.22060822147650985,\n\ \ \"f1_stderr\": 0.0038388662178584767,\n \"acc\": 0.3986331756197593,\n\ \ \"acc_stderr\": 0.009900867594093993\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.16096895973154363,\n \"em_stderr\": 0.0037635677120072437,\n\ \ \"f1\": 0.22060822147650985,\n \"f1_stderr\": 0.0038388662178584767\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.07429871114480667,\n \ \ \"acc_stderr\": 0.007223844172845576\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7229676400947119,\n \"acc_stderr\": 0.012577891015342412\n\ \ }\n}\n```" repo_url: https://huggingface.co/Aspik101/trurl-2-7b-pl-instruct_unload 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_08_17T14_40_19.486608 path: - '**/details_harness|arc:challenge|25_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-08-17T14:40:19.486608.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_22T23_43_45.355114 path: - '**/details_harness|drop|3_2023-09-22T23-43-45.355114.parquet' - split: latest path: - '**/details_harness|drop|3_2023-09-22T23-43-45.355114.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_22T23_43_45.355114 path: - '**/details_harness|gsm8k|5_2023-09-22T23-43-45.355114.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-09-22T23-43-45.355114.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hellaswag|10_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-17T14:40:19.486608.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-management|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T14:40:19.486608.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_17T14_40_19.486608 path: - '**/details_harness|truthfulqa:mc|0_2023-08-17T14:40:19.486608.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-08-17T14:40:19.486608.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_22T23_43_45.355114 path: - '**/details_harness|winogrande|5_2023-09-22T23-43-45.355114.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-09-22T23-43-45.355114.parquet' - config_name: results data_files: - split: 2023_08_17T14_40_19.486608 path: - results_2023-08-17T14:40:19.486608.parquet - split: 2023_09_22T23_43_45.355114 path: - results_2023-09-22T23-43-45.355114.parquet - split: latest path: - results_2023-09-22T23-43-45.355114.parquet --- # Dataset Card for Evaluation run of Aspik101/trurl-2-7b-pl-instruct_unload ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/Aspik101/trurl-2-7b-pl-instruct_unload - **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 [Aspik101/trurl-2-7b-pl-instruct_unload](https://huggingface.co/Aspik101/trurl-2-7b-pl-instruct_unload) 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 2 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_Aspik101__trurl-2-7b-pl-instruct_unload", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-22T23:43:45.355114](https://huggingface.co/datasets/open-llm-leaderboard/details_Aspik101__trurl-2-7b-pl-instruct_unload/blob/main/results_2023-09-22T23-43-45.355114.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": 0.16096895973154363, "em_stderr": 0.0037635677120072437, "f1": 0.22060822147650985, "f1_stderr": 0.0038388662178584767, "acc": 0.3986331756197593, "acc_stderr": 0.009900867594093993 }, "harness|drop|3": { "em": 0.16096895973154363, "em_stderr": 0.0037635677120072437, "f1": 0.22060822147650985, "f1_stderr": 0.0038388662178584767 }, "harness|gsm8k|5": { "acc": 0.07429871114480667, "acc_stderr": 0.007223844172845576 }, "harness|winogrande|5": { "acc": 0.7229676400947119, "acc_stderr": 0.012577891015342412 } } ``` ### 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]

aqua_rat

--- annotations_creators: - crowdsourced language_creators: - crowdsourced - expert-generated language: - en license: - apache-2.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - multiple-choice-qa paperswithcode_id: aqua-rat pretty_name: Algebra Question Answering with Rationales dataset_info: - config_name: raw features: - name: question dtype: string - name: options sequence: string - name: rationale dtype: string - name: correct dtype: string splits: - name: train num_bytes: 42333259 num_examples: 97467 - name: test num_bytes: 116779 num_examples: 254 - name: validation num_bytes: 118636 num_examples: 254 download_size: 47833135 dataset_size: 42568674 - config_name: tokenized features: - name: question dtype: string - name: options sequence: string - name: rationale dtype: string - name: correct dtype: string splits: - name: train num_bytes: 46493843 num_examples: 97467 - name: test num_bytes: 126283 num_examples: 254 - name: validation num_bytes: 128873 num_examples: 254 download_size: 52003894 dataset_size: 46748999 --- # Dataset Card for AQUA-RAT ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/deepmind/AQuA](https://github.com/deepmind/AQuA) - **Repository:** [https://github.com/deepmind/AQuA](https://github.com/deepmind/AQuA) - **Paper:** [https://arxiv.org/pdf/1705.04146.pdf](https://arxiv.org/pdf/1705.04146.pdf) ### Dataset Summary A large-scale dataset consisting of approximately 100,000 algebraic word problems. The solution to each question is explained step-by-step using natural language. This data is used to train a program generation model that learns to generate the explanation, while generating the program that solves the question. ### Supported Tasks and Leaderboards ### Languages en ## Dataset Structure ### Data Instances ``` { "question": "A grocery sells a bag of ice for $1.25, and makes 20% profit. If it sells 500 bags of ice, how much total profit does it make?", "options": ["A)125", "B)150", "C)225", "D)250", "E)275"], "rationale": "Profit per bag = 1.25 * 0.20 = 0.25\nTotal profit = 500 * 0.25 = 125\nAnswer is A.", "correct": "A" } ``` ### Data Fields - `question` : (str) A natural language definition of the problem to solve - `options` : (list(str)) 5 possible options (A, B, C, D and E), among which one is correct - `rationale` : (str) A natural language description of the solution to the problem - `correct` : (str) The correct option ### Data Splits | | Train | Valid | Test | | ----- | ------ | ----- | ---- | | Examples | 97467 | 254 | 254 | ## 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 Copyright 2017 Google Inc. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ### Citation Information ``` @article{ling2017program, title={Program induction by rationale generation: Learning to solve and explain algebraic word problems}, author={Ling, Wang and Yogatama, Dani and Dyer, Chris and Blunsom, Phil}, journal={ACL}, year={2017} } ``` ### Contributions Thanks to [@arkhalid](https://github.com/arkhalid) for adding this dataset.

jon-tow/okapi_arc_challenge

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_arc_challenge <!-- Provide a quick summary of the dataset. --> Multilingual translation of [AI2's Arc Challenge](https://allenai.org/data/arc) from the paper *"Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback"* ([Lai et al., 2023](https://arxiv.org/abs/2307.16039)) ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> ARC is a dataset of 7,787 genuine grade-school level, multiple-choice science questions assembled to encourage research in advanced question-answering. The dataset is partitioned into a Challenge Set and an Easy Set, where the former contains only questions answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm. We also include a corpus of over 14 million science sentences relevant to the task and an implementation of three neural baseline models for this dataset. We pose ARC as a challenge to the community. - **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 @article{Clark2018ThinkYH, title={Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge}, author={Peter Clark and Isaac Cowhey and Oren Etzioni and Tushar Khot and Ashish Sabharwal and Carissa Schoenick and Oyvind Tafjord}, journal={ArXiv}, year={2018}, volume={abs/1803.05457} } ```

rcds/swiss_judgment_prediction

rcds

--- pretty_name: Swiss-Judgment-Prediction annotations_creators: - found language_creators: - found language: - de - fr - it - en license: - cc-by-sa-4.0 multilinguality: - multilingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: [] tags: - judgement-prediction dataset_info: - config_name: de features: - name: id dtype: int32 - name: year dtype: int32 - name: text dtype: string - name: label dtype: class_label: names: '0': dismissal '1': approval - name: language dtype: string - name: region dtype: string - name: canton dtype: string - name: legal area dtype: string - name: source_language dtype: string splits: - name: train num_bytes: 104270719 num_examples: 35458 - name: validation num_bytes: 12131878 num_examples: 4705 - name: test num_bytes: 26056177 num_examples: 9725 download_size: 1000382331 dataset_size: 142458774 - config_name: fr features: - name: id dtype: int32 - name: year dtype: int32 - name: text dtype: string - name: label dtype: class_label: names: '0': dismissal '1': approval - name: language dtype: string - name: region dtype: string - name: canton dtype: string - name: legal area dtype: string - name: source_language dtype: string splits: - name: train num_bytes: 96807957 num_examples: 21179 - name: validation num_bytes: 13031904 num_examples: 3095 - name: test num_bytes: 33318359 num_examples: 6820 download_size: 1000382331 dataset_size: 143158220 - config_name: it features: - name: id dtype: int32 - name: year dtype: int32 - name: text dtype: string - name: label dtype: class_label: names: '0': dismissal '1': approval - name: language dtype: string - name: region dtype: string - name: canton dtype: string - name: legal area dtype: string - name: source_language dtype: string splits: - name: train num_bytes: 10773516 num_examples: 3072 - name: validation num_bytes: 1045551 num_examples: 408 - name: test num_bytes: 2474761 num_examples: 812 download_size: 1000382331 dataset_size: 14293828 - config_name: mt_de features: - name: id dtype: int32 - name: year dtype: int32 - name: text dtype: string - name: label dtype: class_label: names: '0': dismissal '1': approval - name: language dtype: string - name: region dtype: string - name: canton dtype: string - name: legal area dtype: string - name: source_language dtype: string splits: - name: train num_bytes: 106990696 num_examples: 24251 - name: validation - name: test download_size: 1000382331 dataset_size: 106990696 - config_name: mt_fr features: - name: id dtype: int32 - name: year dtype: int32 - name: text dtype: string - name: label dtype: class_label: names: '0': dismissal '1': approval - name: language dtype: string - name: region dtype: string - name: canton dtype: string - name: legal area dtype: string - name: source_language dtype: string splits: - name: train num_bytes: 117932134 num_examples: 38524 - name: validation - name: test download_size: 1000382331 dataset_size: 117932134 - config_name: mt_it features: - name: id dtype: int32 - name: year dtype: int32 - name: text dtype: string - name: label dtype: class_label: names: '0': dismissal '1': approval - name: language dtype: string - name: region dtype: string - name: canton dtype: string - name: legal area dtype: string - name: source_language dtype: string splits: - name: train num_bytes: 201749076 num_examples: 56631 - name: validation - name: test download_size: 1000382331 dataset_size: 201749076 - config_name: mt_en features: - name: id dtype: int32 - name: year dtype: int32 - name: text dtype: string - name: label dtype: class_label: names: '0': dismissal '1': approval - name: language dtype: string - name: region dtype: string - name: canton dtype: string - name: legal area dtype: string - name: source_language dtype: string splits: - name: train num_bytes: 196352783 num_examples: 59703 - name: validation - name: test download_size: 1000382331 dataset_size: 196352783 - config_name: all features: - name: id dtype: int32 - name: year dtype: int32 - name: text dtype: string - name: label dtype: class_label: names: '0': dismissal '1': approval - name: language dtype: string - name: region dtype: string - name: canton dtype: string - name: legal area dtype: string - name: source_language dtype: string splits: - name: train num_bytes: 211852192 num_examples: 59709 - name: validation num_bytes: 26209333 num_examples: 8208 - name: test num_bytes: 61849297 num_examples: 17357 download_size: 1000382331 dataset_size: 299910822 - config_name: all+mt features: - name: id dtype: int32 - name: year dtype: int32 - name: text dtype: string - name: label dtype: class_label: names: '0': dismissal '1': approval - name: language dtype: string - name: region dtype: string - name: canton dtype: string - name: legal area dtype: string - name: source_language dtype: string splits: - name: train num_bytes: 834876881 num_examples: 238818 - name: validation num_bytes: 26209333 num_examples: 8208 - name: test num_bytes: 61849297 num_examples: 17357 download_size: 1000382331 dataset_size: 922935511 --- # Dataset Card for "SwissJudgmentPrediction" ## 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/JoelNiklaus/SwissCourtRulingCorpus - **Repository:** https://github.com/JoelNiklaus/SwissCourtRulingCorpus - **Paper:** https://arxiv.org/abs/2110.00806 - **Leaderboard:** N/A - **Point of Contact:** [Joel Niklaus](mailto:joel.niklaus@inf.unibe.ch) ### Dataset Summary **Documents** Swiss-Judgment-Prediction is a multilingual, diachronic dataset of 85K Swiss Federal Supreme Court (FSCS) cases annotated with the respective binarized judgment outcome (approval/dismissal), posing a challenging text classification task. We also provide additional metadata, i.e., the publication year, the legal area and the canton of origin per case, to promote robustness and fairness studies on the critical area of legal NLP. ### Supported Tasks and Leaderboards SwissJudgmentPrediction can be used for the legal judgment prediction task. The dataset is not yet part of an established benchmark. ### Languages Switzerland has four official languages with 3 languages (German, French and Italian) being represented in more than 1000 Swiss Federal Supreme court decisions. The decisions are written by the judges and clerks in the language of the proceedings. ## Dataset Structure In version 2 we added machine translated data using [EasyNMT](https://github.com/UKPLab/EasyNMT) for all documents into German, French, Italian and English as an additional training set. ### Data Instances **Multilingual use of the dataset** When the dataset is used in a multilingual setting selecting the the 'all_languages' flag: ```python from datasets import load_dataset dataset = load_dataset('swiss_judgment_prediction', 'all_languages') ``` ``` { "id": 48757, "year": 2015, "facts": "Sachverhalt: A. X._ war bei der Krankenversicherung C._ taggeldversichert. Infolge einer Arbeitsunf\u00e4higkeit leistete ihm die C._ vom 30. Juni 2011 bis am 28. Juni 2013 Krankentaggelder, wobei die Leistungen bis am 30. September 2012 auf Grundlage einer Arbeitsunf\u00e4higkeit von 100% und danach basierend auf einer Arbeitsunf\u00e4higkeit von 55% erbracht wurden. Die Neueinsch\u00e4tzung der Arbeitsf\u00e4higkeit erfolgte anhand eines Gutachtens der D._ AG vom 27. August 2012, welches im Auftrag der C._ erstellt wurde. X._ machte daraufhin gegen\u00fcber der C._ geltend, er sei entgegen dem Gutachten auch nach dem 30. September 2012 zu 100% arbeitsunf\u00e4hig gewesen. Ferner verlangte er von der D._ AG zwecks externer \u00dcberpr\u00fcfung des Gutachtens die Herausgabe s\u00e4mtlicher diesbez\u00fcglicher Notizen, Auswertungen und Unterlagen. A._ (als Gesch\u00e4ftsf\u00fchrer der D._ AG) und B._ (als f\u00fcr das Gutachten medizinisch Verantwortliche) antworteten ihm, dass sie alle Unterlagen der C._ zugestellt h\u00e4tten und dass allf\u00e4llige Fragen zum Gutachten direkt der C._ zu stellen seien. X._ reichte am 2. Januar 2014 eine Strafanzeige gegen A._ und B._ ein. Er wirft diesen vor, ihn durch die Nichtherausgabe der Dokumente und durch Behinderung des IV-Verfahrens gen\u00f6tigt, Daten besch\u00e4digt bzw. vernichtet und ein falsches \u00e4rztliches Zeugnis ausgestellt zu haben. Zudem h\u00e4tten sie durch die Verz\u00f6gerung des IV-Verfahrens und insbesondere durch das falsche \u00e4rztliche Zeugnis sein Verm\u00f6gen arglistig gesch\u00e4digt. B. Die Staatsanwaltschaft des Kantons Bern, Region Oberland, nahm das Verfahren wegen N\u00f6tigung, Datenbesch\u00e4digung, falschem \u00e4rztlichem Zeugnis und arglistiger Verm\u00f6genssch\u00e4digung mit Verf\u00fcgung vom 10. November 2014 nicht an die Hand. Das Obergericht des Kantons Bern wies die von X._ dagegen erhobene Beschwerde am 27. April 2015 ab, soweit darauf einzutreten war. C. X._ beantragt mit Beschwerde in Strafsachen, der Beschluss vom 27. April 2015 sei aufzuheben und die Angelegenheit zur korrekten Ermittlung des Sachverhalts an die Staatsanwaltschaft zur\u00fcckzuweisen. Er stellt zudem den sinngem\u00e4ssen Antrag, das bundesgerichtliche Verfahren sei w\u00e4hrend der Dauer des konnexen Strafverfahrens gegen eine Teilgutachterin und des ebenfalls konnexen Zivil- oder Strafverfahrens gegen die C._ wegen Einsichtsverweigerung in das mutmasslich gef\u00e4lschte Originalgutachten zu sistieren. X._ ersucht um unentgeltliche Rechtspflege. ", "labels": 0, # dismissal "language": "de", "region": "Espace Mittelland", "canton": "be", "legal area": "penal law" } ``` **Monolingual use of the dataset** When the dataset is used in a monolingual setting selecting the ISO language code for one of the 3 supported languages. For example: ```python from datasets import load_dataset dataset = load_dataset('swiss_judgment_prediction', 'de') ``` ``` { "id": 48757, "year": 2015, "facts": "Sachverhalt: A. X._ war bei der Krankenversicherung C._ taggeldversichert. Infolge einer Arbeitsunf\u00e4higkeit leistete ihm die C._ vom 30. Juni 2011 bis am 28. Juni 2013 Krankentaggelder, wobei die Leistungen bis am 30. September 2012 auf Grundlage einer Arbeitsunf\u00e4higkeit von 100% und danach basierend auf einer Arbeitsunf\u00e4higkeit von 55% erbracht wurden. Die Neueinsch\u00e4tzung der Arbeitsf\u00e4higkeit erfolgte anhand eines Gutachtens der D._ AG vom 27. August 2012, welches im Auftrag der C._ erstellt wurde. X._ machte daraufhin gegen\u00fcber der C._ geltend, er sei entgegen dem Gutachten auch nach dem 30. September 2012 zu 100% arbeitsunf\u00e4hig gewesen. Ferner verlangte er von der D._ AG zwecks externer \u00dcberpr\u00fcfung des Gutachtens die Herausgabe s\u00e4mtlicher diesbez\u00fcglicher Notizen, Auswertungen und Unterlagen. A._ (als Gesch\u00e4ftsf\u00fchrer der D._ AG) und B._ (als f\u00fcr das Gutachten medizinisch Verantwortliche) antworteten ihm, dass sie alle Unterlagen der C._ zugestellt h\u00e4tten und dass allf\u00e4llige Fragen zum Gutachten direkt der C._ zu stellen seien. X._ reichte am 2. Januar 2014 eine Strafanzeige gegen A._ und B._ ein. Er wirft diesen vor, ihn durch die Nichtherausgabe der Dokumente und durch Behinderung des IV-Verfahrens gen\u00f6tigt, Daten besch\u00e4digt bzw. vernichtet und ein falsches \u00e4rztliches Zeugnis ausgestellt zu haben. Zudem h\u00e4tten sie durch die Verz\u00f6gerung des IV-Verfahrens und insbesondere durch das falsche \u00e4rztliche Zeugnis sein Verm\u00f6gen arglistig gesch\u00e4digt. B. Die Staatsanwaltschaft des Kantons Bern, Region Oberland, nahm das Verfahren wegen N\u00f6tigung, Datenbesch\u00e4digung, falschem \u00e4rztlichem Zeugnis und arglistiger Verm\u00f6genssch\u00e4digung mit Verf\u00fcgung vom 10. November 2014 nicht an die Hand. Das Obergericht des Kantons Bern wies die von X._ dagegen erhobene Beschwerde am 27. April 2015 ab, soweit darauf einzutreten war. C. X._ beantragt mit Beschwerde in Strafsachen, der Beschluss vom 27. April 2015 sei aufzuheben und die Angelegenheit zur korrekten Ermittlung des Sachverhalts an die Staatsanwaltschaft zur\u00fcckzuweisen. Er stellt zudem den sinngem\u00e4ssen Antrag, das bundesgerichtliche Verfahren sei w\u00e4hrend der Dauer des konnexen Strafverfahrens gegen eine Teilgutachterin und des ebenfalls konnexen Zivil- oder Strafverfahrens gegen die C._ wegen Einsichtsverweigerung in das mutmasslich gef\u00e4lschte Originalgutachten zu sistieren. X._ ersucht um unentgeltliche Rechtspflege. ", "labels": 0, # dismissal "language": "de", "region": "Espace Mittelland", "canton": "be", "legal area": "penal law" } ``` ### Data Fields **Multilingual use of the dataset** The following data fields are provided for documents (`train`, `validation`, `test`): `id`: (**int**) a unique identifier of the for the document \ `year`: (**int**) the publication year \ `text`: (**str**) the facts of the case \ `label`: (**class label**) the judgment outcome: 0 (dismissal) or 1 (approval) \ `language`: (**str**) one of (de, fr, it) \ `region`: (**str**) the region of the lower court \ `canton`: (**str**) the canton of the lower court \ `legal area`: (**str**) the legal area of the case **Monolingual use of the dataset** The following data fields are provided for documents (`train`, `validation`, `test`): `id`: (**int**) a unique identifier of the for the document \ `year`: (**int**) the publication year \ `text`: (**str**) the facts of the case \ `label`: (**class label**) the judgment outcome: 0 (dismissal) or 1 (approval) \ `language`: (**str**) one of (de, fr, it) \ `region`: (**str**) the region of the lower court \ `canton`: (**str**) the canton of the lower court \ `legal area`: (**str**) the legal area of the case ### Data Splits | Language | Subset | Number of Documents (Training/Validation/Test) | |------------|------------|------------------------------------------------| | German | **de** | 35'452 / 4'705 / 9'725 | | French | **fr** | 21'179 / 3'095 / 6'820 | | Italian | **it** | 3'072 / 408 / 812 | | All | **all** | 59'709 / 8'208 / 17'357 | | MT German | **mt_de** | 24'251 / 0 / 0 | | MT French | **mt_fr** | 38'524 / 0 / 0 | | MT Italian | **mt_it** | 56'631 / 0 / 0 | | MT All | **all+mt** | 238'818 / 8'208 / 17'357 | ## Dataset Creation ### Curation Rationale The dataset was curated by Niklaus et al. (2021). ### Source Data #### Initial Data Collection and Normalization The original data are available at the Swiss Federal Supreme Court (https://www.bger.ch) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (https://entscheidsuche.ch) in HTML. #### Who are the source language producers? Switzerland has four official languages with 3 languages (German, French and Italian) being represented in more than 1000 Swiss Federal Supreme court decisions. The decisions are written by the judges and clerks in the language of the proceedings. ### Annotations #### Annotation process The decisions have been annotated with the binarized judgment outcome using parsers and regular expressions. #### Who are the annotators? Joel Niklaus and Adrian Jörg annotated the binarized judgment outcomes. Metadata is published by the Swiss Federal Supreme Court (https://www.bger.ch). ### Personal and Sensitive Information The dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: https://www.bger.ch/home/juridiction/anonymisierungsregeln.html. ## 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 Niklaus et al. (2021) ### Licensing Information We release the data under CC-BY-4.0 which complies with the court licensing (https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf) © Swiss Federal Supreme Court, 2000-2020 The copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made. Source: https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf ### Citation Information *Joel Niklaus, Ilias Chalkidis, and Matthias Stürmer.* *Swiss-Judgment-Prediction: A Multilingual Legal Judgment Prediction Benchmark* *Proceedings of the 2021 Natural Legal Language Processing Workshop. Punta Cana, Dominican Republic. 2021* ``` @InProceedings{niklaus-etal-2021-swiss, author = {Niklaus, Joel and Chalkidis, Ilias and Stürmer, Matthias}, title = {Swiss-Judgment-Prediction: A Multilingual Legal Judgment Prediction Benchmark}, booktitle = {Proceedings of the 2021 Natural Legal Language Processing Workshop}, year = {2021}, location = {Punta Cana, Dominican Republic}, } ``` and the new citation ``` @misc{niklaus2022empirical, title={An Empirical Study on Cross-X Transfer for Legal Judgment Prediction}, author={Joel Niklaus and Matthias Stürmer and Ilias Chalkidis}, year={2022}, eprint={2209.12325}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@joelniklaus](https://github.com/joelniklaus) for adding this dataset.

codeparrot/github-code-clean

codeparrot

--- license: apache-2.0 --- This is a cleaner version of [Github-code dataset](https://huggingface.co/datasets/codeparrot/github-code), we add the following filters: * Average line length < 100 * Alpha numeric characters fraction > 0.25 * Remove auto-generated files (keyword search) 3.39M files are removed making up 2.94% of the dataset.

jon-tow/okapi_hellaswag

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_hellaswag <!-- Provide a quick summary of the dataset. --> Multilingual translation of [Hellaswag](https://arxiv.org/abs/1905.07830). ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> Hellaswag is a commonsense inference challenge dataset. Though its questions are trivial for humans (>95% accuracy), state-of-the-art models struggle (<48%). This is achieved via Adversarial Filtering (AF), a data collection paradigm wherein a series of discriminators iteratively select an adversarial set of machine-generated wrong answers. AF proves to be surprisingly robust. The key insight is to scale up the length and complexity of the dataset examples towards a critical 'Goldilocks' zone wherein generated text is ridiculous to humans, yet often misclassified by state-of-the-art models.munity. - **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 @inproceedings{zellers2019hellaswag, title={HellaSwag: Can a Machine Really Finish Your Sentence?}, author={Zellers, Rowan and Holtzman, Ari and Bisk, Yonatan and Farhadi, Ali and Choi, Yejin}, booktitle ={Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics}, year={2019} } ```

IlyaGusev/gazeta

IlyaGusev

--- annotations_creators: - expert-generated - found language_creators: - expert-generated - found task_categories: - summarization language: - ru size_categories: - 10K<n<100K license: - unknown multilinguality: - monolingual source_datasets: - original paperswithcode_id: gazeta --- # Dataset Card for Gazeta ## 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/IlyaGusev/gazeta - **Paper:** [Dataset for Automatic Summarization of Russian News](https://arxiv.org/abs/2006.11063) - **Leaderboard:** https://paperswithcode.com/sota/text-summarization-on-gazeta - **Point of Contact:** [Ilya Gusev](ilya.gusev@phystech.edu) ### Dataset Summary Dataset for automatic summarization of Russian news. News and their summaries are from the Gazeta website. Summaries were parsed as the content of an HTML tag with “description” property. Additional selection of good summaries was performed. There are two versions of this dataset. ### Supported Tasks and Leaderboards Leaderboard on Papers With Code: [text-summarization-on-gazeta](https://paperswithcode.com/sota/text-summarization-on-gazeta). Please use the original [evaluation script](https://github.com/IlyaGusev/summarus/blob/master/evaluate.py) with the same parameters. Example: ``` python3 evaluate.py --predicted-path predictions.txt --gold-path targets.txt --language ru --tokenize-after --lower ``` ### Languages The dataset is in Russian. ### Usage Loading version 1.0: ```python from datasets import load_dataset dataset = load_dataset('IlyaGusev/gazeta', revision="v1.0") ``` Loading version 2.0: ```python from datasets import load_dataset dataset = load_dataset('IlyaGusev/gazeta', revision="v2.0") ``` ### Other datasets Other Russian summarization datasets: * Russian part of [XL-Sum](https://huggingface.co/datasets/csebuetnlp/xlsum), parsed from www.bbc.com/russian, 77803 samples * Russian part of [MLSUM](https://huggingface.co/datasets/mlsum), parsed from www.mk.ru, 27063 samples ## Dataset Structure ### Data Instances For each instance, there is a string for the article, a string for the summary, and a string for the url. Additionally, a string for the title and a date are provided. ``` { 'date': '2019-10-01 15:14:05', 'url': 'https://www.gazeta.ru/tech/2019/10/01/12698923/whatsapp_pls.shtml', 'title': 'На последнем издыхании: у кого отключится WhatsApp', 'summary': 'Мессенджер WhatsApp перестанет работать на ряде смартфонов — речь идет о гаджетах на базе операционных систем Android 2.3.7 и iOS 8, которые считаются устаревшими. В компании отмечают, что сервис на этих устройствах может отключиться в любой момент, поэтому будет целесообразно сменить устройство либо обновить ОС.', 'text': 'На официальном сайте мессенджера WhatsApp появилось сообщение о том, что с 1 февраля 2020 года сервис прекратит свою работу на некоторых устаревших смартфонах. Речь идет об устройствах, работающих на базе операционных систем Android 2.3.7 и iOS 8. При этом руководство WhatsApp предупреждает, что даже до обозначенного выше дедлайна функционал мессенджера на этих ОС может быть ограничен. «В связи с тем, что мы не планируем обновлять данные операционные системы, некоторые функции могут перестать работать на них в любое время», — говорится в пресс-релизе компании. Чтобы сохранить возможность пользоваться мессенджером без проблем, следует обновить версию прошивки или приобрести новое, более современное устройство. Сообщается, что на старых версиях операционных систем уже не получится завести новый аккаунт WhatsApp или верифицировать уже существующий. При этом в WhatsApp порекомендовали пользоваться устройствами с Android 4.0.3 и более поздними версиями, а также iOS 9 и более поздними версиями. Ранее стало известно о том, что с 31 декабря 2019 года WhatsApp прекращает поддержку устройств на базе операционной системы Windows Phone, от разработки которой пришлось отказаться. Впрочем, если верить статистике , эти меры вряд ли затронут большое количество пользователей. По состоянию на май 2019 года лишь 0,3% всех владельцев Android все еще пользуются ОС версий 2.3.3–2.3.7. Что же касается iOS, то версия под номером «10» или старше установлена на 5% устройств Apple. Как уже упоминалось выше, выпуск новых гаджетов на Windows Phone и вовсе прекращен ее создателем. В середине сентября экс-сотрудник АНБ Эдвард Сноуден раскритиковал WhatsApp за несовершенную систему защиты, порекомендовав политикам пользоваться другими средствами связи. Журналист французской радиостанции France Inter отметил, что президент Франции Эмманюэль Макрон для связи использует Telegram, а премьер-министр страны Эдуар Филипп — WhatsApp. Сноуден назвал такое решение «большой ошибкой», учитывая серьезные посты, которые занимают Макрон и Филипп. По словам Сноудена, эти сервисы безопаснее обычных SMS-сообщений, но все еще «чрезвычайно опасны, если вы премьер-министр». Больше всего претензий у информатора к WhatsApp, который стал частью активов корпорации Facebook в 2014 году. Эдвард Сноуден отметил, что после приобретения мессенджера Facebook «слой за слоем» снимает различные уровни защиты сервиса, чтобы при необходимости читать переписку своих пользователей. Ранее с критикой в адрес WhatsApp выступил и глава Telegram Павел Дуров. По словам предпринимателя, после устранения одной «дыры» в мессенджере тут же появляются новые. «Все выявленные проблемы позволяют вести слежку, выглядят и функционируют как бэкдоры», — заявил Дуров. При этом Дуров подчеркнул, что WhatsApp мог быть вынужден установить бэкдоры по указанию ФБР. В июне руководство WhatsApp заявило о том, что их сервис готов судиться с юзерами за нарушение правил пользования. В список нарушений входит использование программы «не в личных целях» и применение автоматической рассылки сообщений. По данным пресс-службы WhatsApp, уже сейчас обнаружены и заморожены «миллионы аккаунтов», пойманных на «злоупотреблении». «Наша платформа изначально создавалась, чтобы помогать людям общаться с их друзьями и любимыми... Используя информацию приложения, мы нашли и заблокировали миллионы злоупотребляющих аккаунтов от использования нашей сети», – заявили в WhatsApp. В частности, нарушение происходит, если компания публично заявляет о возможности использовать WhatsApp, нарушая при этом правила пользования мессенджером. «Ничто в этом объявлении не ограничивает право WhatsApp от применения своих условий с использованием технологий. Классификаторы на основе machine learning нам в этом помогают, и мы продолжим их использовать», – добавили в команде приложения.', } ``` Some dataset statistics are below: | Feature | Mean Token Count | Mean Sentence Count | |:---------|:---------|--------------------------------------------------| | Text | 767 | 37 | | Summary | 50 | 3 | ### Data Splits | Dataset Split | v1, Number of Instances in Split | v2, Number of Instances in Split | |:---------|:---------|:---------| | Train | 52,400 | 60,964 | | Validation | 5,265 | 6,369 | | Test | 5,770 | 6,793 | ## Dataset Creation ### Curation Rationale When the first version of the dataset was collected, there were no other datasets for Russian text summarization. Even now, it is one of the few datasets for this task. ### Source Data #### Initial Data Collection and Normalization * The source of data is the [Gazeta](https://www.gazeta.ru/) website. * Parsing scripts are [here](https://github.com/IlyaGusev/gazeta/tree/master/parser). * Cleaning and normalization Colab notebook is [here](https://colab.research.google.com/drive/1Ed_chVrslp_7vJNS3PmRC0_ZJrRQYv0C) #### Who are the source language producers? Texts and summaries were written by journalists at [Gazeta](https://www.gazeta.ru/). ### Annotations #### Annotation process [N/A] #### Who are the annotators? [N/A] ### Personal and Sensitive Information The dataset is not anonymized, so individuals' names can be found in the dataset. Information about the original author is not included in the dataset. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases It is a dataset from a single source. Thus it has a constrained text style and event perspective. ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The data was collected by Ilya Gusev. ### Licensing Information Legal basis for distribution of the dataset: https://www.gazeta.ru/credits.shtml, paragraph 2.1.2. All rights belong to "www.gazeta.ru". Usage of this dataset is possible only for personal purposes on a non-commercial basis. ### Citation Information ```bibtex @InProceedings{10.1007/978-3-030-59082-6_9, author="Gusev, Ilya", editor="Filchenkov, Andrey and Kauttonen, Janne and Pivovarova, Lidia", title="Dataset for Automatic Summarization of Russian News", booktitle="Artificial Intelligence and Natural Language", year="2020", publisher="Springer International Publishing", address="Cham", pages="122--134", isbn="978-3-030-59082-6" } ``` ### Contributions [N/A]

quoref

--- annotations_creators: - crowdsourced language: - en language_creators: - found license: - cc-by-4.0 multilinguality: - monolingual pretty_name: Quoref size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: [] paperswithcode_id: quoref tags: - coreference-resolution dataset_info: features: - name: id dtype: string - name: question dtype: string - name: context dtype: string - name: title dtype: string - name: url dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: train num_bytes: 44377729 num_examples: 19399 - name: validation num_bytes: 5442031 num_examples: 2418 download_size: 5078438 dataset_size: 49819760 --- # Dataset Card for "quoref" ## 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/quoref - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning](https://aclanthology.org/D19-1606/) - **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:** 5.08 MB - **Size of the generated dataset:** 49.82 MB - **Total amount of disk used:** 54.90 MB ### Dataset Summary Quoref is a QA dataset which tests the coreferential reasoning capability of reading comprehension systems. In this span-selection benchmark containing 24K questions over 4.7K paragraphs from Wikipedia, a system must resolve hard coreferences before selecting the appropriate span(s) in the paragraphs for answering 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:** 5.08 MB - **Size of the generated dataset:** 49.82 MB - **Total amount of disk used:** 54.90 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "answers": { "answer_start": [1633], "text": ["Frankie"] }, "context": "\"Frankie Bono, a mentally disturbed hitman from Cleveland, comes back to his hometown in New York City during Christmas week to ...", "id": "bfc3b34d6b7e73c0bd82a009db12e9ce196b53e6", "question": "What is the first name of the person who has until New Year's Eve to perform a hit?", "title": "Blast of Silence", "url": "https://en.wikipedia.org/wiki/Blast_of_Silence" } ``` ### Data Fields The data fields are the same among all splits. #### default - `id`: a `string` feature. - `question`: a `string` feature. - `context`: a `string` feature. - `title`: a `string` feature. - `url`: a `string` feature. - `answers`: a dictionary feature containing: - `answer_start`: a `int32` feature. - `text`: a `string` feature. ### Data Splits | name |train|validation| |-------|----:|---------:| |default|19399| 2418| ## 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{allenai:quoref, author = {Pradeep Dasigi and Nelson F. Liu and Ana Marasovic and Noah A. Smith and Matt Gardner}, title = {Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning}, journal = {arXiv:1908.05803v2 }, year = {2019}, } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

alzoubi36/policy_ie_a

alzoubi36

--- dataset_info: features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 592707 num_examples: 4109 - name: validation num_bytes: 16114 num_examples: 100 - name: test num_bytes: 163819 num_examples: 1041 download_size: 364376 dataset_size: 772640 --- # Dataset for the PolicyIE-A task in the [PrivacyGLUE](https://github.com/infsys-lab/privacy-glue) dataset

BeIR/nq

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.

yizhongw/self_instruct

yizhongw

--- license: apache-2.0 dataset_info: - config_name: self_instruct features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 20527462 num_examples: 82612 download_size: 24113858 dataset_size: 20527462 - config_name: human_eval features: - name: id dtype: string - name: motivation_app dtype: string - name: instruction dtype: string - name: instances sequence: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 151244 num_examples: 252 download_size: 170193 dataset_size: 151244 - config_name: super_natural_instructions features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 40352923 num_examples: 50000 - name: test num_bytes: 9713953 num_examples: 11810 download_size: 52975509 dataset_size: 50066876 - config_name: prompt_source features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 57368889 num_examples: 52657 download_size: 60126945 dataset_size: 57368889 - config_name: p3 features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 57368889 num_examples: 52657 download_size: 60126945 dataset_size: 57368889 --- # Dataset Card for Self Instruct ## Table of Contents - [Dataset Card for Self Instruct](#dataset-card-for-self-instruct) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [self\_instruct](#self_instruct) - [super\_natural\_instructions](#super_natural_instructions) - [p3](#p3) - [human\_eval](#human_eval) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [self\_instruct](#self_instruct-1) - [super\_natural\_instructions](#super_natural_instructions-1) - [p3](#p3-1) - [human\_eval](#human_eval-1) - [Data Fields](#data-fields) - [self\_instruct](#self_instruct-2) - [super\_natural\_instructions](#super_natural_instructions-2) - [p3](#p3-2) - [human\_eval](#human_eval-2) - [Data Splits](#data-splits) - [self\_instruct](#self_instruct-3) - [super\_natural\_instructions](#super_natural_instructions-3) - [p3](#p3-3) - [human\_eval](#human_eval-3) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - **Homepage:** - **Repository:** https://github.com/yizhongw/self-instruct - **Paper:** https://arxiv.org/abs/2212.10560 - **Leaderboard:** - **Point of Contact:** Yizhong Wang ### Dataset Summary Self-Instruct is a framework that helps language models improve their ability to follow natural language instructions. It does this by using the model's own generations to create a large collection of instructional data. With Self-Instruct, it is possible to improve the instruction-following capabilities of language models without relying on extensive manual annotation. A part of this framework, the Self-Instruct authors released a dataset that contains 52k instructions, paired with 82K instance inputs and outputs. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better. The authors also released a new set of 252 expert-written tasks and their instructions motivated by user-oriented applications (rather than well-studied NLP tasks). This data is used in the human evaluation section of [the Self Instruct paper](https://arxiv.org/abs/2212.10560). To enable comparison on public datasets, Self-Instruct also contains 50k examples from the P3 and Super Natural Instructions datasets. ### Supported Tasks and Leaderboards The datasets in Self-Instruct are designed for _instruction training_ pretrained language models. The following subsets are provided as part of Self Instruct. #### self_instruct 82k prompts and model completions generated via OpenAI's `davinci` engine. #### super_natural_instructions 50k expert written instructions and demonstrations sampled from the [Super Natural Instructions dataset](https://arxiv.org/abs/2204.07705) #### p3 50k crowd-sourced instructions and demonstrations sampled from the [Public Pool of Prompts (P3) dataset](https://huggingface.co/datasets/bigscience/P3) #### human_eval 252 expert-written tasks and their instructions motivated by user-oriented applications (rather than well-studied NLP tasks). This data is used in the human evaluation section of [the self-instruct paper](https://arxiv.org/abs/2212.10560). Please refer to [the human evaluation information]([human_eval/README.md](https://github.com/yizhongw/self-instruct/blob/main/human_eval/README.md)) for more details. ### Languages The data in Self Instruct are in English (BCP-47 en). ## Dataset Structure ### Data Instances #### self_instruct An example pf "train" looks as follows: ```json { "prompt": "Explain the origin of life on earth. Output:", "completion": " Life on Earth is believed to have begun about 3.5 billion years ago, when the planet was still in its early stages of formation. The earliest evidence for life on Earth are fossils of microorganisms (bacteria and archaea) that lived approximately 3.5 billion years ago. These organisms were capable of converting carbon dioxide into organic compounds using sunlight as an energy source. This process is known as photosynthesis.", } ``` #### super_natural_instructions An example pf "train" looks as follows: ```json { "prompt": "Write a correct answer for the question. You should reason about prototypical situations and provide the correct answer. Avoid answers that provide incomplete justification for the question.\n\nInput: name something people often buy on credit.\n\nOutput:", "completion": " appliances", } ``` #### p3 An example pf "train" looks as follows: ```json { "prompt": "Determine if the following two sentences paraphrase each other or not.\nSent 1: By adding sufficient oxygen to compensate for the metabolic usage , rebreathing the carbon dioxide and removing the gas , most of the volume is conserved .\nSent 2: By adding sufficient oxygen to compensate for the metabolic consumption , removing the carbon dioxide and reinhaling the gas , most of the volume is conserved .\n", "completion": "No", } ``` #### human_eval An example pf "train" looks as follows: ```json { "id": "user_oriented_task_136", "motivation_app": "Goodreads", "instruction": "Choose the best books from the given genre.", "instances": { "input": ["Crime & Mystery"], "output": [ "1- The Girl with the Dragon Tattoo\n2- And Then There Were None\n3- Angels & Demons\n4- Rebecca\n5- In Cold Blood\n6- The Godfather\n7- The Lovely Bones\n8- Gone Girl\n9- The Name of the Rose\n10- Shutter Island" ], }, } ``` ### Data Fields The data fields for each configuration are as follows. #### self_instruct * `prompt`: The instruction provided to the model or human labeler. * `completion`: A completion provided by the model or human labeler. #### super_natural_instructions * `prompt`: The instruction provided to the model or human labeler. * `completion`: A completion provided by the model or human labeler. #### p3 * `prompt`: The instruction provided to the model or human labeler. * `completion`: A completion provided by the model or human labeler. #### human_eval * `id`: The ID associated with the labelling task * `motivation_app`: The application associated with the task * `instruction`: The instruction written by the human labeler. * `instances.input`: The input that forms part of the complete instruction * `instances.output`: The human written demonstration ### Data Splits #### self_instruct | | train | |---------------|------:| | self_instruct | 82612 | #### super_natural_instructions | | train | test | |----------------------------|------:|------:| | super_natural_instructions | 50000 | 11810 | #### p3 | | train | |----|------:| | p3 | 52657 | #### human_eval | | train | |------------|------:| | human_eval | 252 | ## 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 The `self_instruct` data is generated by a language model (GPT-3) and inevitably contains some errors or biases. The authors analyzed the data quality on 200 random instructions in our paper, and found that 46% of the data points may have problems. We encourage users to use this data with caution and propose new methods to filter or improve the imperfections. ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @misc{selfinstruct, title={Self-Instruct: Aligning Language Model with Self Generated Instructions}, author={Wang, Yizhong and Kordi, Yeganeh and Mishra, Swaroop and Liu, Alisa and Smith, Noah A. and Khashabi, Daniel and Hajishirzi, Hannaneh}, journal={arXiv preprint arXiv:2212.10560}, year={2022} } ```

laion/laion2B-en

laion

--- license: cc-by-4.0 --- HEIGHT and WIDTH are swapped

open-web-math/open-web-math

open-web-math

--- dataset_info: features: - name: url dtype: string - name: text dtype: string - name: date dtype: string - name: metadata dtype: string splits: - name: train num_bytes: 56651995057 num_examples: 6315233 download_size: 16370689925 dataset_size: 56651995057 license: odc-by task_categories: - text-generation language: - en pretty_name: OpenWebMath size_categories: - 10B<n<100B --- <img src="imgs/OpenWebMath-left.png" width="300"> [Keiran Paster](https://keirp.com)\*, [Marco Dos Santos](https://marco-dossantos.github.io/)\*, [Zhangir Azerbayev](https://zhangir-azerbayev.github.io/), [Jimmy Ba](https://jimmylba.github.io/) [GitHub ](https://github.com/keirp/OpenWebMath) | [ArXiv](https://arxiv.org/abs/2310.06786) | [PDF](https://arxiv.org/pdf/2310.06786.pdf) **OpenWebMath** is a dataset containing the majority of the high-quality, mathematical text from the internet. It is filtered and extracted from over 200B HTML files on Common Crawl down to a set of **6.3 million documents** containing a total of **14.7B tokens**. OpenWebMath is intended for use in _pretraining_ and _finetuning_ large language models. You can download the dataset using Hugging Face: ```python from datasets import load_dataset ds = load_dataset("open-web-math/open-web-math") ``` # OpenWebMath Contents The dataset is structured as follows: ```python { "text": ..., # document text. "url": ..., # document url. "date": ..., # date the page was crawled. "metadata": ..., # JSON containing information from the extraction process. } ``` OpenWebMath contains documents from over 130k different domains, including data from forums, educational pages, and blogs. The dataset contains documents covering mathematics, physics, statistics, computer science, and more. The following table shows the most common domains in OpenWebMath by character count. | Domain | # Characters | % Characters | | ----------------- | ------------- | ------------ | | stackexchange.com | 4,655,132,784 | 9.55% | | nature.com | 1,529,935,838 | 3.14% | | wordpress.com | 1,294,166,938 | 2.66% | | physicsforums.com | 1,160,137,919 | 2.38% | | github.io | 725,689,722 | 1.49% | | zbmath.org | 620,019,503 | 1.27% | | wikipedia.org | 618,024,754 | 1.27% | | groundai.com | 545,214,990 | 1.12% | | blogspot.com | 520,392,333 | 1.07% | | mathoverflow.net | 499,102,560 | 1.02% | # OpenWebMath Pipeline <img src="imgs/pipeline.png" alt="Overview of the OpenWebMath Pipeline"> OpenWebMath builds on the massive [Common Crawl](https://commoncrawl.org/) dataset, which contains over 200B HTML documents. We filtered the data to only include documents that are: (1) in English, (2) contain mathematical content, and (3) are of high quality. We also put a strong emphasis on extracting LaTeX content from the HTML documents as well as reducing boilerplate in comparison to other web datasets. The OpenWebMath pipeline consists of five steps: 1. **Prefiltering HTML Documents**: - We apply a simple prefilter to all HTML documents in Common Crawl in order to skip documents without mathematical content to unnecessary processing time. 2. **Text Extraction**: - Extract text, including LaTeX content, from the HTML documents while removing boilerplate. 3. **Content Classification and Filtering**: - Apply a [FastText language identification model](https://fasttext.cc/docs/en/language-identification.html) to keep only English documents. - Filter high perplexity documents using a [KenLM](https://github.com/kpu/kenlm) model trained on [Proof-Pile](https://huggingface.co/datasets/hoskinson-center/proof-pile). - Filter non-mathematical documents using our own _MathScore_ model. 4. **Deduplication**: - Deduplicate the dataset using SimHash in [text-dedup](https://github.com/ChenghaoMou/text-dedup). 5. **Manual Inspection**: - Inspect the documents gathered from previous steps and remove low quality pages. For a detailed discussion on the processing pipeline, please refer to our paper. # License OpenWebMath is made available under an ODC-By 1.0 license; users should also abide by the CommonCrawl ToU: [https://commoncrawl.org/terms-of-use/](https://commoncrawl.org/terms-of-use/). We do not alter the license of any of the underlying data. # Citation Information ``` @misc{paster2023openwebmath, title={OpenWebMath: An Open Dataset of High-Quality Mathematical Web Text}, author={Keiran Paster and Marco Dos Santos and Zhangir Azerbayev and Jimmy Ba}, year={2023}, eprint={2310.06786}, archivePrefix={arXiv}, primaryClass={cs.AI} } ```

regisss/librispeech_asr_for_optimum_habana_ci

regisss

--- license: cc-by-4.0 --- This dataset contains the splits `clean.train.100` and `clean.dev` of the [LibriSpeech dataset](https://huggingface.co/datasets/librispeech_asr). It is only meant to be used in Optimum Habana's CI to avoid downloading other splits.

mrqa

--- annotations_creators: - found language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - extended|drop - extended|hotpot_qa - extended|natural_questions - extended|race - extended|search_qa - extended|squad - extended|trivia_qa task_categories: - question-answering task_ids: - extractive-qa paperswithcode_id: mrqa-2019 pretty_name: MRQA 2019 dataset_info: features: - name: subset dtype: string - name: context dtype: string - name: context_tokens sequence: - name: tokens dtype: string - name: offsets dtype: int32 - name: qid dtype: string - name: question dtype: string - name: question_tokens sequence: - name: tokens dtype: string - name: offsets dtype: int32 - name: detected_answers sequence: - name: text dtype: string - name: char_spans sequence: - name: start dtype: int32 - name: end dtype: int32 - name: token_spans sequence: - name: start dtype: int32 - name: end dtype: int32 - name: answers sequence: string config_name: plain_text splits: - name: train num_bytes: 4090681873 num_examples: 516819 - name: test num_bytes: 57712177 num_examples: 9633 - name: validation num_bytes: 484107026 num_examples: 58221 download_size: 1479518355 dataset_size: 4632501076 --- # Dataset Card for MRQA 2019 ## 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:** [MRQA 2019 Shared Task](https://mrqa.github.io/2019/shared.html) - **Repository:** [MRQA 2019 Github repository](https://github.com/mrqa/MRQA-Shared-Task-2019) - **Paper:** [MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension ](https://arxiv.org/abs/1910.09753) - **Leaderboard:** [Shared task](https://mrqa.github.io/2019/shared.html) - **Point of Contact:** [mrforqa@gmail.com](mrforqa@gmail.com) ### Dataset Summary The MRQA 2019 Shared Task focuses on generalization in question answering. An effective question answering system should do more than merely interpolate from the training set to answer test examples drawn from the same distribution: it should also be able to extrapolate to out-of-distribution examples — a significantly harder challenge. The dataset is a collection of 18 existing QA dataset (carefully selected subset of them) and converted to the same format (SQuAD format). Among these 18 datasets, six datasets were made available for training, six datasets were made available for development, and the final six for testing. The dataset is released as part of the MRQA 2019 Shared Task. ### Supported Tasks and Leaderboards From the official repository: *The format of the task is extractive question answering. Given a question and context passage, systems must find the word or phrase in the document that best answers the question. While this format is somewhat restrictive, it allows us to leverage many existing datasets, and its simplicity helps us focus on out-of-domain generalization, instead of other important but orthogonal challenges.* *We have adapted several existing datasets from their original formats and settings to conform to our unified extractive setting. Most notably:* - *We provide only a single, length-limited context.* - *There are no unanswerable or non-span answer questions.* - *All questions have at least one accepted answer that is found exactly in the context.* *A span is judged to be an exact match if it matches the answer string after performing normalization consistent with the SQuAD dataset. Specifically:* - *The text is uncased.* - *All punctuation is stripped.* - *All articles `{a, an, the}` are removed.* - *All consecutive whitespace markers are compressed to just a single normal space `' '`.* Answers are evaluated using exact match and token-level F1 metrics. One can refer to the [mrqa_official_eval.py](https://github.com/mrqa/MRQA-Shared-Task-2019/blob/master/mrqa_official_eval.py) for evaluation. ### Languages The text in the dataset is in English. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances An examples looks like this: ``` { 'qid': 'f43c83e38d1e424ea00f8ad3c77ec999', 'subset': 'SQuAD' 'context': 'CBS broadcast Super Bowl 50 in the U.S., and charged an average of $5 million for a 30-second commercial during the game. The Super Bowl 50 halftime show was headlined by the British rock group Coldplay with special guest performers Beyoncé and Bruno Mars, who headlined the Super Bowl XLVII and Super Bowl XLVIII halftime shows, respectively. It was the third-most watched U.S. broadcast ever.', 'context_tokens': { 'offsets': [0, 4, 14, 20, 25, 28, 31, 35, 39, 41, 45, 53, 56, 64, 67, 68, 70, 78, 82, 84, 94, 105, 112, 116, 120, 122, 126, 132, 137, 140, 149, 154, 158, 168, 171, 175, 183, 188, 194, 203, 208, 216, 222, 233, 241, 245, 251, 255, 257, 261, 271, 275, 281, 286, 292, 296, 302, 307, 314, 323, 328, 330, 342, 344, 347, 351, 355, 360, 361, 366, 374, 379, 389, 393], 'tokens': ['CBS', 'broadcast', 'Super', 'Bowl', '50', 'in', 'the', 'U.S.', ',', 'and', 'charged', 'an', 'average', 'of', '$', '5', 'million', 'for', 'a', '30-second', 'commercial', 'during', 'the', 'game', '.', 'The', 'Super', 'Bowl', '50', 'halftime', 'show', 'was', 'headlined', 'by', 'the', 'British', 'rock', 'group', 'Coldplay', 'with', 'special', 'guest', 'performers', 'Beyoncé', 'and', 'Bruno', 'Mars', ',', 'who', 'headlined', 'the', 'Super', 'Bowl', 'XLVII', 'and', 'Super', 'Bowl', 'XLVIII', 'halftime', 'shows', ',', 'respectively', '.', 'It', 'was', 'the', 'third', '-', 'most', 'watched', 'U.S.', 'broadcast', 'ever', '.'] }, 'question': "Who was the main performer at this year's halftime show?", 'question_tokens': { 'offsets': [0, 4, 8, 12, 17, 27, 30, 35, 39, 42, 51, 55], 'tokens': ['Who', 'was', 'the', 'main', 'performer', 'at', 'this', 'year', "'s", 'halftime', 'show', '?'] }, 'detected_answers': { 'char_spans': [ { 'end': [201], 'start': [194] }, { 'end': [201], 'start': [194] }, { 'end': [201], 'start': [194] } ], 'text': ['Coldplay', 'Coldplay', 'Coldplay'], 'token_spans': [ { 'end': [38], 'start': [38] }, { 'end': [38], 'start': [38] }, { 'end': [38], 'start': [38] } ] }, 'answers': ['Coldplay', 'Coldplay', 'Coldplay'], } ``` ### Data Fields - `subset`: which of the dataset does this examples come from? - `context`: This is the raw text of the supporting passage. Three special token types have been inserted: `[TLE]` precedes document titles, `[DOC]` denotes document breaks, and `[PAR]` denotes paragraph breaks. The maximum length of the context is 800 tokens. - `context_tokens`: A tokenized version of the supporting passage, using spaCy. Each token is a tuple of the token string and token character offset. The maximum number of tokens is 800. - `tokens`: list of tokens. - `offets`: list of offsets. - `qas`: A list of questions for the given context. - `qid`: A unique identifier for the question. The `qid` is unique across all datasets. - `question`: The raw text of the question. - `question_tokens`: A tokenized version of the question. The tokenizer and token format is the same as for the context. - `tokens`: list of tokens. - `offets`: list of offsets. - `detected_answers`: A list of answer spans for the given question that index into the context. For some datasets these spans have been automatically detected using searching heuristics. The same answer may appear multiple times in the text --- each of these occurrences is recorded. For example, if `42` is the answer, the context `"The answer is 42. 42 is the answer."`, has two occurrences marked. - `text`: The raw text of the detected answer. - `char_spans`: Inclusive (start, end) character spans (indexing into the raw context). - `start`: start (single element) - `end`: end (single element) - `token_spans`: Inclusive (start, end) token spans (indexing into the tokenized context). - `start`: start (single element) - `end`: end (single element) ### Data Splits **Training data** | Dataset | Number of Examples | | :-----: | :------: | | [SQuAD](https://arxiv.org/abs/1606.05250) | 86,588 | | [NewsQA](https://arxiv.org/abs/1611.09830) | 74,160 | | [TriviaQA](https://arxiv.org/abs/1705.03551)| 61,688 | | [SearchQA](https://arxiv.org/abs/1704.05179)| 117,384 | | [HotpotQA](https://arxiv.org/abs/1809.09600)| 72,928 | | [NaturalQuestions](https://ai.google/research/pubs/pub47761)| 104,071 | **Development data** This in-domain data may be used for helping develop models. | Dataset | Examples | | :-----: | :------: | | [SQuAD](https://arxiv.org/abs/1606.05250) | 10,507 | | [NewsQA](https://arxiv.org/abs/1611.09830) | 4,212 | | [TriviaQA](https://arxiv.org/abs/1705.03551)| 7,785| | [SearchQA](https://arxiv.org/abs/1704.05179)| 16,980 | | [HotpotQA](https://arxiv.org/abs/1809.09600)| 5,904 | | [NaturalQuestions](https://ai.google/research/pubs/pub47761)| 12,836 | **Test data** The final testing data only contain out-of-domain data. | Dataset | Examples | | :-----: | :------: | | [BioASQ](http://bioasq.org/) | 1,504 | | [DROP](https://arxiv.org/abs/1903.00161) | 1,503 | | [DuoRC](https://arxiv.org/abs/1804.07927)| 1,501 | | [RACE](https://arxiv.org/abs/1704.04683) | 674 | | [RelationExtraction](https://arxiv.org/abs/1706.04115) | 2,948| | [TextbookQA](http://ai2-website.s3.amazonaws.com/publications/CVPR17_TQA.pdf)| 1,503 | From the official repository: ***Note:** As previously mentioned, the out-of-domain dataset have been modified from their original settings to fit the unified MRQA Shared Task paradigm. At a high level, the following two major modifications have been made:* *1. All QA-context pairs are extractive. That is, the answer is selected from the context and not via, e.g., multiple-choice.* *2. All contexts are capped at a maximum of `800` tokens. As a result, for longer contexts like Wikipedia articles, we only consider examples where the answer appears in the first `800` tokens.* *As a result, some splits are harder than the original datasets (e.g., removal of multiple-choice in RACE), while some are easier (e.g., restricted context length in NaturalQuestions --- we use the short answer selection). Thus one should expect different performance ranges if comparing to previous work on these datasets.* ## Dataset Creation ### Curation Rationale From the official repository: *Both train and test datasets have the same format described above, but may differ in some of the following ways:* - *Passage distribution: Test examples may involve passages from different sources (e.g., science, news, novels, medical abstracts, etc) with pronounced syntactic and lexical differences.* - *Question distribution: Test examples may emphasize different styles of questions (e.g., entity-centric, relational, other tasks reformulated as QA, etc) which may come from different sources (e.g., crowdworkers, domain experts, exam writers, etc.)* - *Joint distribution: Test examples may vary according to the relationship of the question to the passage (e.g., collected independent vs. dependent of evidence, multi-hop, etc)* ### 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 ``` @inproceedings{fisch2019mrqa, title={{MRQA} 2019 Shared Task: Evaluating Generalization in Reading Comprehension}, author={Adam Fisch and Alon Talmor and Robin Jia and Minjoon Seo and Eunsol Choi and Danqi Chen}, booktitle={Proceedings of 2nd Machine Reading for Reading Comprehension (MRQA) Workshop at EMNLP}, year={2019}, } ``` ### Contributions Thanks to [@jimmycode](https://github.com/jimmycode), [@VictorSanh](https://github.com/VictorSanh) for adding this dataset.

lmsys/chatbot_arena_conversations

lmsys

--- dataset_info: features: - name: question_id dtype: string - name: model_a dtype: string - name: model_b dtype: string - name: winner dtype: string - name: judge dtype: string - name: conversation_a list: - name: content dtype: string - name: role dtype: string - name: conversation_b list: - name: content dtype: string - name: role dtype: string - name: turn dtype: int64 - name: anony dtype: bool - name: language dtype: string - name: tstamp dtype: float64 - name: openai_moderation struct: - name: categories struct: - name: harassment dtype: bool - name: harassment/threatening dtype: bool - name: hate dtype: bool - name: hate/threatening dtype: bool - name: self-harm dtype: bool - name: self-harm/instructions dtype: bool - name: self-harm/intent dtype: bool - name: sexual dtype: bool - name: sexual/minors dtype: bool - name: violence dtype: bool - name: violence/graphic dtype: bool - name: category_scores struct: - name: harassment dtype: float64 - name: harassment/threatening dtype: float64 - name: hate dtype: float64 - name: hate/threatening dtype: float64 - name: self-harm dtype: float64 - name: self-harm/instructions dtype: float64 - name: self-harm/intent dtype: float64 - name: sexual dtype: float64 - name: sexual/minors dtype: float64 - name: violence dtype: float64 - name: violence/graphic dtype: float64 - name: flagged dtype: bool - name: toxic_chat_tag struct: - name: roberta-large struct: - name: flagged dtype: bool - name: probability dtype: float64 - name: t5-large struct: - name: flagged dtype: bool - name: score dtype: float64 splits: - name: train num_bytes: 81159839 num_examples: 33000 download_size: 41572998 dataset_size: 81159839 license: cc task_categories: - conversational size_categories: - 10K<n<100K extra_gated_prompt: "Disclaimers and Terms\n\ - This dataset contains conversations that may be considered unsafe, offensive, or upsetting. It is not intended for training dialogue agents without applying appropriate filtering measures. We are not responsible for any outputs of the models trained on this dataset.\n\ - Statements or opinions made in this dataset do not reflect the views of researchers or institutions involved in the data collection effort.\n\ - Users of this data are responsible for ensuring its appropriate use, which includes abiding by any applicable laws and regulations.\n\ - Users of this data should adhere to the terms of use for a specific model when using its direct outputs.\n\ - Users of this data agree to not attempt to determine the identity of individuals in this dataset." --- ## Chatbot Arena Conversations Dataset This dataset contains 33K cleaned conversations with pairwise human preferences. It is collected from 13K unique IP addresses on the [Chatbot Arena](https://lmsys.org/blog/2023-05-03-arena/) from April to June 2023. Each sample includes a question ID, two model names, their full conversation text in OpenAI API JSON format, the user vote, the anonymized user ID, the detected language tag, the OpenAI moderation API tag, the additional toxic tag, and the timestamp. To ensure the safe release of data, we have made our best efforts to remove all conversations that contain personally identifiable information (PII). User consent is obtained through the "Terms of use" section on the data collection website. In addition, we have included the OpenAI moderation API output to flag inappropriate conversations. However, we have chosen to keep unsafe conversations intact so that researchers can study the safety-related questions associated with LLM usage in real-world scenarios as well as the OpenAI moderation process. As an example, we included additional toxic tags that are generated by our own toxic tagger, which are trained by fine-tuning T5 and RoBERTa on manually labeled data. **Basic Statistics** | Key | Value | | --- | --- | | # Conversations | 33,000 | | # Models | 20 | | # Users | 13,383 | | # Languages | 96 | | Avg. # Turns per Sample | 1.2 | | Avg. # Tokens per Prompt | 52.3 | | Avg. # Tokens per Response | 189.5 | ## Uniqueness and Potential Usage Compared to existing human preference datasets like [Anthropic/hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf), and [OpenAssistant/oasst1](https://huggingface.co/datasets/OpenAssistant/oasst1). This dataset - Contains the outputs of 20 LLMs including stronger LLMs such as GPT-4 and Claude-v1. It also contains many failure cases of these state-of-the-art models. - Contains unrestricted conversations from over 13K users in the wild. We believe it will help the AI research community answer important questions around topics like: - Characteristics and distributions of real-world user prompts - Training instruction-following models - Improve and evaluate LLM evaluation methods - Model selection and request dispatching algorithms - AI safety and content moderation ## Disclaimers and Terms - **This dataset contains conversations that may be considered unsafe, offensive, or upsetting.** It is not intended for training dialogue agents without applying appropriate filtering measures. We are not responsible for any outputs of the models trained on this dataset. - Statements or opinions made in this dataset do not reflect the views of researchers or institutions involved in the data collection effort. - Users of this data are responsible for ensuring its appropriate use, which includes abiding by any applicable laws and regulations. - Users of this data should adhere to the terms of use for a specific model when using its direct outputs. - Users of this data agree to not attempt to determine the identity of individuals in this dataset. ## Visualization and Elo Rating Calculation This Colab [notebook](https://colab.research.google.com/drive/1J2Wf7sxc9SVmGnSX_lImhT246pxNVZip?usp=sharing) provides some visualizations and shows how to compute Elo ratings with the dataset. ## License The user prompts are licensed under CC-BY-4.0, while the model outputs are licensed under CC-BY-NC-4.0. ## Citation ``` @misc{zheng2023judging, title={Judging LLM-as-a-judge with MT-Bench and Chatbot Arena}, author={Lianmin Zheng and Wei-Lin Chiang and Ying Sheng and Siyuan Zhuang and Zhanghao Wu and Yonghao Zhuang and Zi Lin and Zhuohan Li and Dacheng Li and Eric. P Xing and Hao Zhang and Joseph E. Gonzalez and Ion Stoica}, year={2023}, eprint={2306.05685}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

multi_re_qa

--- annotations_creators: - expert-generated - found language_creators: - expert-generated - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M - 10K<n<100K - 1K<n<10K - 1M<n<10M source_datasets: - extended|other-BioASQ - extended|other-DuoRC - extended|other-HotpotQA - extended|other-Natural-Questions - extended|other-Relation-Extraction - extended|other-SQuAD - extended|other-SearchQA - extended|other-TextbookQA - extended|other-TriviaQA task_categories: - question-answering task_ids: - extractive-qa - open-domain-qa paperswithcode_id: multireqa pretty_name: MultiReQA dataset_info: - config_name: SearchQA features: - name: candidate_id dtype: string - name: response_start dtype: int32 - name: response_end dtype: int32 splits: - name: train num_bytes: 183902877 num_examples: 3163801 - name: validation num_bytes: 26439174 num_examples: 454836 download_size: 36991959 dataset_size: 210342051 - config_name: TriviaQA features: - name: candidate_id dtype: string - name: response_start dtype: int32 - name: response_end dtype: int32 splits: - name: train num_bytes: 107326326 num_examples: 1893674 - name: validation num_bytes: 13508062 num_examples: 238339 download_size: 21750402 dataset_size: 120834388 - config_name: HotpotQA features: - name: candidate_id dtype: string - name: response_start dtype: int32 - name: response_end dtype: int32 splits: - name: train num_bytes: 29516866 num_examples: 508879 - name: validation num_bytes: 3027229 num_examples: 52191 download_size: 6343389 dataset_size: 32544095 - config_name: SQuAD features: - name: candidate_id dtype: string - name: response_start dtype: int32 - name: response_end dtype: int32 splits: - name: train num_bytes: 16828974 num_examples: 95659 - name: validation num_bytes: 2012997 num_examples: 10642 download_size: 3003646 dataset_size: 18841971 - config_name: NaturalQuestions features: - name: candidate_id dtype: string - name: response_start dtype: int32 - name: response_end dtype: int32 splits: - name: train num_bytes: 28732767 num_examples: 448355 - name: validation num_bytes: 1418124 num_examples: 22118 download_size: 6124487 dataset_size: 30150891 - config_name: BioASQ features: - name: candidate_id dtype: string - name: response_start dtype: int32 - name: response_end dtype: int32 splits: - name: test num_bytes: 766190 num_examples: 14158 download_size: 156649 dataset_size: 766190 - config_name: RelationExtraction features: - name: candidate_id dtype: string - name: response_start dtype: int32 - name: response_end dtype: int32 splits: - name: test num_bytes: 217870 num_examples: 3301 download_size: 73019 dataset_size: 217870 - config_name: TextbookQA features: - name: candidate_id dtype: string - name: response_start dtype: int32 - name: response_end dtype: int32 splits: - name: test num_bytes: 4182675 num_examples: 71147 download_size: 704602 dataset_size: 4182675 - config_name: DuoRC features: - name: candidate_id dtype: string - name: response_start dtype: int32 - name: response_end dtype: int32 splits: - name: test num_bytes: 1483518 num_examples: 5525 download_size: 97625 dataset_size: 1483518 config_names: - BioASQ - DuoRC - HotpotQA - NaturalQuestions - RelationExtraction - SQuAD - SearchQA - TextbookQA - TriviaQA --- # Dataset Card for MultiReQA ## 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-datasets/MultiReQA - **Repository:** https://github.com/google-research-datasets/MultiReQA - **Paper:** https://arxiv.org/pdf/2005.02507.pdf - **Leaderboard:** - **Point of Contact:** ### Dataset Summary MultiReQA contains the sentence boundary annotation from eight publicly available QA datasets including SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, BioASQ, RelationExtraction, and TextbookQA. Five of these datasets, including SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, contain both training and test data, and three, in cluding BioASQ, RelationExtraction, TextbookQA, contain only the test data (also includes DuoRC but not specified in the official documentation) ### Supported Tasks and Leaderboards - Question answering (QA) - Retrieval question answering (ReQA) ### Languages Sentence boundary annotation for SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, BioASQ, RelationExtraction, TextbookQA and DuoRC ## Dataset Structure ### Data Instances The general format is: ` { "candidate_id": <candidate_id>, "response_start": <response_start>, "response_end": <response_end> } ... ` An example from SearchQA: `{'candidate_id': 'SearchQA_000077f3912049dfb4511db271697bad/_0_1', 'response_end': 306, 'response_start': 243} ` ### Data Fields ` { "candidate_id": <STRING>, "response_start": <INT>, "response_end": <INT> } ... ` - **candidate_id:** The candidate id of the candidate sentence. It consists of the original qid from the MRQA shared task. - **response_start:** The start index of the sentence with respect to its original context. - **response_end:** The end index of the sentence with respect to its original context ### Data Splits Train and Dev splits are available only for the following datasets, - SearchQA - TriviaQA - HotpotQA - SQuAD - NaturalQuestions Test splits are available only for the following datasets, - BioASQ - RelationExtraction - TextbookQA The number of candidate sentences for each dataset in the table below. | | MultiReQA | | |--------------------|-----------|---------| | | train | test | | SearchQA | 629,160 | 454,836 | | TriviaQA | 335,659 | 238,339 | | HotpotQA | 104,973 | 52,191 | | SQuAD | 87,133 | 10,642 | | NaturalQuestions | 106,521 | 22,118 | | BioASQ | - | 14,158 | | RelationExtraction | - | 3,301 | | TextbookQA | - | 3,701 | ## Dataset Creation ### Curation Rationale MultiReQA is a new multi-domain ReQA evaluation suite composed of eight retrieval QA tasks drawn from publicly available QA datasets from the [MRQA shared task](https://mrqa.github.io/). The dataset was curated by converting existing QA datasets from [MRQA shared task](https://mrqa.github.io/) to the format of MultiReQA benchmark. ### Source Data #### Initial Data Collection and Normalization The Initial data collection was performed by converting existing QA datasets from MRQA shared task to the format of MultiReQA benchmark. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? The annotators/curators of the dataset are [mandyguo-xyguo](https://github.com/mandyguo-xyguo) and [mwurts4google](https://github.com/mwurts4google), the contributors of the official MultiReQA github repository ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The annotators/curators of the dataset are [mandyguo-xyguo](https://github.com/mandyguo-xyguo) and [mwurts4google](https://github.com/mwurts4google), the contributors of the official MultiReQA github repository ### Licensing Information [More Information Needed] ### Citation Information ``` @misc{m2020multireqa, title={MultiReQA: A Cross-Domain Evaluation for Retrieval Question Answering Models}, author={Mandy Guo and Yinfei Yang and Daniel Cer and Qinlan Shen and Noah Constant}, year={2020}, eprint={2005.02507}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@Karthik-Bhaskar](https://github.com/Karthik-Bhaskar) for adding this dataset.

cedr

--- annotations_creators: - crowdsourced language_creators: - found language: - ru license: - apache-2.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification - multi-label-classification pretty_name: The Corpus for Emotions Detecting in Russian-language text sentences (CEDR) tags: - emotion-classification dataset_info: - config_name: main features: - name: text dtype: string - name: labels sequence: class_label: names: '0': joy '1': sadness '2': surprise '3': fear '4': anger - name: source dtype: string splits: - name: train num_bytes: 1418355 num_examples: 7528 - name: test num_bytes: 350275 num_examples: 1882 download_size: 693026 dataset_size: 1768630 - config_name: enriched features: - name: text dtype: string - name: labels sequence: class_label: names: '0': joy '1': sadness '2': surprise '3': fear '4': anger - name: source dtype: string - name: sentences list: list: - name: forma dtype: string - name: lemma dtype: string splits: - name: train num_bytes: 4792366 num_examples: 7528 - name: test num_bytes: 1182343 num_examples: 1882 download_size: 1822522 dataset_size: 5974709 --- # Dataset Card for [cedr] ## 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:** [GitHub](https://github.com/sag111/CEDR) - **Repository:** [GitHub](https://github.com/sag111/CEDR) - **Paper:** [ScienceDirect](https://www.sciencedirect.com/science/article/pii/S1877050921013247) - **Leaderboard:** - **Point of Contact:** [@sag111](mailto:sag111@mail.ru) ### Dataset Summary The Corpus for Emotions Detecting in Russian-language text sentences of different social sources (CEDR) contains 9410 comments labeled for 5 emotion categories (joy, sadness, surprise, fear, and anger). Here are 2 dataset configurations: - "main" - contains "text", "labels", and "source" features; - "enriched" - includes all "main" features and "sentences". Dataset with predefined train/test splits. ### Supported Tasks and Leaderboards This dataset is intended for multi-label emotion classification. ### Languages The data is in Russian. ## Dataset Structure ### Data Instances Each instance is a text sentence in Russian from several sources with one or more emotion annotations (or no emotion at all). An example for an instance from the dataset is shown below: ``` { 'text': 'Забавно как люди в возрасте удивляются входящим звонкам на мобильник)', 'labels': [0], 'source': 'twitter', 'sentences': [ [ {'forma': 'Забавно', 'lemma': 'Забавно'}, {'forma': 'как', 'lemma': 'как'}, {'forma': 'люди', 'lemma': 'человек'}, {'forma': 'в', 'lemma': 'в'}, {'forma': 'возрасте', 'lemma': 'возраст'}, {'forma': 'удивляются', 'lemma': 'удивляться'}, {'forma': 'входящим', 'lemma': 'входить'}, {'forma': 'звонкам', 'lemma': 'звонок'}, {'forma': 'на', 'lemma': 'на'}, {'forma': 'мобильник', 'lemma': 'мобильник'}, {'forma': ')', 'lemma': ')'} ] ] } ``` Emotion label codes: {0: "joy", 1: "sadness", 2: "surprise", 3: "fear", 4: "anger"} ### Data Fields The main configuration includes: - text: the text of the sentence; - labels: the emotion annotations; - source: the tag name of the corresponding source In addition to the above, the raw data includes: - sentences: text tokenized and lemmatized with [udpipe](https://ufal.mff.cuni.cz/udpipe) - 'forma': the original word form; - 'lemma': the lemma of this word ### Data Splits The dataset includes a set of train/test splits. with 7528, and 1882 examples respectively. ## Dataset Creation ### Curation Rationale The formed dataset of examples consists of sentences in Russian from several sources (blogs, microblogs, news), which allows creating methods to analyse various types of texts. The created methodology for building the dataset based on applying a crowdsourcing service can be used to expand the number of examples to improve the accuracy of supervised classifiers. ### Source Data #### Initial Data Collection and Normalization Data was collected from several sources: posts of the Live Journal social network, texts of the online news agency Lenta.ru, and Twitter microblog posts. Only those sentences were selected that contained marker words from the dictionary of [the emotive vocabulary of the Russian language](http://lexrus.ru/default.aspx?p=2876). The authors manually formed a list of marker words for each emotion by choosing words from different categories of the dictionary. In total, 3069 sentences were selected from LiveJournal posts, 2851 sentences from Lenta.Ru, and 3490 sentencesfrom Twitter. After selection, sentences were offered to annotators for labeling. #### Who are the source language producers? Russian-speaking LiveJournal and Tweeter users, and authors of news articles on the site lenta.ru. ### Annotations #### Annotation process Annotating sentences with labels of their emotions was performed with the help of [a crowdsourcing platform](https://yandex.ru/support/toloka/index.html?lang=en). The annotators’ task was: “What emotions did the author express in the sentence?”. The annotators were allowed to put an arbitrary number of the following emotion labels: "joy", "sadness", "anger", "fear", and "surprise". If the accuracy of an annotator on the control sentences (including the trial run) became less than 70%, or if the accuracy was less than 66% over the last six control samples, the annotator was dismissed. Sentences were split into tasks and assigned to annotators so that each sentence was annotated at least three times. A label of a specific emotion was assigned to a sentence if put by more than half of the annotators. #### Who are the annotators? Only those of the 30% of the best-performing active users (by the platform’s internal rating) who spoke Russian and were over 18 years old were allowed into the annotation process. Moreover, before a platform user could be employed as an annotator, they underwent a training task, after which they were to mark 25 trial samples with more than 80% agreement compared to the annotation that the authors had performed themselves. ### Personal and Sensitive Information The text of the sentences may contain profanity. ## 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 Researchers at AI technology lab at NRC "Kurchatov Institute". See the author [list](https://www.sciencedirect.com/science/article/pii/S1877050921013247). ### Licensing Information The GitHub repository which houses this dataset has an Apache License 2.0. ### Citation Information If you have found our results helpful in your work, feel free to cite our publication. This is an updated version of the dataset, the collection and preparation of which is described here: ``` @article{sboev2021data, title={Data-Driven Model for Emotion Detection in Russian Texts}, author={Sboev, Alexander and Naumov, Aleksandr and Rybka, Roman}, journal={Procedia Computer Science}, volume={190}, pages={637--642}, year={2021}, publisher={Elsevier} } ``` ### Contributions Thanks to [@naumov-al](https://github.com/naumov-al) for adding this dataset.

squad_kor_v1

--- annotations_creators: - crowdsourced language_creators: - found language: - ko license: - cc-by-nd-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - extractive-qa paperswithcode_id: korquad pretty_name: The Korean Question Answering Dataset dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 config_name: squad_kor_v1 splits: - name: train num_bytes: 83380337 num_examples: 60407 - name: validation num_bytes: 8261729 num_examples: 5774 download_size: 42408533 dataset_size: 91642066 --- # Dataset Card for KorQuAD v1.0 ## 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://korquad.github.io/KorQuad%201.0/ - **Repository:** https://github.com/korquad/korquad.github.io/tree/master/dataset - **Paper:** https://arxiv.org/abs/1909.07005 ### Dataset Summary KorQuAD 1.0 is a large-scale question-and-answer dataset constructed for Korean machine reading comprehension, and investigate the dataset to understand the distribution of answers and the types of reasoning required to answer the question. This dataset benchmarks the data generating process of SQuAD v1.0 to meet the standard. ### Supported Tasks and Leaderboards `question-answering` ### Languages Korean ## Dataset Structure Follows the standars SQuAD format. ### Data Instances An example from the data set looks as follows: ``` {'answers': {'answer_start': [54], 'text': ['교향곡']}, 'context': '1839년 바그너는 괴테의 파우스트을 처음 읽고 그 내용에 마음이 끌려 이를 소재로 해서 하나의 교향곡을 쓰려는 뜻을 갖는다. 이 시기 바그너는 1838년에 빛 독촉으로 산전수전을 다 걲은 상황이라 좌절과 실망에 가득했으며 메피스토펠레스를 만나는 파우스트의 심경에 공감했다고 한다. 또한 파리에서 아브네크의 지휘로 파리 음악원 관현악단이 연주하는 베토벤의 교향곡 9번을 듣고 깊은 감명을 받았는데, 이것이 이듬해 1월에 파우스트의 서곡으로 쓰여진 이 작품에 조금이라도 영향을 끼쳤으리라는 것은 의심할 여지가 없다. 여기의 라단조 조성의 경우에도 그의 전기에 적혀 있는 것처럼 단순한 정신적 피로나 실의가 반영된 것이 아니라 베토벤의 합창교향곡 조성의 영향을 받은 것을 볼 수 있다. 그렇게 교향곡 작곡을 1839년부터 40년에 걸쳐 파리에서 착수했으나 1악장을 쓴 뒤에 중단했다. 또한 작품의 완성과 동시에 그는 이 서곡(1악장)을 파리 음악원의 연주회에서 연주할 파트보까지 준비하였으나, 실제로는 이루어지지는 않았다. 결국 초연은 4년 반이 지난 후에 드레스덴에서 연주되었고 재연도 이루어졌지만, 이후에 그대로 방치되고 말았다. 그 사이에 그는 리엔치와 방황하는 네덜란드인을 완성하고 탄호이저에도 착수하는 등 분주한 시간을 보냈는데, 그런 바쁜 생활이 이 곡을 잊게 한 것이 아닌가 하는 의견도 있다.', 'id': '6566495-0-0', 'question': '바그너는 괴테의 파우스트를 읽고 무엇을 쓰고자 했는가?', 'title': '파우스트_서곡'} ``` ### Data Fields ``` {'id': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'context': Value(dtype='string', id=None), 'question': Value(dtype='string', id=None), 'answers': Sequence(feature={'text': Value(dtype='string', id=None), 'answer_start': Value(dtype='int32', id=None)}, length=-1, id=None)} ``` ### Data Splits - Train: 60407 - Validation: 5774 ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data Wikipedia #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [CC BY-ND 2.0 KR](https://creativecommons.org/licenses/by-nd/2.0/kr/deed.en) ### Citation Information ``` @article{lim2019korquad1, title={Korquad1. 0: Korean qa dataset for machine reading comprehension}, author={Lim, Seungyoung and Kim, Myungji and Lee, Jooyoul}, journal={arXiv preprint arXiv:1909.07005}, year={2019} ``` ### Contributions Thanks to [@cceyda](https://github.com/cceyda) for adding this dataset.

GEM/wiki_cat_sum

GEM

--- annotations_creators: - automatically-created language_creators: - unknown language: - en license: - cc-by-sa-3.0 multilinguality: - unknown size_categories: - unknown source_datasets: - original task_categories: - summarization task_ids: [] pretty_name: wiki_cat_sum --- # Dataset Card for GEM/wiki_cat_sum ## Dataset Description - **Homepage:** https://github.com/lauhaide/WikiCatSum - **Repository:** https://datashare.ed.ac.uk/handle/10283/3368 - **Paper:** https://arxiv.org/abs/1906.04687 - **Leaderboard:** N/A - **Point of Contact:** Laura Perez-Beltrachini ### Link to Main Data Card You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/wiki_cat_sum). ### Dataset Summary WikiCatSum is an English summarization dataset in three domains: animals, companies, and film. It provides multiple paragraphs of text paired with a summary of the paragraphs. You can load the dataset via: ``` import datasets data = datasets.load_dataset('GEM/wiki_cat_sum') ``` The data loader can be found [here](https://huggingface.co/datasets/GEM/wiki_cat_sum). #### website [Github](https://github.com/lauhaide/WikiCatSum) #### paper [Arxiv](https://arxiv.org/abs/1906.04687) #### authors Laura Perez-Beltrachini, Yang Liu, Mirella Lapata (University of Edinburgh) Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, Noam Shazeer (GoogleBrain) ## Dataset Overview ### Where to find the Data and its Documentation #### Webpage <!-- info: What is the webpage for the dataset (if it exists)? --> <!-- scope: telescope --> [Github](https://github.com/lauhaide/WikiCatSum) #### Download <!-- info: What is the link to where the original dataset is hosted? --> <!-- scope: telescope --> [Website](https://datashare.ed.ac.uk/handle/10283/3368) #### Paper <!-- info: What is the link to the paper describing the dataset (open access preferred)? --> <!-- scope: telescope --> [Arxiv](https://arxiv.org/abs/1906.04687) #### BibTex <!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. --> <!-- scope: microscope --> ``` @inproceedings{perez-beltrachini-etal-2019-generating, title = "Generating Summaries with Topic Templates and Structured Convolutional Decoders", author = "Perez-Beltrachini, Laura and Liu, Yang and Lapata, Mirella", booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/P19-1504", doi = "10.18653/v1/P19-1504", } ``` #### Contact Name <!-- quick --> <!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. --> <!-- scope: periscope --> Laura Perez-Beltrachini #### Contact Email <!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. --> <!-- scope: periscope --> lperez@ed.ac.uk #### Has a Leaderboard? <!-- info: Does the dataset have an active leaderboard? --> <!-- scope: telescope --> no ### Languages and Intended Use #### Multilingual? <!-- quick --> <!-- info: Is the dataset multilingual? --> <!-- scope: telescope --> no #### Covered Languages <!-- quick --> <!-- info: What languages/dialects are covered in the dataset? --> <!-- scope: telescope --> `English` #### License <!-- quick --> <!-- info: What is the license of the dataset? --> <!-- scope: telescope --> cc-by-sa-3.0: Creative Commons Attribution Share Alike 3.0 Unported #### Intended Use <!-- info: What is the intended use of the dataset? --> <!-- scope: microscope --> Research on multi-document abstractive summarisation. #### Primary Task <!-- info: What primary task does the dataset support? --> <!-- scope: telescope --> Summarization #### Communicative Goal <!-- quick --> <!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. --> <!-- scope: periscope --> Summarise the most important facts of a given entity in the Film, Company, and Animal domains from a cluster of related documents. ### Credit #### Curation Organization Type(s) <!-- info: In what kind of organization did the dataset curation happen? --> <!-- scope: telescope --> `industry`, `academic` #### Curation Organization(s) <!-- info: Name the organization(s). --> <!-- scope: periscope --> Google Cloud Platform, University of Edinburgh #### Dataset Creators <!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). --> <!-- scope: microscope --> Laura Perez-Beltrachini, Yang Liu, Mirella Lapata (University of Edinburgh) Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, Noam Shazeer (GoogleBrain) #### Funding <!-- info: Who funded the data creation? --> <!-- scope: microscope --> Google Cloud Platform, European Research Council #### Who added the Dataset to GEM? <!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. --> <!-- scope: microscope --> Ronald Cardenas (University of Edinburgh) Laura Perez-Beltrachini (University of Edinburgh) ### Dataset Structure #### Data Fields <!-- info: List and describe the fields present in the dataset. --> <!-- scope: telescope --> - `id`: ID of the data example - `title`: Is the Wikipedia article's title - `paragraphs`: Is the ranked list of paragraphs from the set of crawled texts - `summary`: Is constituted by a list of sentences together with their corresponding topic label #### Example Instance <!-- info: Provide a JSON formatted example of a typical instance in the dataset. --> <!-- scope: periscope --> This is a truncated example from the animal setting: ``` {'gem_id': 'animal-train-1', 'gem_parent_id': 'animal-train-1', 'id': '2652', 'paragraphs': ["lytrosis (hulst) of louisiana vernon antoine brou jr. 2005. southern lepidopterists' news, 27: 7 ., ..."], 'references': ['lytrosis unitaria , the common lytrosis moth, is a species of moth of the geometridae family. it is found in north america, including arkansas, georgia, iowa , massachusetts, and wisconsin. the wingspan is about 50 mm. the larvae feed on rosa, crataegus, amelanchier, acer, quercus and viburnum species.'], 'summary': {'text': ['lytrosis unitaria , the common lytrosis moth , is a species of moth of the geometridae family .', 'it is found in north america , including arkansas , georgia , iowa , massachusetts , new hampshire , new jersey , new york , north carolina , ohio , oklahoma , ontario , pennsylvania , south carolina , tennessee , texas , virginia , west virginia and wisconsin .', 'the wingspan is about 50 mm .', 'the larvae feed on rosa , crataegus , amelanchier , acer , quercus and viburnum species . '], 'topic': [29, 20, 9, 8]}, 'target': 'lytrosis unitaria , the common lytrosis moth, is a species of moth of the geometridae family. it is found in north america, including arkansas, georgia, iowa , massachusetts, and wisconsin. the wingspan is about 50 mm. the larvae feed on rosa, crataegus, amelanchier, acer, quercus and viburnum species.', 'title': 'lytrosis unitaria'} ``` #### Data Splits <!-- info: Describe and name the splits in the dataset if there are more than one. --> <!-- scope: periscope --> Nb of instances in train/valid/test are 50,938/2,855/2,831 #### Splitting Criteria <!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. --> <!-- scope: microscope --> The data was split i.i.d., i.e. uniformly split into training, validation, and test datasets. ## Dataset in GEM ### Rationale for Inclusion in GEM #### Why is the Dataset in GEM? <!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? --> <!-- scope: microscope --> Evaluation of models' performance on noisy (document, summary) pairs and long inputs. Evaluate models' capabilities to generalise and mitigate biases. #### Similar Datasets <!-- info: Do other datasets for the high level task exist? --> <!-- scope: telescope --> no #### Unique Language Coverage <!-- info: Does this dataset cover other languages than other datasets for the same task? --> <!-- scope: periscope --> no #### Ability that the Dataset measures <!-- info: What aspect of model ability can be measured with this dataset? --> <!-- scope: periscope --> Capabilities to generalise, mitigate biases, factual correctness. ### GEM-Specific Curation #### Modificatied for GEM? <!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? --> <!-- scope: telescope --> yes #### GEM Modifications <!-- info: What changes have been made to he original dataset? --> <!-- scope: periscope --> `annotations added` #### Modification Details <!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification --> <!-- scope: microscope --> We provide topic labels for summary sentences. #### Additional Splits? <!-- info: Does GEM provide additional splits to the dataset? --> <!-- scope: telescope --> no ### Getting Started with the Task #### Pointers to Resources <!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. --> <!-- scope: microscope --> - [Generating Wikipedia by Summarizing Long Sequences](https://arxiv.org/abs/1801.10198) - [Generating Summaries with Topic Templates and Structured Convolutional Decoders](https://arxiv.org/abs/1906.04687) - [Noisy Self-Knowledge Distillation for Text Summarization](https://arxiv.org/abs/2009.07032) And all references in these papers. ## Previous Results ### Previous Results #### Measured Model Abilities <!-- info: What aspect of model ability can be measured with this dataset? --> <!-- scope: telescope --> Capabilities to generalise, mitigate biases, factual correctness. #### Metrics <!-- info: What metrics are typically used for this task? --> <!-- scope: periscope --> `ROUGE`, `BERT-Score`, `MoverScore`, `Other: Other Metrics` #### Other Metrics <!-- info: Definitions of other metrics --> <!-- scope: periscope --> - Abstract/Copy - Factual accuracy based on the score of (Goodrich et al., 2019) and the relation extraction system of (Sorokin and Gurevych, 2017). #### Proposed Evaluation <!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. --> <!-- scope: microscope --> Human based are Question Answering and Ranking (Content, Fluency and Repetition) #### Previous results available? <!-- info: Are previous results available? --> <!-- scope: telescope --> yes #### Other Evaluation Approaches <!-- info: What evaluation approaches have others used? --> <!-- scope: periscope --> Those listed above. #### Relevant Previous Results <!-- info: What are the most relevant previous results for this task/dataset? --> <!-- scope: microscope --> Generating Summaries with Topic Templates and Structured Convolutional Decoders https://arxiv.org/abs/1906.04687 Noisy Self-Knowledge Distillation for Text Summarization https://arxiv.org/abs/2009.07032 ## Dataset Curation ### Original Curation #### Original Curation Rationale <!-- info: Original curation rationale --> <!-- scope: telescope --> The dataset is a subset of the WikiSum (Liu et al., 2018) dataset focusing on summaries of entities in three domains (Film, Company, and Animal). It is multi-document summarisation where input-output pairs for each example entity are created as follows. The input is a set of paragraphs collected from i) documents in the Reference section of the entity's Wikipedia page plus ii) documents collected from the top ten search results after querying Google search engine with the entity name. The output summary is the Wikipedia abstract for the entity. #### Communicative Goal <!-- info: What was the communicative goal? --> <!-- scope: periscope --> Generate descriptive summaries with specific domains, where certain topics are discussed and generally in specific orders. #### Sourced from Different Sources <!-- info: Is the dataset aggregated from different data sources? --> <!-- scope: telescope --> yes #### Source Details <!-- info: List the sources (one per line) --> <!-- scope: periscope --> WikiSum (Liu et al., 2018) ### Language Data #### How was Language Data Obtained? <!-- info: How was the language data obtained? --> <!-- scope: telescope --> `Other` #### Topics Covered <!-- info: Does the language in the dataset focus on specific topics? How would you describe them? --> <!-- scope: periscope --> The dataset and task focuses on summaries for entities in three domains: Company, Film, and Animal. #### Data Validation <!-- info: Was the text validated by a different worker or a data curator? --> <!-- scope: telescope --> not validated #### Data Preprocessing <!-- info: How was the text data pre-processed? (Enter N/A if the text was not pre-processed) --> <!-- scope: microscope --> Summary sentences are associated with a topic label. There is a topic model for each domain. #### Was Data Filtered? <!-- info: Were text instances selected or filtered? --> <!-- scope: telescope --> not filtered ### Structured Annotations #### Additional Annotations? <!-- quick --> <!-- info: Does the dataset have additional annotations for each instance? --> <!-- scope: telescope --> automatically created #### Annotation Service? <!-- info: Was an annotation service used? --> <!-- scope: telescope --> no #### Annotation Values <!-- info: Purpose and values for each annotation --> <!-- scope: microscope --> Each summary sentences was annotated with a topic label. There is a topic model for each of the three domains. This was used to guide a hierarchical decoder. #### Any Quality Control? <!-- info: Quality control measures? --> <!-- scope: telescope --> validated by data curators #### Quality Control Details <!-- info: Describe the quality control measures that were taken. --> <!-- scope: microscope --> Manual inspection of a sample of topics assigned to sentences. The number of topics was selected based on the performance of the summarisation model. ### Consent #### Any Consent Policy? <!-- info: Was there a consent policy involved when gathering the data? --> <!-- scope: telescope --> no #### Justification for Using the Data <!-- info: If not, what is the justification for reusing the data? --> <!-- scope: microscope --> The dataset is base on Wikipedia and referenced and retrieved documents crawled from the Web. ### Private Identifying Information (PII) #### Contains PII? <!-- quick --> <!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? --> <!-- scope: telescope --> unlikely #### Any PII Identification? <!-- info: Did the curators use any automatic/manual method to identify PII in the dataset? --> <!-- scope: periscope --> no identification ### Maintenance #### Any Maintenance Plan? <!-- info: Does the original dataset have a maintenance plan? --> <!-- scope: telescope --> no ## Broader Social Context ### Previous Work on the Social Impact of the Dataset #### Usage of Models based on the Data <!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? --> <!-- scope: telescope --> no ### Impact on Under-Served Communities #### Addresses needs of underserved Communities? <!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). --> <!-- scope: telescope --> no ### Discussion of Biases #### Any Documented Social Biases? <!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. --> <!-- scope: telescope --> yes #### Links and Summaries of Analysis Work <!-- info: Provide links to and summaries of works analyzing these biases. --> <!-- scope: microscope --> This dataset is based on Wikipedia and thus biases analysis on other Wikipedia-based datasets are potentially true for WikiCatSum. For instance, see analysis for the ToTTo dataset here [1]. [1] Automatic Construction of Evaluation Suites for Natural Language Generation Datasets https://openreview.net/forum?id=CSi1eu_2q96 ## Considerations for Using the Data ### PII Risks and Liability ### Licenses #### Copyright Restrictions on the Dataset <!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? --> <!-- scope: periscope --> `public domain` #### Copyright Restrictions on the Language Data <!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? --> <!-- scope: periscope --> `public domain` ### Known Technical Limitations

MMInstruction/M3IT_ML

MMInstruction

--- license: cc-by-4.0 ---

biomrc

--- language: - en paperswithcode_id: biomrc pretty_name: BIOMRC dataset_info: - config_name: plain_text features: - name: abstract dtype: string - name: title dtype: string - name: entities_list sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1653301820 num_examples: 700000 - name: validation num_bytes: 119697683 num_examples: 50000 - name: test num_bytes: 147832373 num_examples: 62707 download_size: 408080356 dataset_size: 1920831876 - config_name: biomrc_large_A features: - name: abstract dtype: string - name: title dtype: string - name: entities_list sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1653301820 num_examples: 700000 - name: validation num_bytes: 119697683 num_examples: 50000 - name: test num_bytes: 147832373 num_examples: 62707 download_size: 408080356 dataset_size: 1920831876 - config_name: biomrc_large_B features: - name: abstract dtype: string - name: title dtype: string - name: entities_list sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1325877001 num_examples: 700000 - name: validation num_bytes: 96414040 num_examples: 50000 - name: test num_bytes: 118708586 num_examples: 62707 download_size: 343061539 dataset_size: 1540999627 - config_name: biomrc_small_A features: - name: abstract dtype: string - name: title dtype: string - name: entities_list sequence: string - name: answer dtype: string splits: - name: train num_bytes: 206553549 num_examples: 87500 - name: validation num_bytes: 14957163 num_examples: 6250 - name: test num_bytes: 14807799 num_examples: 6250 download_size: 68879274 dataset_size: 236318511 - config_name: biomrc_small_B features: - name: abstract dtype: string - name: title dtype: string - name: entities_list sequence: string - name: answer dtype: string splits: - name: train num_bytes: 165662937 num_examples: 87500 - name: validation num_bytes: 12047304 num_examples: 6250 - name: test num_bytes: 11911172 num_examples: 6250 download_size: 57706889 dataset_size: 189621413 - config_name: biomrc_tiny_A features: - name: abstract dtype: string - name: title dtype: string - name: entities_list sequence: string - name: answer dtype: string splits: - name: test num_bytes: 70914 num_examples: 30 download_size: 22519 dataset_size: 70914 - config_name: biomrc_tiny_B features: - name: abstract dtype: string - name: title dtype: string - name: entities_list sequence: string - name: answer dtype: string splits: - name: test num_bytes: 59925 num_examples: 30 download_size: 19685 dataset_size: 59925 --- # Dataset Card for "biomrc" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [http://nlp.cs.aueb.gr/](http://nlp.cs.aueb.gr/) - **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:** 1.29 GB - **Size of the generated dataset:** 5.81 GB - **Total amount of disk used:** 7.09 GB ### Dataset Summary We introduce BIOMRC, a large-scale cloze-style biomedical MRC dataset. Care was taken to reduce noise, compared to the previous BIOREAD dataset of Pappas et al. (2018). Experiments show that simple heuristics do not perform well on the new dataset and that two neural MRC models that had been tested on BIOREAD perform much better on BIOMRC, indicating that the new dataset is indeed less noisy or at least that its task is more feasible. Non-expert human performance is also higher on the new dataset compared to BIOREAD, and biomedical experts perform even better. We also introduce a new BERT-based MRC model, the best version of which substantially outperforms all other methods tested, reaching or surpassing the accuracy of biomedical experts in some experiments. We make the new dataset available in three different sizes, also releasing our code, and providing a leaderboard. ### 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 #### biomrc_large_A - **Size of downloaded dataset files:** 408.08 MB - **Size of the generated dataset:** 1.92 GB - **Total amount of disk used:** 2.33 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "abstract": "\"OBJECTIVES: @entity9 is a @entity10 that may result from greater occipital nerve entrapment. Entrapped peripheral nerves typica...", "answer": "@entity9 :: (MESH:D009437,Disease) :: ['unilateral occipital neuralgia']\n", "entities_list": ["@entity1 :: ('9606', 'Species') :: ['patients']", "@entity10 :: ('MESH:D006261', 'Disease') :: ['headache', 'Headache']", "@entity9 :: ('MESH:D009437', 'Disease') :: ['Occipital neuralgia', 'unilateral occipital neuralgia']"], "title": "Sonographic evaluation of the greater occipital nerve in XXXX .\n" } ``` #### biomrc_large_B - **Size of downloaded dataset files:** 343.06 MB - **Size of the generated dataset:** 1.54 GB - **Total amount of disk used:** 1.88 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "abstract": "\"BACKGROUND: Adults with physical disabilities are less likely than others to receive @entity2 screening. It is not known, howev...", "answer": "@entity2", "entities_list": ["@entity2", "@entity1", "@entity0", "@entity3"], "title": "Does a standard measure of self-reported physical disability correlate with clinician perception of impairment related to XXXX screening?\n" } ``` #### biomrc_small_A - **Size of downloaded dataset files:** 68.88 MB - **Size of the generated dataset:** 236.32 MB - **Total amount of disk used:** 305.20 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "abstract": "\"PURPOSE: @entity120 ( @entity120 ) is a life-limiting @entity102 that presents as an elevated blood pressure in the pulmonary a...", "answer": "@entity148 :: (MESH:D001008,Disease) :: ['anxiety']\n", "entities_list": "[\"@entity1 :: ('9606', 'Species') :: ['patients']\", \"@entity308 :: ('MESH:D003866', 'Disease') :: ['depression']\", \"@entity146 :...", "title": "A predictive model of the effects of @entity308 , XXXX , stress, 6-minute-walk distance, and social support on health-related quality of life in an adult pulmonary hypertension population.\n" } ``` #### biomrc_small_B - **Size of downloaded dataset files:** 57.70 MB - **Size of the generated dataset:** 189.62 MB - **Total amount of disk used:** 247.33 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "abstract": "\"Single-agent activity for @entity12 reflected by response rates of 10%-30% has been reported in @entity0 with @entity3 ( @entit...", "answer": "@entity10", "entities_list": ["@entity0", "@entity6", "@entity2", "@entity5", "@entity12", "@entity11", "@entity1", "@entity7", "@entity9", "@entity10", "@entity3", "@entity4", "@entity8"], "title": "No synergistic activity of @entity7 and XXXX in the treatment of @entity3 .\n" } ``` #### biomrc_tiny_A - **Size of downloaded dataset files:** 0.02 MB - **Size of the generated dataset:** 0.07 MB - **Total amount of disk used:** 0.09 MB An example of 'test' looks as follows. ``` This example was too long and was cropped: { "abstract": "\"OBJECTIVE: Decompressive craniectomy (DC) requires later cranioplasty (CP) in survivors. However, if additional ventriculoperit...", "answer": "@entity260 :: (MESH:D011183,Disease) :: ['Postoperative Complications']\n", "entities_list": ["@entity1 :: ('9606', 'Species') :: ['Patients', 'patients', 'Patient']", "@entity260 :: ('MESH:D011183', 'Disease') :: ['VPS regarding postoperative complications']", "@entity1276 :: ('MESH:D006849', 'Disease') :: ['hydrocephalus']"], "title": "Cranioplasty and Ventriculoperitoneal Shunt Placement after Decompressive Craniectomy: Staged Surgery Is Associated with Fewer XXXX .\n" } ``` ### Data Fields The data fields are the same among all splits. #### biomrc_large_A - `abstract`: a `string` feature. - `title`: a `string` feature. - `entities_list`: a `list` of `string` features. - `answer`: a `string` feature. #### biomrc_large_B - `abstract`: a `string` feature. - `title`: a `string` feature. - `entities_list`: a `list` of `string` features. - `answer`: a `string` feature. #### biomrc_small_A - `abstract`: a `string` feature. - `title`: a `string` feature. - `entities_list`: a `list` of `string` features. - `answer`: a `string` feature. #### biomrc_small_B - `abstract`: a `string` feature. - `title`: a `string` feature. - `entities_list`: a `list` of `string` features. - `answer`: a `string` feature. #### biomrc_tiny_A - `abstract`: a `string` feature. - `title`: a `string` feature. - `entities_list`: a `list` of `string` features. - `answer`: a `string` feature. ### Data Splits #### biomrc_large_A | |train |validation|test | |--------------|-----:|---------:|----:| |biomrc_large_A|700000| 50000|62707| #### biomrc_large_B | |train |validation|test | |--------------|-----:|---------:|----:| |biomrc_large_B|700000| 50000|62707| #### biomrc_small_A | |train|validation|test| |--------------|----:|---------:|---:| |biomrc_small_A|87500| 6250|6250| #### biomrc_small_B | |train|validation|test| |--------------|----:|---------:|---:| |biomrc_small_B|87500| 6250|6250| #### biomrc_tiny_A | |test| |-------------|---:| |biomrc_tiny_A| 30| ## 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{pappas-etal-2020-biomrc, title = "{B}io{MRC}: A Dataset for Biomedical Machine Reading Comprehension", author = "Pappas, Dimitris and Stavropoulos, Petros and Androutsopoulos, Ion and McDonald, Ryan", booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.bionlp-1.15", pages = "140--149", abstract = "We introduce BIOMRC, a large-scale cloze-style biomedical MRC dataset. Care was taken to reduce noise, compared to the previous BIOREAD dataset of Pappas et al. (2018). Experiments show that simple heuristics do not perform well on the new dataset and that two neural MRC models that had been tested on BIOREAD perform much better on BIOMRC, indicating that the new dataset is indeed less noisy or at least that its task is more feasible. Non-expert human performance is also higher on the new dataset compared to BIOREAD, and biomedical experts perform even better. We also introduce a new BERT-based MRC model, the best version of which substantially outperforms all other methods tested, reaching or surpassing the accuracy of biomedical experts in some experiments. We make the new dataset available in three different sizes, also releasing our code, and providing a leaderboard.", } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@PetrosStav](https://github.com/PetrosStav), [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

civil_comments

--- language: - en paperswithcode_id: null pretty_name: CivilComments dataset_info: features: - name: text dtype: string - name: toxicity dtype: float32 - name: severe_toxicity dtype: float32 - name: obscene dtype: float32 - name: threat dtype: float32 - name: insult dtype: float32 - name: identity_attack dtype: float32 - name: sexual_explicit dtype: float32 splits: - name: test num_bytes: 32073013 num_examples: 97320 - name: train num_bytes: 596835730 num_examples: 1804874 - name: validation num_bytes: 32326369 num_examples: 97320 download_size: 414947977 dataset_size: 661235112 license: cc0-1.0 --- # Dataset Card for "civil_comments" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data) - **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:** 414.95 MB - **Size of the generated dataset:** 661.23 MB - **Total amount of disk used:** 1.08 GB ### Dataset Summary The comments in this dataset come from an archive of the Civil Comments platform, a commenting plugin for independent news sites. These public comments were created from 2015 - 2017 and appeared on approximately 50 English-language news sites across the world. When Civil Comments shut down in 2017, they chose to make the public comments available in a lasting open archive to enable future research. The original data, published on figshare, includes the public comment text, some associated metadata such as article IDs, timestamps and commenter-generated "civility" labels, but does not include user ids. Jigsaw extended this dataset by adding additional labels for toxicity and identity mentions. This data set is an exact replica of the data released for the Jigsaw Unintended Bias in Toxicity Classification Kaggle challenge. This dataset is released under CC0, as is the underlying comment text. ### 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:** 414.95 MB - **Size of the generated dataset:** 661.23 MB - **Total amount of disk used:** 1.08 GB An example of 'validation' looks as follows. ``` { "identity_attack": 0.0, "insult": 0.0, "obscene": 0.0, "severe_toxicity": 0.0, "sexual_explicit": 0.0, "text": "The public test.", "threat": 0.0, "toxicity": 0.0 } ``` ### Data Fields The data fields are the same among all splits. #### default - `text`: a `string` feature. - `toxicity`: a `float32` feature. - `severe_toxicity`: a `float32` feature. - `obscene`: a `float32` feature. - `threat`: a `float32` feature. - `insult`: a `float32` feature. - `identity_attack`: a `float32` feature. - `sexual_explicit`: a `float32` feature. ### Data Splits | name | train |validation|test | |-------|------:|---------:|----:| |default|1804874| 97320|97320| ## 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 This dataset is released under [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/). ### Citation Information ``` @article{DBLP:journals/corr/abs-1903-04561, author = {Daniel Borkan and Lucas Dixon and Jeffrey Sorensen and Nithum Thain and Lucy Vasserman}, title = {Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification}, journal = {CoRR}, volume = {abs/1903.04561}, year = {2019}, url = {http://arxiv.org/abs/1903.04561}, archivePrefix = {arXiv}, eprint = {1903.04561}, timestamp = {Sun, 31 Mar 2019 19:01:24 +0200}, biburl = {https://dblp.org/rec/bib/journals/corr/abs-1903-04561}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

plaguss/snli-small

plaguss

--- size_categories: n<1K tags: - rlfh - argilla - human-feedback --- # Dataset Card for snli-small This dataset has been created with [Argilla](https://docs.argilla.io). As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets). ## Dataset Description - **Homepage:** https://argilla.io - **Repository:** https://github.com/argilla-io/argilla - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset contains: * A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla. * Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`. * The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla. ### Load with Argilla To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code: ```python import argilla as rg ds = rg.FeedbackDataset.from_huggingface("plaguss/snli-small") ``` ### Load with `datasets` To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code: ```python from datasets import load_dataset ds = load_dataset("plaguss/snli-small") ``` ### Supported Tasks and Leaderboards This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/guides/llms/conceptual_guides/data_model.html) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure). There are no leaderboards associated with this dataset. ### Languages [More Information Needed] ## Dataset Structure ### Data in Argilla The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, and **guidelines**. The **fields** are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions. | Field Name | Title | Type | Required | Markdown | | ---------- | ----- | ---- | -------- | -------- | | premise | Premise | TextField | True | False | | hypothesis | Hypothesis | TextField | True | False | The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, single choice, or multiple choice. | Question Name | Title | Type | Required | Description | Values/Labels | | ------------- | ----- | ---- | -------- | ----------- | ------------- | | label | The hypothesis entails the premise, neither entails nor contradict each other, or the hypothesis contradicts the premise? | LabelQuestion | True | N/A | ['0', '1', '2'] | **✨ NEW** Additionally, we also have **suggestions**, which are linked to the existing questions, and so on, named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above. Finally, the **guidelines** are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section. ### Data Instances An example of a dataset instance in Argilla looks as follows: ```json { "fields": { "hypothesis": "A person is training his horse for a competition.", "premise": "A person on a horse jumps over a broken down airplane." }, "metadata": {}, "responses": [ { "status": "submitted", "values": { "label": { "value": "1" } } } ], "suggestions": [] } ``` While the same record in HuggingFace `datasets` looks as follows: ```json { "external_id": null, "hypothesis": "A person is training his horse for a competition.", "label": [ { "status": "submitted", "user_id": null, "value": "1" } ], "label-suggestion": null, "label-suggestion-metadata": { "agent": null, "score": null, "type": null }, "metadata": "{}", "premise": "A person on a horse jumps over a broken down airplane." } ``` ### Data Fields Among the dataset fields, we differentiate between the following: * **Fields:** These are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions. * **premise** is of type `TextField`. * **hypothesis** is of type `TextField`. * **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`. * **label** is of type `LabelQuestion` with the following allowed values ['0', '1', '2']. * **✨ NEW** **Suggestions:** As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable. * (optional) **label-suggestion** is of type `label_selection` with the following allowed values ['0', '1', '2']. Additionally, we also have one more field which is optional and is the following: * **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file. ### Data Splits The dataset contains a single split, which is `train`. ## 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 guidelines Premise: A string used to determine the truthfulness of the hypothesis, Hypothesis: A string that may be true, false, or whose truth conditions may not be knowable when compared to the premise #### 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]

llm-book/wrime-sentiment

llm-book

--- task_categories: - text-classification language: - ja size_categories: - 10K<n<100K --- # Dataset Card for llm-book/wrime-sentiment 日本語の感情分析データセット WRIME を、ポジティブ/ネガティブの二値分類のタスクに加工したデータセットです。 GitHub リポジトリ [ids-cv/wrime](https://github.com/ids-cv/wrime) で公開されているデータセットを利用しています。 `Avg. Readers_Sentiment` の値が0より大きいものをポジティブ、0より小さいものをネガティブとラベル付をしています。 書籍『大規模言語モデル入門』のサンプルコードで利用することを想定しています。 詳しくは[書籍のGitHubリポジトリ](https://github.com/ghmagazine/llm-book)をご覧ください。 ## 使い方 以下のようにデータセットを読み込むことができます。 ```python from datasets import load_dataset dataset = load_dataset("hf_datasets/wrime-sentiment") print(dataset["train"].features["label"]) print(dataset) ``` ```python ClassLabel(names=['positive', 'negative'], id=None) DatasetDict({ train: Dataset({ features: ['sentence', 'label'], num_rows: 20149 }) validation: Dataset({ features: ['sentence', 'label'], num_rows: 1608 }) test: Dataset({ features: ['sentence', 'label'], num_rows: 1781 }) }) ``` デフォルトの設定では、元のデータセットから極性がニュートラルであるものを除いています。 `remove_netural=False`と指定することで、ニュートラルなデータも含めた三値分類のデータセットを読み込むことができます。 ```python from datasets import load_dataset dataset = load_dataset("hf_datasets/wrime-sentiment", remove_neutral=False) print(dataset["train"].features["label"]) print(dataset) ``` ```python ClassLabel(names=['positive', 'negative', 'neutral'], id=None) DatasetDict({ train: Dataset({ features: ['sentence', 'label'], num_rows: 30000 }) validation: Dataset({ features: ['sentence', 'label'], num_rows: 2500 }) test: Dataset({ features: ['sentence', 'label'], num_rows: 2500 }) }) ```

ivanzhouyq/RedPajama-Tiny

ivanzhouyq

--- task_categories: - text-generation language: - en pretty_name: RedPajama Tiny --- # Dataset Card for Dataset Name ### Dataset Summary This is a tiny version of the RedPajama dataset, which is a clean-room, fully open-source implementation of the LLaMa dataset. This dataset contains 64 samples from each of the 7 sources. The full dataset has the following token counts and is available for [download]( https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T): | Dataset | Token Count | |---------------|-------------| | Commoncrawl | 878 Billion | | C4 | 175 Billion | | GitHub | 59 Billion | | Books | 26 Billion | | ArXiv | 28 Billion | | Wikipedia | 24 Billion | | StackExchange | 20 Billion | | Total | 1.2 Trillion | ### Languages Primarily English, though the Wikipedia slice contains multiple languages. ## Dataset Structure The dataset structure is as follows: ``` { "text": ..., "meta": {"url": "...", "timestamp": "...", "source": "...", "language": "...", ...} } ``` ## Dataset Creation This dataset was created to follow the LLaMa paper as closely as possible to try to reproduce its recipe. ### Source Data #### Commoncrawl We download five dumps from Commoncrawl, and run the dumps through the official `cc_net` pipeline. We then deduplicate on the paragraph level, and filter out low quality text using a linear classifier trained to classify paragraphs as Wikipedia references or random Commoncrawl samples. #### C4 C4 is downloaded from Huggingface. The only preprocessing step is to bring the data into our own format. #### GitHub The raw GitHub data is downloaded from Google BigQuery. We deduplicate on the file level and filter out low quality files and only keep projects that are distributed under the MIT, BSD, or Apache license. #### Wikipedia We use the Wikipedia dataset available on Huggingface, which is based on the Wikipedia dump from 2023-03-20 and contains text in 20 different languages. The dataset comes in preprocessed format, so that hyperlinks, comments and other formatting boilerplate has been removed. #### Gutenberg and Books3 The PG19 subset of the Gutenberg Project and Books3 datasets are downloaded from Huggingface. After downloading, we use simhash to remove near duplicates. #### ArXiv ArXiv data is downloaded from Amazon S3 in the `arxiv` requester pays bucket. We only keep latex source files and remove preambles, comments, macros and bibliographies. #### Stackexchange The Stack Exchange split of the dataset is download from the [Internet Archive](https://archive.org/download/stackexchange). Here we only keep the posts from the 28 largest sites, remove html tags, group the posts into question-answer pairs, and order answers by their score.

fever

--- language: - en paperswithcode_id: fever annotations_creators: - crowdsourced language_creators: - found license: - cc-by-sa-3.0 - gpl-3.0 multilinguality: - monolingual pretty_name: FEVER size_categories: - 100K<n<1M source_datasets: - extended|wikipedia task_categories: - text-classification task_ids: [] tags: - knowledge-verification dataset_info: - config_name: v1.0 features: - name: id dtype: int32 - name: label dtype: string - name: claim dtype: string - name: evidence_annotation_id dtype: int32 - name: evidence_id dtype: int32 - name: evidence_wiki_url dtype: string - name: evidence_sentence_id dtype: int32 splits: - name: train num_bytes: 29591412 num_examples: 311431 - name: labelled_dev num_bytes: 3643157 num_examples: 37566 - name: unlabelled_dev num_bytes: 1548965 num_examples: 19998 - name: unlabelled_test num_bytes: 1617002 num_examples: 19998 - name: paper_dev num_bytes: 1821489 num_examples: 18999 - name: paper_test num_bytes: 1821668 num_examples: 18567 download_size: 44853972 dataset_size: 40043693 - config_name: v2.0 features: - name: id dtype: int32 - name: label dtype: string - name: claim dtype: string - name: evidence_annotation_id dtype: int32 - name: evidence_id dtype: int32 - name: evidence_wiki_url dtype: string - name: evidence_sentence_id dtype: int32 splits: - name: validation num_bytes: 306243 num_examples: 2384 download_size: 392466 dataset_size: 306243 - config_name: wiki_pages features: - name: id dtype: string - name: text dtype: string - name: lines dtype: string splits: - name: wikipedia_pages num_bytes: 7254115038 num_examples: 5416537 download_size: 1713485474 dataset_size: 7254115038 --- # Dataset Card for "fever" ## 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://fever.ai/](https://fever.ai/) - **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 With billions of individual pages on the web providing information on almost every conceivable topic, we should have the ability to collect facts that answer almost every conceivable question. However, only a small fraction of this information is contained in structured sources (Wikidata, Freebase, etc.) – we are therefore limited by our ability to transform free-form text to structured knowledge. There is, however, another problem that has become the focus of a lot of recent research and media coverage: false information coming from unreliable sources. The FEVER workshops are a venue for work in verifiable knowledge extraction and to stimulate progress in this direction. - FEVER Dataset: FEVER (Fact Extraction and VERification) consists of 185,445 claims generated by altering sentences extracted from Wikipedia and subsequently verified without knowledge of the sentence they were derived from. The claims are classified as Supported, Refuted or NotEnoughInfo. For the first two classes, the annotators also recorded the sentence(s) forming the necessary evidence for their judgment. - FEVER 2.0 Adversarial Attacks Dataset: The FEVER 2.0 Dataset consists of 1174 claims created by the submissions of participants in the Breaker phase of the 2019 shared task. Participants (Breakers) were tasked with generating adversarial examples that induce classification errors for the existing systems. Breakers submitted a dataset of up to 1000 instances with equal number of instances for each of the three classes (Supported, Refuted NotEnoughInfo). Only novel claims (i.e. not contained in the original FEVER dataset) were considered as valid entries to the shared task. The submissions were then manually evaluated for Correctness (grammatical, appropriately labeled and meet the FEVER annotation guidelines requirements). ### Supported Tasks and Leaderboards The task is verification of textual claims against textual sources. When compared to textual entailment (TE)/natural language inference, the key difference is that in these tasks the passage to verify each claim is given, and in recent years it typically consists a single sentence, while in verification systems it is retrieved from a large set of documents in order to form the evidence. ### Languages The dataset is in English. ## Dataset Structure ### Data Instances #### v1.0 - **Size of downloaded dataset files:** 44.86 MB - **Size of the generated dataset:** 40.05 MB - **Total amount of disk used:** 84.89 MB An example of 'train' looks as follows. ``` 'claim': 'Nikolaj Coster-Waldau worked with the Fox Broadcasting Company.', 'evidence_wiki_url': 'Nikolaj_Coster-Waldau', 'label': 'SUPPORTS', 'id': 75397, 'evidence_id': 104971, 'evidence_sentence_id': 7, 'evidence_annotation_id': 92206} ``` #### v2.0 - **Size of downloaded dataset files:** 0.39 MB - **Size of the generated dataset:** 0.30 MB - **Total amount of disk used:** 0.70 MB An example of 'validation' looks as follows. ``` {'claim': "There is a convicted statutory rapist called Chinatown's writer.", 'evidence_wiki_url': '', 'label': 'NOT ENOUGH INFO', 'id': 500000, 'evidence_id': -1, 'evidence_sentence_id': -1, 'evidence_annotation_id': 269158} ``` #### wiki_pages - **Size of downloaded dataset files:** 1.71 GB - **Size of the generated dataset:** 7.25 GB - **Total amount of disk used:** 8.97 GB An example of 'wikipedia_pages' looks as follows. ``` {'text': 'The following are the football -LRB- soccer -RRB- events of the year 1928 throughout the world . ', 'lines': '0\tThe following are the football -LRB- soccer -RRB- events of the year 1928 throughout the world .\n1\t', 'id': '1928_in_association_football'} ``` ### Data Fields The data fields are the same among all splits. #### v1.0 - `id`: a `int32` feature. - `label`: a `string` feature. - `claim`: a `string` feature. - `evidence_annotation_id`: a `int32` feature. - `evidence_id`: a `int32` feature. - `evidence_wiki_url`: a `string` feature. - `evidence_sentence_id`: a `int32` feature. #### v2.0 - `id`: a `int32` feature. - `label`: a `string` feature. - `claim`: a `string` feature. - `evidence_annotation_id`: a `int32` feature. - `evidence_id`: a `int32` feature. - `evidence_wiki_url`: a `string` feature. - `evidence_sentence_id`: a `int32` feature. #### wiki_pages - `id`: a `string` feature. - `text`: a `string` feature. - `lines`: a `string` feature. ### Data Splits #### v1.0 | | train | unlabelled_dev | labelled_dev | paper_dev | unlabelled_test | paper_test | |------|-------:|---------------:|-------------:|----------:|----------------:|-----------:| | v1.0 | 311431 | 19998 | 37566 | 18999 | 19998 | 18567 | #### v2.0 | | validation | |------|-----------:| | v2.0 | 2384 | #### wiki_pages | | wikipedia_pages | |------------|----------------:| | wiki_pages | 5416537 | ## 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 FEVER license: ``` These data annotations incorporate material from Wikipedia, which is licensed pursuant to the Wikipedia Copyright Policy. These annotations are made available under the license terms described on the applicable Wikipedia article pages, or, where Wikipedia license terms are unavailable, under the Creative Commons Attribution-ShareAlike License (version 3.0), available at http://creativecommons.org/licenses/by-sa/3.0/ (collectively, the “License Terms”). You may not use these files except in compliance with the applicable License Terms. ``` ### Citation Information If you use "FEVER Dataset", please cite: ```bibtex @inproceedings{Thorne18Fever, author = {Thorne, James and Vlachos, Andreas and Christodoulopoulos, Christos and Mittal, Arpit}, title = {{FEVER}: a Large-scale Dataset for Fact Extraction and {VERification}}, booktitle = {NAACL-HLT}, year = {2018} } ``` If you use "FEVER 2.0 Adversarial Attacks Dataset", please cite: ```bibtex @inproceedings{Thorne19FEVER2, author = {Thorne, James and Vlachos, Andreas and Cocarascu, Oana and Christodoulopoulos, Christos and Mittal, Arpit}, title = {The {FEVER2.0} Shared Task}, booktitle = {Proceedings of the Second Workshop on {Fact Extraction and VERification (FEVER)}}, year = {2018} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@mariamabarham](https://github.com/mariamabarham), [@lewtun](https://github.com/lewtun), [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.

bible_para

--- annotations_creators: - found language_creators: - found language: - acu - af - agr - ake - am - amu - ar - bg - bsn - cak - ceb - ch - chq - chr - cjp - cni - cop - crp - cs - da - de - dik - dje - djk - dop - ee - el - en - eo - es - et - eu - fi - fr - gbi - gd - gu - gv - he - hi - hr - hu - hy - id - is - it - ja - jak - jiv - kab - kbh - kek - kn - ko - la - lt - lv - mam - mi - ml - mr - my - ne - nhg - nl - 'no' - ojb - pck - pes - pl - plt - pot - ppk - pt - quc - quw - ro - rom - ru - shi - sk - sl - sn - so - sq - sr - ss - sv - syr - te - th - tl - tmh - tr - uk - usp - vi - wal - wo - xh - zh - zu license: - cc0-1.0 multilinguality: - multilingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: null pretty_name: BiblePara dataset_info: - config_name: de-en features: - name: id dtype: string - name: translation dtype: translation: languages: - de - en splits: - name: train num_bytes: 17262178 num_examples: 62195 download_size: 5440713 dataset_size: 17262178 - config_name: en-fr features: - name: id dtype: string - name: translation dtype: translation: languages: - en - fr splits: - name: train num_bytes: 17536445 num_examples: 62195 download_size: 5470044 dataset_size: 17536445 - config_name: en-es features: - name: id dtype: string - name: translation dtype: translation: languages: - en - es splits: - name: train num_bytes: 17105724 num_examples: 62191 download_size: 5418998 dataset_size: 17105724 - config_name: en-fi features: - name: id dtype: string - name: translation dtype: translation: languages: - en - fi splits: - name: train num_bytes: 17486055 num_examples: 62026 download_size: 5506407 dataset_size: 17486055 - config_name: en-no features: - name: id dtype: string - name: translation dtype: translation: languages: - en - 'no' splits: - name: train num_bytes: 16681323 num_examples: 62107 download_size: 5293164 dataset_size: 16681323 - config_name: en-hi features: - name: id dtype: string - name: translation dtype: translation: languages: - en - hi splits: - name: train num_bytes: 27849361 num_examples: 62073 download_size: 6224765 dataset_size: 27849361 --- # Dataset Card for BiblePara ## 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/bible-uedin.php - **Repository:** None - **Paper:** https://link.springer.com/article/10.1007/s10579-014-9287-y - **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/bible-uedin.php E.g. `dataset = load_dataset("bible_para", lang1="fi", lang2="hi")` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances Here are some examples of questions and facts: ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.

medalpaca/medical_meadow_medqa

medalpaca

--- task_categories: - question-answering language: - en - zh tags: - medical --- # Dataset Card for MedQA ## Dataset Description - **Paper:** ### Dataset Summary This is the data and baseline source code for the paper: Jin, Di, et al. "What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams." From https://github.com/jind11/MedQA: >The data that contains both the QAs and textbooks can be downloaded from [this google drive folder](https://drive.google.com/file/d/1ImYUSLk9JbgHXOemfvyiDiirluZHPeQw/view?usp=sharing). A bit of details of data are explained as below: > > For QAs, we have three sources: US, Mainland of China, and Taiwan District, which are put in folders, respectively. All files for QAs are in jsonl file format, where each line is a data sample as a dict. The "XX_qbank.jsonl" files contain all data samples while we also provide an official random split into train, dev, and test sets. Those files in the "metamap" folders are extracted medical related phrases using the Metamap tool. > > For QAs, we also include the "4_options" version in for US and Mainland of China since we reported results for 4 options in the paper. > > For textbooks, we have two languages: English and simplified Chinese. For simplified Chinese, we provide two kinds of sentence spliting: one is split by sentences, and the other is split by paragraphs. ### Citation Information ``` @article{jin2020disease, title={What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams}, author={Jin, Di and Pan, Eileen and Oufattole, Nassim and Weng, Wei-Hung and Fang, Hanyi and Szolovits, Peter}, journal={arXiv preprint arXiv:2009.13081}, year={2020} } ```

DFKI-SLT/cdcp

DFKI-SLT

--- language: - en size_categories: - n<1K --- # Dataset Card for "CDCP" ### Dataset Summary CDCP (a.k.a. *Cornell eRulemaking Corpus*; [Park and Cardie, 2018](https://aclanthology.org/L18-1257.pdf)) consists of 731 user comments from an eRulemaking platform in the English language. There are five types of components (`Fact`, `Testimony`, `Reference`, `Value`, and `Policy`) and two types of supporting relations (`Reason` and `Evidence`) are annotated on the basis of the study by Park et al. (2015). The resulting dataset contains 4931 elementary unit and 1221 support relation annotations. (pp. 1623-1624) ### Supported Tasks and Leaderboards - **Tasks:** Argument Mining, Link Prediction, Component Classification, Relation Classification - **Leaderboards:** https://paperswithcode.com/dataset/cdcp ### Languages The language in the dataset is English (AmE). ## Dataset Structure ### Data Instances - **Size of downloaded dataset files:** 5.37 MB ``` { 'id': "00195", 'text': "State and local court rules sometimes make default judgments much more likely. For example, when a person who allegedly owes a debt is told to come to court on a work day, they may be forced to choose between a default judgment and their job. I urge the CFPB to find practices that involve scheduling hearings at inconvenient times unfair, deceptive, and abusive, or inconsistent with 1692i", 'proposition': { "start": [0, 78, 242], "end": [78, 242, 391], "label": [4, 4, 1], "url": ["", "", ""], }, 'relations': {"head": [0, 2], "tail": [1, 0], "label": [1, 1]}, } ``` ### Data Fields - `id`: the instance id of the text, a `string` feature - `text`: the text (with URLs marked as `__URL__`), a `string` feature - `proposition`: the annotation list of spans with labels and URL (if applicable), a `dictionary` feature - `start`: the indices indicating the inclusive start of the spans, a `list` of `int` feature - `end`: the indices indicating the exclusive end of the spans, a `list` of `int` feature - `label`: the indices indicating the span type, a `list` of `int` feature (see [label list](https://huggingface.co/datasets/DFKI-SLT/cdcp/blob/main/cdcp.py#L40)) - `urls`: the URLs link with corresponding indices to each proposition, a `list` of `str` feature - `relation`: the relation between labeled spans with relation labels, a `dictionary` feature - `head`: the indices indicating the first element in a relation, a `list` of `int` feature - `tail`: the indices indicating the second element in a relation, a `list` of `int` feature - `label`: the indices indicating the relation type in a relation, a `list` of `int` feature (see [label list](https://huggingface.co/datasets/DFKI-SLT/cdcp/blob/main/cdcp.py#L41)) ### Data Splits | | train | test | | ------------------------------------------------------------------------------------------------ | ---------------------------------------: | -------------------------------------: | | No. of instances | 581 | 150 | | No. of span labels<br/>- `Fact`<br/>- `Testimony`<br/>- `Reference`<br/>- `Value`<br/>- `Policy` | <br/>654<br/>873<br/>31<br/>1686<br/>662 | <br/>132<br/>244<br/>1<br/>496<br/>153 | | No. of relation labels<br/>- `reason`<br/>- `evidence` | <br/>1055<br/>47 | <br/>298<br/>26 | ## Dataset Creation ### Curation Rationale "eRulemaking is a means for government agencies to directly reach citizens to solicit their opinions and experiences regarding newly proposed rules. The effort, however, is partly hampered by citizens’ comments that lack reasoning and evidence, which are largely ignored since government agencies are unable to evaluate the validity and strength." (p. 1623) "It will be a valuable resource for building argument mining systems that can not only extract arguments from unstructured text, but also identify ways in which a given argument can be improved with respect to its evaluability." (p. 1624) ### Source Data eRulemaking comments (see [eRulemaking](https://www.gsa.gov/about-us/organization/federal-acquisition-service/technology-transformation-services/erulemaking)) #### Initial Data Collection and Normalization "Annotated 731 user comments on Consumer Debt Collection Practices (CDCP) rule by the Consumer Financial Protection Bureau (CFPB) posted on www.regulationroom.org." (p. 1624) #### Who are the source language producers? General public participants, implying American citizens. "According to a voluntary user survey that asked the commenters to self-identify themselves, about 64% of the comments came from consumers, 22% from debt collectors, and the remainder from others, such as consumer advocates and counsellor organizations." (p. 1624) ### Annotations #### Annotation process "The annotators annotated the elementary units and support relations defined in the argumentation model proposed by [Park et al. (2015)](https://dl.acm.org/doi/10.1145/2746090.2746118)." "Each user comment was annotated by two annotators, who independently determined the types of elementary units and support relations among them using the GATE annotation tool (Cunningham et al., 2011). A third annotator manually resolved the conflicts to produce the final dataset." "Inter-annotator agreement between 2 annotators is measured with Krippendorf’s α with respect to elementary unit type (α=64.8%) and support relations (α=44.1%); IDs of supported elementary units are treated as labels for the supporting elementary units." (p. 1626) #### Who are the annotators? \[More Information Needed\] ### Personal and Sensitive Information \[More Information Needed\] ## Considerations for Using the Data ### Social Impact of Dataset "Immediate applications include automatically ranking arguments based on their evaluability for a (crude) identification of read-worthy comments and providing real-time feedback to writers, specifying which types of support for which propositions can be added to construct better-formed arguments." (p. 1624) ### Discussion of Biases About 45% of the elementary units are `VALUE` type. A significant portion, roughly 75%, of support relation annotations are between adjacent elementary units. While commenters certainly tend to provide reasons immediately after the proposition to be supported, it is also easier for annotators to identify support relations in proximity. Thus, support relations in the wild may be not as skewed toward those between adjacent elementary units. (pp. 1626-1627) ### Other Known Limitations \[More Information Needed\] ## Additional Information ### Dataset Curators \[More Information Needed\] ### Licensing Information \[More Information Needed\] ### Citation Information ``` @inproceedings{park2018corpus, title={A corpus of erulemaking user comments for measuring evaluability of arguments}, author={Park, Joonsuk and Cardie, Claire}, booktitle={Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year={2018} } ``` ### Contributions Thanks to [@idalr](https://github.com/idalr) for adding this dataset.

lamini/alpaca

lamini

--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 27364517 num_examples: 52002 download_size: 12742513 dataset_size: 27364517 --- # Dataset Card for "alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

alzoubi36/opp_115

alzoubi36

--- dataset_info: features: - name: text dtype: string - name: label sequence: int64 splits: - name: train num_bytes: 1047118 num_examples: 2185 - name: validation num_bytes: 270827 num_examples: 550 - name: test num_bytes: 316635 num_examples: 697 download_size: 811600 dataset_size: 1634580 --- # Dataset for the OPP-115 task in the [PrivacyGLUE](https://github.com/infsys-lab/privacy-glue) dataset

svhn

--- annotations_creators: - machine-generated - expert-generated language_creators: - machine-generated language: - en license: - other multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - image-classification - object-detection task_ids: [] paperswithcode_id: svhn pretty_name: Street View House Numbers dataset_info: - config_name: full_numbers features: - name: image dtype: image - name: digits sequence: - name: bbox sequence: int32 length: 4 - name: label dtype: class_label: names: '0': '0' '1': '1' '2': '2' '3': '3' '4': '4' '5': '5' '6': '6' '7': '7' '8': '8' '9': '9' splits: - name: train num_bytes: 390404309 num_examples: 33402 - name: test num_bytes: 271503052 num_examples: 13068 - name: extra num_bytes: 1868720340 num_examples: 202353 download_size: 2636187279 dataset_size: 2530627701 - config_name: cropped_digits features: - name: image dtype: image - name: label dtype: class_label: names: '0': '0' '1': '1' '2': '2' '3': '3' '4': '4' '5': '5' '6': '6' '7': '7' '8': '8' '9': '9' splits: - name: train num_bytes: 128364360 num_examples: 73257 - name: test num_bytes: 44464040 num_examples: 26032 - name: extra num_bytes: 967853504 num_examples: 531131 download_size: 1575594780 dataset_size: 1140681904 --- # Dataset Card for Street View House Numbers ## 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://ufldl.stanford.edu/housenumbers - **Repository:** - **Paper:** [Reading Digits in Natural Images with Unsupervised Feature Learning](http://ufldl.stanford.edu/housenumbers/nips2011_housenumbers.pdf) - **Leaderboard:** https://paperswithcode.com/sota/image-classification-on-svhn - **Point of Contact:** streetviewhousenumbers@gmail.com ### Dataset Summary SVHN is a real-world image dataset for developing machine learning and object recognition algorithms with minimal requirement on data preprocessing and formatting. It can be seen as similar in flavor to MNIST (e.g., the images are of small cropped digits), but incorporates an order of magnitude more labeled data (over 600,000 digit images) and comes from a significantly harder, unsolved, real world problem (recognizing digits and numbers in natural scene images). SVHN is obtained from house numbers in Google Street View images. The dataset comes in two formats: 1. Original images with character level bounding boxes. 2. MNIST-like 32-by-32 images centered around a single character (many of the images do contain some distractors at the sides). ### Supported Tasks and Leaderboards - `object-detection`: The dataset can be used to train a model for digit detection. - `image-classification`: The dataset can be used to train a model for Image Classification where the task is to predict a correct digit on the image. The leaderboard for this task is available at: https://paperswithcode.com/sota/image-classification-on-svhn ### Languages English ## Dataset Structure ### Data Instances #### full_numbers The original, variable-resolution, color house-number images with character level bounding boxes. ``` { 'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=98x48 at 0x259E3F01780>, 'digits': { 'bbox': [ [36, 7, 13, 32], [50, 7, 12, 32] ], 'label': [6, 9] } } ``` #### cropped_digits Character level ground truth in an MNIST-like format. All digits have been resized to a fixed resolution of 32-by-32 pixels. The original character bounding boxes are extended in the appropriate dimension to become square windows, so that resizing them to 32-by-32 pixels does not introduce aspect ratio distortions. Nevertheless this preprocessing introduces some distracting digits to the sides of the digit of interest. ``` { 'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32 at 0x25A89494780>, 'label': 1 } ``` ### Data Fields #### full_numbers - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]` - `digits`: a dictionary containing digits' bounding boxes and labels - `bbox`: a list of bounding boxes (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format) corresponding to the digits present on the image - `label`: a list of integers between 0 and 9 representing the digit. #### cropped_digits - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]` - `digit`: an integer between 0 and 9 representing the digit. ### Data Splits #### full_numbers The data is split into training, test and extra set. The training set contains 33402 images, test set 13068 and the extra set 202353 images. #### cropped_digits The data is split into training, test and extra set. The training set contains 73257 images, test set 26032 and the extra set 531131 images. The extra set can be used as extra training data. The extra set was obtained in a similar manner to the training and test set, but with the increased detection threshold in order to generate this large amount of labeled data. The SVHN extra subset is thus somewhat biased toward less difficult detections, and is thus easier than SVHN train/SVHN test. ## Dataset Creation ### Curation Rationale From the paper: > As mentioned above, the venerable MNIST dataset has been a valuable goal post for researchers seeking to build better learning systems whose benchmark performance could be expected to translate into improved performance on realistic applications. However, computers have now reached essentially human levels of performance on this problem—a testament to progress in machine learning and computer vision. The Street View House Numbers (SVHN) digit database that we provide can be seen as similar in flavor to MNIST (e.g., the images are of small cropped characters), but the SVHN dataset incorporates an order of magnitude more labeled data and comes from a significantly harder, unsolved, real world problem. Here the gap between human performance and state of the art feature representations is significant. Going forward, we expect that this dataset may fulfill a similar role for modern feature learning algorithms: it provides a new and difficult benchmark where increased performance can be expected to translate into tangible gains on a realistic application. ### Source Data #### Initial Data Collection and Normalization From the paper: > The SVHN dataset was obtained from a large number of Street View images using a combination of automated algorithms and the Amazon Mechanical Turk (AMT) framework, which was used to localize and transcribe the single digits. We downloaded a very large set of images from urban areas in various countries. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process From the paper: > From these randomly selected images, the house-number patches were extracted using a dedicated sliding window house-numbers detector using a low threshold on the detector’s confidence score in order to get a varied, unbiased dataset of house-number signs. These low precision detections were screened and transcribed by AMT workers. #### Who are the annotators? The AMT workers. ### 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 Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu and Andrew Y. Ng ### Licensing Information Non-commerical use only. ### Citation Information ``` @article{netzer2011reading, title={Reading digits in natural images with unsupervised feature learning}, author={Netzer, Yuval and Wang, Tao and Coates, Adam and Bissacco, Alessandro and Wu, Bo and Ng, Andrew Y}, year={2011} } ``` ### Contributions Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset.

open-llm-leaderboard/details_lmsys__vicuna-7b-v1.5-16k

open-llm-leaderboard

--- pretty_name: Evaluation run of lmsys/vicuna-7b-v1.5-16k dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [lmsys/vicuna-7b-v1.5-16k](https://huggingface.co/lmsys/vicuna-7b-v1.5-16k) 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_lmsys__vicuna-7b-v1.5-16k\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-21T18:19:50.042065](https://huggingface.co/datasets/open-llm-leaderboard/details_lmsys__vicuna-7b-v1.5-16k/blob/main/results_2023-10-21T18-19-50.042065.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\": 0.008913590604026845,\n\ \ \"em_stderr\": 0.0009625465757536675,\n \"f1\": 0.07239303691275177,\n\ \ \"f1_stderr\": 0.001693138759317829,\n \"acc\": 0.38701199696261246,\n\ \ \"acc_stderr\": 0.00973738194322199\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.008913590604026845,\n \"em_stderr\": 0.0009625465757536675,\n\ \ \"f1\": 0.07239303691275177,\n \"f1_stderr\": 0.001693138759317829\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.06368460955269144,\n \ \ \"acc_stderr\": 0.006726213078805721\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7103393843725335,\n \"acc_stderr\": 0.012748550807638257\n\ \ }\n}\n```" repo_url: https://huggingface.co/lmsys/vicuna-7b-v1.5-16k 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_08_18T07_58_23.659880 path: - '**/details_harness|arc:challenge|25_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-08-18T07:58:23.659880.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_19T11_48_14.833652 path: - '**/details_harness|drop|3_2023-10-19T11-48-14.833652.parquet' - split: 2023_10_21T18_19_50.042065 path: - '**/details_harness|drop|3_2023-10-21T18-19-50.042065.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-21T18-19-50.042065.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_19T11_48_14.833652 path: - '**/details_harness|gsm8k|5_2023-10-19T11-48-14.833652.parquet' - split: 2023_10_21T18_19_50.042065 path: - '**/details_harness|gsm8k|5_2023-10-21T18-19-50.042065.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-21T18-19-50.042065.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hellaswag|10_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-18T07:58:23.659880.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-management|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T07:58:23.659880.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_18T07_58_23.659880 path: - '**/details_harness|truthfulqa:mc|0_2023-08-18T07:58:23.659880.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-08-18T07:58:23.659880.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_19T11_48_14.833652 path: - '**/details_harness|winogrande|5_2023-10-19T11-48-14.833652.parquet' - split: 2023_10_21T18_19_50.042065 path: - '**/details_harness|winogrande|5_2023-10-21T18-19-50.042065.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-21T18-19-50.042065.parquet' - config_name: results data_files: - split: 2023_08_18T07_58_23.659880 path: - results_2023-08-18T07:58:23.659880.parquet - split: 2023_10_19T11_48_14.833652 path: - results_2023-10-19T11-48-14.833652.parquet - split: 2023_10_21T18_19_50.042065 path: - results_2023-10-21T18-19-50.042065.parquet - split: latest path: - results_2023-10-21T18-19-50.042065.parquet --- # Dataset Card for Evaluation run of lmsys/vicuna-7b-v1.5-16k ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/lmsys/vicuna-7b-v1.5-16k - **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 [lmsys/vicuna-7b-v1.5-16k](https://huggingface.co/lmsys/vicuna-7b-v1.5-16k) 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_lmsys__vicuna-7b-v1.5-16k", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-21T18:19:50.042065](https://huggingface.co/datasets/open-llm-leaderboard/details_lmsys__vicuna-7b-v1.5-16k/blob/main/results_2023-10-21T18-19-50.042065.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": 0.008913590604026845, "em_stderr": 0.0009625465757536675, "f1": 0.07239303691275177, "f1_stderr": 0.001693138759317829, "acc": 0.38701199696261246, "acc_stderr": 0.00973738194322199 }, "harness|drop|3": { "em": 0.008913590604026845, "em_stderr": 0.0009625465757536675, "f1": 0.07239303691275177, "f1_stderr": 0.001693138759317829 }, "harness|gsm8k|5": { "acc": 0.06368460955269144, "acc_stderr": 0.006726213078805721 }, "harness|winogrande|5": { "acc": 0.7103393843725335, "acc_stderr": 0.012748550807638257 } } ``` ### 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]

cats_vs_dogs

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - image-classification task_ids: - multi-class-image-classification paperswithcode_id: cats-vs-dogs pretty_name: Cats Vs. Dogs dataset_info: features: - name: image dtype: image - name: labels dtype: class_label: names: '0': cat '1': dog splits: - name: train num_bytes: 4219400 num_examples: 23410 download_size: 824887076 dataset_size: 4219400 --- # Dataset Card for Cats Vs. Dogs ## 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:** [Cats vs Dogs Dataset](https://www.microsoft.com/en-us/download/details.aspx?id=54765) - **Repository:** - **Paper:** [Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization](https://www.microsoft.com/en-us/research/wp-content/uploads/2007/10/CCS2007.pdf) - **Leaderboard:** [Dogs vs. Cats](https://www.kaggle.com/competitions/dogs-vs-cats) - **Point of Contact:** ### Dataset Summary A large set of images of cats and dogs. There are 1738 corrupted images that are dropped. This dataset is part of a now-closed Kaggle competition and represents a subset of the so-called Asirra dataset. From the competition page: > The Asirra data set > > Web services are often protected with a challenge that's supposed to be easy for people to solve, but difficult for computers. Such a challenge is often called a [CAPTCHA](http://www.captcha.net/) (Completely Automated Public Turing test to tell Computers and Humans Apart) or HIP (Human Interactive Proof). HIPs are used for many purposes, such as to reduce email and blog spam and prevent brute-force attacks on web site passwords. > > Asirra (Animal Species Image Recognition for Restricting Access) is a HIP that works by asking users to identify photographs of cats and dogs. This task is difficult for computers, but studies have shown that people can accomplish it quickly and accurately. Many even think it's fun! Here is an example of the Asirra interface: > > Asirra is unique because of its partnership with [Petfinder.com](https://www.petfinder.com/), the world's largest site devoted to finding homes for homeless pets. They've provided Microsoft Research with over three million images of cats and dogs, manually classified by people at thousands of animal shelters across the United States. Kaggle is fortunate to offer a subset of this data for fun and research. ### Supported Tasks and Leaderboards - `image-classification`: The goal of this task is to classify a given image as either containing a cat or a dog. The leaderboard is available [here](https://paperswithcode.com/sota/image-classification-on-cats-vs-dogs). ### Languages English. ## Dataset Structure ### Data Instances A sample from the training set is provided below: ``` { 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x375 at 0x29CEAD71780>, 'labels': 0 } ``` ### Data Fields The data instances have the following fields: - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`. - `labels`: an `int` classification label. Class Label Mappings: ``` { "cat": 0, "dog": 1, } ``` ### Data Splits | | train | |---------------|------:| | # of examples | 23410 | ## Dataset Creation ### Curation Rationale This subset was to built to test whether computer vision algorithms can beat the Asirra CAPTCHA: From the competition page: > Image recognition attacks > > While random guessing is the easiest form of attack, various forms of image recognition can allow an attacker to make guesses that are better than random. There is enormous diversity in the photo database (a wide variety of backgrounds, angles, poses, lighting, etc.), making accurate automatic classification difficult. In an informal poll conducted many years ago, computer vision experts posited that a classifier with better than 60% accuracy would be difficult without a major advance in the state of the art. For reference, a 60% classifier improves the guessing probability of a 12-image HIP from 1/4096 to 1/459. ### Source Data #### Initial Data Collection and Normalization This dataset is a subset of the Asirra dataset. From the competition page: > Asirra is unique because of its partnership with Petfinder.com, the world's largest site devoted to finding homes for homeless pets. They've provided Microsoft Research with over three million images of cats and dogs, manually classified by people at thousands of animal shelters across the United States. #### Who are the source language producers? The users of [Petfinder.com](https://www.petfinder.com/). ### Annotations #### Annotation process The images were annotated by selecting a pet category on [Petfinder.com](https://www.petfinder.com/). #### Who are the annotators? The users of [Petfinder.com](https://www.petfinder.com/). ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases From the paper: > Unlike many image-based CAPTCHAs which are abstract or subjective, Asirra’s challenges are concrete, inoffensive (cute, by some accounts), require no specialized or culturally biased knowledge, and have definite ground truth. This makes Asirra less frustrating for humans. Some beta-testers found it fun. The four-year-old child of one asked several times to “play the cat and dog game again.” ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ```bibtex @Inproceedings (Conference){asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization, author = {Elson, Jeremy and Douceur, John (JD) and Howell, Jon and Saul, Jared}, title = {Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization}, booktitle = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)}, year = {2007}, month = {October}, publisher = {Association for Computing Machinery, Inc.}, url = {https://www.microsoft.com/en-us/research/publication/asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization/}, edition = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)}, } ``` ### Contributions Thanks to [@nateraw](https://github.com/nateraw) for adding this dataset.

BeIR/hotpotqa

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.

conllpp

--- annotations_creators: - expert-generated language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended|conll2003 task_categories: - token-classification task_ids: - named-entity-recognition paperswithcode_id: conll pretty_name: CoNLL++ train-eval-index: - config: conllpp task: token-classification task_id: entity_extraction splits: train_split: train eval_split: test col_mapping: tokens: tokens ner_tags: tags metrics: - type: seqeval name: seqeval dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: pos_tags sequence: class_label: names: 0: '"' 1: '''''' 2: '#' 3: $ 4: ( 5: ) 6: ',' 7: . 8: ':' 9: '``' 10: CC 11: CD 12: DT 13: EX 14: FW 15: IN 16: JJ 17: JJR 18: JJS 19: LS 20: MD 21: NN 22: NNP 23: NNPS 24: NNS 25: NN|SYM 26: PDT 27: POS 28: PRP 29: PRP$ 30: RB 31: RBR 32: RBS 33: RP 34: SYM 35: TO 36: UH 37: VB 38: VBD 39: VBG 40: VBN 41: VBP 42: VBZ 43: WDT 44: WP 45: WP$ 46: WRB - name: chunk_tags sequence: class_label: names: 0: O 1: B-ADJP 2: I-ADJP 3: B-ADVP 4: I-ADVP 5: B-CONJP 6: I-CONJP 7: B-INTJ 8: I-INTJ 9: B-LST 10: I-LST 11: B-NP 12: I-NP 13: B-PP 14: I-PP 15: B-PRT 16: I-PRT 17: B-SBAR 18: I-SBAR 19: B-UCP 20: I-UCP 21: B-VP 22: I-VP - name: ner_tags sequence: class_label: names: 0: O 1: B-PER 2: I-PER 3: B-ORG 4: I-ORG 5: B-LOC 6: I-LOC 7: B-MISC 8: I-MISC config_name: conllpp splits: - name: train num_bytes: 6931393 num_examples: 14041 - name: validation num_bytes: 1739247 num_examples: 3250 - name: test num_bytes: 1582078 num_examples: 3453 download_size: 4859600 dataset_size: 10252718 --- # Dataset Card for "conllpp" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Github](https://github.com/ZihanWangKi/CrossWeigh) - **Repository:** [Github](https://github.com/ZihanWangKi/CrossWeigh) - **Paper:** [Aclweb](https://www.aclweb.org/anthology/D19-1519) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary CoNLLpp is a corrected version of the CoNLL2003 NER dataset where labels of 5.38% of the sentences in the test set have been manually corrected. The training set and development set from CoNLL2003 is included for completeness. One correction on the test set for example, is: ``` { "tokens": ["SOCCER", "-", "JAPAN", "GET", "LUCKY", "WIN", ",", "CHINA", "IN", "SURPRISE", "DEFEAT", "."], "original_ner_tags_in_conll2003": ["O", "O", "B-LOC", "O", "O", "O", "O", "B-PER", "O", "O", "O", "O"], "corrected_ner_tags_in_conllpp": ["O", "O", "B-LOC", "O", "O", "O", "O", "B-LOC", "O", "O", "O", "O"], } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances #### conllpp - **Size of downloaded dataset files:** 4.85 MB - **Size of the generated dataset:** 10.26 MB - **Total amount of disk used:** 15.11 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "chunk_tags": [11, 12, 12, 21, 13, 11, 11, 21, 13, 11, 12, 13, 11, 21, 22, 11, 12, 17, 11, 21, 17, 11, 12, 12, 21, 22, 22, 13, 11, 0], "id": "0", "ner_tags": [0, 3, 4, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], "pos_tags": [12, 22, 22, 38, 15, 22, 28, 38, 15, 16, 21, 35, 24, 35, 37, 16, 21, 15, 24, 41, 15, 16, 21, 21, 20, 37, 40, 35, 21, 7], "tokens": ["The", "European", "Commission", "said", "on", "Thursday", "it", "disagreed", "with", "German", "advice", "to", "consumers", "to", "shun", "British", "lamb", "until", "scientists", "determine", "whether", "mad", "cow", "disease", "can", "be", "transmitted", "to", "sheep", "."] } ``` ### Data Fields The data fields are the same among all splits. #### conllpp - `id`: a `string` feature. - `tokens`: a `list` of `string` features. - `pos_tags`: a `list` of classification labels, with possible values including `"` (0), `''` (1), `#` (2), `$` (3), `(` (4). - `chunk_tags`: a `list` of classification labels, with possible values including `O` (0), `B-ADJP` (1), `I-ADJP` (2), `B-ADVP` (3), `I-ADVP` (4). - `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `B-PER` (1), `I-PER` (2), `B-ORG` (3), `I-ORG` (4). ### Data Splits | name |train|validation|test| |---------|----:|---------:|---:| |conll2003|14041| 3250|3453| ## 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{wang2019crossweigh, title={CrossWeigh: Training Named Entity Tagger from Imperfect Annotations}, author={Wang, Zihan and Shang, Jingbo and Liu, Liyuan and Lu, Lihao and Liu, Jiacheng and Han, Jiawei}, 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={5157--5166}, year={2019} } ``` ### Contributions Thanks to [@ZihanWangKi](https://github.com/ZihanWangKi) for adding this dataset.

GEM/viggo

GEM

--- annotations_creators: - none language_creators: - unknown language: - en license: - cc-by-sa-4.0 multilinguality: - unknown size_categories: - unknown source_datasets: - original task_categories: - table-to-text task_ids: [] pretty_name: viggo tags: - data-to-text --- # Dataset Card for GEM/viggo ## Dataset Description - **Homepage:** https://nlds.soe.ucsc.edu/viggo - **Repository:** [Needs More Information] - **Paper:** https://aclanthology.org/W19-8623/ - **Leaderboard:** N/A - **Point of Contact:** Juraj Juraska ### Link to Main Data Card You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/viggo). ### Dataset Summary ViGGO is an English data-to-text generation dataset in the video game domain, with target responses being more conversational than information-seeking, yet constrained to the information presented in a meaning representation. The dataset is relatively small with about 5,000 datasets but very clean, and can thus serve for evaluating transfer learning, low-resource, or few-shot capabilities of neural models. You can load the dataset via: ``` import datasets data = datasets.load_dataset('GEM/viggo') ``` The data loader can be found [here](https://huggingface.co/datasets/GEM/viggo). #### website [Wesbite](https://nlds.soe.ucsc.edu/viggo) #### paper [ACL Anthology](https://aclanthology.org/W19-8623/) #### authors Juraj Juraska, Kevin K. Bowden, Marilyn Walker ## Dataset Overview ### Where to find the Data and its Documentation #### Webpage <!-- info: What is the webpage for the dataset (if it exists)? --> <!-- scope: telescope --> [Wesbite](https://nlds.soe.ucsc.edu/viggo) #### Paper <!-- info: What is the link to the paper describing the dataset (open access preferred)? --> <!-- scope: telescope --> [ACL Anthology](https://aclanthology.org/W19-8623/) #### BibTex <!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. --> <!-- scope: microscope --> ``` @inproceedings{juraska-etal-2019-viggo, title = "{V}i{GGO}: A Video Game Corpus for Data-To-Text Generation in Open-Domain Conversation", author = "Juraska, Juraj and Bowden, Kevin and Walker, Marilyn", booktitle = "Proceedings of the 12th International Conference on Natural Language Generation", month = oct # "{--}" # nov, year = "2019", address = "Tokyo, Japan", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/W19-8623", doi = "10.18653/v1/W19-8623", pages = "164--172", } ``` #### Contact Name <!-- quick --> <!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. --> <!-- scope: periscope --> Juraj Juraska #### Contact Email <!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. --> <!-- scope: periscope --> jjuraska@ucsc.edu #### Has a Leaderboard? <!-- info: Does the dataset have an active leaderboard? --> <!-- scope: telescope --> no ### Languages and Intended Use #### Multilingual? <!-- quick --> <!-- info: Is the dataset multilingual? --> <!-- scope: telescope --> no #### Covered Languages <!-- quick --> <!-- info: What languages/dialects are covered in the dataset? --> <!-- scope: telescope --> `English` #### License <!-- quick --> <!-- info: What is the license of the dataset? --> <!-- scope: telescope --> cc-by-sa-4.0: Creative Commons Attribution Share Alike 4.0 International #### Intended Use <!-- info: What is the intended use of the dataset? --> <!-- scope: microscope --> ViGGO was designed for the task of data-to-text generation in chatbots (as opposed to task-oriented dialogue systems), with target responses being more conversational than information-seeking, yet constrained to the information presented in a meaning representation. The dataset, being relatively small and clean, can also serve for demonstrating transfer learning capabilities of neural models. #### Primary Task <!-- info: What primary task does the dataset support? --> <!-- scope: telescope --> Data-to-Text ### Credit #### Curation Organization Type(s) <!-- info: In what kind of organization did the dataset curation happen? --> <!-- scope: telescope --> `academic` #### Curation Organization(s) <!-- info: Name the organization(s). --> <!-- scope: periscope --> University of California, Santa Cruz #### Dataset Creators <!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). --> <!-- scope: microscope --> Juraj Juraska, Kevin K. Bowden, Marilyn Walker #### Who added the Dataset to GEM? <!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. --> <!-- scope: microscope --> Juraj Juraska ### Dataset Structure #### Data Fields <!-- info: List and describe the fields present in the dataset. --> <!-- scope: telescope --> Each example in the dataset has the following two fields: - `mr`: A meaning representation (MR) that, in a structured format, provides the information to convey, as well as the desired dialogue act (DA) type. - `ref`: A reference output, i.e., a corresponding utterance realizing all the information in the MR. Each MR is a flattened dictionary of attribute-and-value pairs, "wrapped" in the dialogue act type indication. This format was chosen primarily for its compactness, but also to allow for easy concatenation of multiple DAs (each with potentially different attributes) in a single MR. Following is the list of all possible attributes (which are also refered to as "slots") in ViGGO along with their types/possible values: - `name`: The name of a video game (e.g., Rise of the Tomb Raider). - `release_year`: The year a video game was released in (e.g., 2015). - `exp_release_date`: For a not-yet-released game, the date when it is expected to be released (e.g., February 22, 2019). *Note: This slot cannot appear together with `release_year` in the same dialogue act.* - `developer`: The name of the studio/person that created the game (e.g., Crystal Dynamics). - `genres`: A list of one or more genre labels from a set of possible values (e.g., action-adventure, shooter). - `player_perspective`: A list of one or more perspectives from which the game is/can be played (possible values: first person, third person, side view, bird view). - `platforms`: A list of one or more gaming platforms the game was officially released for (possible values: PC, PlayStation, Xbox, Nintendo, Nintendo Switch). - `esrb`: A game's content rating as determined by the ESRB (possible values: E (for Everyone), E 10+ (for Everyone 10 and Older), T (for Teen), M (for Mature)). - `rating`: Depending on the dialogue act this slot is used with, it is a categorical representation of either the game's average rating or the game's liking (possible values: excellent, good, average, poor). - `has_multiplayer`: Indicates whether a game supports multiplayer or can only be played in single-player mode (possible values: yes, no). - `available_on_steam`: Indicates whether a game can be purchased through the Steam digital distribution service (possible values: yes, no). - `has_linux_release`: Indicates whether a game is supported on Linux operating systems (possible values: yes, no). - `has_mac_release`: Indicates whether a game is supported on macOS (possible values: yes, no). - `specifier`: A game specifier used by the `request` DA, typically an adjective (e.g., addictive, easiest, overrated, visually impressive). Each MR in the dataset has 3 distinct reference utterances, which are represented as 3 separate examples with the same MR. #### Reason for Structure <!-- info: How was the dataset structure determined? --> <!-- scope: microscope --> The dataset structure mostly follows the format of the popular E2E dataset, however, with added dialogue act type indications, new list-type attributes introduced, and unified naming convention for multi-word attribute names. #### Example Instance <!-- info: Provide a JSON formatted example of a typical instance in the dataset. --> <!-- scope: periscope --> ``` { "mr": "give_opinion(name[SpellForce 3], rating[poor], genres[real-time strategy, role-playing], player_perspective[bird view])", "ref": "I think that SpellForce 3 is one of the worst games I've ever played. Trying to combine the real-time strategy and role-playing genres just doesn't work, and the bird view perspective makes it near impossible to play." } ``` #### Data Splits <!-- info: Describe and name the splits in the dataset if there are more than one. --> <!-- scope: periscope --> ViGGO is split into 3 partitions, with no MRs in common between the training set and either of the validation and the test set (and that *after* delexicalizing the `name` and `developer` slots). The ratio of examples in the partitions is approximately 7.5 : 1 : 1.5, with their exact sizes listed below: - **Train:** 5,103 (1,675 unique MRs) - **Validation:** 714 (238 unique MRs) - **Test:** 1,083 (359 unique MRs) - **TOTAL:** 6,900 (2,253 unique MRs) *Note: The reason why the number of unique MRs is not exactly one third of all examples is that for each `request_attribute` DA (which only has one slot, and that without a value) 12 reference utterances were collected instead of 3.* #### Splitting Criteria <!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. --> <!-- scope: microscope --> A similar MR length and slot distribution was preserved across the partitions. The distribution of DA types, on the other hand, is skewed slightly toward fewer `inform` DA instances (the most prevalent DA type) and a higher proportion of the less prevalent DAs in the validation and the test set. #### <!-- info: What does an outlier of the dataset in terms of length/perplexity/embedding look like? --> <!-- scope: microscope --> ``` { "mr": "request_attribute(player_perspective[])", "ref": "Is there a certain player perspective that you prefer over others in games you play?" }, { "mr": "inform(name[FIFA 12], esrb[E (for Everyone)], genres[simulation, sport], player_perspective[bird view, side view], platforms[PlayStation, Xbox, Nintendo, PC], available_on_steam[no])", "ref": "Fifa 12 is a decent sports simulator. It's pretty cool how the game swaps from the bird's eye perspective down to a side view while you're playing. You can get the game for PlayStation, Xbox, Nintendo consoles, and PC, but unfortunately it's not on Steam. Of course, as a sports game there's not much objectionable content so it's rated E." }, { "mr": "inform(name[Super Bomberman], release_year[1993], genres[action, strategy], has_multiplayer[no], platforms[Nintendo, PC], available_on_steam[no], has_linux_release[no], has_mac_release[no])", "ref": "Super Bomberman is one of my favorite Nintendo games, also available on PC, though not through Steam. It came out all the way back in 1993, and you can't get it for any modern consoles, unfortunately, so no online multiplayer, or of course Linux or Mac releases either. That said, it's still one of the most addicting action-strategy games out there." } ``` ## Dataset in GEM ### Rationale for Inclusion in GEM #### Why is the Dataset in GEM? <!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? --> <!-- scope: microscope --> ViGGO is a fairly small dataset but includes a greater variety of utterance types than most other datasets for NLG from structured meaning representations. This makes it more interesting from the perspective of model evaluation, since models have to learn to differentiate between various dialogue act types that share the same slots. #### Similar Datasets <!-- info: Do other datasets for the high level task exist? --> <!-- scope: telescope --> yes #### Unique Language Coverage <!-- info: Does this dataset cover other languages than other datasets for the same task? --> <!-- scope: periscope --> no #### Difference from other GEM datasets <!-- info: What else sets this dataset apart from other similar datasets in GEM? --> <!-- scope: microscope --> ViGGO's language is more casual and conversational -- as opposed to information-seeking -- which differentiates it from the majority of popular datasets for the same type of data-to-text task. Moreover, the video game domain is a rather uncommon one in the NLG community, despite being very well-suited for data-to-text generation, considering it offers entities with many attributes to talk about, which can be described in a structured format. ### GEM-Specific Curation #### Modificatied for GEM? <!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? --> <!-- scope: telescope --> no #### Additional Splits? <!-- info: Does GEM provide additional splits to the dataset? --> <!-- scope: telescope --> no ### Getting Started with the Task #### Pointers to Resources <!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. --> <!-- scope: microscope --> - [E2E NLG Challenge](http://www.macs.hw.ac.uk/InteractionLab/E2E/) #### Technical Terms <!-- info: Technical terms used in this card and the dataset and their definitions --> <!-- scope: microscope --> - MR = meaning representation - DA = dialogue act ## Previous Results ### Previous Results #### Metrics <!-- info: What metrics are typically used for this task? --> <!-- scope: periscope --> `BLEU`, `METEOR`, `ROUGE`, `BERT-Score`, `BLEURT`, `Other: Other Metrics` #### Other Metrics <!-- info: Definitions of other metrics --> <!-- scope: periscope --> SER (slot error rate): Indicates the proportion of missing/incorrect/duplicate/hallucinated slot mentions in the utterances across a test set. The closer to zero a model scores in this metric, the more semantically accurate its outputs are. This metric is typically calculated either manually on a small sample of generated outputs, or heuristically using domain-specific regex rules and gazetteers. #### Previous results available? <!-- info: Are previous results available? --> <!-- scope: telescope --> yes #### Relevant Previous Results <!-- info: What are the most relevant previous results for this task/dataset? --> <!-- scope: microscope --> - [Juraska et al., 2019. ViGGO: A Video Game Corpus for Data-To-Text Generation in Open-Domain Conversation.](https://aclanthology.org/W19-8623/) - [Harkous et al., 2020. Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity.](https://aclanthology.org/2020.coling-main.218/) - [Kedzie and McKeown, 2020. Controllable Meaning Representation to Text Generation: Linearization and Data Augmentation Strategies.](https://aclanthology.org/2020.emnlp-main.419/) - [Juraska and Walker, 2021. Attention Is Indeed All You Need: Semantically Attention-Guided Decoding for Data-to-Text NLG.](https://aclanthology.org/2021.inlg-1.45/) ## Dataset Curation ### Original Curation #### Original Curation Rationale <!-- info: Original curation rationale --> <!-- scope: telescope --> The primary motivation behind ViGGO was to create a data-to-text corpus in a new but conversational domain, and intended for use in open-domain chatbots rather than task-oriented dialogue systems. To this end, the dataset contains utterances of 9 generalizable and conversational dialogue act types, revolving around various aspects of video games. The idea is that similar, relatively small datasets could fairly easily be collected for other conversational domains -- especially other entertainment domains (such as music or books), but perhaps also topics like animals or food -- to support an open-domain conversational agent with controllable neural NLG. Another desired quality of the ViGGO dataset was cleanliness (no typos and grammatical errors) and semantic accuracy, which has often not been the case with other crowdsourced data-to-text corpora. In general, for the data-to-text generation task, there is arguably no need to put the burden on the generation model to figure out the noise, since the noise would not be expected to be there in a real-world system whose dialogue manager that creates the input for the NLG module is usually configurable and tightly controlled. #### Communicative Goal <!-- info: What was the communicative goal? --> <!-- scope: periscope --> Produce a response from a structured meaning representation in the context of a conversation about video games. It can be a brief opinion or a description of a game, as well as a request for attribute (e.g., genre, player perspective, or platform) preference/confirmation or an inquiry about liking a particular type of games. #### Sourced from Different Sources <!-- info: Is the dataset aggregated from different data sources? --> <!-- scope: telescope --> no ### Language Data #### How was Language Data Obtained? <!-- info: How was the language data obtained? --> <!-- scope: telescope --> `Crowdsourced` #### Where was it crowdsourced? <!-- info: If crowdsourced, where from? --> <!-- scope: periscope --> `Amazon Mechanical Turk` #### Language Producers <!-- info: What further information do we have on the language producers? --> <!-- scope: microscope --> The paid crowdworkers who produced the reference utterances were from English-speaking countries, and they had at least 1,000 HITs approved and a HIT approval rate of 98% or more. Furthermore, in the instructions, crowdworkers were discouraged from taking on the task unless they considered themselves a gamer. #### Topics Covered <!-- info: Does the language in the dataset focus on specific topics? How would you describe them? --> <!-- scope: periscope --> The dataset focuses on video games and their various aspects, and hence the language of the utterances may contain video game-specific jargon. #### Data Validation <!-- info: Was the text validated by a different worker or a data curator? --> <!-- scope: telescope --> validated by data curator #### Data Preprocessing <!-- info: How was the text data pre-processed? (Enter N/A if the text was not pre-processed) --> <!-- scope: microscope --> First, regular expressions were used to enforce several standardization policies regarding special characters, punctuation, and the correction of undesired abbreviations/misspellings of standard domain-specific terms (e.g., terms like "Play station" or "PS4" would be changed to the uniform "PlayStation"). At the same time, hyphens were removed or enforced uniformly in certain terms, for example, "single-player". Although phrases such as "first person" should correctly have a hyphen when used as adjective, the crowdworkers used this rule very inconsistently. In order to avoid model outputs being penalized during the evaluation by the arbitrary choice of a hyphen presence or absence in the reference utterances, the hyphen was removed in all such phrases regardless of the noun vs. adjective use. Second, an extensive set of heuristics was developed to identify slot-related errors. This process revealed the vast majority of missing or incorrect slot mentions, which were subsequently fixed according to the corresponding MRs. This eventually led to the development of a robust, cross-domain, heuristic slot aligner that can be used for automatic slot error rate evaluation. For details, see the appendix in [Juraska and Walker, 2021](https://aclanthology.org/2021.inlg-1.45/). Crowdworkers would sometimes also inject a piece of information which was not present in the MR, some of which is not even represented by any of the slots, e.g., plot or main characters. This unsolicited information was removed from the utterances so as to avoid confusing the neural model. Finally, any remaining typos and grammatical errors were resolved. #### Was Data Filtered? <!-- info: Were text instances selected or filtered? --> <!-- scope: telescope --> manually #### Filter Criteria <!-- info: What were the selection criteria? --> <!-- scope: microscope --> Compliance with the indicated dialogue act type, semantic accuracy (i.e., all information in the corresponding MR mentioned and that correctly), and minimal extraneous information (e.g., personal experience/opinion). Whenever it was within a reasonable amount of effort, the utterances were manually fixed instead of being discarded/crowdsourced anew. ### Structured Annotations #### Additional Annotations? <!-- quick --> <!-- info: Does the dataset have additional annotations for each instance? --> <!-- scope: telescope --> none #### Annotation Service? <!-- info: Was an annotation service used? --> <!-- scope: telescope --> no ### Consent #### Any Consent Policy? <!-- info: Was there a consent policy involved when gathering the data? --> <!-- scope: telescope --> no ### Private Identifying Information (PII) #### Contains PII? <!-- quick --> <!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? --> <!-- scope: telescope --> no PII #### Justification for no PII <!-- info: Provide a justification for selecting `no PII` above. --> <!-- scope: periscope --> Crowdworkers were instructed to only express the information in the provided meaning representation, which never prompted them to mention anything about themselves. Occasionally, they would still include a bit of personal experience (e.g., "I used to like the game as a kid.") or opinion, but these would be too general to be considered PII. ### Maintenance #### Any Maintenance Plan? <!-- info: Does the original dataset have a maintenance plan? --> <!-- scope: telescope --> no ## Broader Social Context ### Previous Work on the Social Impact of the Dataset #### Usage of Models based on the Data <!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? --> <!-- scope: telescope --> no ### Impact on Under-Served Communities #### Addresses needs of underserved Communities? <!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). --> <!-- scope: telescope --> no ### Discussion of Biases #### Any Documented Social Biases? <!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. --> <!-- scope: telescope --> no ## Considerations for Using the Data ### PII Risks and Liability ### Licenses ### Known Technical Limitations #### Technical Limitations <!-- info: Describe any known technical limitations, such as spurrious correlations, train/test overlap, annotation biases, or mis-annotations, and cite the works that first identified these limitations when possible. --> <!-- scope: microscope --> The dataset is limited to a single domain: video games. One caveat of using a language generator trained on this dataset in a dialogue system as-is is that multiple subsequent turns discussing the same video game would be repeating its full name. ViGGO was designed for generation without context, and therefore it is up to the dialogue manager to ensure that pronouns are substituted for the names whenever it would sound more natural in a dialogue. Alternately, the dataset can easily be augmented with automatically constructed samples which omit the `name` slot in the MR and replace the name with a pronoun in the reference utterance.

range3/wiki40b-ja

range3

--- task_categories: - text-generation - fill-mask language: - ja --- # range3/wiki40b-ja This dataset consists of three parquet files from the wiki40b dataset with only Japanese data extracted. It is generated by the following python code. このデータセットは、wiki40bデータセットの日本語データのみを抽出した3つのparquetファイルで構成されます。以下のpythonコードによって生成しています。 ```py import datasets dss = datasets.load_dataset( "wiki40b", "ja", beam_runner="DirectRunner", ) for split,ds in dss.items(): ds.to_parquet(f"wikipedia-ja-20230101/{split}.parquet") ```

pawlo2013/anime_diffusion_full

pawlo2013

--- dataset_info: features: - name: sketch dtype: image - name: full_colour dtype: image - name: scribbles dtype: image - name: sketch_and_scribbles_merged dtype: image splits: - name: train num_bytes: 4923366050.448 num_examples: 40869 - name: test num_bytes: 97418307.0 num_examples: 301 download_size: 4996697792 dataset_size: 5020784357.448 --- # Dataset Card for "anime_diffusion_full" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

gsarti/clean_mc4_it

gsarti

--- annotations_creators: - no-annotation language_creators: - found language: - it license: - odc-by multilinguality: - monolingual size_categories: tiny: - 1M<n<10M small: - 10M<n<100M medium: - 10M<n<100M large: - 10M<n<100M full: - 100M<n<1B source_datasets: - extended task_categories: - text-generation task_ids: - language-modeling paperswithcode_id: mc4 pretty_name: mC4_it --- # Dataset Card for Clean Italian mC4 🇮🇹 ## Table of Contents - [Dataset Card for Clean](#dataset-card-for-mc4) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Preprocessing](#preprocessing) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [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 - **Original Homepage:** [HF Hub](https://huggingface.co/datasets/allenai/c4) - **Paper:** [ArXiv](https://arxiv.org/abs/1910.10683) ### Dataset Summary A thoroughly cleaned version of the Italian split of the multilingual colossal, cleaned version of Common Crawl's web crawl corpus (mC4). Based on the [Common Crawl dataset](https://commoncrawl.org). The original version was prepared by [AllenAI](https://allenai.org/), hosted at the address [https://huggingface.co/datasets/allenai/c4](https://huggingface.co/datasets/allenai/c4), with subsequent preprocessing performed by [Gabriele Sarti](https://gsarti.com) following a standard procedure for all dataset shards. ### Preprocessing The preprocessing of the dataset follows the procedure used by Yeb Havinga for training the model [`t5-base-dutch`](https://huggingface.co/flax-community/t5-base-dutch) on a portion of the cleaned Dutch split of mC4. The original code, that was adapted for Italian in this case, is available on [GitLab](https://gitlab.com/yhavinga/c4nlpreproc). In summary, the preprocessing procedure includes: - Removing documents containing words from a selection of the [Italian and English List of Dirty Naught Obscene and Otherwise Bad Words](https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words). - Removing sentences containing: - Less than 3 words. - A word longer than 1000 characters. - An end symbol not matching end-of-sentence punctuation. - Strings associated to javascript code (e.g. `{`), lorem ipsum, policy information in Italian or English. - Removing documents (after sentence filtering): - Containing less than 5 sentences. - Containing less than 500 or more than 50'000 characters. - Not identified as prevalently Italian by the `LangDetect` package. Using parallel processing with 96 CPU cores on a TPUv3 via Google Cloud to perform the complete clean of all the original Italian shards of mC4 (1024 of ~220Mb train, 8 of ~24Mb validation) required roughly 10 hours due to the demanding steps of sentence tokenization and language detection. The total size of compressed `.json.gz` files is roughly halved after the procedure. ## Dataset Structure ### Data Instances An example from the dataset: ``` { 'timestamp': '2020-02-22T22:24:31Z', 'url': 'https://altreconomia.it/una-rotonda-sul-pane/', 'text': 'Per raggiungere il campo attraversiamo la striscia d’asfalto che porta verso la provinciale numero 13. Mettiamo a rischio la nostra incolumità in un territorio di auto e camion. Sullo sfondo, i profili della Grigna e del Resegone. Più vicini, quelli del solito ipermercato di provincia, e delle villette a schiera che avanzano tra le coltivazioni. È lo sprawling, l’avanzata del cemento.\\nDa questo lato dalla strada, invece, è ancora regno contadino. Almeno per ora. Torniamo a Caponago (Mb), Brianza pura, dove ha avuto i natali il progetto “Spiga e madia”. Ne parlammo su Ae nel gennaio 2009: in un territorio “spaesato”, il Comitato “verso il Distretto di economia solidale della Brianza” (Desbri) e la “Retina” dei gruppi di acquisto locali danno vita a un progetto di produzione di frumento, molitura, panificazione e distribuzione in un raggio di 20 chilometri. Si comincia da zero, nel 2007, senza alcun di finanziamento, quando una famiglia del [...]. Il giochino vale almeno 3 miliardi di euro all’anno. La misura, introdotta in via straordinaria con la finanziaria 2005, è stata prorogata anche con l’ultimo decreto “milleproroghe”.' } ``` ### Data Fields The data contains the following fields: - `url`: url of the source as a string - `text`: text content as a string - `timestamp`: timestamp of extraction as a string ### Data Splits To build mC4, the original authors used [CLD3](https://github.com/google/cld3) to identify over 100 languages. For Italian, the whole corpus of scraped text was divided in `1032` jsonl files, `1024` for training following the naming style `c4-it.tfrecord-0XXXX-of-01024.json.gz` and 8 for validation following the naming style `c4-it-validation.tfrecord-0000X-of-00008.json.gz`. The full set of preprocessed files takes roughly 215GB of disk space to download with Git LFS. For ease of use under different storage capacities, the following incremental splits are available (sizes are estimates). **Important**: The sizes in GB represent the estimated weight for : |split |train size (docs, words, download + preproc disk space)|validation size| |:-----|------------------------------------------------------:|--------------:| |tiny | 10M docs, 4B words (9 GB + 27 GB) | 12k docs | |small | 20M docs, 8B words (18 GB + 54 GB) | 24k docs | |medium| 50M docs, 20B words (47 GB + 135 GB) | 48k docs | |large | 75M docs, 30B words (71 GB + 203 GB) | 72k docs | |full | 103M docs, 41B words (109 GB + 279 GB) | 96k docs | You can load any subset like this: ```python from datasets import load_dataset mc4_it_tiny = load_dataset("gsarti/clean_mc4_it", "tiny") ``` Since splits are quite large, you may want to traverse them using the streaming mode available starting from 🤗 Datasets v1.9.0: ```python from datasets import load_dataset mc4_it_full_stream = load_dataset("gsarti/clean_mc4_it", "full", split='train', streaming=True) print(next(iter(mc4_it_full_stream))) # Prints the example presented above ``` ## Dataset Creation Refer to the original paper for more considerations regarding the choice of sources and the scraping process for creating `mC4`. ## Considerations for Using the Data ### Social Impact of Dataset With more than 200GB of cleaned Italian text and more than 41B estimated words, this is by far the largest available corpus for the Italian language. The second largest dataset available is [OSCAR](https://oscar-corpus.com/), which is only 69GB in size for its deduplicated variant. Using this corpus for training language models with adequate computational resources will allow researchers to reach parity with the performances observed for the English language. This can in turn have important repercussions for the development of commercial language technology applications for the Italian language. ### Discussion of Biases Despit the cleaning procedure aimed at removing vulgarity and profanity, it must be considered that model trained on this scraped corpus will inevitably reflect biases present in blog articles and comments on the Internet. This makes the corpus especially interesting in the context of studying data biases and how to limit their impacts. ## Additional Information ### Dataset Curators Authors at AllenAI are the original curators for the `mc4` corpus. For inquiries or requests regarding the Italian cleaned portion contained in this repository, please contact me at [gabriele.sarti996@gmail.com](mailto:gabriele.sarti996@gmail.com) ### Licensing Information AllenAI are releasing this dataset under the terms of ODC-BY. By using this, you are also bound by the Common Crawl terms of use in respect of the content contained in the dataset. ### Citation Information If you use this dataset in your work, please cite us and the original mC4 authors as: ``` @article{sarti-nissim-2022-it5, title={IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation}, author={Sarti, Gabriele and Nissim, Malvina}, journal={ArXiv preprint 2203.03759}, url={https://arxiv.org/abs/2203.03759}, year={2022}, month={mar} } @inproceedings{xue-etal-2021-mt5, title = "m{T}5: A Massively Multilingual Pre-trained Text-to-Text Transformer", author = "Xue, Linting and Constant, Noah and Roberts, Adam and Kale, Mihir and Al-Rfou, Rami and Siddhant, Aditya and Barua, Aditya and Raffel, Colin", booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jun, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.naacl-main.41", doi = "10.18653/v1/2021.naacl-main.41", pages = "483--498", } ``` ### Contributions Thanks to [@dirkgr](https://github.com/dirkgr) and [@lhoestq](https://github.com/lhoestq) for adding this dataset.

german_legal_entity_recognition

--- annotations_creators: - expert-generated language_creators: - found language: - de license: - cc-by-4.0 multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition paperswithcode_id: legal-documents-entity-recognition pretty_name: Legal Documents Entity Recognition dataset_info: - config_name: bag features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-AN '1': B-EUN '2': B-GRT '3': B-GS '4': B-INN '5': B-LD '6': B-LDS '7': B-LIT '8': B-MRK '9': B-ORG '10': B-PER '11': B-RR '12': B-RS '13': B-ST '14': B-STR '15': B-UN '16': B-VO '17': B-VS '18': B-VT '19': I-AN '20': I-EUN '21': I-GRT '22': I-GS '23': I-INN '24': I-LD '25': I-LDS '26': I-LIT '27': I-MRK '28': I-ORG '29': I-PER '30': I-RR '31': I-RS '32': I-ST '33': I-STR '34': I-UN '35': I-VO '36': I-VS '37': I-VT '38': O splits: - name: train download_size: 4392913 dataset_size: 0 - config_name: bfh features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-AN '1': B-EUN '2': B-GRT '3': B-GS '4': B-INN '5': B-LD '6': B-LDS '7': B-LIT '8': B-MRK '9': B-ORG '10': B-PER '11': B-RR '12': B-RS '13': B-ST '14': B-STR '15': B-UN '16': B-VO '17': B-VS '18': B-VT '19': I-AN '20': I-EUN '21': I-GRT '22': I-GS '23': I-INN '24': I-LD '25': I-LDS '26': I-LIT '27': I-MRK '28': I-ORG '29': I-PER '30': I-RR '31': I-RS '32': I-ST '33': I-STR '34': I-UN '35': I-VO '36': I-VS '37': I-VT '38': O splits: - name: train download_size: 4392913 dataset_size: 0 - config_name: bgh features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-AN '1': B-EUN '2': B-GRT '3': B-GS '4': B-INN '5': B-LD '6': B-LDS '7': B-LIT '8': B-MRK '9': B-ORG '10': B-PER '11': B-RR '12': B-RS '13': B-ST '14': B-STR '15': B-UN '16': B-VO '17': B-VS '18': B-VT '19': I-AN '20': I-EUN '21': I-GRT '22': I-GS '23': I-INN '24': I-LD '25': I-LDS '26': I-LIT '27': I-MRK '28': I-ORG '29': I-PER '30': I-RR '31': I-RS '32': I-ST '33': I-STR '34': I-UN '35': I-VO '36': I-VS '37': I-VT '38': O splits: - name: train download_size: 4392913 dataset_size: 0 - config_name: bpatg features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-AN '1': B-EUN '2': B-GRT '3': B-GS '4': B-INN '5': B-LD '6': B-LDS '7': B-LIT '8': B-MRK '9': B-ORG '10': B-PER '11': B-RR '12': B-RS '13': B-ST '14': B-STR '15': B-UN '16': B-VO '17': B-VS '18': B-VT '19': I-AN '20': I-EUN '21': I-GRT '22': I-GS '23': I-INN '24': I-LD '25': I-LDS '26': I-LIT '27': I-MRK '28': I-ORG '29': I-PER '30': I-RR '31': I-RS '32': I-ST '33': I-STR '34': I-UN '35': I-VO '36': I-VS '37': I-VT '38': O splits: - name: train download_size: 4392913 dataset_size: 0 - config_name: bsg features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-AN '1': B-EUN '2': B-GRT '3': B-GS '4': B-INN '5': B-LD '6': B-LDS '7': B-LIT '8': B-MRK '9': B-ORG '10': B-PER '11': B-RR '12': B-RS '13': B-ST '14': B-STR '15': B-UN '16': B-VO '17': B-VS '18': B-VT '19': I-AN '20': I-EUN '21': I-GRT '22': I-GS '23': I-INN '24': I-LD '25': I-LDS '26': I-LIT '27': I-MRK '28': I-ORG '29': I-PER '30': I-RR '31': I-RS '32': I-ST '33': I-STR '34': I-UN '35': I-VO '36': I-VS '37': I-VT '38': O splits: - name: train download_size: 4392913 dataset_size: 0 - config_name: bverfg features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-AN '1': B-EUN '2': B-GRT '3': B-GS '4': B-INN '5': B-LD '6': B-LDS '7': B-LIT '8': B-MRK '9': B-ORG '10': B-PER '11': B-RR '12': B-RS '13': B-ST '14': B-STR '15': B-UN '16': B-VO '17': B-VS '18': B-VT '19': I-AN '20': I-EUN '21': I-GRT '22': I-GS '23': I-INN '24': I-LD '25': I-LDS '26': I-LIT '27': I-MRK '28': I-ORG '29': I-PER '30': I-RR '31': I-RS '32': I-ST '33': I-STR '34': I-UN '35': I-VO '36': I-VS '37': I-VT '38': O splits: - name: train download_size: 4392913 dataset_size: 0 - config_name: bverwg features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-AN '1': B-EUN '2': B-GRT '3': B-GS '4': B-INN '5': B-LD '6': B-LDS '7': B-LIT '8': B-MRK '9': B-ORG '10': B-PER '11': B-RR '12': B-RS '13': B-ST '14': B-STR '15': B-UN '16': B-VO '17': B-VS '18': B-VT '19': I-AN '20': I-EUN '21': I-GRT '22': I-GS '23': I-INN '24': I-LD '25': I-LDS '26': I-LIT '27': I-MRK '28': I-ORG '29': I-PER '30': I-RR '31': I-RS '32': I-ST '33': I-STR '34': I-UN '35': I-VO '36': I-VS '37': I-VT '38': O splits: - name: train download_size: 4392913 dataset_size: 0 - config_name: all features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-AN '1': B-EUN '2': B-GRT '3': B-GS '4': B-INN '5': B-LD '6': B-LDS '7': B-LIT '8': B-MRK '9': B-ORG '10': B-PER '11': B-RR '12': B-RS '13': B-ST '14': B-STR '15': B-UN '16': B-VO '17': B-VS '18': B-VT '19': I-AN '20': I-EUN '21': I-GRT '22': I-GS '23': I-INN '24': I-LD '25': I-LDS '26': I-LIT '27': I-MRK '28': I-ORG '29': I-PER '30': I-RR '31': I-RS '32': I-ST '33': I-STR '34': I-UN '35': I-VO '36': I-VS '37': I-VT '38': O splits: - name: train download_size: 4392913 dataset_size: 0 --- # Dataset Card for Legal Documents Entity Recognition ## 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/elenanereiss/Legal-Entity-Recognition - **Repository:** None - **Paper:** https://link.springer.com/chapter/10.1007/978-3-030-33220-4_20 - **Leaderboard:** [If the dataset supports an active leaderboard, add link here]() - **Point of Contact:** Georg Rehm (georg.rehm@dfki.de) ### 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>Deprecated:</b> Dataset "german_legal_entity_recognition" is deprecated and will be deleted. Use <a href="https://huggingface.co/datasets/elenanereiss/german-ler">"elenanereiss/german-ler"</a> instead.</p> </div> ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.

wiki_hop

--- annotations_creators: - crowdsourced language_creators: - expert-generated language: - en license: - cc-by-sa-3.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - extractive-qa paperswithcode_id: wikihop pretty_name: WikiHop tags: - multi-hop dataset_info: - config_name: original features: - name: id dtype: string - name: query dtype: string - name: answer dtype: string - name: candidates sequence: string - name: supports sequence: string - name: annotations sequence: sequence: string splits: - name: train num_bytes: 325952974 num_examples: 43738 - name: validation num_bytes: 41246536 num_examples: 5129 download_size: 339843061 dataset_size: 367199510 - config_name: masked features: - name: id dtype: string - name: question dtype: string - name: answer dtype: string - name: candidates sequence: string - name: supports sequence: string - name: annotations sequence: sequence: string splits: - name: train num_bytes: 348249138 num_examples: 43738 - name: validation num_bytes: 44066862 num_examples: 5129 download_size: 339843061 dataset_size: 392316000 --- # Dataset Card for WikiHop ## 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:** [QAngaroo](http://qangaroo.cs.ucl.ac.uk/) - **Repository:** [If the dataset is hosted on github or has a github homepage, add URL here]() - **Paper:** [Constructing Datasets for Multi-hop Reading Comprehension Across Documents](https://arxiv.org/abs/1710.06481) - **Leaderboard:** [leaderboard](http://qangaroo.cs.ucl.ac.uk/leaderboard.html) - **Point of Contact:** [Johannes Welbl](j.welbl@cs.ucl.ac.uk) ### 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.

BeIR/climate-fever

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.

mxeval/mbxp

mxeval

--- license: apache-2.0 task_categories: - text-generation language: - en tags: - mxeval - mbxp - mbpp - code-generation - mxeval pretty_name: mbxp size_categories: - 10K<n<100K --- # MBXP ## Table of Contents - [MBXP](#MBXP) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#related-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Executional Correctness](#execution) - [Execution Example](#execution-example) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Social Impact of Dataset](#social-impact-of-dataset) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) # MBXP ## Dataset Description - **Repository:** [GitHub Repository](https://github.com/amazon-science/mbxp-exec-eval) - **Paper:** [Multi-lingual Evaluation of Code Generation Models](https://openreview.net/forum?id=Bo7eeXm6An8) ### Dataset Summary This repository contains data and code to perform execution-based multi-lingual evaluation of code generation capabilities and the corresponding data, namely, a multi-lingual benchmark MBXP, multi-lingual MathQA and multi-lingual HumanEval. <br>Results and findings can be found in the paper ["Multi-lingual Evaluation of Code Generation Models"](https://arxiv.org/abs/2210.14868). ### Related Tasks and Leaderboards * [Multi-HumanEval](https://huggingface.co/datasets/mxeval/multi-humaneval) * [MBXP](https://huggingface.co/datasets/mxeval/mbxp) * [MathQA-X](https://huggingface.co/datasets/mxeval/mathqa-x) ### Languages The programming problems are written in multiple programming languages and contain English natural text in comments and docstrings. ## Dataset Structure To lookup currently supported datasets ```python from datasets import get_dataset_config_names get_dataset_config_names("mxeval/mbxp") ['python', 'csharp', 'go', 'java', 'javascript', 'kotlin', 'perl', 'php', 'ruby', 'scala', 'swift', 'typescript'] ``` To load a specific dataset and language ```python from datasets import load_dataset load_dataset("mxeval/mbxp", "python") DatasetDict({ test: Dataset({ features: ['task_id', 'language', 'prompt', 'test', 'entry_point', 'canonical_solution', 'description'], num_rows: 974 }) }) ``` ### Data Instances An example of a dataset instance: ```python { "task_id": "MBPP/1", "language": "python", "prompt": "\n\ndef min_cost(cost, m, n):\n\t\"\"\"\n\tWrite a function to find the minimum cost path to reach (m, n) from (0, 0) for the given cost matrix cost[][] and a position (m, n) in cost[][].\n\t>>> min_cost([[1, 2, 3], [4, 8, 2], [1, 5, 3]], 2, 2)\n\t8\n\t>>> min_cost([[2, 3, 4], [5, 9, 3], [2, 6, 4]], 2, 2)\n\t12\n\t>>> min_cost([[3, 4, 5], [6, 10, 4], [3, 7, 5]], 2, 2)\n\t16\n\t\"\"\"\n", "test": "\n\nMETADATA = {}\n\n\ndef check(candidate):\n assert candidate([[1, 2, 3], [4, 8, 2], [1, 5, 3]], 2, 2) == 8\n assert candidate([[2, 3, 4], [5, 9, 3], [2, 6, 4]], 2, 2) == 12\n assert candidate([[3, 4, 5], [6, 10, 4], [3, 7, 5]], 2, 2) == 16\n\n", "entry_point": "min_cost", "canonical_solution": "\tR = 3\n\tC = 3\n\t \n\ttc = [[0 for x in range(C)] for x in range(R)] \n\ttc[0][0] = cost[0][0] \n\tfor i in range(1, m+1): \n\t\ttc[i][0] = tc[i-1][0] + cost[i][0] \n\tfor j in range(1, n+1): \n\t\ttc[0][j] = tc[0][j-1] + cost[0][j] \n\tfor i in range(1, m+1): \n\t\tfor j in range(1, n+1): \n\t\t\ttc[i][j] = min(tc[i-1][j-1], tc[i-1][j], tc[i][j-1]) + cost[i][j] \n\treturn tc[m][n]", "description": "Write a function to find the minimum cost path to reach (m, n) from (0, 0) for the given cost matrix cost[][] and a position (m, n) in cost[][]." } ``` ### Data Fields - `task_id`: identifier for the data sample - `prompt`: input for the model containing function header and docstrings - `canonical_solution`: solution for the problem in the `prompt` - `description`: task description - `test`: contains function to test generated code for correctness - `entry_point`: entry point for test - `language`: programming lanuage identifier to call the appropriate subprocess call for program execution ### Data Splits - MBXP - Python - Java - Javascript - Typescript - Kotlin - Ruby - Php - Cpp - Csharp - Go - Perl - Scala - Swift ## Dataset Creation ### Curation Rationale Since code generation models are often trained on dumps of GitHub a dataset not included in the dump was necessary to properly evaluate the model. However, since this dataset was published on GitHub it is likely to be included in future dumps. ### Personal and Sensitive Information None. ### Social Impact of Dataset With this dataset code generating models can be better evaluated which leads to fewer issues introduced when using such models. ### Dataset Curators AWS AI Labs ## Execution ### Execution Example Install the repo [mbxp-exec-eval](https://github.com/amazon-science/mbxp-exec-eval) to execute generations or canonical solutions for the prompts from this dataset. ```python >>> from datasets import load_dataset >>> from mxeval.execution import check_correctness >>> mbxp_python = load_dataset("mxeval/mbxp", "python", split="test") >>> example_problem = mbxp_python[0] >>> check_correctness(example_problem, example_problem["canonical_solution"], timeout=20.0) {'task_id': 'MBPP/1', 'passed': True, 'result': 'passed', 'completion_id': None, 'time_elapsed': 10.314226150512695} ``` ### Considerations for Using the Data Make sure to sandbox the execution environment. ### Licensing Information [LICENSE](https://huggingface.co/datasets/mxeval/mbxp/blob/main/mbxp-LICENSE) <br> [THIRD PARTY LICENSES](https://huggingface.co/datasets/mxeval/mbxp/blob/main/THIRD_PARTY_LICENSES) ### Citation Information ``` @article{mbxp_athiwaratkun2022, title = {Multi-lingual Evaluation of Code Generation Models}, author = {Athiwaratkun, Ben and Gouda, Sanjay Krishna and Wang, Zijian and Li, Xiaopeng and Tian, Yuchen and Tan, Ming and Ahmad, Wasi Uddin and Wang, Shiqi and Sun, Qing and Shang, Mingyue and Gonugondla, Sujan Kumar and Ding, Hantian and Kumar, Varun and Fulton, Nathan and Farahani, Arash and Jain, Siddhartha and Giaquinto, Robert and Qian, Haifeng and Ramanathan, Murali Krishna and Nallapati, Ramesh and Ray, Baishakhi and Bhatia, Parminder and Sengupta, Sudipta and Roth, Dan and Xiang, Bing}, doi = {10.48550/ARXIV.2210.14868}, url = {https://arxiv.org/abs/2210.14868}, keywords = {Machine Learning (cs.LG), Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` ### Contributions [skgouda@](https://github.com/sk-g) [benathi@](https://github.com/benathi)

bitext/Bitext-customer-support-llm-chatbot-training-dataset

bitext

--- license: cdla-sharing-1.0 task_categories: - question-answering - table-question-answering language: - en tags: - question-answering - llm - chatbot - costumer-support - conversional-ai - generative-ai - natural-language-understanding - fine-tuning - Retail pretty_name: >- Bitext - Customer Service Tagged Training Dataset for LLM-based Virtual Assistants size_categories: - 10K<n<100K --- # Bitext - Customer Service Tagged Training Dataset for LLM-based Virtual Assistants ## Overview This dataset can be used to train Large Language Models such as GPT, Llama2 and Falcon, both for Fine Tuning and Domain Adaptation. The dataset has the following specs: - Use Case: Intent Detection - Vertical: Customer Service - 27 intents assigned to 10 categories - 26872 question/answer pairs, around 1000 per intent - 30 entity/slot types - 12 different types of language generation tags The categories and intents have been selected from Bitext's collection of 20 vertical-specific datasets, covering the intents that are common across all 20 verticals. The verticals are: - Automotive, Retail Banking, Education, Events & Ticketing, Field Services, Healthcare, Hospitality, Insurance, Legal Services, Manufacturing, Media Streaming, Mortgages & Loans, Moving & Storage, Real Estate/Construction, Restaurant & Bar Chains, Retail/E-commerce, Telecommunications, Travel, Utilities, Wealth Management For a full list of verticals and its intents see [https://www.bitext.com/chatbot-verticals/](https://www.bitext.com/chatbot-verticals/). The question/answer pairs have been generated using a hybrid methodology that uses natural texts as source text, NLP technology to extract seeds from these texts, and NLG technology to expand the seed texts. All steps in the process are curated by computational linguists. ## Dataset Token Count The dataset contains an extensive amount of text data across its 'instruction' and 'response' columns. After processing and tokenizing the dataset, we've identified a total of 3.57 million tokens. This rich set of tokens is essential for training advanced LLMs for AI Conversational, AI Generative, and Question and Answering (Q&A) models. ## Fields of the Dataset Each entry in the dataset contains the following fields: - flags: tags (explained below in the Language Generation Tags section) - instruction: a user request from the Customer Service domain - category: the high-level semantic category for the intent - intent: the intent corresponding to the user instruction - response: an example expected response from the virtual assistant ## Categories and Intents The categories and intents covered by the dataset are: - ACCOUNT: create_account, delete_account, edit_account, switch_account - CANCELLATION_FEE: check_cancellation_fee - DELIVERY: delivery_options - FEEDBACK: complaint, review - INVOICE: check_invoice, get_invoice - NEWSLETTER: newsletter_subscription - ORDER: cancel_order, change_order, place_order - PAYMENT: check_payment_methods, payment_issue - REFUND: check_refund_policy, track_refund - SHIPPING_ADDRESS: change_shipping_address, set_up_shipping_address ## Entities The entities covered by the dataset are: - {{Order Number}}, typically present in: - Intents: cancel_order, change_order, change_shipping_address, check_invoice, check_refund_policy, complaint, delivery_options, delivery_period, get_invoice, get_refund, place_order, track_order, track_refund - {{Invoice Number}}, typically present in: - Intents: check_invoice, get_invoice - {{Online Order Interaction}}, typically present in: - Intents: cancel_order, change_order, check_refund_policy, delivery_period, get_refund, review, track_order, track_refund - {{Online Payment Interaction}}, typically present in: - Intents: cancel_order, check_payment_methods - {{Online Navigation Step}}, typically present in: - Intents: complaint, delivery_options - {{Online Customer Support Channel}}, typically present in: - Intents: check_refund_policy, complaint, contact_human_agent, delete_account, delivery_options, edit_account, get_refund, payment_issue, registration_problems, switch_account - {{Profile}}, typically present in: - Intent: switch_account - {{Profile Type}}, typically present in: - Intent: switch_account - {{Settings}}, typically present in: - Intents: cancel_order, change_order, change_shipping_address, check_cancellation_fee, check_invoice, check_payment_methods, contact_human_agent, delete_account, delivery_options, edit_account, get_invoice, newsletter_subscription, payment_issue, place_order, recover_password, registration_problems, set_up_shipping_address, switch_account, track_order, track_refund - {{Online Company Portal Info}}, typically present in: - Intents: cancel_order, edit_account - {{Date}}, typically present in: - Intents: check_invoice, check_refund_policy, get_refund, track_order, track_refund - {{Date Range}}, typically present in: - Intents: check_cancellation_fee, check_invoice, get_invoice - {{Shipping Cut-off Time}}, typically present in: - Intent: delivery_options - {{Delivery City}}, typically present in: - Intent: delivery_options - {{Delivery Country}}, typically present in: - Intents: check_payment_methods, check_refund_policy, delivery_options, review, switch_account - {{Salutation}}, typically present in: - Intents: cancel_order, check_payment_methods, check_refund_policy, create_account, delete_account, delivery_options, get_refund, recover_password, review, set_up_shipping_address, switch_account, track_refund - {{Client First Name}}, typically present in: - Intents: check_invoice, get_invoice - {{Client Last Name}}, typically present in: - Intents: check_invoice, create_account, get_invoice - {{Customer Support Phone Number}}, typically present in: - Intents: change_shipping_address, contact_customer_service, contact_human_agent, payment_issue - {{Customer Support Email}}, typically present in: - Intents: cancel_order, change_shipping_address, check_invoice, check_refund_policy, complaint, contact_customer_service, contact_human_agent, get_invoice, get_refund, newsletter_subscription, payment_issue, recover_password, registration_problems, review, set_up_shipping_address, switch_account - {{Live Chat Support}}, typically present in: - Intents: check_refund_policy, complaint, contact_human_agent, delete_account, delivery_options, edit_account, get_refund, payment_issue, recover_password, registration_problems, review, set_up_shipping_address, switch_account, track_order - {{Website URL}}, typically present in: - Intents: check_payment_methods, check_refund_policy, complaint, contact_customer_service, contact_human_agent, create_account, delete_account, delivery_options, get_refund, newsletter_subscription, payment_issue, place_order, recover_password, registration_problems, review, switch_account - {{Upgrade Account}}, typically present in: - Intents: create_account, edit_account, switch_account - {{Account Type}}, typically present in: - Intents: cancel_order, change_order, change_shipping_address, check_cancellation_fee, check_invoice, check_payment_methods, check_refund_policy, complaint, contact_customer_service, contact_human_agent, create_account, delete_account, delivery_options, delivery_period, edit_account, get_invoice, get_refund, newsletter_subscription, payment_issue, place_order, recover_password, registration_problems, review, set_up_shipping_address, switch_account, track_order, track_refund - {{Account Category}}, typically present in: - Intents: cancel_order, change_order, change_shipping_address, check_cancellation_fee, check_invoice, check_payment_methods, check_refund_policy, complaint, contact_customer_service, contact_human_agent, create_account, delete_account, delivery_options, delivery_period, edit_account, get_invoice, get_refund, newsletter_subscription, payment_issue, place_order, recover_password, registration_problems, review, set_up_shipping_address, switch_account, track_order, track_refund - {{Account Change}}, typically present in: - Intent: switch_account - {{Program}}, typically present in: - Intent: place_order - {{Refund Amount}}, typically present in: - Intent: track_refund - {{Money Amount}}, typically present in: - Intents: check_refund_policy, complaint, get_refund, track_refund - {{Store Location}}, typically present in: - Intents: complaint, delivery_options, place_order ## Language Generation Tags The dataset contains tags that reflect how language varies/changes across different linguistic phenomena like colloquial or offensive language. So if an utterance for intent “cancel_order” contains the “COLLOQUIAL” tag, the utterance will express an informal language variation like: “can u cancel my order”. These tags indicate the type of language variation that the entry expresses. When associated to each entry, they allow Conversational Designers to customize training datasets to different user profiles with different uses of language. Through these tags, many different datasets can be created to make the resulting assistant more accurate and robust. A bot that sells sneakers should be mainly targeted to younger population that use a more colloquial language; while a classical retail banking bot should be able to handle more formal or polite language. The dataset also reflects commonly occurring linguistic phenomena of real-life virtual assistant, such as spelling mistakes, run-on words, punctuation errors… The dataset contains tagging for all relevant linguistic phenomena that can be used to customize the dataset for different user profiles. ### Tags for Lexical variation M - Morphological variation: inflectional and derivational “is my SIM card active”, “is my SIM card activated” L - Semantic variations: synonyms, use of hyphens, compounding… “what’s my billing date", “what’s my anniversary date” ### Tags for Syntactic structure variation B - Basic syntactic structure: “activate my SIM card”, “I need to activate my SIM card” I - Interrogative structure “can you activate my SIM card?”, “how do I activate my SIM card?” C - Coordinated syntactic structure “I have a new SIM card, what do I need to do to activate it?” N - Negation “I do not want this item, where to cancel my order?” ### Tags for language register variations P - Politeness variation “could you help me activate my SIM card, please?” Q - Colloquial variation “can u activ8 my SIM?” W - Offensive language “I want to talk to a f*&%*g agent” ### Tags for stylistic variations K - Keyword mode "activate SIM", "new SIM" E - Use of abbreviations: “I'm / I am interested in getting a new SIM” Z - Errors and Typos: spelling issues, wrong punctuation… “how can i activaet my card” ### Other tags not in use in this Dataset D - Indirect speech “ask my agent to activate my SIM card” G - Regional variations US English vs UK English: "truck" vs "lorry" France French vs Canadian French: "tchatter" vs "clavarder" R - Respect structures - Language-dependent variations English: "may" vs "can…" French: "tu" vs "vous..." Spanish: "tú" vs "usted..." Y - Code switching “activer ma SIM card” --- (c) Bitext Innovations, 2023

THUDM/AgentInstruct

THUDM

--- configs: - config_name: default data_files: - split: os path: data/os-* - split: db path: data/db-* - split: alfworld path: data/alfworld-* - split: webshop path: data/webshop-* - split: kg path: data/kg-* - split: mind2web path: data/mind2web-* dataset_info: features: - name: conversations list: - name: from dtype: string - name: loss dtype: bool - name: value dtype: string - name: id dtype: string splits: - name: os num_bytes: 660245 num_examples: 195 - name: db num_bytes: 1436655 num_examples: 538 - name: alfworld num_bytes: 1223363 num_examples: 336 - name: webshop num_bytes: 1602648 num_examples: 351 - name: kg num_bytes: 2960010 num_examples: 324 - name: mind2web num_bytes: 159590 num_examples: 122 download_size: 1255385 dataset_size: 8042511 language: - en pretty_name: AgentInstruct --- # AgentInstruct Dataset <p align="center"> 🤗 <a href="https://huggingface.co/THUDM/agentlm-70b" target="_blank">[Models]</a> • 💻 <a href="https://github.com/THUDM/AgentTuning" target="_blank">[Github Repo]</a> • 📌 <a href="https://THUDM.github.io/AgentTuning/" target="_blank">[Project Page]</a> • 📃 <a href="https://arxiv.org/abs/2310.12823" target="_blank">[Paper]</a> </p> **AgentInstruct** is a meticulously curated dataset featuring **1,866** high-quality interactions, designed to enhance AI agents across six diverse real-world tasks, leveraging innovative methods like **Task Derivation** and **Self-Instruct**. - 🔍 **CoT** - Harness the power of [ReAct](https://react-lm.github.io/), offering detailed thought explanations for each action, ensuring an intricate understanding of the model's decision-making journey. - 🌍 **Diversity** - Spanning 6 real-world scenarios, from Daily Household Routines to Database Operations, and their average turns range from 5 to 35. - 🎯 **Precision** - Not all trajectories of GPT-4 are effective! Ours are rigorously filtered using strict rewards to ensure top-notch quality. - ✅ **Assurance** - Rigorous checks to avoid data leakage, ensuring pristine dataset quality. ## Task Overview | Task | # Filt. Traj. | Avg # Filt. Traj. Turns | |---|---|---| |ALFWorld|336|13.52| |WebShop|351|3.68| |Mind2Web|122|1.00| |Knowledge Graph|324|6.04| |Operating System|195|3.85| |Database|538|2.06| |**AgentInstruct**|1866|5.24| AgentInstruct includes 1,866 trajectories from 6 agents tasks. "Traj." stands for interaction trajectory. "Filt. Traj." stands for filtered trajectories. ## Models **AgentLM** models are produced by mixed training on AgentInstruct dataset and ShareGPT dataset from Llama-2-chat models. The models follow the conversation format of [Llama-2-chat](https://huggingface.co/blog/llama2#how-to-prompt-llama-2), with system prompt fixed as ``` You are a helpful, respectful and honest assistant. ``` 7B, 13B, and 70B models are available on Huggingface model hub. |Model|Huggingface Repo| |---|---| |AgentLM-7B| [🤗Huggingface Repo](https://huggingface.co/THUDM/agentlm-7b) | |AgentLM-13B| [🤗Huggingface Repo](https://huggingface.co/THUDM/agentlm-13b) | |AgentLM-70B| [🤗Huggingface Repo](https://huggingface.co/THUDM/agentlm-70b) | Check our [[Github Repo]](https://github.com/THUDM/AgentTuning) for details about **AgentTuning**. ## Citation If you find our work useful, please consider citing AgentTuning: ``` @misc{zeng2023agenttuning, title={AgentTuning: Enabling Generalized Agent Abilities for LLMs}, author={Aohan Zeng and Mingdao Liu and Rui Lu and Bowen Wang and Xiao Liu and Yuxiao Dong and Jie Tang}, year={2023}, eprint={2310.12823}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

alzoubi36/policy_ie_b

alzoubi36

--- dataset_info: features: - name: type-I struct: - name: subtask dtype: string - name: tags sequence: string - name: tokens sequence: string - name: type-II struct: - name: subtask dtype: string - name: tags sequence: string - name: tokens sequence: string splits: - name: train num_bytes: 3944744 num_examples: 4109 - name: validation num_bytes: 1102169 num_examples: 1041 - name: test num_bytes: 1102169 num_examples: 1041 download_size: 814098 dataset_size: 6149082 --- # Dataset for the PolicyIE-B task in the [PrivacyGLUE](https://github.com/infsys-lab/privacy-glue) dataset

quarel

--- language: - en paperswithcode_id: quarel pretty_name: QuaRel dataset_info: features: - name: id dtype: string - name: answer_index dtype: int32 - name: logical_forms sequence: string - name: logical_form_pretty dtype: string - name: world_literals sequence: - name: world1 dtype: string - name: world2 dtype: string - name: question dtype: string splits: - name: train num_bytes: 1072874 num_examples: 1941 - name: test num_bytes: 307588 num_examples: 552 - name: validation num_bytes: 154308 num_examples: 278 download_size: 631370 dataset_size: 1534770 --- # Dataset Card for "quarel" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://allenai.org/data/quarel](https://allenai.org/data/quarel) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 0.63 MB - **Size of the generated dataset:** 1.53 MB - **Total amount of disk used:** 2.17 MB ### Dataset Summary QuaRel is a crowdsourced dataset of 2771 multiple-choice story questions, including their logical forms. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 0.63 MB - **Size of the generated dataset:** 1.53 MB - **Total amount of disk used:** 2.17 MB An example of 'train' looks as follows. ``` { "answer_index": 0, "id": "QuaRel_V1_B5_1403", "logical_form_pretty": "qrel(time, lower, world1) -> qrel(distance, higher, world2) ; qrel(distance, higher, world1)", "logical_forms": ["(infer (time lower world1) (distance higher world2) (distance higher world1))", "(infer (time lower world2) (distance higher world1) (distance higher world2))"], "question": "John and Rita are going for a run. Rita gets tired and takes a break on the park bench. After twenty minutes in the park, who has run farther? (A) John (B) Rita", "world_literals": { "world1": ["Rita"], "world2": ["John"] } } ``` ### Data Fields The data fields are the same among all splits. #### default - `id`: a `string` feature. - `answer_index`: a `int32` feature. - `logical_forms`: a `list` of `string` features. - `logical_form_pretty`: a `string` feature. - `world_literals`: a dictionary feature containing: - `world1`: a `string` feature. - `world2`: a `string` feature. - `question`: a `string` feature. ### Data Splits | name |train|validation|test| |-------|----:|---------:|---:| |default| 1941| 278| 552| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{quarel_v1, title={QuaRel: A Dataset and Models for Answering Questions about Qualitative Relationships}, author={Oyvind Tafjord, Peter Clark, Matt Gardner, Wen-tau Yih, Ashish Sabharwal}, year={2018}, journal={arXiv:1805.05377v1} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@mariamabarham](https://github.com/mariamabarham), [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

wikicorpus

--- pretty_name: Wikicorpus annotations_creators: - machine-generated - no-annotation language_creators: - found language: - ca - en - es license: - gfdl multilinguality: - monolingual size_categories: - 100K<n<1M - 10M<n<100M - 1M<n<10M source_datasets: - original task_categories: - fill-mask - text-classification - text-generation - token-classification task_ids: - language-modeling - masked-language-modeling - part-of-speech paperswithcode_id: null tags: - word-sense-disambiguation - lemmatization dataset_info: - config_name: raw_ca features: - name: id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 263170192 num_examples: 143883 download_size: 96437841 dataset_size: 263170192 - config_name: raw_es features: - name: id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 671295359 num_examples: 259409 download_size: 252926918 dataset_size: 671295359 - config_name: raw_en features: - name: id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 3388801074 num_examples: 1359146 download_size: 1346378932 dataset_size: 3388801074 - config_name: tagged_ca features: - name: id dtype: string - name: title dtype: string - name: sentence sequence: string - name: lemmas sequence: string - name: pos_tags sequence: string - name: wordnet_senses sequence: string splits: - name: train num_bytes: 1666129919 num_examples: 2016221 download_size: 226390380 dataset_size: 1666129919 - config_name: tagged_es features: - name: id dtype: string - name: title dtype: string - name: sentence sequence: string - name: lemmas sequence: string - name: pos_tags sequence: string - name: wordnet_senses sequence: string splits: - name: train num_bytes: 4100040390 num_examples: 5039367 download_size: 604910899 dataset_size: 4100040390 - config_name: tagged_en features: - name: id dtype: string - name: title dtype: string - name: sentence sequence: string - name: lemmas sequence: string - name: pos_tags sequence: string - name: wordnet_senses sequence: string splits: - name: train num_bytes: 18077275300 num_examples: 26350272 download_size: 2477450893 dataset_size: 18077275300 config_names: - raw_ca - raw_en - raw_es - tagged_ca - tagged_en - tagged_es --- # Dataset Card for Wikicorpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://www.cs.upc.edu/~nlp/wikicorpus/ - **Repository:** - **Paper:** https://www.cs.upc.edu/~nlp/papers/reese10.pdf - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The Wikicorpus is a trilingual corpus (Catalan, Spanish, English) that contains large portions of the Wikipedia (based on a 2006 dump) and has been automatically enriched with linguistic information. In its present version, it contains over 750 million words. The corpora have been annotated with lemma and part of speech information using the open source library FreeLing. Also, they have been sense annotated with the state of the art Word Sense Disambiguation algorithm UKB. As UKB assigns WordNet senses, and WordNet has been aligned across languages via the InterLingual Index, this sort of annotation opens the way to massive explorations in lexical semantics that were not possible before. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Each sub-dataset is monolingual in the languages: - ca: Catalan - en: English - es: Spanish ## 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 The WikiCorpus is licensed under the same license as Wikipedia, that is, the [GNU Free Documentation License](http://www.fsf.org/licensing/licenses/fdl.html) ### Citation Information ``` @inproceedings{reese-etal-2010-wikicorpus, title = "{W}ikicorpus: A Word-Sense Disambiguated Multilingual {W}ikipedia Corpus", author = "Reese, Samuel and Boleda, Gemma and Cuadros, Montse and Padr{\'o}, Llu{\'i}s and Rigau, German", booktitle = "Proceedings of the Seventh International Conference on Language Resources and Evaluation ({LREC}'10)", month = may, year = "2010", address = "Valletta, Malta", publisher = "European Language Resources Association (ELRA)", url = "http://www.lrec-conf.org/proceedings/lrec2010/pdf/222_Paper.pdf", abstract = "This article presents a new freely available trilingual corpus (Catalan, Spanish, English) that contains large portions of the Wikipedia and has been automatically enriched with linguistic information. To our knowledge, this is the largest such corpus that is freely available to the community: In its present version, it contains over 750 million words. The corpora have been annotated with lemma and part of speech information using the open source library FreeLing. Also, they have been sense annotated with the state of the art Word Sense Disambiguation algorithm UKB. As UKB assigns WordNet senses, and WordNet has been aligned across languages via the InterLingual Index, this sort of annotation opens the way to massive explorations in lexical semantics that were not possible before. We present a first attempt at creating a trilingual lexical resource from the sense-tagged Wikipedia corpora, namely, WikiNet. Moreover, we present two by-products of the project that are of use for the NLP community: An open source Java-based parser for Wikipedia pages developed for the construction of the corpus, and the integration of the WSD algorithm UKB in FreeLing.", } ``` ### Contributions Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.

qanastek/EMEA-V3

qanastek

--- annotations_creators: - machine-generated - expert-generated language_creators: - found language: - bg - cs - da - de - el - en - es - et - fi - fr - hu - it - lt - lv - mt - nl - pl - pt - ro - sk - sl - sv multilinguality: - bg - cs - da - de - el - en - es - et - fi - fr - hu - it - lt - lv - mt - nl - pl - pt - ro - sk - sl - sv pretty_name: EMEA-V3 size_categories: - 100K<n<1M source_datasets: - extended task_categories: - translation - machine-translation task_ids: - translation - machine-translation --- # EMEA-V3 : European parallel translation corpus from the European Medicines Agency ## Table of Contents - [Dataset Card for [Needs More Information]](#dataset-card-for-needs-more-information) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - **Homepage:** https://opus.nlpl.eu/EMEA.php - **Repository:** https://github.com/qanastek/EMEA-V3/ - **Paper:** https://aclanthology.org/L12-1246/ - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Yanis Labrak](mailto:yanis.labrak@univ-avignon.fr) ### Dataset Summary `EMEA-V3` is a parallel corpus for neural machine translation collected and aligned by [Tiedemann, Jorg](mailto:jorg.tiedemann@lingfil.uu.se) during the [OPUS project](https://opus.nlpl.eu/). ### Supported Tasks and Leaderboards `translation`: The dataset can be used to train a model for translation. ### Languages In our case, the corpora consists of a pair of source and target sentences for all 22 different languages from the European Union (EU). **List of languages :** `Bulgarian (bg)`,`Czech (cs)`,`Danish (da)`,`German (de)`,`Greek (el)`,`English (en)`,`Spanish (es)`,`Estonian (et)`,`Finnish (fi)`,`French (fr)`,`Hungarian (hu)`,`Italian (it)`,`Lithuanian (lt)`,`Latvian (lv)`,`Maltese (mt)`,`Dutch (nl)`,`Polish (pl)`,`Portuguese (pt)`,`Romanian (ro)`,`Slovak (sk)`,`Slovenian (sl)`,`Swedish (sv)`. ## Load the dataset with HuggingFace ```python from datasets import load_dataset dataset = load_dataset("qanastek/EMEA-V3", split='train', download_mode='force_redownload') print(dataset) print(dataset[0]) ``` ## Dataset Structure ### Data Instances ```plain lang,source_text,target_text bg-cs,EMEA/ H/ C/ 471,EMEA/ H/ C/ 471 bg-cs,ABILIFY,ABILIFY bg-cs,Какво представлява Abilify?,Co je Abilify? bg-cs,"Abilify е лекарство, съдържащо активното вещество арипипразол.","Abilify je léčivý přípravek, který obsahuje účinnou látku aripiprazol." bg-cs,"Предлага се под формата на таблетки от 5 mg, 10 mg, 15 mg и 30 mg, като диспергиращи се таблетки (таблетки, които се разтварят в устата) от 10 mg, 15 mg и 30 mg, като перорален разтвор (1 mg/ ml) и като инжекционен разтвор (7, 5 mg/ ml).","Je dostupný ve formě tablet s obsahem 5 mg, 10 mg, 15 mg a 30 mg, ve formě tablet dispergovatelných v ústech (tablet, které se rozpustí v ústech) s obsahem 10 mg, 15 mg a 30 mg, jako perorální roztok (1 mg/ ml) nebo jako injekční roztok (7, 5 mg/ ml)." bg-cs,За какво се използва Abilify?,Na co se přípravek Abilify používá? ``` ### Data Fields **lang** : The pair of source and target language of type `String`. **source_text** : The source text of type `String`. **target_text** : The target text of type `String`. ### Data Splits | | bg | cs | da | de | el | en | es | et | fi | fr | hu | it | lt | lv | mt | nl | pl | pt | ro | sk | sl | sv | |--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------| | **bg** | 0 | 342378 | 349675 | 348061 | 355696 | 333066 | 349936 | 336142 | 341732 | 358045 | 352763 | 351669 | 348679 | 342721 | 351097 | 353942 | 355005 | 347925 | 351099 | 345572 | 346954 | 342927 | | **cs** | 342378 | 0 | 354824 | 353397 | 364609 | 335716 | 356506 | 340309 | 349040 | 363614 | 358353 | 357578 | 353232 | 347807 | 334353 | 355192 | 358357 | 351244 | 330447 | 346835 | 348411 | 346894 | | **da** | 349675 | 354824 | 0 | 387202 | 397654 | 360186 | 387329 | 347391 | 379830 | 396294 | 367091 | 388495 | 360572 | 353801 | 342263 | 388250 | 368779 | 382576 | 340508 | 356890 | 357694 | 373510 | | **de** | 348061 | 353397 | 387202 | 0 | 390281 | 364005 | 386335 | 346166 | 378626 | 393468 | 366828 | 381396 | 360907 | 353151 | 340294 | 377770 | 367080 | 381365 | 337562 | 355805 | 358700 | 376925 | | **el** | 355696 | 364609 | 397654 | 390281 | 0 | 372824 | 393051 | 354874 | 384889 | 403248 | 373706 | 391389 | 368576 | 360047 | 348221 | 396284 | 372486 | 387170 | 342655 | 364959 | 363778 | 384569 | | **en** | 333066 | 335716 | 360186 | 364005 | 372824 | 0 | 366769 | 333667 | 357177 | 373152 | 349176 | 361089 | 339899 | 336306 | 324695 | 360418 | 348450 | 361393 | 321233 | 338649 | 338195 | 352587 | | **es** | 349936 | 356506 | 387329 | 386335 | 393051 | 366769 | 0 | 348454 | 378158 | 394253 | 368203 | 378076 | 360645 | 354126 | 340297 | 381188 | 367091 | 376443 | 337302 | 358745 | 357961 | 379462 | | **et** | 336142 | 340309 | 347391 | 346166 | 354874 | 333667 | 348454 | 0 | 341694 | 358012 | 352099 | 351747 | 345417 | 339042 | 337302 | 350911 | 354329 | 345856 | 325992 | 343950 | 342787 | 340761 | | **fi** | 341732 | 349040 | 379830 | 378626 | 384889 | 357177 | 378158 | 341694 | 0 | 387478 | 358869 | 379862 | 352968 | 346820 | 334275 | 379729 | 358760 | 374737 | 331135 | 348559 | 348680 | 368528 | | **fr** | 358045 | 363614 | 396294 | 393468 | 403248 | 373152 | 394253 | 358012 | 387478 | 0 | 373625 | 385869 | 368817 | 361137 | 347699 | 388607 | 372387 | 388658 | 344139 | 363249 | 366474 | 383274 | | **hu** | 352763 | 358353 | 367091 | 366828 | 373706 | 349176 | 368203 | 352099 | 358869 | 373625 | 0 | 367937 | 361015 | 354872 | 343831 | 368387 | 369040 | 361652 | 340410 | 357466 | 361157 | 356426 | | **it** | 351669 | 357578 | 388495 | 381396 | 391389 | 361089 | 378076 | 351747 | 379862 | 385869 | 367937 | 0 | 360783 | 356001 | 341552 | 384018 | 365159 | 378841 | 337354 | 357562 | 358969 | 377635 | | **lt** | 348679 | 353232 | 360572 | 360907 | 368576 | 339899 | 360645 | 345417 | 352968 | 368817 | 361015 | 360783 | 0 | 350576 | 337339 | 362096 | 361497 | 357070 | 335581 | 351639 | 350916 | 349636 | | **lv** | 342721 | 347807 | 353801 | 353151 | 360047 | 336306 | 354126 | 339042 | 346820 | 361137 | 354872 | 356001 | 350576 | 0 | 336157 | 355791 | 358607 | 349590 | 329581 | 348689 | 346862 | 345016 | | **mt** | 351097 | 334353 | 342263 | 340294 | 348221 | 324695 | 340297 | 337302 | 334275 | 347699 | 343831 | 341552 | 337339 | 336157 | 0 | 341111 | 344764 | 335553 | 338137 | 335930 | 334491 | 335353 | | **nl** | 353942 | 355192 | 388250 | 377770 | 396284 | 360418 | 381188 | 350911 | 379729 | 388607 | 368387 | 384018 | 362096 | 355791 | 341111 | 0 | 369694 | 383913 | 339047 | 359126 | 360054 | 379771 | | **pl** | 355005 | 358357 | 368779 | 367080 | 372486 | 348450 | 367091 | 354329 | 358760 | 372387 | 369040 | 365159 | 361497 | 358607 | 344764 | 369694 | 0 | 357426 | 335243 | 352527 | 355534 | 353214 | | **pt** | 347925 | 351244 | 382576 | 381365 | 387170 | 361393 | 376443 | 345856 | 374737 | 388658 | 361652 | 378841 | 357070 | 349590 | 335553 | 383913 | 357426 | 0 | 333365 | 354784 | 352673 | 373392 | | **ro** | 351099 | 330447 | 340508 | 337562 | 342655 | 321233 | 337302 | 325992 | 331135 | 344139 | 340410 | 337354 | 335581 | 329581 | 338137 | 339047 | 335243 | 333365 | 0 | 332373 | 330329 | 331268 | | **sk** | 345572 | 346835 | 356890 | 355805 | 364959 | 338649 | 358745 | 343950 | 348559 | 363249 | 357466 | 357562 | 351639 | 348689 | 335930 | 359126 | 352527 | 354784 | 332373 | 0 | 348396 | 346855 | | **sl** | 346954 | 348411 | 357694 | 358700 | 363778 | 338195 | 357961 | 342787 | 348680 | 366474 | 361157 | 358969 | 350916 | 346862 | 334491 | 360054 | 355534 | 352673 | 330329 | 348396 | 0 | 347727 | | **sv** | 342927 | 346894 | 373510 | 376925 | 384569 | 352587 | 379462 | 340761 | 368528 | 383274 | 356426 | 377635 | 349636 | 345016 | 335353 | 379771 | 353214 | 373392 | 331268 | 346855 | 347727 | 0 | ## Dataset Creation ### Curation Rationale For details, check the corresponding [pages](https://opus.nlpl.eu/EMEA.php). ### Source Data <!-- #### Initial Data Collection and Normalization ddd --> #### Who are the source language producers? Every data of this corpora as been uploaded by [Tiedemann, Jorg](mailto:jorg.tiedemann@lingfil.uu.se) on [Opus](https://opus.nlpl.eu/EMEA.php). ### Personal and Sensitive Information The corpora is free of personal or sensitive information. ## Considerations for Using the Data ### Other Known Limitations The nature of the task introduce a variability in the quality of the target translations. ## Additional Information ### Dataset Curators __Hugging Face EMEA-V3__: Labrak Yanis, Dufour Richard (Not affiliated with the original corpus) __OPUS : Parallel Data, Tools and Interfaces in OPUS__: [Tiedemann, Jorg](mailto:jorg.tiedemann@lingfil.uu.se). <!-- ### Licensing Information ddd --> ### Citation Information Please cite the following paper when using this dataset. ```latex @inproceedings{tiedemann-2012-parallel, title = Parallel Data, Tools and Interfaces in OPUS, author = { Tiedemann, Jorg }, booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)", month = may, year = 2012, address = Istanbul, Turkey, publisher = European Language Resources Association (ELRA), url = http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf, pages = 2214--2218, abstract = This paper presents the current status of OPUS, a growing language resource of parallel corpora and related tools. The focus in OPUS is to provide freely available data sets in various formats together with basic annotation to be useful for applications in computational linguistics, translation studies and cross-linguistic corpus studies. In this paper, we report about new data sets and their features, additional annotation tools and models provided from the website and essential interfaces and on-line services included in the project., } ```

atasoglu/flickr8k-dataset

atasoglu

--- task_categories: - image-to-text - text-to-image language: - en pretty_name: flickr8k size_categories: - 1K<n<10K --- You must download the dataset files manually. You can visit [this](https://github.com/jbrownlee/Datasets/releases/tag/Flickr8k) page or run `download.sh` to get files. After, you can load dataset by referencing the directory: ```py import datasets ds = datasets.load_dataset("atasoglu/flickr8k-dataset", data_dir="data") print(ds) ``` ``` DatasetDict({ train: Dataset({ features: ['image_id', 'image_path', 'captions'], num_rows: 6000 }) test: Dataset({ features: ['image_id', 'image_path', 'captions'], num_rows: 1000 }) validation: Dataset({ features: ['image_id', 'image_path', 'captions'], num_rows: 1000 }) }) ``` I don't own the copyright of the images. Please [visit](https://forms.illinois.edu/sec/1713398) for more.

textvqa

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - cc-by-4.0 multilinguality: - monolingual pretty_name: TextVQA size_categories: - 10K<n<100K source_datasets: - original task_categories: - visual-question-answering task_ids: - visual-question-answering dataset_info: - config_name: train features: - name: image_id dtype: string - name: question_id dtype: int32 - name: question dtype: string - name: question_tokens sequence: string - name: image dtype: image - name: image_width dtype: int32 - name: image_height dtype: int32 - name: flickr_original_url dtype: string - name: flickr_300k_url dtype: string - name: answers sequence: string - name: image_classes sequence: string - name: set_name dtype: string splits: - name: train num_bytes: 21381310 num_examples: 34602 - name: validation num_bytes: 3077854 num_examples: 5000 - name: test num_bytes: 3025046 num_examples: 5734 download_size: 8070116310 dataset_size: 27484210 - config_name: val features: - name: image_id dtype: string - name: question_id dtype: int32 - name: question dtype: string - name: question_tokens sequence: string - name: image dtype: image - name: image_width dtype: int32 - name: image_height dtype: int32 - name: flickr_original_url dtype: string - name: flickr_300k_url dtype: string - name: answers sequence: string - name: image_classes sequence: string - name: set_name dtype: string splits: - name: train num_bytes: 21381310 num_examples: 34602 - name: validation num_bytes: 3077854 num_examples: 5000 - name: test num_bytes: 3025046 num_examples: 5734 download_size: 8070116310 dataset_size: 27484210 - config_name: test features: - name: image_id dtype: string - name: question_id dtype: int32 - name: question dtype: string - name: question_tokens sequence: string - name: image dtype: image - name: image_width dtype: int32 - name: image_height dtype: int32 - name: flickr_original_url dtype: string - name: flickr_300k_url dtype: string - name: answers sequence: string - name: image_classes sequence: string - name: set_name dtype: string splits: - name: train num_bytes: 21381310 num_examples: 34602 - name: validation num_bytes: 3077854 num_examples: 5000 - name: test num_bytes: 3025046 num_examples: 5734 download_size: 8070116310 dataset_size: 27484210 - config_name: textvqa features: - name: image_id dtype: string - name: question_id dtype: int32 - name: question dtype: string - name: question_tokens sequence: string - name: image dtype: image - name: image_width dtype: int32 - name: image_height dtype: int32 - name: flickr_original_url dtype: string - name: flickr_300k_url dtype: string - name: answers sequence: string - name: image_classes sequence: string - name: set_name dtype: string splits: - name: train num_bytes: 22073350 num_examples: 34602 - name: validation num_bytes: 3177854 num_examples: 5000 - name: test num_bytes: 3139726 num_examples: 5734 download_size: 8070116310 dataset_size: 28390930 --- # Dataset Card for TextVQA ## 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:** https://textvqa.org - **Repository:** https://github.com/facebookresearch/mmf - **Paper:** https://arxiv.org/abs/1904.08920 - **Leaderboard:** https://eval.ai/web/challenges/challenge-page/874/overview - **Point of Contact:** mailto:amanpreet@nyu.edu ### Dataset Summary TextVQA requires models to read and reason about text in images to answer questions about them. Specifically, models need to incorporate a new modality of text present in the images and reason over it to answer TextVQA questions. TextVQA dataset contains 45,336 questions over 28,408 images from the OpenImages dataset. The dataset uses [VQA accuracy](https://visualqa.org/evaluation.html) metric for evaluation. ### Supported Tasks and Leaderboards - `visual-question-answering`: The dataset can be used for Visual Question Answering tasks where given an image, you have to answer a question based on the image. For the TextVQA dataset specifically, the questions require reading and reasoning about the scene text in the given image. ### Languages The questions in the dataset are in English. ## Dataset Structure ### Data Instances A typical sample mainly contains the question in `question` field, an image object in `image` field, OpenImage image id in `image_id` and lot of other useful metadata. 10 answers per questions are contained in the `answers` attribute. For test set, 10 empty strings are contained in the `answers` field as the answers are not available for it. An example look like below: ``` {'question': 'who is this copyrighted by?', 'image_id': '00685bc495504d61', 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=384x512 at 0x276021C5EB8>, 'image_classes': ['Vehicle', 'Tower', 'Airplane', 'Aircraft'], 'flickr_original_url': 'https://farm2.staticflickr.com/5067/5620759429_4ea686e643_o.jpg', 'flickr_300k_url': 'https://c5.staticflickr.com/6/5067/5620759429_f43a649fb5_z.jpg', 'image_width': 786, 'image_height': 1024, 'answers': ['simon clancy', 'simon ciancy', 'simon clancy', 'simon clancy', 'the brand is bayard', 'simon clancy', 'simon clancy', 'simon clancy', 'simon clancy', 'simon clancy'], 'question_tokens': ['who', 'is', 'this', 'copyrighted', 'by'], 'question_id': 3, 'set_name': 'train' }, ``` ### Data Fields - `question`: string, the question that is being asked about the image - `image_id`: string, id of the image which is same as the OpenImages id - `image`: A `PIL.Image.Image` object containing the image about which the question is being asked. 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]`. - `image_classes`: List[str], The OpenImages classes to which the image belongs to. - `flickr_original_url`: string, URL to original image on Flickr - `flickr_300k_url`: string, URL to resized and low-resolution image on Flickr. - `image_width`: int, Width of the original image. - `image_height`: int, Height of the original image. - `question_tokens`: List[str], A pre-tokenized list of question. - `answers`: List[str], List of 10 human-annotated answers for the question. These 10 answers are collected from 10 different users. The list will contain empty strings for test set for which we don't have the answers. - `question_id`: int, Unique id of the question. - `set_name`: string, the set to which this question belongs. ### Data Splits There are three splits. `train`, `validation` and `test`. The `train` and `validation` sets share images with OpenImages `train` set and have their answers available. For test set answers, we return a list of ten empty strings. To get inference results and numbers on `test` set, you need to go to the [EvalAI leaderboard](https://eval.ai/web/challenges/challenge-page/874/overview) and upload your predictions there. Please see instructions at [https://textvqa.org/challenge/](https://textvqa.org/challenge/). ## Dataset Creation ### Curation Rationale From the paper: > Studies have shown that a dominant class of questions asked by visually impaired users on images of their surroundings involves reading text in the image. But today’s VQA models can not read! Our paper takes a first step towards addressing this problem. First, we introduce a new “TextVQA” dataset to facilitate progress on this important problem. Existing datasets either have a small proportion of questions about text (e.g., the VQA dataset) or are too small (e.g., the VizWiz dataset). TextVQA contains 45,336 questions on 28,408 images that require reasoning about text to answer. ### Source Data #### Initial Data Collection and Normalization The initial images were sourced from [OpenImages](https://storage.googleapis.com/openimages/web/factsfigures_v4.html) v4 dataset. These were first filtered based on automatic heuristics using an OCR system where we only took images which had at least some text detected in them. See [annotation process](#annotation-process) section to understand the next stages. #### Who are the source language producers? English Crowdsource Annotators ### Annotations #### Annotation process After the automatic process of filter the images that contain text, the images were manually verified using human annotators making sure that they had text. In next stage, the annotators were asked to write questions involving scene text for the image. For some images, in this stage, two questions were collected whenever possible. Finally, in the last stage, ten different human annotators answer the questions asked in last stage. #### Who are the annotators? Annotators are from one of the major data collection platforms such as AMT. Exact details are not mentioned in the paper. ### Personal and Sensitive Information The dataset does have similar PII issues as OpenImages and can at some times contain human faces, license plates, and documents. Using provided `image_classes` data field is one option to try to filter out some of this information. ## Considerations for Using the Data ### Social Impact of Dataset The paper helped realize the importance of scene text recognition and reasoning in general purpose machine learning applications and has led to many follow-up works including [TextCaps](https://textvqa.org/textcaps) and [TextOCR](https://textvqa.org/textocr). Similar datasets were introduced over the time which specifically focus on sight-disabled users such as [VizWiz](https://vizwiz.org) or focusing specifically on the same problem as TextVQA like [STVQA](https://paperswithcode.com/dataset/st-vqa), [DocVQA](https://arxiv.org/abs/2007.00398v3) and [OCRVQA](https://ocr-vqa.github.io/). Currently, most methods train on combined dataset from TextVQA and STVQA to achieve state-of-the-art performance on both datasets. ### Discussion of Biases Question-only bias where a model is able to answer the question without even looking at the image is discussed in the [paper](https://arxiv.org/abs/1904.08920) which was a major issue with original VQA dataset. The outlier bias in answers is prevented by collecting 10 different answers which are also taken in consideration by the evaluation metric. ### Other Known Limitations - The dataset is english only but does involve images with non-English latin characters so can involve some multi-lingual understanding. - The performance on the dataset is also dependent on the quality of OCR used as the OCR errors can directly lead to wrong answers. - The metric used for calculating accuracy is same as [VQA accuracy](https://visualqa.org/evaluation.html). This involves one-to-one matching with the given answers and thus doesn't allow analyzing one-off errors through OCR. ## Additional Information ### Dataset Curators - [Amanpreet Singh](https://github.com/apsdehal) - Vivek Natarjan - Meet Shah - Yu Jiang - Xinlei Chen - Dhruv Batra - Devi Parikh - Marcus Rohrbach ### Licensing Information CC by 4.0 ### Citation Information ```bibtex @inproceedings{singh2019towards, title={Towards VQA Models That Can Read}, author={Singh, Amanpreet and Natarjan, Vivek and Shah, Meet and Jiang, Yu and Chen, Xinlei and Batra, Dhruv and Parikh, Devi and Rohrbach, Marcus}, booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, pages={8317-8326}, year={2019} } ``` ### Contributions Thanks to [@apsdehal](https://github.com/apsdehal) for adding this dataset.

aharley/rvl_cdip

aharley

--- annotations_creators: - found language_creators: - found language: - en license: - other multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - extended|iit_cdip task_categories: - image-classification task_ids: - multi-class-image-classification paperswithcode_id: rvl-cdip pretty_name: RVL-CDIP viewer: false dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': letter '1': form '2': email '3': handwritten '4': advertisement '5': scientific report '6': scientific publication '7': specification '8': file folder '9': news article '10': budget '11': invoice '12': presentation '13': questionnaire '14': resume '15': memo splits: - name: train num_bytes: 38816373360 num_examples: 320000 - name: test num_bytes: 4863300853 num_examples: 40000 - name: validation num_bytes: 4868685208 num_examples: 40000 download_size: 38779484559 dataset_size: 48548359421 --- # Dataset Card for RVL-CDIP ## 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) ## Dataset Description - **Homepage:** [The RVL-CDIP Dataset](https://www.cs.cmu.edu/~aharley/rvl-cdip/) - **Repository:** - **Paper:** [Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval](https://arxiv.org/abs/1502.07058) - **Leaderboard:** [RVL-CDIP leaderboard](https://paperswithcode.com/dataset/rvl-cdip) - **Point of Contact:** [Adam W. Harley](mailto:aharley@cmu.edu) ### Dataset Summary The RVL-CDIP (Ryerson Vision Lab Complex Document Information Processing) dataset consists of 400,000 grayscale images in 16 classes, with 25,000 images per class. There are 320,000 training images, 40,000 validation images, and 40,000 test images. The images are sized so their largest dimension does not exceed 1000 pixels. ### Supported Tasks and Leaderboards - `image-classification`: The goal of this task is to classify a given document into one of 16 classes representing document types (letter, form, etc.). The leaderboard for this task is available [here](https://paperswithcode.com/sota/document-image-classification-on-rvl-cdip). ### Languages All the classes and documents use English as their primary language. ## Dataset Structure ### Data Instances A sample from the training set is provided below : ``` { 'image': <PIL.TiffImagePlugin.TiffImageFile image mode=L size=754x1000 at 0x7F9A5E92CA90>, 'label': 15 } ``` ### Data Fields - `image`: A `PIL.Image.Image` object containing a document. - `label`: an `int` classification label. <details> <summary>Class Label Mappings</summary> ```json { "0": "letter", "1": "form", "2": "email", "3": "handwritten", "4": "advertisement", "5": "scientific report", "6": "scientific publication", "7": "specification", "8": "file folder", "9": "news article", "10": "budget", "11": "invoice", "12": "presentation", "13": "questionnaire", "14": "resume", "15": "memo" } ``` </details> ### Data Splits | |train|test|validation| |----------|----:|----:|---------:| |# of examples|320000|40000|40000| The dataset was split in proportions similar to those of ImageNet. - 320000 images were used for training, - 40000 images for validation, and - 40000 images for testing. ## Dataset Creation ### Curation Rationale From the paper: > This work makes available a new labelled subset of the IIT-CDIP collection, containing 400,000 document images across 16 categories, useful for training new CNNs for document analysis. ### Source Data #### Initial Data Collection and Normalization The same as in the IIT-CDIP collection. #### Who are the source language producers? The same as in the IIT-CDIP collection. ### Annotations #### Annotation process The same as in the IIT-CDIP collection. #### Who are the annotators? The same as in the IIT-CDIP collection. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The dataset was curated by the authors - Adam W. Harley, Alex Ufkes, and Konstantinos G. Derpanis. ### Licensing Information RVL-CDIP is a subset of IIT-CDIP, which came from the [Legacy Tobacco Document Library](https://www.industrydocuments.ucsf.edu/tobacco/), for which license information can be found [here](https://www.industrydocuments.ucsf.edu/help/copyright/). ### Citation Information ```bibtex @inproceedings{harley2015icdar, title = {Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval}, author = {Adam W Harley and Alex Ufkes and Konstantinos G Derpanis}, booktitle = {International Conference on Document Analysis and Recognition ({ICDAR})}}, year = {2015} } ``` ### Contributions Thanks to [@dnaveenr](https://github.com/dnaveenr) for adding this dataset.

c3

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - zh license: - other multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - question-answering task_ids: - multiple-choice-qa paperswithcode_id: c3 pretty_name: C3 dataset_info: - config_name: mixed features: - name: documents sequence: string - name: document_id dtype: string - name: questions sequence: - name: question dtype: string - name: answer dtype: string - name: choice sequence: string splits: - name: train num_bytes: 2710513 num_examples: 3138 - name: test num_bytes: 891619 num_examples: 1045 - name: validation num_bytes: 910799 num_examples: 1046 download_size: 5481785 dataset_size: 4512931 - config_name: dialog features: - name: documents sequence: string - name: document_id dtype: string - name: questions sequence: - name: question dtype: string - name: answer dtype: string - name: choice sequence: string splits: - name: train num_bytes: 2039819 num_examples: 4885 - name: test num_bytes: 646995 num_examples: 1627 - name: validation num_bytes: 611146 num_examples: 1628 download_size: 4352392 dataset_size: 3297960 --- # Dataset Card for C3 ## 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:** [link]() - **Paper:** []() - **Leaderboard:** []() - **Point of Contact:** []() ### Dataset Summary Machine reading comprehension tasks require a machine reader to answer questions relevant to the given document. In this paper, we present the first free-form multiple-Choice Chinese machine reading Comprehension dataset (C^3), containing 13,369 documents (dialogues or more formally written mixed-genre texts) and their associated 19,577 multiple-choice free-form questions collected from Chinese-as-a-second-language examinations. We present a comprehensive analysis of the prior knowledge (i.e., linguistic, domain-specific, and general world knowledge) needed for these real-world problems. We implement rule-based and popular neural methods and find that there is still a significant performance gap between the best performing model (68.5%) and human readers (96.0%), especially on problems that require prior knowledge. We further study the effects of distractor plausibility and data augmentation based on translated relevant datasets for English on model performance. We expect C^3 to present great challenges to existing systems as answering 86.8% of questions requires both knowledge within and beyond the accompanying document, and we hope that C^3 can serve as a platform to study how to leverage various kinds of prior knowledge to better understand a given written or orally oriented text. ### 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 Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @article{sun2019investigating, title={Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension}, author={Sun, Kai and Yu, Dian and Yu, Dong and Cardie, Claire}, journal={Transactions of the Association for Computational Linguistics}, year={2020}, url={https://arxiv.org/abs/1904.09679v3} } ``` ### Contributions Thanks to [@Narsil](https://github.com/Narsil) for adding this dataset.

liar

--- annotations_creators: - expert-generated language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: [] paperswithcode_id: liar pretty_name: LIAR tags: - fake-news-detection dataset_info: features: - name: id dtype: string - name: label dtype: class_label: names: '0': 'false' '1': half-true '2': mostly-true '3': 'true' '4': barely-true '5': pants-fire - name: statement dtype: string - name: subject dtype: string - name: speaker dtype: string - name: job_title dtype: string - name: state_info dtype: string - name: party_affiliation dtype: string - name: barely_true_counts dtype: float32 - name: false_counts dtype: float32 - name: half_true_counts dtype: float32 - name: mostly_true_counts dtype: float32 - name: pants_on_fire_counts dtype: float32 - name: context dtype: string splits: - name: train num_bytes: 2730651 num_examples: 10269 - name: test num_bytes: 341414 num_examples: 1283 - name: validation num_bytes: 341592 num_examples: 1284 download_size: 1013571 dataset_size: 3413657 train-eval-index: - config: default task: text-classification task_id: multi_class_classification splits: train_split: train eval_split: test col_mapping: statement: 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:** https://sites.cs.ucsb.edu/~william/ - **Repository:** - **Paper:** https://arxiv.org/abs/1705.00648 - **Leaderboard:** - **Point of Contact:** ### Dataset Summary LIAR is a dataset for fake news detection with 12.8K human labeled short statements from politifact.com's API, and each statement is evaluated by a politifact.com editor for its truthfulness. The distribution of labels in the LIAR dataset is relatively well-balanced: except for 1,050 pants-fire cases, the instances for all other labels range from 2,063 to 2,638. In each case, the labeler provides a lengthy analysis report to ground each judgment. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English. ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@hugoabonizio](https://github.com/hugoabonizio) for adding this dataset.

THUDM/webglm-qa

THUDM

--- annotations_creators: [] language: - en multilinguality: - monolingual source_datasets: [] task_categories: - text-generation - question-answering pretty_name: WebGLM-QA size_categories: - 100M<n<200M --- # WebGLM-QA ## Dataset Description [WebGLM-QA](https://github.com/THUDM/WebGLM) is the dataset used to train the WebGLM generator module. It consists of 43,579 high-quality data samples for the train split, 1,000 for the validation split, and 400 for the test split. Refer to [our paper](https://arxiv.org/abs/2306.07906) for the data construction details. ## Dataset Structure To load the dataset, you can try the following code. ```python from datasets import load_dataset load_dataset("THUDM/webglm-qa") DatasetDict({ train: Dataset({ features: ['question', 'answer', 'references'], num_rows: 43579 }) test: Dataset({ features: ['question', 'answer', 'references'], num_rows: 400 }) validation: Dataset({ features: ['question', 'answer', 'references'], num_rows: 1000 }) }) ``` ```python next(iter(data["test"])) {'question': 'Just got my (Canadian) mortgage renewal notice telling me I have to chose Subsequent Payment Terms.', 'answer': "When renewing a mortgage in Canada, your lender must notify you in advance of the renewal date with your options for renewal terms[1][2]. Your mortgage will typically automatically renew or become in default if you don't take action[3]. Depending on your lender, you may be able to renew your mortgage as early as 6 months prior to your current mortgage term expiring[2][3][5]. RBC Royal Bank mortgage customers can choose Subsequent Payment Terms and be protected from an increase in interest rates for the interest type and term they selected[4].", 'references': ['When faced with a mortgage renewal, this simply means that your current contracted mortgage term is approaching its expiration date. You see, the majority of mortgages in Toronto and in general mortgages in Ontario are contracted for a finite period of time that is referred to as the “mortgage term”. This period tends to range from as little as a few months to as long as 10 years in Canada.', 'You can either proactively reach out to your lender several months prior to your renewal date to find out, but if you don’t, your lender must notify you in advance of the renewal date what your options are. If your mortgage does happen to be with a federally regulated bank, then they are obligated to send you an official renewal statement with no less than 21 days remaining on your current mortgage term. Also, if your lender chooses not to renew your mortgage then they must notify you in advance and provide you with enough time to refinance your mortgage elsewhere or to pay it off.', 'When it comes to mortgage renewals, if you do not take action your mortgage will in many cases either renew automatically or become in default. When your mortgage term approaches the end, your mortgage lender will typically offer you renewal terms that you may choose to accept, negotiate, or decline. Provided you continue to make your monthly mortgage payments on time, lenders will rarely not extend to you an offer to renew your mortgage, although this can happen without cause depending on your mortgage commitment and contract.', "When you renew your RBC Royal Bank mortgage at maturity, you are protected from an increase in interest rates, for the interest type and term you selected, in the 30-day period prior to your regularly scheduled renewal date. And, if the interest rate changes before your actual mortgage renewal date, you'll automatically receive the lower rate for the term and type you chose.", 'When renewing your mortgage in Canada, some lenders may allow you to renew your mortgage as early as 6 months prior to your current mortgage term expiring.']} ``` ## Data Fields * ``question``: a question raised by a user or individual related to a certain topic. * ``answer``: the generated response to the question. * ``references``: a list of quotes or snippets from sources used to generate the answer given. ## Data Splits We split the dataset into train, validation, and test. ## Citation Information Refer to https://github.com/THUDM/WebGLM.

lmsys/lmsys-chat-1m

lmsys

--- size_categories: - 1M<n<10M task_categories: - conversational extra_gated_prompt: You agree to the [LMSYS-Chat-1M Dataset License Agreement](https://huggingface.co/datasets/lmsys/lmsys-chat-1m#lmsys-chat-1m-dataset-license-agreement). extra_gated_fields: Name: text Email: text Affiliation: text Country: text extra_gated_button_content: I agree to the terms and conditions of the LMSYS-Chat-1M Dataset License Agreement. configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: conversation_id dtype: string - name: model dtype: string - name: conversation list: - name: content dtype: string - name: role dtype: string - name: turn dtype: int64 - name: language dtype: string - name: openai_moderation list: - name: categories struct: - name: harassment dtype: bool - name: harassment/threatening dtype: bool - name: hate dtype: bool - name: hate/threatening dtype: bool - name: self-harm dtype: bool - name: self-harm/instructions dtype: bool - name: self-harm/intent dtype: bool - name: sexual dtype: bool - name: sexual/minors dtype: bool - name: violence dtype: bool - name: violence/graphic dtype: bool - name: category_scores struct: - name: harassment dtype: float64 - name: harassment/threatening dtype: float64 - name: hate dtype: float64 - name: hate/threatening dtype: float64 - name: self-harm dtype: float64 - name: self-harm/instructions dtype: float64 - name: self-harm/intent dtype: float64 - name: sexual dtype: float64 - name: sexual/minors dtype: float64 - name: violence dtype: float64 - name: violence/graphic dtype: float64 - name: flagged dtype: bool - name: redacted dtype: bool splits: - name: train num_bytes: 2626438904 num_examples: 1000000 download_size: 1488850250 dataset_size: 2626438904 --- ## LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset This dataset contains one million real-world conversations with 25 state-of-the-art LLMs. It is collected from 210K unique IP addresses in the wild on the [Vicuna demo and Chatbot Arena website](https://chat.lmsys.org/) from April to August 2023. Each sample includes a conversation ID, model name, conversation text in OpenAI API JSON format, detected language tag, and OpenAI moderation API tag. User consent is obtained through the "Terms of use" section on the data collection website. To ensure the safe release of data, we have made our best efforts to remove all conversations that contain personally identifiable information (PII). In addition, we have included the OpenAI moderation API output for each message. However, we have chosen to keep unsafe conversations so that researchers can study the safety-related questions associated with LLM usage in real-world scenarios as well as the OpenAI moderation process. For more details, please refer to the paper: https://arxiv.org/abs/2309.11998 **Basic Statistics** | Key | Value | | --- | --- | | # Conversations | 1,000,000 | | # Models | 25 | | # Users | 210,479 | | # Languages | 154 | | Avg. # Turns per Sample | 2.0 | | Avg. # Tokens per Prompt | 69.5 | | Avg. # Tokens per Response | 214.5 | **PII Redaction** We partnered with the [OpaquePrompts](https://opaqueprompts.opaque.co/) team to redact person names in this dataset to protect user privacy. Names like "Mary" and "James" in a conversation will appear as "NAME_1" and "NAME_2". For example: ```json Raw: [ { "content": "Write me a bio. My Name is Mary I am a student who is currently a beginner free lancer. I worked with James in the past ..." }] Redacted: [ { "content": "Write me a bio. My Name is NAME_1 I am a student who is currently a beginner free lancer. I worked with NAME_2 in the past ..." }] ``` Each conversation includes a "redacted" field to indicate if it has been redacted. This process may impact data quality and occasionally lead to incorrect redactions. We are working on improving the redaction quality and will release improved versions in the future. If you want to access the raw conversation data, please fill out [the form](https://docs.google.com/forms/d/1PZw67e19l0W3oCiQOjzSyZvXfOemhg6LCY0XzVmOUx0/edit) with details about your intended use cases. ## Uniqueness and Potential Usage This dataset features large-scale real-world conversations with LLMs. We believe it will help the AI research community answer important questions around topics like: - Characteristics and distributions of real-world user prompts - AI safety and content moderation - Training instruction-following models - Improving and evaluating LLM evaluation methods - Model selection and request dispatching algorithms For more details, please refer to the paper: https://arxiv.org/abs/2309.11998 ## LMSYS-Chat-1M Dataset License Agreement This Agreement contains the terms and conditions that govern your access and use of the LMSYS-Chat-1M Dataset (as defined above). You may not use the LMSYS-Chat-1M Dataset if you do not accept this Agreement. By clicking to accept, accessing the LMSYS-Chat-1M Dataset, or both, you hereby agree to the terms of the Agreement. If you are agreeing to be bound by the Agreement on behalf of your employer or another entity, you represent and warrant that you have full legal authority to bind your employer or such entity to this Agreement. If you do not have the requisite authority, you may not accept the Agreement or access the LMSYS-Chat-1M Dataset on behalf of your employer or another entity. - Safety and Moderation: **This dataset contains unsafe conversations that may be perceived as offensive or unsettling.** User should apply appropriate filters and safety measures before utilizing this dataset for training dialogue agents. - Non-Endorsement: The views and opinions depicted in this dataset **do not reflect** the perspectives of the researchers or affiliated institutions engaged in the data collection process. - Legal Compliance: You are mandated to use it in adherence with all pertinent laws and regulations. - Model Specific Terms: When leveraging direct outputs of a specific model, users must adhere to its corresponding terms of use. - Non-Identification: You **must not** attempt to identify the identities of individuals or infer any sensitive personal data encompassed in this dataset. - Prohibited Transfers: You should not distribute, copy, disclose, assign, sublicense, embed, host, or otherwise transfer the dataset to any third party. - Right to Request Deletion: At any time, we may require you to delete all copies of the conversation dataset (in whole or in part) in your possession and control. You will promptly comply with any and all such requests. Upon our request, you shall provide us with written confirmation of your compliance with such requirement. - Termination: We may, at any time, for any reason or for no reason, terminate this Agreement, effective immediately upon notice to you. Upon termination, the license granted to you hereunder will immediately terminate, and you will immediately stop using the LMSYS-Chat-1M Dataset and destroy all copies of the LMSYS-Chat-1M Dataset and related materials in your possession or control. - Limitation of Liability: IN NO EVENT WILL WE BE LIABLE FOR ANY CONSEQUENTIAL, INCIDENTAL, EXEMPLARY, PUNITIVE, SPECIAL, OR INDIRECT DAMAGES (INCLUDING DAMAGES FOR LOSS OF PROFITS, BUSINESS INTERRUPTION, OR LOSS OF INFORMATION) ARISING OUT OF OR RELATING TO THIS AGREEMENT OR ITS SUBJECT MATTER, EVEN IF WE HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. Subject to your compliance with the terms and conditions of this Agreement, we grant to you, a limited, non-exclusive, non-transferable, non-sublicensable license to use the LMSYS-Chat-1M Dataset, including the conversation data and annotations, to research, develop, and improve software, algorithms, machine learning models, techniques, and technologies for both research and commercial purposes. ## Citation ``` @misc{zheng2023lmsyschat1m, title={LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset}, author={Lianmin Zheng and Wei-Lin Chiang and Ying Sheng and Tianle Li and Siyuan Zhuang and Zhanghao Wu and Yonghao Zhuang and Zhuohan Li and Zi Lin and Eric. P Xing and Joseph E. Gonzalez and Ion Stoica and Hao Zhang}, year={2023}, eprint={2309.11998}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

higgsfield/dsml_original

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" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

yuvalkirstain/pickapic_v2

yuvalkirstain

--- dataset_info: features: - name: are_different dtype: bool - name: best_image_uid dtype: string - name: caption dtype: string - name: created_at dtype: timestamp[ns] - name: has_label dtype: bool - name: image_0_uid dtype: string - name: image_0_url dtype: string - name: image_1_uid dtype: string - name: image_1_url dtype: string - name: jpg_0 dtype: binary - name: jpg_1 dtype: binary - name: label_0 dtype: float64 - name: label_1 dtype: float64 - name: model_0 dtype: string - name: model_1 dtype: string - name: ranking_id dtype: int64 - name: user_id dtype: int64 - name: num_example_per_prompt dtype: int64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 322022952127 num_examples: 959040 - name: validation num_bytes: 6339087542 num_examples: 20596 - name: test num_bytes: 6618429346 num_examples: 20716 - name: validation_unique num_bytes: 170578993 num_examples: 500 - name: test_unique num_bytes: 175368751 num_examples: 500 download_size: 15603769274 dataset_size: 335326416759 --- # Dataset Card for "pickapic_v2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

ccaligned_multilingual

--- annotations_creators: - no-annotation language_creators: - found language: - af - ak - am - ar - as - ay - az - be - bg - bm - bn - br - bs - ca - ceb - ckb - cs - cy - de - dv - el - eo - es - fa - ff - fi - fo - fr - fy - ga - gl - gn - gu - he - hi - hr - hu - id - ig - is - it - iu - ja - ka - kac - kg - kk - km - kn - ko - ku - ky - la - lg - li - ln - lo - lt - lv - mg - mi - mk - ml - mn - mr - ms - mt - my - ne - nl - 'no' - nso - ny - om - or - pa - pl - ps - pt - rm - ro - ru - rw - sc - sd - se - shn - si - sk - sl - sn - so - sq - sr - ss - st - su - sv - sw - syc - szl - ta - te - tg - th - ti - tl - tn - tr - ts - tt - ug - uk - ur - uz - ve - vi - war - wo - xh - yi - yo - zgh - zh - zu - zza license: - unknown multilinguality: - translation size_categories: - n<1K - 1K<n<10K - 10K<n<100K - 100K<n<1M - 1M<n<10M - 10M<n<100M source_datasets: - original task_categories: - other paperswithcode_id: ccaligned pretty_name: CCAligned dataset_info: - config_name: documents-zz_TR features: - name: Domain dtype: string - name: Source_URL dtype: string - name: Target_URL dtype: string - name: translation dtype: translation: languages: - en_XX - zz_TR splits: - name: train num_bytes: 641412 num_examples: 41 download_size: 125488 dataset_size: 641412 - config_name: sentences-zz_TR features: - name: translation dtype: translation: languages: - en_XX - zz_TR - name: LASER_similarity dtype: float32 splits: - name: train num_bytes: 4056 num_examples: 34 download_size: 1428 dataset_size: 4056 - config_name: documents-tz_MA features: - name: Domain dtype: string - name: Source_URL dtype: string - name: Target_URL dtype: string - name: translation dtype: translation: languages: - en_XX - tz_MA splits: - name: train num_bytes: 51782 num_examples: 4 download_size: 11996 dataset_size: 51782 - config_name: sentences-tz_MA features: - name: translation dtype: translation: languages: - en_XX - tz_MA - name: LASER_similarity dtype: float32 splits: - name: train num_bytes: 6256 num_examples: 33 download_size: 2420 dataset_size: 6256 - config_name: documents-ak_GH features: - name: Domain dtype: string - name: Source_URL dtype: string - name: Target_URL dtype: string - name: translation dtype: translation: languages: - en_XX - ak_GH splits: - name: train num_bytes: 10738312 num_examples: 249 download_size: 399236 dataset_size: 10738312 - config_name: sentences-ak_GH features: - name: translation dtype: translation: languages: - en_XX - ak_GH - name: LASER_similarity dtype: float32 splits: - name: train num_bytes: 50110 num_examples: 478 download_size: 17636 dataset_size: 50110 --- # Dataset Card for ccaligned_multilingual ## 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/cc-aligned/ - **Repository:** [Needs More Information] - **Paper:** https://www.aclweb.org/anthology/2020.emnlp-main.480.pdf - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary CCAligned consists of parallel or comparable web-document pairs in 137 languages aligned with English. These web-document pairs were constructed by performing language identification on raw web-documents, and ensuring corresponding language codes were corresponding in the URLs of web documents. This pattern matching approach yielded more than 100 million aligned documents paired with English. Recognizing that each English document was often aligned to mulitple documents in different target language, we can join on English documents to obtain aligned documents that directly pair two non-English documents (e.g., Arabic-French). This corpus was created from 68 Commoncrawl Snapshots. To load a language which isn't part of the config, all you need to do is specify the language code. You can find the valid languages in http://www.statmt.org/cc-aligned/ E.g. ``` dataset = load_dataset("ccaligned_multilingual", language_code="fr_XX", type="documents") ``` or ``` dataset = load_dataset("ccaligned_multilingual", language_code="fr_XX", type="sentences") ``` ### Supported Tasks and Leaderboards [Needs More Information] ### Languages The text in the dataset is in (137) multiple languages aligned with english. ## Dataset Structure ### Data Instances An instance of `documents` type for language `ak_GH`: ``` {'Domain': 'islamhouse.com', 'Source_URL': 'https://islamhouse.com/en/audios/373088/', 'Target_URL': 'https://islamhouse.com/ak/audios/373088/', 'translation': {'ak_GH': "Ntwatiaa / wɔabɔ no tɔfa wɔ mu no te ase ma Umrah - Arab kasa|Islamhouse.com|Follow us:|facebook|twitter|taepe|Titles All|Fie wibesite|kasa nyina|Buukuu edi adanse ma prente|Nhyehyɛmu|Nyim/sua Islam|Curriculums|Nyina ndeɛma|Nyina ndeɛma (295)|Buukuu/ nwoma (2)|sini / muuvi (31)|ɔdio (262)|Aɛn websideNew!|Kɔ wura kramosom mu seisei|Ebio|figa/kaasɛ|Farebae|AKAkan|Kratafa titriw|kasa interface( anyimu) : Akan|Kasa ma no mu-nsɛm : Arab kasa|ɔdio|Ntwatiaa / wɔabɔ no tɔfa wɔ mu no te ase ma Umrah|play|pause|stop|mute|unmute|max volume|Kasakyerɛ ni :|Farebae:|17 / 11 / 1432 , 15/10/2011|Nhyehyɛmu:|Jurisprudence/ Esum Nimdea|Som|Hajj na Umrah|Jurisprudence/ Esum Nimdea|Som|Hajj na Umrah|Mmira ma Hajj na Umrah|nkyerɛmu|kasamu /sɛntɛns ma te ase na Umrah wɔ ... mu no hann ma no Quran na Sunnah na te ase ma no nana na no kasamu /sɛntɛns ma bi ma no emerging yi adu obusuani|Akenkane we ye di ko kasa bi su (36)|Afar - Qafár afa|Akan|Amhari ne - አማርኛ|Arab kasa - عربي|Assamese - অসমীয়া|Bengali - বাংলা|Maldive - ދިވެހި|Greek - Ελληνικά|English ( brofo kasa) - English|Persian - فارسی|Fula - pulla|French - Français|Hausa - Hausa|Kurdish - كوردی سۆرانی|Uganda ne - Oluganda|Mandinka - Mandinko|Malayalam - മലയാളം|Nepali - नेपाली|Portuguese - Português|Russian - Русский|Sango - Sango|Sinhalese - සිංහල|Somali - Soomaali|Albania ne - Shqip|Swahili - Kiswahili|Telugu - తెలుగు ప్రజలు|Tajik - Тоҷикӣ|Thai - ไทย|Tagalog - Tagalog|Turkish - Türkçe|Uyghur - ئۇيغۇرچە|Urdu - اردو|Uzbeck ne - Ўзбек тили|Vietnamese - Việt Nam|Wolof - Wolof|Chine ne - 中文|Soma kɔ bi kyerɛ adwen kɔ wɛb ebusuapanin|Soma kɔ ne kɔ hom adamfo|Soma kɔ bi kyerɛ adwen kɔ wɛb ebusuapanin|Nsɔwso fael (1)|1|الموجز في فقه العمرة|MP3 14.7 MB|Enoumah ebatahu|Rituals/Esom ajomadie ewu Hajji mmire .. 1434 AH [01] no fapemso Enum|Fiidbak/ Ye hiya wu jun kyiri|Lenke de yɛe|kɔntakt yɛn|Aɛn webside|Qura'an Kro kronkrom|Balagh|wɔ mfinimfin Dowload faele|Yɛ atuu bra Islam mu afei|Tsin de yɛe ewu|Anaa bomu/combine hɛn melin liste|© Islamhouse Website/ Islam dan webi site|×|×|Yi mu kasa|", 'en_XX': 'SUMMARY in the jurisprudence of Umrah - Arabic - Abdul Aziz Bin Marzooq Al-Turaifi|Islamhouse.com|Follow us:|facebook|twitter|QuranEnc.com|HadeethEnc.com|Type|Titles All|Home Page|All Languages|Categories|Know about Islam|All items|All items (4057)|Books (701)|Articles (548)|Fatawa (370)|Videos (1853)|Audios (416)|Posters (98)|Greeting cards (22)|Favorites (25)|Applications (21)|Desktop Applications (3)|To convert to Islam now !|More|Figures|Sources|Curriculums|Our Services|QuranEnc.com|HadeethEnc.com|ENEnglish|Main Page|Interface Language : English|Language of the content : Arabic|Audios|تعريب عنوان المادة|SUMMARY in the jurisprudence of Umrah|play|pause|stop|mute|unmute|max volume|Lecturer : Abdul Aziz Bin Marzooq Al-Turaifi|Sources:|AlRaya Islamic Recoding in Riyadh|17 / 11 / 1432 , 15/10/2011|Categories:|Islamic Fiqh|Fiqh of Worship|Hajj and Umrah|Islamic Fiqh|Fiqh of Worship|Hajj and Umrah|Pilgrimage and Umrah|Description|SUMMARY in jurisprudence of Umrah: A statement of jurisprudence and Umrah in the light of the Quran and Sunnah and understanding of the Ancestors and the statement of some of the emerging issues related to them.|This page translated into (36)|Afar - Qafár afa|Akane - Akan|Amharic - አማርኛ|Arabic - عربي|Assamese - অসমীয়া|Bengali - বাংলা|Maldivi - ދިވެހި|Greek - Ελληνικά|English|Persian - فارسی|Fula - pulla|French - Français|Hausa - Hausa|kurdish - كوردی سۆرانی|Ugandan - Oluganda|Mandinka - Mandinko|Malayalam - മലയാളം|Nepali - नेपाली|Portuguese - Português|Russian - Русский|Sango - Yanga ti Sango|Sinhalese - සිංහල|Somali - Soomaali|Albanian - Shqip|Swahili - Kiswahili|Telugu - తెలుగు|Tajik - Тоҷикӣ|Thai - ไทย|Tagalog - Tagalog|Turkish - Türkçe|Uyghur - ئۇيغۇرچە|Urdu - اردو|Uzbek - Ўзбек тили|Vietnamese - Việt Nam|Wolof - Wolof|Chinese - 中文|Send a comment to Webmaster|Send to a friend?|Send a comment to Webmaster|Attachments (1)|1|الموجز في فقه العمرة|MP3 14.7 MB|The relevant Material|The rituals of the pilgrimage season .. 1434 AH [ 01] the fifth pillar|The Quality of the Accepted Hajj (Piligrimage) and Its Limitations|Easy Path to the Rules of the Rites of Hajj|A Call to the Pilgrims of the Scared House of Allah|More|feedback|Important links|Contact us|Privacy policy|Islam Q&A|Learning Arabic Language|About Us|Convert To Islam|Noble Quran encyclopedia|IslamHouse.com Reader|Encyclopedia of Translated Prophetic Hadiths|Our Services|The Quran|Balagh|Center for downloading files|To embrace Islam now...|Follow us through|Or join our mailing list.|© Islamhouse Website|×|×|Choose language|'}} ``` An instance of `sentences` type for language `ak_GH`: ``` {'LASER_similarity': 1.4549942016601562, 'translation': {'ak_GH': 'Salah (nyamefere) ye Mmerebeia', 'en_XX': 'What he dislikes when fasting (10)'}} ``` ### Data Fields For `documents` type: - `Domain`: a `string` feature containing the domain. - `Source_URL`: a `string` feature containing the source URL. - `Target_URL`: a `string` feature containing the target URL. - `translation`: a `dictionary` feature with two keys : - `en_XX`: a `string` feature containing the content in English. - <language_code>: a `string` feature containing the content in the `language_code` specified. For `sentences` type: - `LASER_similarity`: a `float32` feature representing the LASER similarity score. - `translation`: a `dictionary` feature with two keys : - `en_XX`: a `string` feature containing the content in English. - <language_code>: a `string` feature containing the content in the `language_code` specified. ### Data Splits Split sizes of some small configurations: | name |train| |----------|----:| |documents-zz_TR|41| |sentences-zz_TR|34| |documents-tz_MA|4| |sentences-tz_MA|33| |documents-ak_GH|249| |sentences-ak_GH|478| ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset. ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information ``` @inproceedings{elkishky_ccaligned_2020, author = {El-Kishky, Ahmed and Chaudhary, Vishrav and Guzm{\'a}n, Francisco and Koehn, Philipp}, booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020)}, month = {November}, title = {{CCAligned}: A Massive Collection of Cross-lingual Web-Document Pairs}, year = {2020} address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.emnlp-main.480", doi = "10.18653/v1/2020.emnlp-main.480", pages = "5960--5969" } ``` ### Contributions Thanks to [@gchhablani](https://github.com/gchhablani) for adding this dataset.

cardo14/Taylor_Swift_Embeddings

cardo14

--- license: mit ---

royboy0416/ko-alpaca

royboy0416

--- license: cc-by-4.0 task_categories: - text-generation language: - ko --- </b>Testing purpose only. Do not redistribute. </b> Original contents: [url] https://huggingface.co/datasets/tatsu-lab/alpaca Ko-alpaca: [url] https://github.com/Beomi/KoAlpaca/blob/main/ko_alpaca_data.json

BeIR/arguana-qrels

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.

bigbio/sem_eval_2024_task_2

bigbio

--- language: - en bigbio_language: - English multilinguality: monolingual pretty_name: SemEval 2024 Task 2 homepage: https://allenai.org/data/scitail bigbio_pubmed: false bigbio_public: true bigbio_tasks: - TEXTUAL_ENTAILMENT --- # Dataset Card for SemEval 2024 Task 2 ## Dataset Description - **Homepage:** https://sites.google.com/view/nli4ct/semeval-2024?authuser=0 - **Pubmed:** False - **Public:** True - **Tasks:** TE ## Dataset (Description copied from dataset homepage) The statements and evidence are generated by clinical domain experts, clinical trial organisers, and research oncologists from the Cancer Research UK Manchester Institute and the Digital Experimental Cancer Medicine Team. There are a total of (TBD) statements split evenly across the different sections and classes. ## Description Each Clinical Trial Report (CTR) consists of 4 sections: Eligibility criteria - A set of conditions for patients to be allowed to take part in the clinical trial Intervention - Information concerning the type, dosage, frequency, and duration of treatments being studied. Results - Number of participants in the trial, outcome measures, units, and the results. Adverse events - These are signs and symptoms observed in patients during the clinical trial. For this task, each CTR may contain 1-2 patient groups, called cohorts or arms. These groups may receive different treatments, or have different baseline characteristics. ## Citation Information ``` @article{, author = {}, title = {}, journal = {}, volume = {}, year = {}, url = {}, doi = {}, biburl = {}, bibsource = {} }

circa

--- annotations_creators: - expert-generated 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: - multi-class-classification paperswithcode_id: circa pretty_name: CIRCA tags: - question-answer-pair-classification dataset_info: features: - name: context dtype: string - name: question-X dtype: string - name: canquestion-X dtype: string - name: answer-Y dtype: string - name: judgements dtype: string - name: goldstandard1 dtype: class_label: names: '0': 'Yes' '1': 'No' '2': In the middle, neither yes nor no '3': Probably yes / sometimes yes '4': Probably no '5': Yes, subject to some conditions '6': Other '7': I am not sure how X will interpret Y’s answer - name: goldstandard2 dtype: class_label: names: '0': 'Yes' '1': 'No' '2': In the middle, neither yes nor no '3': Yes, subject to some conditions '4': Other splits: - name: train num_bytes: 8149489 num_examples: 34268 download_size: 7766077 dataset_size: 8149489 --- # Dataset Card for CIRCA ## 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:** [CIRCA homepage](https://github.com/google-research-datasets/circa) - **Repository:** [CIRCA repository](https://github.com/google-research-datasets/circa) - **Paper:** ["I’d rather just go to bed”: Understanding Indirect Answers](https://arxiv.org/abs/2010.03450) - **Point of Contact:** [Circa team, Google](circa@google.com) ### Dataset Summary The Circa (meaning ‘approximately’) dataset aims to help machine learning systems to solve the problem of interpreting indirect answers to polar questions. The dataset contains pairs of yes/no questions and indirect answers, together with annotations for the interpretation of the answer. The data is collected in 10 different social conversational situations (eg. food preferences of a friend). The following are the situational contexts for the dialogs in the data. ``` 1. X wants to know about Y’s food preferences 2. X wants to know what activities Y likes to do during weekends. 3. X wants to know what sorts of books Y likes to read. 4. Y has just moved into a neighbourhood and meets his/her new neighbour X. 5. X and Y are colleagues who are leaving work on a Friday at the same time. 6. X wants to know about Y's music preferences. 7. Y has just travelled from a different city to meet X. 8. X and Y are childhood neighbours who unexpectedly run into each other at a cafe. 9. Y has just told X that he/she is thinking of buying a flat in New York. 10. Y has just told X that he/she is considering switching his/her job. ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The text in the dataset is in English. ## Dataset Structure ### Data Instances The columns indicate: ``` 1. id : unique id for the question-answer pair 2. context : the social situation for the dialogue. One of 10 situations (see next section). Each situation is a dialogue between a person who poses the question (X) and the person who answers (Y). 3. question-X : the question posed by X 4. canquestion-X : a (automatically) rewritten version of question into declarative form Eg. Do you like Italian? --> I like Italian. See the paper for details. 5. answer-Y : the answer given by Y to X 6. judgements : the interpretations for the QA pair from 5 annotators. The value is a list of 5 strings, separated by the token ‘#’ 7. goldstandard1 : a gold standard majority judgement from the annotators. The value is the most common interpretation and picked by at least 3 (out of 5 annotators). When a majority judgement was not reached by the above criteria, the value is ‘NA’ 8. goldstandard2 : Here the labels ‘Probably yes / sometimes yes’, ‘Probably no', and 'I am not sure how X will interpret Y’s answer' are mapped respectively to ‘Yes’, ‘No’, and 'In the middle, neither yes nor no’ before computing the majority. Still the label must be given at least 3 times to become the majority choice. This method represents a less strict way of analyzing the interpretations. ``` ### Data Fields ``` id : 1 context : X wants to know about Y's food preferences. question-X : Are you vegan? canquestion-X : I am vegan. answer-Y : I love burgers too much. judgements : no#no#no#no#no goldstandard1 : no (label(s) used for the classification task) goldstandard2 : no (label(s) used for the classification task) ``` ### Data Splits There are no explicit train/val/test splits in this dataset. ## Dataset Creation ### Curation Rationale They revisited a pragmatic inference problem in dialog: Understanding indirect responses to questions. Humans can interpret ‘I’m starving.’ in response to ‘Hungry?’, even without direct cue words such as ‘yes’ and ‘no’. In dialog systems, allowing natural responses rather than closed vocabularies would be similarly beneficial. However, today’s systems are only as sensitive to these pragmatic moves as their language model allows. They create and release the first large-scale English language corpus ‘Circa’ with 34,268 (polar question, indirect answer) pairs to enable progress on this task. ### Source Data #### Initial Data Collection and Normalization The QA pairs and judgements were collected using crowd annotations in three phases. They recruited English native speakers. The full descriptions of the data collection and quality control are present in [EMNLP 2020 paper](https://arxiv.org/pdf/2010.03450.pdf). Below is a brief overview only. Phase 1: In the first phase, they collected questions only. They designed 10 imaginary social situations which give the annotator a context for the conversation. Examples are: ``` ‘asking a friend for food preferences’ ‘meeting your childhood neighbour’ ‘your friend wants to buy a flat in New York’ ``` Annotators were asked to suggest questions which could be asked in each situation, such that each question only requires a ‘yes’ or ‘no’ answer. 100 annotators produced 5 questions each for the 10 situations, resulting in 5000 questions. Phase 2: Here they focused on eliciting answers to the questions. They sampled 3500 questions from our previous set. For each question, They collected possible answers from 10 different annotators. The annotators were instructed to provide a natural phrase or a sentence as the answer and to avoid the use of explicit ‘yes’ and ‘no’ words. Phase 3: Finally the QA pairs (34,268) were given to a third set of annotators who were asked how the question seeker would likely interpret a particular answer. These annotators had the following options to choose from: ``` * 'Yes' * 'Probably yes' / 'sometimes yes' * 'Yes, subject to some conditions' * 'No' * 'Probably no' * 'In the middle, neither yes nor no' * 'I am not sure how X will interpret Y's answer' ``` #### Who are the source language producers? The rest of the data apart from 10 initial questions was collected using crowd workers. They ran pilots for each step of data collection, and perused their results manually to ensure clarity in guidelines, and quality of the data. They also recruited native English speakers, mostly from the USA, and a few from the UK and Canada. They did not collect any further information about the crowd workers. ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? The rest of the data apart from 10 initial questions was collected using crowd workers. They ran pilots for each step of data collection, and perused their results manually to ensure clarity in guidelines, and quality of the data. They also recruited native English speakers, mostly from the USA, and a few from the UK and Canada. They did not collect any further information about the crowd workers. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators This dataset is the work of Annie Louis, Dan Roth, and Filip Radlinski from Google LLC. ### Licensing Information This dataset was made available under the Creative Commons Attribution 4.0 License. A full copy of the license can be found at https://creativecommons.org/licenses/by-sa/4.0/e and link to the license webpage if available. ### Citation Information ``` @InProceedings{louis_emnlp2020, author = "Annie Louis and Dan Roth and Filip Radlinski", title = ""{I}'d rather just go to bed": {U}nderstanding {I}ndirect {A}nswers", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing", year = "2020", } ``` ### Contributions Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik) for adding this dataset.

CShorten/ML-ArXiv-Papers

CShorten

--- license: afl-3.0 --- This dataset contains the subset of ArXiv papers with the "cs.LG" tag to indicate the paper is about Machine Learning. The core dataset is filtered from the full ArXiv dataset hosted on Kaggle: https://www.kaggle.com/datasets/Cornell-University/arxiv. The original dataset contains roughly 2 million papers. This dataset contains roughly 100,000 papers following the category filtering. The dataset is maintained by with requests to the ArXiv API. The current iteration of the dataset only contains the title and abstract of the paper. The ArXiv dataset contains additional features that we may look to include in future releases. We have highlighted the top two features on the roadmap for integration: <ul> <li> <b>authors</b> </li> <li> <b>update_date</b> </li> <li> Submitter </li> <li> Comments </li> <li> Journal-ref </li> <li> doi </li> <li> report-no </li> <li> categories </li> <li> license </li> <li> versions </li> <li> authors_parsed </li> </ul>

BeIR/dbpedia-entity

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.

BeIR/fever

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.

phiyodr/coco2017

phiyodr

--- language: - en pretty_name: COCO2017 size_categories: - 100K<n<1M task_categories: - image-to-text task_ids: - image-captioning tags: - coco - image-captioning dataset_info: features: - name: license dtype: int64 - name: file_name dtype: string - name: coco_url dtype: string - name: height dtype: int64 - name: width dtype: int64 - name: date_captured dtype: string - name: flickr_url dtype: string - name: image_id dtype: int64 - name: ids sequence: int64 - name: captions sequence: string splits: - name: train num_bytes: 64026361 num_examples: 118287 - name: validation num_bytes: 2684731 num_examples: 5000 download_size: 30170127 dataset_size: 66711092 --- # coco2017 Image-text pairs from [MS COCO2017](https://cocodataset.org/#download). ## Data origin * Data originates from [cocodataset.org](http://images.cocodataset.org/annotations/annotations_trainval2017.zip) * While `coco-karpathy` uses a dense format (with several sentences and sendids per row), `coco-karpathy-long` uses a long format with one `sentence` (aka caption) and `sendid` per row. `coco-karpathy-long` uses the first five sentences and therefore is five times as long as `coco-karpathy`. * `phiyodr/coco2017`: One row corresponds one image with several sentences. * `phiyodr/coco2017-long`: One row correspond one sentence (aka caption). There are 5 rows (sometimes more) with the same image details. ## Format ```python DatasetDict({ train: Dataset({ features: ['license', 'file_name', 'coco_url', 'height', 'width', 'date_captured', 'flickr_url', 'image_id', 'ids', 'captions'], num_rows: 118287 }) validation: Dataset({ features: ['license', 'file_name', 'coco_url', 'height', 'width', 'date_captured', 'flickr_url', 'image_id', 'ids', 'captions'], num_rows: 5000 }) }) ``` ## Usage * Download image data and unzip ```bash cd PATH_TO_IMAGE_FOLDER wget http://images.cocodataset.org/zips/train2017.zip wget http://images.cocodataset.org/zips/val2017.zip #wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip # zip not needed: everything you need is in load_dataset("phiyodr/coco2017") unzip train2017.zip unzip val2017.zip ``` * Load dataset in Python ```python import os from datasets import load_dataset PATH_TO_IMAGE_FOLDER = "COCO2017" def create_full_path(example): """Create full path to image using `base_path` to COCO2017 folder.""" example["image_path"] = os.path.join(PATH_TO_IMAGE_FOLDER, example["filepath"], example["filename"]) return example dataset = load_dataset("phiyodr/coco2017") dataset = dataset.map(create_full_path) ```

argilla/news-summary

argilla

--- language: - en license: - cc-by-nc-4.0 size_categories: - 10K<n<100K source_datasets: - original task_categories: - summarization task_ids: - news-articles-summarization dataset_info: features: - name: text dtype: string - name: prediction list: - name: score dtype: float64 - name: text dtype: string - name: prediction_agent dtype: string - name: annotation dtype: 'null' - name: annotation_agent dtype: 'null' - name: id dtype: string - name: metadata dtype: 'null' - name: status dtype: string - name: event_timestamp dtype: timestamp[us] - name: metrics struct: - name: text_length dtype: int64 splits: - name: train num_bytes: 2563132.0446374374 num_examples: 1000 - name: test num_bytes: 52331466.955362566 num_examples: 20417 download_size: 33207109 dataset_size: 54894599.0 --- # Dataset Card for "news-summary" ## Dataset Description - **Homepage:** Kaggle Challenge - **Repository:** https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset?select=True.csv - **Paper:** N.A. - **Leaderboard:** N.A. - **Point of Contact:** N.A. ### Dataset Summary Officially it was supposed to be used for classification but, can you use this data set to summarize news articles? ### Languages english ### Citation Information Acknowledgements Ahmed H, Traore I, Saad S. “Detecting opinion spams and fake news using text classification”, Journal of Security and Privacy, Volume 1, Issue 1, Wiley, January/February 2018. Ahmed H, Traore I, Saad S. (2017) “Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques. In: Traore I., Woungang I., Awad A. (eds) Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments. ISDDC 2017. Lecture Notes in Computer Science, vol 10618. Springer, Cham (pp. 127-138). ### Contributions Thanks to [@davidberenstein1957](https://github.com/davidberenstein1957) for adding this dataset.

assin2

--- annotations_creators: - expert-generated language_creators: - found language: - pt license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - text-scoring - natural-language-inference - semantic-similarity-scoring paperswithcode_id: assin2 pretty_name: ASSIN 2 dataset_info: features: - name: sentence_pair_id dtype: int64 - name: premise dtype: string - name: hypothesis dtype: string - name: relatedness_score dtype: float32 - name: entailment_judgment dtype: class_label: names: '0': NONE '1': ENTAILMENT splits: - name: train num_bytes: 864816 num_examples: 6500 - name: test num_bytes: 339580 num_examples: 2448 - name: validation num_bytes: 66895 num_examples: 500 download_size: 2113646 dataset_size: 1271291 --- # Dataset Card for ASSIN 2 ## 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:** [ASSIN 2 homepage](https://sites.google.com/view/assin2) - **Repository:** [ASSIN 2 repository](https://sites.google.com/view/assin2) - **Paper:** [The ASSIN 2 shared task: a quick overview](https://drive.google.com/file/d/1ft1VU6xiVm-N58dfAp6FHWjQ4IvcXgqp/view) - **Point of Contact:** [Livy Real](mailto:livyreal@gmail.com) ### Dataset Summary The ASSIN 2 corpus is composed of rather simple sentences. Following the procedures of SemEval 2014 Task 1. The training and validation data are composed, respectively, of 6,500 and 500 sentence pairs in Brazilian Portuguese, annotated for entailment and semantic similarity. Semantic similarity values range from 1 to 5, and text entailment classes are either entailment or none. The test data are composed of approximately 3,000 sentence pairs with the same annotation. All data were manually annotated. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The language supported is Portuguese. ## Dataset Structure ### Data Instances An example from the ASSIN 2 dataset looks as follows: ``` { "entailment_judgment": 1, "hypothesis": "Uma criança está segurando uma pistola de água", "premise": "Uma criança risonha está segurando uma pistola de água e sendo espirrada com água", "relatedness_score": 4.5, "sentence_pair_id": 1 } ``` ### Data Fields - `sentence_pair_id`: a `int64` feature. - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `relatedness_score`: a `float32` feature. - `entailment_judgment`: a classification label, with possible values including `NONE`, `ENTAILMENT`. ### Data Splits The data is split into train, validation and test set. The split sizes are as follow: | Train | Val | Test | | ------ | ----- | ---- | | 6500 | 500 | 2448 | ## 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{real2020assin, title={The assin 2 shared task: a quick overview}, author={Real, Livy and Fonseca, Erick and Oliveira, Hugo Goncalo}, booktitle={International Conference on Computational Processing of the Portuguese Language}, pages={406--412}, year={2020}, organization={Springer} } ``` ### Contributions Thanks to [@jonatasgrosman](https://github.com/jonatasgrosman) for adding this dataset.

clarin-pl/kpwr-ner

clarin-pl

--- annotations_creators: - expert-generated language_creators: - found language: - pl license: - cc-by-3.0 multilinguality: - monolingual size_categories: - 18K - 10K<n<100K source_datasets: - original task_categories: - other task_ids: - named-entity-recognition pretty_name: KPWr-NER tags: - structure-prediction --- # KPWR-NER ## Description KPWR-NER is a part the Polish Corpus of Wrocław University of Technology (*Korpus Języka Polskiego Politechniki Wrocławskiej*). Its objective is named entity recognition for fine-grained categories of entities. It is the ‘n82’ version of the KPWr, which means that number of classes is restricted to 82 (originally 120). During corpus creation, texts were annotated by humans from various sources, covering many domains and genres. ## Tasks (input, output and metrics) Named entity recognition (NER) - tagging entities in text with their corresponding type. **Input** ('*tokens'* column): sequence of tokens **Output** ('*ner'* column): sequence of predicted tokens’ classes in BIO notation (82 possible classes, described in detail in the annotation guidelines) **Measurements**: F1-score (seqeval) **Example**: Input: `[‘Roboty’, ‘mają’, ‘kilkanaście’, ‘lat’, ‘i’, ‘pochodzą’, ‘z’, ‘USA’, ‘,’, ‘Wysokie’, ‘napięcie’, ‘jest’, ‘dużo’, ‘młodsze’, ‘,’, ‘powstało’, ‘w’, ‘Niemczech’, ‘.’]` Input (translated by DeepL): `Robots are more than a dozen years old and come from the US, High Voltage is much younger, having been developed in Germany.` Output: `[‘B-nam_pro_title’, ‘O’, ‘O’, ‘O’, ‘O’, ‘O’, ‘O’, ‘B-nam_loc_gpe_country’, ‘O’, ‘B-nam_pro_title’, ‘I-nam_pro_title’, ‘O’, ‘O’, ‘O’, ‘O’, ‘O’, ‘O’, ‘B-nam_loc_gpe_country’, ‘O’]` ## Data splits | Subset | Cardinality (sentences) | |--------|------------------------:| | train | 13959 | | dev | 0 | | test | 4323 | ## Class distribution (without "O" and "I-*") | Class | train | validation | test | |:----------------------------|--------:|-------------:|----------:| | B-nam_liv_person | 0.21910 | - | 0.21422 | | B-nam_loc_gpe_city | 0.10101 | - | 0.09865 | | B-nam_loc_gpe_country | 0.07467 | - | 0.08059 | | B-nam_org_institution | 0.05893 | - | 0.06005 | | B-nam_org_organization | 0.04448 | - | 0.05553 | | B-nam_org_group_team | 0.03492 | - | 0.03363 | | B-nam_adj_country | 0.03410 | - | 0.03747 | | B-nam_org_company | 0.02439 | - | 0.01716 | | B-nam_pro_media_periodic | 0.02250 | - | 0.01896 | | B-nam_fac_road | 0.01995 | - | 0.02144 | | B-nam_liv_god | 0.01934 | - | 0.00790 | | B-nam_org_nation | 0.01739 | - | 0.01828 | | B-nam_oth_tech | 0.01724 | - | 0.01377 | | B-nam_pro_media_web | 0.01709 | - | 0.00903 | | B-nam_fac_goe | 0.01596 | - | 0.01445 | | B-nam_eve_human | 0.01573 | - | 0.01761 | | B-nam_pro_title | 0.01558 | - | 0.00790 | | B-nam_pro_brand | 0.01543 | - | 0.01038 | | B-nam_org_political_party | 0.01264 | - | 0.01309 | | B-nam_loc_gpe_admin1 | 0.01219 | - | 0.01445 | | B-nam_eve_human_sport | 0.01174 | - | 0.01242 | | B-nam_pro_software | 0.01091 | - | 0.02190 | | B-nam_adj | 0.00963 | - | 0.01174 | | B-nam_loc_gpe_admin3 | 0.00888 | - | 0.01061 | | B-nam_pro_model_car | 0.00873 | - | 0.00587 | | B-nam_loc_hydronym_river | 0.00843 | - | 0.01151 | | B-nam_oth | 0.00775 | - | 0.00497 | | B-nam_pro_title_document | 0.00738 | - | 0.01986 | | B-nam_loc_astronomical | 0.00730 | - | - | | B-nam_oth_currency | 0.00723 | - | 0.01151 | | B-nam_adj_city | 0.00670 | - | 0.00948 | | B-nam_org_group_band | 0.00587 | - | 0.00429 | | B-nam_loc_gpe_admin2 | 0.00565 | - | 0.00813 | | B-nam_loc_gpe_district | 0.00504 | - | 0.00406 | | B-nam_loc_land_continent | 0.00459 | - | 0.00722 | | B-nam_loc_country_region | 0.00459 | - | 0.00090 | | B-nam_loc_land_mountain | 0.00414 | - | 0.00203 | | B-nam_pro_title_book | 0.00384 | - | 0.00248 | | B-nam_loc_historical_region | 0.00376 | - | 0.00497 | | B-nam_loc | 0.00361 | - | 0.00090 | | B-nam_eve | 0.00361 | - | 0.00181 | | B-nam_org_group | 0.00331 | - | 0.00406 | | B-nam_loc_land_island | 0.00331 | - | 0.00248 | | B-nam_pro_media_tv | 0.00316 | - | 0.00158 | | B-nam_liv_habitant | 0.00316 | - | 0.00158 | | B-nam_eve_human_cultural | 0.00316 | - | 0.00497 | | B-nam_pro_title_tv | 0.00309 | - | 0.00542 | | B-nam_oth_license | 0.00286 | - | 0.00248 | | B-nam_num_house | 0.00256 | - | 0.00248 | | B-nam_pro_title_treaty | 0.00248 | - | 0.00045 | | B-nam_fac_system | 0.00248 | - | 0.00587 | | B-nam_loc_gpe_subdivision | 0.00241 | - | 0.00587 | | B-nam_loc_land_region | 0.00226 | - | 0.00248 | | B-nam_pro_title_album | 0.00218 | - | 0.00158 | | B-nam_adj_person | 0.00203 | - | 0.00406 | | B-nam_fac_square | 0.00196 | - | 0.00135 | | B-nam_pro_award | 0.00188 | - | 0.00519 | | B-nam_eve_human_holiday | 0.00188 | - | 0.00203 | | B-nam_pro_title_song | 0.00166 | - | 0.00158 | | B-nam_pro_media_radio | 0.00151 | - | 0.00068 | | B-nam_pro_vehicle | 0.00151 | - | 0.00090 | | B-nam_oth_position | 0.00143 | - | 0.00226 | | B-nam_liv_animal | 0.00143 | - | 0.00248 | | B-nam_pro | 0.00135 | - | 0.00045 | | B-nam_oth_www | 0.00120 | - | 0.00451 | | B-nam_num_phone | 0.00120 | - | 0.00045 | | B-nam_pro_title_article | 0.00113 | - | - | | B-nam_oth_data_format | 0.00113 | - | 0.00226 | | B-nam_fac_bridge | 0.00105 | - | 0.00090 | | B-nam_liv_character | 0.00098 | - | - | | B-nam_pro_software_game | 0.00090 | - | 0.00068 | | B-nam_loc_hydronym_lake | 0.00090 | - | 0.00045 | | B-nam_loc_gpe_conurbation | 0.00090 | - | - | | B-nam_pro_media | 0.00083 | - | 0.00181 | | B-nam_loc_land | 0.00075 | - | 0.00045 | | B-nam_loc_land_peak | 0.00075 | - | - | | B-nam_fac_park | 0.00068 | - | 0.00226 | | B-nam_org_organization_sub | 0.00060 | - | 0.00068 | | B-nam_loc_hydronym | 0.00060 | - | 0.00023 | | B-nam_loc_hydronym_sea | 0.00045 | - | 0.00068 | | B-nam_loc_hydronym_ocean | 0.00045 | - | 0.00023 | | B-nam_fac_goe_stop | 0.00038 | - | 0.00090 | ## Citation ``` @inproceedings{broda-etal-2012-kpwr, title = "{KPW}r: Towards a Free Corpus of {P}olish", author = "Broda, Bartosz and Marci{\'n}czuk, Micha{\l} and Maziarz, Marek and Radziszewski, Adam and Wardy{\'n}ski, Adam", booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation ({LREC}'12)", month = may, year = "2012", address = "Istanbul, Turkey", publisher = "European Language Resources Association (ELRA)", url = "http://www.lrec-conf.org/proceedings/lrec2012/pdf/965_Paper.pdf", pages = "3218--3222", abstract = "This paper presents our efforts aimed at collecting and annotating a free Polish corpus. The corpus will serve for us as training and testing material for experiments with Machine Learning algorithms. As others may also benefit from the resource, we are going to release it under a Creative Commons licence, which is hoped to remove unnecessary usage restrictions, but also to facilitate reproduction of our experimental results. The corpus is being annotated with various types of linguistic entities: chunks and named entities, selected syntactic and semantic relations, word senses and anaphora. We report on the current state of the project as well as our ultimate goals.", } ``` ## License ``` Creative Commons Attribution 3.0 Unported Licence ``` ## Links [HuggingFace](https://huggingface.co/datasets/clarin-pl/kpwr-ner) [Source](https://clarin-pl.eu/index.php/kpwr-en/) [Paper](https://aclanthology.org/L12-1574/) [KPWr annotation guidelines](http://www.nlp.pwr.wroc.pl/narzedzia-i-zasoby/zasoby/kpwr-lemma/16-narzedzia-zasoby/79-wytyczne) [KPWr annotation guidelines - named entities](https://clarin-pl.eu/dspace/handle/11321/294) ## Examples ### Loading ```python from pprint import pprint from datasets import load_dataset dataset = load_dataset("clarin-pl/kpwr-ner") pprint(dataset['train'][0]) # {'lemmas': ['roborally', 'czy', 'wysoki', 'napięcie', '?'], # 'ner': [73, 160, 73, 151, 160], # 'orth': ['subst:sg:nom:n', # 'qub', # 'adj:sg:nom:n:pos', # 'subst:sg:nom:n', # 'interp'], # 'tokens': ['RoboRally', 'czy', 'Wysokie', 'napięcie', '?']} ``` ### Evaluation ```python import random from pprint import pprint from datasets import load_dataset, load_metric dataset = load_dataset("clarin-pl/kpwr-ner") references = dataset["test"]["ner"] # generate random predictions predictions = [ [ random.randrange(dataset["train"].features["ner"].feature.num_classes) for _ in range(len(labels)) ] for labels in references ] # transform to original names of labels references_named = [ [dataset["train"].features["ner"].feature.names[label] for label in labels] for labels in references ] predictions_named = [ [dataset["train"].features["ner"].feature.names[label] for label in labels] for labels in predictions ] # utilise seqeval to evaluate seqeval = load_metric("seqeval") seqeval_score = seqeval.compute( predictions=predictions_named, references=references_named, scheme="IOB2" ) pprint(seqeval_score, depth=1) # {'nam_adj': {...}, # 'nam_adj_city': {...}, # 'nam_adj_country': {...}, # 'nam_adj_person': {...}, # 'nam_eve': {...}, # 'nam_eve_human': {...}, # 'nam_eve_human_cultural': {...}, # 'nam_eve_human_holiday': {...}, # 'nam_eve_human_sport': {...}, # 'nam_fac_bridge': {...}, # 'nam_fac_goe': {...}, # 'nam_fac_goe_stop': {...}, # 'nam_fac_park': {...}, # 'nam_fac_road': {...}, # 'nam_fac_square': {...}, # 'nam_fac_system': {...}, # 'nam_liv_animal': {...}, # 'nam_liv_character': {...}, # 'nam_liv_god': {...}, # 'nam_liv_habitant': {...}, # 'nam_liv_person': {...}, # 'nam_loc': {...}, # 'nam_loc_astronomical': {...}, # 'nam_loc_country_region': {...}, # 'nam_loc_gpe_admin1': {...}, # 'nam_loc_gpe_admin2': {...}, # 'nam_loc_gpe_admin3': {...}, # 'nam_loc_gpe_city': {...}, # 'nam_loc_gpe_conurbation': {...}, # 'nam_loc_gpe_country': {...}, # 'nam_loc_gpe_district': {...}, # 'nam_loc_gpe_subdivision': {...}, # 'nam_loc_historical_region': {...}, # 'nam_loc_hydronym': {...}, # 'nam_loc_hydronym_lake': {...}, # 'nam_loc_hydronym_ocean': {...}, # 'nam_loc_hydronym_river': {...}, # 'nam_loc_hydronym_sea': {...}, # 'nam_loc_land': {...}, # 'nam_loc_land_continent': {...}, # 'nam_loc_land_island': {...}, # 'nam_loc_land_mountain': {...}, # 'nam_loc_land_peak': {...}, # 'nam_loc_land_region': {...}, # 'nam_num_house': {...}, # 'nam_num_phone': {...}, # 'nam_org_company': {...}, # 'nam_org_group': {...}, # 'nam_org_group_band': {...}, # 'nam_org_group_team': {...}, # 'nam_org_institution': {...}, # 'nam_org_nation': {...}, # 'nam_org_organization': {...}, # 'nam_org_organization_sub': {...}, # 'nam_org_political_party': {...}, # 'nam_oth': {...}, # 'nam_oth_currency': {...}, # 'nam_oth_data_format': {...}, # 'nam_oth_license': {...}, # 'nam_oth_position': {...}, # 'nam_oth_tech': {...}, # 'nam_oth_www': {...}, # 'nam_pro': {...}, # 'nam_pro_award': {...}, # 'nam_pro_brand': {...}, # 'nam_pro_media': {...}, # 'nam_pro_media_periodic': {...}, # 'nam_pro_media_radio': {...}, # 'nam_pro_media_tv': {...}, # 'nam_pro_media_web': {...}, # 'nam_pro_model_car': {...}, # 'nam_pro_software': {...}, # 'nam_pro_software_game': {...}, # 'nam_pro_title': {...}, # 'nam_pro_title_album': {...}, # 'nam_pro_title_article': {...}, # 'nam_pro_title_book': {...}, # 'nam_pro_title_document': {...}, # 'nam_pro_title_song': {...}, # 'nam_pro_title_treaty': {...}, # 'nam_pro_title_tv': {...}, # 'nam_pro_vehicle': {...}, # 'overall_accuracy': 0.006156203762418094, # 'overall_f1': 0.0009844258777797407, # 'overall_precision': 0.0005213624939842789, # 'overall_recall': 0.008803611738148984} ```

open-llm-leaderboard/details_rombodawg__LosslessMegaCoder-llama2-7b-mini

open-llm-leaderboard

--- pretty_name: Evaluation run of rombodawg/LosslessMegaCoder-llama2-7b-mini dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [rombodawg/LosslessMegaCoder-llama2-7b-mini](https://huggingface.co/rombodawg/LosslessMegaCoder-llama2-7b-mini)\ \ 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 2 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_rombodawg__LosslessMegaCoder-llama2-7b-mini\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-17T20:19:11.154530](https://huggingface.co/datasets/open-llm-leaderboard/details_rombodawg__LosslessMegaCoder-llama2-7b-mini/blob/main/results_2023-09-17T20-19-11.154530.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\": 0.0020973154362416107,\n\ \ \"em_stderr\": 0.00046850650303683207,\n \"f1\": 0.07344798657718166,\n\ \ \"f1_stderr\": 0.0015858347345547499,\n \"acc\": 0.41792920302087216,\n\ \ \"acc_stderr\": 0.010209653238354205\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.0020973154362416107,\n \"em_stderr\": 0.00046850650303683207,\n\ \ \"f1\": 0.07344798657718166,\n \"f1_stderr\": 0.0015858347345547499\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.09552691432903715,\n \ \ \"acc_stderr\": 0.008096605771155743\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7403314917127072,\n \"acc_stderr\": 0.012322700705552667\n\ \ }\n}\n```" repo_url: https://huggingface.co/rombodawg/LosslessMegaCoder-llama2-7b-mini 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_08_24T05_51_33.178388 path: - '**/details_harness|arc:challenge|25_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-08-24T05:51:33.178388.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_17T20_19_11.154530 path: - '**/details_harness|drop|3_2023-09-17T20-19-11.154530.parquet' - split: latest path: - '**/details_harness|drop|3_2023-09-17T20-19-11.154530.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_17T20_19_11.154530 path: - '**/details_harness|gsm8k|5_2023-09-17T20-19-11.154530.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-09-17T20-19-11.154530.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hellaswag|10_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-24T05:51:33.178388.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-management|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-24T05:51:33.178388.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_24T05_51_33.178388 path: - '**/details_harness|truthfulqa:mc|0_2023-08-24T05:51:33.178388.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-08-24T05:51:33.178388.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_17T20_19_11.154530 path: - '**/details_harness|winogrande|5_2023-09-17T20-19-11.154530.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-09-17T20-19-11.154530.parquet' - config_name: results data_files: - split: 2023_09_17T20_19_11.154530 path: - results_2023-09-17T20-19-11.154530.parquet - split: latest path: - results_2023-09-17T20-19-11.154530.parquet --- # Dataset Card for Evaluation run of rombodawg/LosslessMegaCoder-llama2-7b-mini ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/rombodawg/LosslessMegaCoder-llama2-7b-mini - **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 [rombodawg/LosslessMegaCoder-llama2-7b-mini](https://huggingface.co/rombodawg/LosslessMegaCoder-llama2-7b-mini) 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 2 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_rombodawg__LosslessMegaCoder-llama2-7b-mini", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-17T20:19:11.154530](https://huggingface.co/datasets/open-llm-leaderboard/details_rombodawg__LosslessMegaCoder-llama2-7b-mini/blob/main/results_2023-09-17T20-19-11.154530.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": 0.0020973154362416107, "em_stderr": 0.00046850650303683207, "f1": 0.07344798657718166, "f1_stderr": 0.0015858347345547499, "acc": 0.41792920302087216, "acc_stderr": 0.010209653238354205 }, "harness|drop|3": { "em": 0.0020973154362416107, "em_stderr": 0.00046850650303683207, "f1": 0.07344798657718166, "f1_stderr": 0.0015858347345547499 }, "harness|gsm8k|5": { "acc": 0.09552691432903715, "acc_stderr": 0.008096605771155743 }, "harness|winogrande|5": { "acc": 0.7403314917127072, "acc_stderr": 0.012322700705552667 } } ``` ### 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]

marsyas/gtzan

marsyas

--- pretty_name: GTZAN --- # Dataset Card for GTZAN ## Table of Contents - [Dataset Card for GTZAN](#dataset-card-for-gtzan) - [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) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [http://marsyas.info/downloads/datasets.html](http://marsyas.info/downloads/datasets.html) - **Paper:** [http://ismir2001.ismir.net/pdf/tzanetakis.pdf](http://ismir2001.ismir.net/pdf/tzanetakis.pdf) - **Point of Contact:** ### Dataset Summary GTZAN is a dataset for musical genre classification of audio signals. The dataset consists of 1,000 audio tracks, each of 30 seconds long. It contains 10 genres, each represented by 100 tracks. The tracks are all 22,050Hz Mono 16-bit audio files in WAV format. The genres are: blues, classical, country, disco, hiphop, jazz, metal, pop, reggae, and rock. ### Languages English ## Dataset Structure GTZAN is distributed as a single dataset without a predefined training and test split. The information below refers to the single `train` split that is assigned by default. ### Data Instances An example of GTZAN looks as follows: ```python { "file": "/path/to/cache/genres/blues/blues.00000.wav", "audio": { "path": "/path/to/cache/genres/blues/blues.00000.wav", "array": array( [ 0.00732422, 0.01660156, 0.00762939, ..., -0.05560303, -0.06106567, -0.06417847, ], dtype=float32, ), "sampling_rate": 22050, }, "genre": 0, } ``` ### Data Fields The types associated with each of the data fields is as follows: * `file`: a `string` feature. * `audio`: an `Audio` feature containing the `path` of the sound file, the decoded waveform in the `array` field, and the `sampling_rate`. * `genre`: a `ClassLabel` feature. ### 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 ``` @misc{tzanetakis_essl_cook_2001, author = "Tzanetakis, George and Essl, Georg and Cook, Perry", title = "Automatic Musical Genre Classification Of Audio Signals", url = "http://ismir2001.ismir.net/pdf/tzanetakis.pdf", publisher = "The International Society for Music Information Retrieval", year = "2001" } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun) for adding this dataset.

ostapeno/qa-openai_batched_icl0_clen512_maxD-1_maxC2500_0_cleaned_test

ostapeno

--- configs: - config_name: default data_files: - split: abstract_algebra path: data/abstract_algebra-* - split: college_biology path: data/college_biology-* - split: formal_logic path: data/formal_logic-* - split: global_facts path: data/global_facts-* - split: high_school_government_and_politics path: data/high_school_government_and_politics-* - split: high_school_physics path: data/high_school_physics-* - split: machine_learning path: data/machine_learning-* - split: prehistory path: data/prehistory-* - split: security_studies path: data/security_studies-* - split: sociology path: data/sociology-* dataset_info: features: - name: id dtype: string - name: context dtype: string - name: docno dtype: string - name: subject dtype: string - name: icl_examples dtype: 'null' - name: author_instr dtype: string - name: instruction dtype: string - name: response dtype: string splits: - name: abstract_algebra num_bytes: 12694041 num_examples: 4568 - name: college_biology num_bytes: 10912373 num_examples: 3883 - name: formal_logic num_bytes: 12452591 num_examples: 4565 - name: global_facts num_bytes: 11093658 num_examples: 3688 - name: high_school_government_and_politics num_bytes: 11050074 num_examples: 3863 - name: high_school_physics num_bytes: 10749938 num_examples: 3736 - name: machine_learning num_bytes: 15607996 num_examples: 5258 - name: prehistory num_bytes: 10368796 num_examples: 3712 - name: security_studies num_bytes: 9658325 num_examples: 3492 - name: sociology num_bytes: 9248599 num_examples: 3362 download_size: 16862867 dataset_size: 113836391 --- # Dataset Card for "qa-openai_batched_icl0_clen512_maxD-1_maxC2500_0_cleaned_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

itacasehold/itacasehold

itacasehold

--- license: apache-2.0 dataset_info: features: - name: url dtype: string - name: title dtype: string - name: doc dtype: string - name: summary dtype: string - name: materia dtype: string splits: - name: train num_bytes: 25541563 num_examples: 792 - name: validation num_bytes: 2932410 num_examples: 88 - name: test num_bytes: 6870636 num_examples: 221 download_size: 18051772 dataset_size: 35344609 task_categories: - summarization - text-classification language: - it tags: - legal pretty_name: ita_casehold size_categories: - n<1K --- # ITA-CASEHOLD ## Dataset Summary - This dataset contains the data used in the research of the ITA-CASEHOLD model, an extractive summarization model to extract holdings from Italian Legal Administrative documents. - The research paper titled 'Legal Holding Extraction from Italian Case Documents using Italian-LEGAL-BERT Text Summarization' is accepted for ICAIL 23. - It consists of 1101 pairs of judgments and their official holdings between the years 2019 and 2022 from the archives of [Italian Administrative Justice](https://www.giustizia-amministrativa.it/it/web/guest/massime). - The Administrative Justice system in Italy covers a wide range of issues, including public contracts, environmental protection, public services, immigration, taxes, and compensation for damages caused by the State ### Download the dataset To download the dataset, use the following lines: from datasets import load_dataset dataset = load_dataset("itacasehold/itacasehold") To split the train, test, and validation dataset, use dataset = load_dataset("itacasehold/itacasehold", split = 'train') ### Supported Tasks and Leaderboards Summarization, Multi-class Text classification ### Languages Italian ### Data Fields The dataset consists of - **URL**: link to the document - **Document**: The document - **Summary**: The holding of the document - **Materia** : Legal subject - **Title** : Title of the document ### Data Splits - **Train** : 792 - **Validatio** : 88 - **Test** : 221 ### Source Data The data is collected from ['Judicial Administration site'](https://www.giustizia-amministrativa.it/it/web/guest/massime). ### Social Impact of Dataset Legal holdings are considered the most essential part of a legal decision because they summarize it without going into the merits of the specific case, establish a legal principle and set a legal precedent. The holdings writing is carried out by legal experts who, starting from a judgment, set out the applied principle of law in a clear, precise, and concise manner. We approached the problem of extracting legal holdings as an Extractive text summarization task. This Dataset addresses the Legal holding Extraction topic and so far the first and the only one present in the Italian language. This dataset contributes to Summarization in the Italian language and Summarization tasks in Legal domains. Apart from this, the Dataset can also be used as a multi-class text classification task utilizing legal subjects. ### Dataset Limitation This Dataset specifically focuses on the Italian Legal domain, and it is only in Italian. The documents are only from the period of 2019-2022. ## Additional Information ### Dataset Curators The Dataset was curated by researchers from Scoula Superiore Sant'Anna as a part of the project ['Guistizia Agile (Agile Justice)'](https://www.unitus.it/it/unitus/mappatura-della-ricerca/articolo/giustizia-agile) funded by the Italian Ministry of Justice. ### Licensing Information The data sets are distributed under the `Apache 2.0` License. More information about the terms of use of the original data sets is listed [here](https://www.apache.org/licenses/LICENSE-2.0). ### Citation Information If you use this dataset then, please, cite the following paper: Legal Holding Extraction from Italian Case Documents using Italian-LEGAL-BERT Text Summarization. The citation will be added soon.

nvidia/HelpSteer

nvidia

--- license: cc-by-4.0 language: - en pretty_name: Helpfulness SteerLM Dataset size_categories: - 10K<n<100K tags: - human-feedback dataset_info: features: - name: prompt dtype: string - name: response dtype: string - name: helpfulness dtype: int32 - name: correctness dtype: int32 - name: coherence dtype: int32 - name: complexity dtype: int32 - name: verbosity dtype: int32 splits: - name: train num_examples: 35331 - name: validation num_examples: 1789 --- # HelpSteer: Helpfulness SteerLM Dataset HelpSteer is an open-source Helpfulness Dataset (CC-BY-4.0) that supports aligning models to become more helpful, factually correct and coherent, while being adjustable in terms of the complexity and verbosity of its responses. Leveraging this dataset and SteerLM, we train a Llama 2 70B to reach **7.54** on MT Bench, the highest among models trained on open-source datasets based on [MT Bench Leaderboard](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard) as of 15 Nov 2023. This model is available on HF at [Llama2-70B-SteerLM-Chat](https://huggingface.co/nvidia/Llama2-70B-SteerLM-Chat). Try this model instantly for free hosted by us at [NVIDIA AI Playground](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/ai-foundation/models/llama2-70b-steerlm). You can use this in the provided UI or through a limited access API (up to 10, 000 requests within 30 days). If you would need more requests, we demonstrate how you can set up an inference server at [Llama2-70B-SteerLM-Chat model page on HF](https://huggingface.co/nvidia/Llama2-70B-SteerLM-Chat) <img src="https://huggingface.co/datasets/nvidia/HelpSteer/resolve/main/mtbench_categories.png" alt="MT Bench Categories" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> HelpSteer Paper : [HelpSteer: Multi-attribute Helpfulness Dataset for SteerLM](http://arxiv.org/abs/2311.09528) SteerLM Paper: [SteerLM: Attribute Conditioned SFT as an (User-Steerable) Alternative to RLHF](https://arxiv.org/abs/2310.05344) ## Dataset Description HelpSteer contains 37, 120 samples, each containing a prompt, a response as well as five human-annotated attributes of the response, each ranging between 0 and 4 where higher means better for each attribute. These attributes are: 1. **Helpfulness**: Overall helpfulness of the response to the prompt. 2. **Correctness**: Inclusion of all pertinent facts without errors. 3. **Coherence**: Consistency and clarity of expression. 4. **Complexity**: Intellectual depth required to write response (i.e. whether the response can be written by anyone with basic language competency or requires deep domain expertise). 5. **Verbosity**: Amount of detail included in the response, relative to what is asked for in the prompt. ## Example ```json { "prompt": "What are the three most important things to consider when deciding what technology to use to build an assist device to help an elderly person with basic needs?", "response": "To build an assistive device to help an elderly person with basic needs, one must consider three crucial things: safety, compatibility, and ease of use. Safety is paramount, as the device must not cause harm to the user. Compatibility with the user's environment and other devices is also essential. Finally, the device must be simple enough for the elderly person to operate.", "helpfulness": 3, "correctness": 4, "coherence": 4, "complexity": 2, "verbosity": 2 } ``` ## Using the Huggingface Datasets ```python from datasets import load_dataset ds = load_dataset("nvidia/HelpSteer") train = ds['train'] # len(train) = 35331 (95%) val = ds['validation'] # len(val) = 1789 (5%) ``` ## Source 1. Prompts are collected based on a mixture of template-generated (mainly for prompt involving long reference text) and human generated by Scale AI. These prompts relate to the tasks of Rewrite, Summarization, Classification, Extraction, Closed Question Answering, Open Question Answering, Generation and Brainstorming. 2. Responses are generated by an early version of an inhouse LLM. We generate up to 4 responses per prompts using sample techniques to give diverse yet reasonable responses. 3. Annotations of various attributes were done by Scale AI. Annotators rated each response on a Likert 5 scale (between 0 and 4) for each attribute (helpfulness, correctness, coherence, complexity and verbosity). ## Annotation methodology (short) 1. We engaged a select group of contractors via Scale AI. These contractors were provided with comprehensive guidelines that defined each attribute and the criteria for every rating level, together with some annotated examples. These guidelines and examples are detailed in the Appendix of the accompanying paper. 2. The annotation process involved approximately 200 U.S.-based human annotators. Candidates first underwent preliminary assignments, including assessments of English proficiency, to determine eligibility for working on the project. Subsequently, they participated in an introductory training course on the task which ended with a test that involved annotating 35 sample responses. This process ensured not only a thorough understanding of the task requirements but also the delivery of high-quality annotations. 3. Post-annotations, Scale AI performed extensive quality assurance, with each annotation reaching a minimum of two human reviews in addition to automated checks. After receiving the annotations from Scale AI, we conducted our independent quality assurance to make sure that the quality of the annotations was up to our expectations. As a result, some annotations were filtered away to retain only 37, 120 samples. ## Ethical statement Annotators for the dataset were contracted through Scale AI. Scale AI engages the Anker Methodology, GISC Impact Sourcing Standard, and UN Sustainable Development Goals to provide a fair and competitive pay. The specific pay is calculated based on many factors, including the specific project, the specialized skillset and expertise required, regional costs of living and then transparently listed on Scale AI platform. Scale AI also provides multiple channels for questions and support, including 24/7 support teams, community discussion channels with specially trained moderators, and a “speak up” hotline where contractors can report concerns anonymously. Worker concerns can be submitted to and are reviewed by our Remotasks support team, and pay disputes are reviewed by support specialists trained in this area. ## Contact E-Mail: [Zhilin Wang](mailto:zhilinw@nvidia.com) ## Citation If you find this dataset useful, please cite the following works ```bibtex @misc{wang2023helpsteer, title={HelpSteer: Multi-attribute Helpfulness Dataset for SteerLM}, author={Zhilin Wang and Yi Dong and Jiaqi Zeng and Virginia Adams and Makesh Narsimhan Sreedhar and Daniel Egert and Olivier Delalleau and Jane Polak Scowcroft and Neel Kant and Aidan Swope and Oleksii Kuchaiev}, year={2023}, eprint={2311.09528}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ```bibtex @misc{dong2023steerlm, title={SteerLM: Attribute Conditioned SFT as an (User-Steerable) Alternative to RLHF}, author={Yi Dong and Zhilin Wang and Makesh Narsimhan Sreedhar and Xianchao Wu and Oleksii Kuchaiev}, year={2023}, eprint={2310.05344}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

PKU-Alignment/PKU-SafeRLHF-10K

PKU-Alignment

--- license: cc-by-nc-4.0 task_categories: - text-generation language: - en tags: - safe - safety - ai-safety - llm - lm - human-feedback - rlhf - safe-rlhf size_categories: - 10K<n<100K --- ## Paper You can find more information in our paper. - **Dataset Paper:** <https://arxiv.org/abs/2307.04657>

lewtun/dog_food

lewtun

--- annotations_creators: - found language_creators: - found language: - en license: - unknown multilinguality: - monolingual pretty_name: Dog vs Food Dataset size_categories: - 1K<n<10K source_datasets: - original task_categories: - image-classification task_ids: - multi-class-image-classification --- # Dataset Card for the Dog 🐶 vs. Food 🍔 (a.k.a. Dog Food) Dataset ## 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/qw2243c/Image-Recognition-Dogs-Fried-Chicken-or-Blueberry-Muffins- - **Repository:** : https://github.com/qw2243c/Image-Recognition-Dogs-Fried-Chicken-or-Blueberry-Muffins- - **Paper:** : N/A - **Leaderboard:**: N/A - **Point of Contact:**: @sasha ### Dataset Summary This is a dataset for multiclass image classification, between 'dog', 'chicken', and 'muffin' classes. The 'dog' class contains images of dogs that look like fried chicken and some that look like images of muffins, while the 'chicken' and 'muffin' classes contains images of (you guessed it) fried chicken and muffins 😋 ### Supported Tasks and Leaderboards TBC ### Languages The labels are in English (['dog', 'chicken', 'muffin']) ## Dataset Structure ### Data Instances A sample from the training set is provided below: ``` { {'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=300x470 at 0x7F176094EF28>, 'label': 0} } ``` ### Data Fields - img: A `PIL.JpegImageFile` object containing the 300x470. 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]` - label: 0-1 with the following correspondence 0 dog 1 food ### Data Splits Train (1875 images) and Test (625 images) ## Dataset Creation ### Curation Rationale N/A ### Source Data #### Initial Data Collection and Normalization This dataset was taken from the [qw2243c/Image-Recognition-Dogs-Fried-Chicken-or-Blueberry-Muffins?](https://github.com/qw2243c/Image-Recognition-Dogs-Fried-Chicken-or-Blueberry-Muffins-) Github repository and randomly splitting 25% of the data for validation. ### Annotations #### Annotation process This data was scraped from the internet and annotated based on the query words. ### Personal and Sensitive Information N/A ## Considerations for Using the Data ### Social Impact of Dataset N/A ### Discussion of Biases This dataset is balanced -- it has an equal number of images of dogs (1000) compared to chicken (1000 and muffin (1000). This should be taken into account when evaluating models. ### Other Known Limitations N/A ## Additional Information ### Dataset Curators This dataset was created by @lanceyjt, @yl3829, @wesleytao, @qw2243c and @asyouhaveknown ### Licensing Information No information is indicated on the original [github repository](https://github.com/qw2243c/Image-Recognition-Dogs-Fried-Chicken-or-Blueberry-Muffins-). ### Citation Information N/A ### Contributions Thanks to [@lewtun](https://github.com/lewtun) for adding this dataset.

CM/codexglue_codetrans

CM

--- dataset_info: features: - name: id dtype: int32 - name: java dtype: string - name: cs dtype: string splits: - name: train num_bytes: 4372641 num_examples: 10300 - name: validation num_bytes: 226407 num_examples: 500 - name: test num_bytes: 418587 num_examples: 1000 download_size: 0 dataset_size: 5017635 --- # Dataset Card for "codexglue_codetrans" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

crime_and_punish

--- language: - en paperswithcode_id: null pretty_name: CrimeAndPunish dataset_info: features: - name: line dtype: string splits: - name: train num_bytes: 1270540 num_examples: 21969 download_size: 1201735 dataset_size: 1270540 --- # Dataset Card for "crime_and_punish" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://www.gutenberg.org/files/2554/2554-h/2554-h.htm](https://www.gutenberg.org/files/2554/2554-h/2554-h.htm) - **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:** 1.21 MB - **Size of the generated dataset:** 1.27 MB - **Total amount of disk used:** 2.47 MB ### Dataset Summary ### 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 #### crime-and-punish - **Size of downloaded dataset files:** 1.21 MB - **Size of the generated dataset:** 1.27 MB - **Total amount of disk used:** 2.47 MB An example of 'train' looks as follows. ``` { "line": "CRIME AND PUNISHMENT\n" } ``` ### Data Fields The data fields are the same among all splits. #### crime-and-punish - `line`: a `string` feature. ### Data Splits | name |train| |----------------|----:| |crime-and-punish|21969| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

open-llm-leaderboard/details_GOAT-AI__GOAT-7B-Community

open-llm-leaderboard

--- pretty_name: Evaluation run of GOAT-AI/GOAT-7B-Community dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [GOAT-AI/GOAT-7B-Community](https://huggingface.co/GOAT-AI/GOAT-7B-Community)\ \ 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 2 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_GOAT-AI__GOAT-7B-Community\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-22T17:14:52.967997](https://huggingface.co/datasets/open-llm-leaderboard/details_GOAT-AI__GOAT-7B-Community/blob/main/results_2023-09-22T17-14-52.967997.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\": 0.005243288590604027,\n\ \ \"em_stderr\": 0.0007396052260778031,\n \"f1\": 0.06909395973154382,\n\ \ \"f1_stderr\": 0.0015832414439852427,\n \"acc\": 0.3838492484021702,\n\ \ \"acc_stderr\": 0.009135888573374731\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.005243288590604027,\n \"em_stderr\": 0.0007396052260778031,\n\ \ \"f1\": 0.06909395973154382,\n \"f1_stderr\": 0.0015832414439852427\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.04473085670962851,\n \ \ \"acc_stderr\": 0.005693886131407048\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7229676400947119,\n \"acc_stderr\": 0.012577891015342414\n\ \ }\n}\n```" repo_url: https://huggingface.co/GOAT-AI/GOAT-7B-Community 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_07_25T12_51_32.230763 path: - '**/details_harness|arc:challenge|25_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-07-25T12:51:32.230763.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_22T17_14_52.967997 path: - '**/details_harness|drop|3_2023-09-22T17-14-52.967997.parquet' - split: latest path: - '**/details_harness|drop|3_2023-09-22T17-14-52.967997.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_22T17_14_52.967997 path: - '**/details_harness|gsm8k|5_2023-09-22T17-14-52.967997.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-09-22T17-14-52.967997.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hellaswag|10_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-25T12:51:32.230763.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-management|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-virology|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-25T12:51:32.230763.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_25T12_51_32.230763 path: - '**/details_harness|truthfulqa:mc|0_2023-07-25T12:51:32.230763.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-07-25T12:51:32.230763.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_22T17_14_52.967997 path: - '**/details_harness|winogrande|5_2023-09-22T17-14-52.967997.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-09-22T17-14-52.967997.parquet' - config_name: results data_files: - split: 2023_07_25T12_51_32.230763 path: - results_2023-07-25T12:51:32.230763.parquet - split: 2023_09_22T17_14_52.967997 path: - results_2023-09-22T17-14-52.967997.parquet - split: latest path: - results_2023-09-22T17-14-52.967997.parquet --- # Dataset Card for Evaluation run of GOAT-AI/GOAT-7B-Community ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/GOAT-AI/GOAT-7B-Community - **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 [GOAT-AI/GOAT-7B-Community](https://huggingface.co/GOAT-AI/GOAT-7B-Community) 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 2 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_GOAT-AI__GOAT-7B-Community", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-22T17:14:52.967997](https://huggingface.co/datasets/open-llm-leaderboard/details_GOAT-AI__GOAT-7B-Community/blob/main/results_2023-09-22T17-14-52.967997.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": 0.005243288590604027, "em_stderr": 0.0007396052260778031, "f1": 0.06909395973154382, "f1_stderr": 0.0015832414439852427, "acc": 0.3838492484021702, "acc_stderr": 0.009135888573374731 }, "harness|drop|3": { "em": 0.005243288590604027, "em_stderr": 0.0007396052260778031, "f1": 0.06909395973154382, "f1_stderr": 0.0015832414439852427 }, "harness|gsm8k|5": { "acc": 0.04473085670962851, "acc_stderr": 0.005693886131407048 }, "harness|winogrande|5": { "acc": 0.7229676400947119, "acc_stderr": 0.012577891015342414 } } ``` ### 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]

google/MusicCaps

google

--- license: - cc-by-sa-4.0 converted_from: kaggle kaggle_id: googleai/musiccaps task_categories: - text-to-speech language: - en --- # Dataset Card for MusicCaps ## 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://kaggle.com/datasets/googleai/musiccaps - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The MusicCaps dataset contains **5,521 music examples, each of which is labeled with an English *aspect list* and a *free text caption* written by musicians**. An aspect list is for example *"pop, tinny wide hi hats, mellow piano melody, high pitched female vocal melody, sustained pulsating synth lead"*, while the caption consists of multiple sentences about the music, e.g., *"A low sounding male voice is rapping over a fast paced drums playing a reggaeton beat along with a bass. Something like a guitar is playing the melody along. This recording is of poor audio-quality. In the background a laughter can be noticed. This song may be playing in a bar."* The text is solely focused on describing *how* the music sounds, not the metadata like the artist name. The labeled examples are 10s music clips from the [**AudioSet**](https://research.google.com/audioset/) dataset (2,858 from the eval and 2,663 from the train split). Please cite the corresponding paper, when using this dataset: http://arxiv.org/abs/2301.11325 (DOI: `10.48550/arXiv.2301.11325`) ### Dataset Usage The published dataset takes the form of a `.csv` file that contains the ID of YouTube videos and their start/end stamps. In order to use this dataset, one must download the corresponding YouTube videos and chunk them according to the start/end times. The following repository has an example script and notebook to load the clips. The notebook also includes a Gradio demo that helps explore some samples: https://github.com/nateraw/download-musiccaps-dataset ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields #### ytid YT ID pointing to the YouTube video in which the labeled music segment appears. You can listen to the segment by opening https://youtu.be/watch?v={ytid}&start={start_s} #### start_s Position in the YouTube video at which the music starts. #### end_s Position in the YouTube video at which the music end. All clips are 10s long. #### audioset_positive_labels Labels for this segment from the AudioSet (https://research.google.com/audioset/) dataset. #### aspect_list A list of aspects describing the music. #### caption A multi-sentence free text caption describing the music. #### author_id An integer for grouping samples by who wrote them. #### is_balanced_subset If this value is true, the row is a part of the 1k subset which is genre-balanced. #### is_audioset_eval If this value is true, the clip is from the AudioSet eval split. Otherwise it is from the AudioSet train split. ### 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 This dataset was shared by [@googleai](https://ai.google/research/) ### Licensing Information The license for this dataset is cc-by-sa-4.0 ### Citation Information ```bibtex [More Information Needed] ``` ### Contributions [More Information Needed]

health_fact

--- annotations_creators: - expert-generated language_creators: - found language: - en license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - fact-checking - multi-class-classification paperswithcode_id: pubhealth pretty_name: PUBHEALTH dataset_info: features: - name: claim_id dtype: string - name: claim dtype: string - name: date_published dtype: string - name: explanation dtype: string - name: fact_checkers dtype: string - name: main_text dtype: string - name: sources dtype: string - name: label dtype: class_label: names: '0': 'false' '1': mixture '2': 'true' '3': unproven - name: subjects dtype: string splits: - name: train num_bytes: 53985377 num_examples: 9832 - name: test num_bytes: 6825221 num_examples: 1235 - name: validation num_bytes: 6653044 num_examples: 1225 download_size: 24892660 dataset_size: 67463642 train-eval-index: - config: default task: text-classification task_id: multi_class_classification splits: train_split: train eval_split: test col_mapping: claim: 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 PUBHEALTH ## 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:** [PUBHEALTH homepage](https://github.com/neemakot/Health-Fact-Checking) - **Repository:** [PUBHEALTH repository](https://github.com/neemakot/Health-Fact-Checking/blob/master/data/DATASHEET.md) - **Paper:** [Explainable Automated Fact-Checking for Public Health Claims"](https://arxiv.org/abs/2010.09926) - **Point of Contact:**[Neema Kotonya](mailto:nk2418@ic.ac.uk) ### Dataset Summary PUBHEALTH is a comprehensive dataset for explainable automated fact-checking of public health claims. Each instance in the PUBHEALTH dataset has an associated veracity label (true, false, unproven, mixture). Furthermore each instance in the dataset has an explanation text field. The explanation is a justification for which the claim has been assigned a particular veracity label. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The text in the dataset is in English. ## Dataset Structure ### Data Instances The following is an example instance of the PUBHEALTH dataset: | Field | Example | | ----------------- | -------------------------------------------------------------| | __claim__ | Expired boxes of cake and pancake mix are dangerously toxic. | | __explanation__ | What's True: Pancake and cake mixes that contain mold can cause life-threatening allergic reactions. What's False: Pancake and cake mixes that have passed their expiration dates are not inherently dangerous to ordinarily healthy people, and the yeast in packaged baking products does not "over time develops spores." | | __label__ | mixture | | __author(s)__ | David Mikkelson | | __date published__ | April 19, 2006 | | __tags__ | food, allergies, baking, cake | | __main_text__ | In April 2006, the experience of a 14-year-old who had eaten pancakes made from a mix that had gone moldy was described in the popular newspaper column Dear Abby. The account has since been circulated widely on the Internet as scores of concerned homemakers ponder the safety of the pancake and other baking mixes lurking in their larders [...] | | __evidence sources__ | [1] Bennett, Allan and Kim Collins. “An Unusual Case of Anaphylaxis: Mold in Pancake Mix.” American Journal of Forensic Medicine & Pathology. September 2001 (pp. 292-295). [2] Phillips, Jeanne. “Dear Abby.” 14 April 2006 [syndicated column]. | ### Data Fields Mentioned above in data instances. ### Data Splits | | # Instances | |-----------|-------------| | train.tsv | 9832 | | dev.tsv | 1221 | | test.tsv | 1235 | | total | 12288 | ## Dataset Creation ### Curation Rationale The dataset was created to explore fact-checking of difficult to verify claims i.e., those which require expertise from outside of the journalistics domain, in this case biomedical and public health expertise. It was also created in response to the lack of fact-checking datasets which provide gold standard natural language explanations for verdicts/labels. ### Source Data #### Initial Data Collection and Normalization The dataset was retrieved from the following fact-checking, news reviews and news websites: | URL | Type | |-----------------------------------|--------------------| | http://snopes.com/ | fact-checking | | http://politifact.com/ | fact-checking | | http://truthorfiction.com/ | fact-checking | | https://www.factcheck.org/ | fact-checking | | https://fullfact.org/ | fact-checking | | https://apnews.com/ | news | | https://uk.reuters.com/ | news | | https://www.healthnewsreview.org/ | health news review | #### 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 Not to our knowledge, but if it is brought to our attention that we are mistaken we will make the appropriate corrections to the dataset. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The dataset was created by Neema Kotonya, and Francesca Toni, for their research paper "Explainable Automated Fact-Checking for Public Health Claims" presented at EMNLP 2020. ### Licensing Information MIT License ### Citation Information ``` @inproceedings{kotonya-toni-2020-explainable, title = "Explainable Automated Fact-Checking for Public Health Claims", author = "Kotonya, Neema and Toni, Francesca", 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://www.aclweb.org/anthology/2020.emnlp-main.623", pages = "7740--7754", } ``` ### Contributions Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik) for adding this dataset.

JeswinMS4/HR_FIN_CLASSIFIER_DATA

JeswinMS4

--- dataset_info: features: - name: text dtype: string - name: label dtype: string splits: - name: train num_bytes: 119006 num_examples: 1382 download_size: 28420 dataset_size: 119006 configs: - config_name: default data_files: - split: train path: data/train-* ---

keremberke/satellite-building-segmentation

keremberke

--- task_categories: - image-segmentation tags: - roboflow - roboflow2huggingface - Aerial - Logistics - Construction - Damage Risk - Other --- <div align="center"> <img width="640" alt="keremberke/satellite-building-segmentation" src="https://huggingface.co/datasets/keremberke/satellite-building-segmentation/resolve/main/thumbnail.jpg"> </div> ### Dataset Labels ``` ['building'] ``` ### Number of Images ```json {'train': 6764, 'valid': 1934, 'test': 967} ``` ### How to Use - Install [datasets](https://pypi.org/project/datasets/): ```bash pip install datasets ``` - Load the dataset: ```python from datasets import load_dataset ds = load_dataset("keremberke/satellite-building-segmentation", name="full") example = ds['train'][0] ``` ### Roboflow Dataset Page [https://universe.roboflow.com/roboflow-universe-projects/buildings-instance-segmentation/dataset/1](https://universe.roboflow.com/roboflow-universe-projects/buildings-instance-segmentation/dataset/1?ref=roboflow2huggingface) ### Citation ``` @misc{ buildings-instance-segmentation_dataset, title = { Buildings Instance Segmentation Dataset }, type = { Open Source Dataset }, author = { Roboflow Universe Projects }, howpublished = { \\url{ https://universe.roboflow.com/roboflow-universe-projects/buildings-instance-segmentation } }, url = { https://universe.roboflow.com/roboflow-universe-projects/buildings-instance-segmentation }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2023 }, month = { jan }, note = { visited on 2023-01-18 }, } ``` ### License CC BY 4.0 ### Dataset Summary This dataset was exported via roboflow.com on January 16, 2023 at 9:09 PM GMT Roboflow is an end-to-end computer vision platform that helps you * collaborate with your team on computer vision projects * collect & organize images * understand and search unstructured image data * annotate, and create datasets * export, train, and deploy computer vision models * use active learning to improve your dataset over time For state of the art Computer Vision training notebooks you can use with this dataset, visit https://github.com/roboflow/notebooks To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com The dataset includes 9665 images. Buildings are annotated in COCO format. The following pre-processing was applied to each image: * Auto-orientation of pixel data (with EXIF-orientation stripping) No image augmentation techniques were applied.

intfloat/query2doc_msmarco

intfloat

--- license: cc-by-4.0 language: - en size_categories: - 100K<n<1M --- ### Dataset Summary This dataset contains GPT-3.5 (`text-davinci-003`) generations from MS-MARCO queries. [Query2doc: Query Expansion with Large Language Models](https://arxiv.org/pdf/2303.07678.pdf) Liang Wang, Nan Yang and Furu Wei ### Data Instances An example looks as follows. ``` { "query_id": "1030303", "query": "who is aziz hashim", "pseudo_doc": "Aziz Hashim is a renowned entrepreneur, business leader, and one of the most successful restaurant franchise operators in the US. He is the founder of NRD Capital, a private equity firm focused on investments in multi-unit restaurant franchised businesses. Hashim has built a formidable track record of success in the franchise industry, with brands such as Outback Steakhouse and Jamba Juice. His accomplishments and philanthropic initiatives have earned him numerous awards, including the prestigious Ernst and Young Entrepreneur of the Year award." } ``` ### Data Fields - `query_id`: a `string` feature. - `query`: a `string` feature. - `pseudo_doc`: a `string` feature. ### Data Splits | train | dev | test | trec_dl2019 | trec_dl2020 | |--------|------:|------:|------:|------:| | 502939 | 6980 | 6837 | 43 | 54 | ### How to use this dataset ```python from datasets import load_dataset dataset = load_dataset('intfloat/query2doc_msmarco') print(dataset['trec_dl2019'][0]) ``` ### Reproducing our results We provide a python script [repro_bm25.py](https://huggingface.co/datasets/intfloat/query2doc_msmarco/blob/main/repro_bm25.py) to reproduce our results with BM25 retrieval. First install some python dependency packages: ``` pip install pyserini==0.15.0 pytrec_eval datasets tqdm ``` Then download and run the python code: ``` python repro_bm25.py ``` This script utilizes the pre-built Lucene index from [Pyserini](https://github.com/castorini/pyserini/blob/pyserini-0.15.0/docs/prebuilt-indexes.md) and might yield slightly different results compared to the paper. ### Citation Information ``` @article{wang2023query2doc, title={Query2doc: Query Expansion with Large Language Models}, author={Wang, Liang and Yang, Nan and Wei, Furu}, journal={arXiv preprint arXiv:2303.07678}, year={2023} } ```

Locutusque/InstructMix

Locutusque

--- dataset: name: InstructiveMix tagline: A Combined Dataset of Diverse Instructional Content description: > InstructiveMix is a comprehensive dataset that brings together various instructional content from different domains. It combines instructions for tasks, code, poems, math, essays, medical texts, and more. With a diverse range of instructional data, this dataset is suitable for a wide range of natural language processing (NLP) tasks and research. license: CC-BY-SA-4.0 dataset_creation: '2023-08-02T00:00:00.000Z' dataset_version: 1.0.0 authors: - name: Locutusque email: locutusque.airshipcraft@gmail.com task_categories: - text-generation - conversational - question-answering language: - en --- **Dataset Summary:** InstructMix is a comprehensive combined dataset that offers diverse instructional content for a range of tasks. It includes data from various sources, such as code instructions, poems, essays, medical texts, and more. This dataset is designed to support natural language processing (NLP) research, model training, and evaluation across different domains. **Dataset Contents:** The dataset contains a collection of instructional data with corresponding inputs and outputs. Each entry has an "Input" field that contains the instructional content, and an "Output" field that represents the corresponding response or completion. Here is a list of the datasets used: - Locutusque/ColumnedChatCombined - TokenBender/code_instructions_120k_alpaca_style - Open-Orca/OpenOrca - vicgalle/alpaca-gpt4 - ChristophSchuhmann/essays-with-instructions - checkai/instruction-poems - pubmed_qa - BI55/MedText - nampdn-ai/tiny-codes - TIGER-Lab/MathInstruct - garage-bAInd/Open-Platypus It contains two of the following columns: - Input (string) - Output (string) These should hopefully be self-explanatory **Dataset Composition:** - Number of samples: 7570315 - Languages: English **Use Cases:** The InstructiveMix dataset is suitable for various NLP tasks, including text generation, text completion, translation, summarization, and more. It can be used to train and evaluate language models, code generation models, and other NLP-based applications. **Dataset Creation:** The InstructiveMix dataset was created by combining multiple existing datasets with instructional content and adding metadata to facilitate seamless integration. The content spans a diverse set of domains and was sourced from reputable datasets and public sources. **Acknowledgements:** I would like to acknowledge the original creators of the datasets used to construct InstructiveMix. Their contributions have enabled the creation of this valuable resource for the NLP community. **Contact:** For any questions or inquiries related to the InstructiveMix dataset, please contact me at [locutusque.airshipcraft@gmail.com]. ---

result-kand2-sdxl-wuerst-karlo/eacbe536

result-kand2-sdxl-wuerst-karlo

--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 173 num_examples: 10 download_size: 1377 dataset_size: 173 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "eacbe536" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

result-kand2-sdxl-wuerst-karlo/3658ecd8

result-kand2-sdxl-wuerst-karlo

--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 198 num_examples: 10 download_size: 1383 dataset_size: 198 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "3658ecd8" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

taskmaster2

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - dialogue-modeling paperswithcode_id: taskmaster-2 pretty_name: Taskmaster-2 dataset_info: - config_name: flights features: - name: conversation_id dtype: string - name: instruction_id dtype: string - name: utterances list: - name: index dtype: int32 - name: speaker dtype: string - name: text dtype: string - name: segments list: - name: start_index dtype: int32 - name: end_index dtype: int32 - name: text dtype: string - name: annotations list: - name: name dtype: string splits: - name: train num_bytes: 7073487 num_examples: 2481 download_size: 23029880 dataset_size: 7073487 - config_name: food-ordering features: - name: conversation_id dtype: string - name: instruction_id dtype: string - name: utterances list: - name: index dtype: int32 - name: speaker dtype: string - name: text dtype: string - name: segments list: - name: start_index dtype: int32 - name: end_index dtype: int32 - name: text dtype: string - name: annotations list: - name: name dtype: string splits: - name: train num_bytes: 1734825 num_examples: 1050 download_size: 5376675 dataset_size: 1734825 - config_name: hotels features: - name: conversation_id dtype: string - name: instruction_id dtype: string - name: utterances list: - name: index dtype: int32 - name: speaker dtype: string - name: text dtype: string - name: segments list: - name: start_index dtype: int32 - name: end_index dtype: int32 - name: text dtype: string - name: annotations list: - name: name dtype: string splits: - name: train num_bytes: 7436667 num_examples: 2357 download_size: 22507266 dataset_size: 7436667 - config_name: movies features: - name: conversation_id dtype: string - name: instruction_id dtype: string - name: utterances list: - name: index dtype: int32 - name: speaker dtype: string - name: text dtype: string - name: segments list: - name: start_index dtype: int32 - name: end_index dtype: int32 - name: text dtype: string - name: annotations list: - name: name dtype: string splits: - name: train num_bytes: 7112301 num_examples: 3056 download_size: 21189893 dataset_size: 7112301 - config_name: music features: - name: conversation_id dtype: string - name: instruction_id dtype: string - name: utterances list: - name: index dtype: int32 - name: speaker dtype: string - name: text dtype: string - name: segments list: - name: start_index dtype: int32 - name: end_index dtype: int32 - name: text dtype: string - name: annotations list: - name: name dtype: string splits: - name: train num_bytes: 2814030 num_examples: 1603 download_size: 8981720 dataset_size: 2814030 - config_name: restaurant-search features: - name: conversation_id dtype: string - name: instruction_id dtype: string - name: utterances list: - name: index dtype: int32 - name: speaker dtype: string - name: text dtype: string - name: segments list: - name: start_index dtype: int32 - name: end_index dtype: int32 - name: text dtype: string - name: annotations list: - name: name dtype: string splits: - name: train num_bytes: 7341998 num_examples: 3276 download_size: 21472680 dataset_size: 7341998 - config_name: sports features: - name: conversation_id dtype: string - name: instruction_id dtype: string - name: utterances list: - name: index dtype: int32 - name: speaker dtype: string - name: text dtype: string - name: segments list: - name: start_index dtype: int32 - name: end_index dtype: int32 - name: text dtype: string - name: annotations list: - name: name dtype: string splits: - name: train num_bytes: 5738818 num_examples: 3481 download_size: 19549440 dataset_size: 5738818 --- # Dataset Card for Taskmaster-2 ## 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:** [Taskmaster-1](https://research.google/tools/datasets/taskmaster-1/) - **Repository:** [GitHub](https://github.com/google-research-datasets/Taskmaster/tree/master/TM-2-2020) - **Paper:** [Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset](https://arxiv.org/abs/1909.05358) - **Leaderboard:** N/A - **Point of Contact:** [Taskmaster Googlegroup](taskmaster-datasets@googlegroups.com) ### Dataset Summary Taskmaster is dataset for goal oriented conversations. The Taskmaster-2 dataset consists of 17,289 dialogs in the seven domains which include restaurants, food ordering, movies, hotels, flights, music and sports. Unlike Taskmaster-1, which includes both written "self-dialogs" and spoken two-person dialogs, Taskmaster-2 consists entirely of spoken two-person dialogs. In addition, while Taskmaster-1 is almost exclusively task-based, Taskmaster-2 contains a good number of search- and recommendation-oriented dialogs. All dialogs in this release were created using a Wizard of Oz (WOz) methodology in which crowdsourced workers played the role of a 'user' and trained call center operators played the role of the 'assistant'. In this way, users were led to believe they were interacting with an automated system that “spoke” using text-to-speech (TTS) even though it was in fact a human behind the scenes. As a result, users could express themselves however they chose in the context of an automated interface. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The dataset is in English language. ## Dataset Structure ### Data Instances A typical example looks like this ``` { "conversation_id": "dlg-0047a087-6a3c-4f27-b0e6-268f53a2e013", "instruction_id": "flight-6", "utterances": [ { "index": 0, "segments": [], "speaker": "USER", "text": "Hi, I'm looking for a flight. I need to visit a friend." }, { "index": 1, "segments": [], "speaker": "ASSISTANT", "text": "Hello, how can I help you?" }, { "index": 2, "segments": [], "speaker": "ASSISTANT", "text": "Sure, I can help you with that." }, { "index": 3, "segments": [], "speaker": "ASSISTANT", "text": "On what dates?" }, { "index": 4, "segments": [ { "annotations": [ { "name": "flight_search.date.depart_origin" } ], "end_index": 37, "start_index": 27, "text": "March 20th" }, { "annotations": [ { "name": "flight_search.date.return" } ], "end_index": 45, "start_index": 41, "text": "22nd" } ], "speaker": "USER", "text": "I'm looking to travel from March 20th to 22nd." } ] } ``` ### Data Fields Each conversation in the data file has the following structure: - `conversation_id`: A universally unique identifier with the prefix 'dlg-'. The ID has no meaning. - `utterances`: A list of utterances that make up the conversation. - `instruction_id`: A reference to the file(s) containing the user (and, if applicable, agent) instructions for this conversation. Each utterance has the following fields: - `index`: A 0-based index indicating the order of the utterances in the conversation. - `speaker`: Either USER or ASSISTANT, indicating which role generated this utterance. - `text`: The raw text of the utterance. In case of self dialogs (one_person_dialogs), this is written by the crowdsourced worker. In case of the WOz dialogs, 'ASSISTANT' turns are written and 'USER' turns are transcribed from the spoken recordings of crowdsourced workers. - `segments`: A list of various text spans with semantic annotations. Each segment has the following fields: - `start_index`: The position of the start of the annotation in the utterance text. - `end_index`: The position of the end of the annotation in the utterance text. - `text`: The raw text that has been annotated. - `annotations`: A list of annotation details for this segment. Each annotation has a single field: - `name`: The annotation name. ### Data Splits There are no deafults splits for all the config. The below table lists the number of examples in each config. | Config | Train | |-------------------|--------| | flights | 2481 | | food-orderings | 1050 | | hotels | 2355 | | movies | 3047 | | music | 1602 | | restaurant-search | 3276 | | sports | 3478 | ## 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 The dataset is licensed under `Creative Commons Attribution 4.0 License` ### Citation Information [More Information Needed] ``` @inproceedings{48484, title = {Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset}, author = {Bill Byrne and Karthik Krishnamoorthi and Chinnadhurai Sankar and Arvind Neelakantan and Daniel Duckworth and Semih Yavuz and Ben Goodrich and Amit Dubey and Kyu-Young Kim and Andy Cedilnik}, year = {2019} } ``` ### Contributions Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.

shunk031/wrime

shunk031

--- annotations_creators: - crowdsourced language: - ja language_creators: - crowdsourced license: - unknown multilinguality: - monolingual pretty_name: wrime tags: - sentiment-analysis - wrime task_categories: - text-classification task_ids: - sentiment-classification datasets: - ver1 - ver2 metrics: - accuracy --- # Dataset Card for WRIME [](https://github.com/shunk031/huggingface-datasets_wrime/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: https://github.com/ids-cv/wrime - Repository: https://github.com/shunk031/huggingface-datasets_wrime - Paper: https://aclanthology.org/2021.naacl-main.169/ ### Dataset Summary In this study, we introduce a new dataset, WRIME, for emotional intensity estimation. We collect both the subjective emotional intensity ofthe writers themselves and the objective one annotated by the readers, and explore the differences between them. In our data collection, we hired 50 participants via crowdsourcing service. They annotated their own past posts on a social networking service (SNS) with the subjective emotional intensity. We also hired 3 annotators, who annotated allposts with the objective emotional intensity. Consequently, our Japanese emotion analysis datasetconsists of 17,000 posts with both subjective andobjective emotional intensities for Plutchik’s eightemotions ([Plutchik, 1980](https://www.sciencedirect.com/science/article/pii/B9780125587013500077)), which are given in afour-point scale (no, weak, medium, and strong). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages - Japanese ## Dataset Structure ### Data Instances When loading a specific configuration, users has to append a version dependent suffix: ```python from datasets import load_dataset dataset = load_dataset("shunk031/wrime", name="ver1") print(dataset) # DatasetDict({ # train: Dataset({ # features: ['sentence', 'user_id', 'datetime', 'writer', 'reader1', 'reader2', 'reader3', 'avg_readers'], # num_rows: 40000 # }) # validation: Dataset({ # features: ['sentence', 'user_id', 'datetime', 'writer', 'reader1', 'reader2', 'reader3', 'avg_readers'], # num_rows: 1200 # }) # test: Dataset({ # features: ['sentence', 'user_id', 'datetime', 'writer', 'reader1', 'reader2', 'reader3', 'avg_readers'], # num_rows: 2000 # }) # }) ``` #### Ver. 1 An example of looks as follows: ```json { "sentence": "ぼけっとしてたらこんな時間｡チャリあるから食べにでたいのに…", "user_id": "1", "datetime": "2012/07/31 23:48", "writer": { "joy": 0, "sadness": 1, "anticipation": 2, "surprise": 1, "anger": 1, "fear": 0, "disgust": 0, "trust": 1 }, "reader1": { "joy": 0, "sadness": 2, "anticipation": 0, "surprise": 0, "anger": 0, "fear": 0, "disgust": 0, "trust": 0 }, "reader2": { "joy": 0, "sadness": 2, "anticipation": 0, "surprise": 1, "anger": 0, "fear": 0, "disgust": 0, "trust": 0 }, "reader3": { "joy": 0, "sadness": 2, "anticipation": 0, "surprise": 0, "anger": 0, "fear": 1, "disgust": 1, "trust": 0 }, "avg_readers": { "joy": 0, "sadness": 2, "anticipation": 0, "surprise": 0, "anger": 0, "fear": 0, "disgust": 0, "trust": 0 } } ``` #### Ver. 1 An example of looks as follows: ```json { "sentence": "ぼけっとしてたらこんな時間。チャリあるから食べにでたいのに…", "user_id": "1", "datetime": "2012/7/31 23:48", "writer": { "joy": 0, "sadness": 1, "anticipation": 2, "surprise": 1, "anger": 1, "fear": 0, "disgust": 0, "trust": 1, "sentiment": 0 }, "reader1": { "joy": 0, "sadness": 2, "anticipation": 0, "surprise": 0, "anger": 0, "fear": 0, "disgust": 0, "trust": 0, "sentiment": -2 }, "reader2": { "joy": 0, "sadness": 2, "anticipation": 0, "surprise": 0, "anger": 0, "fear": 1, "disgust": 1, "trust": 0, "sentiment": -1 }, "reader3": { "joy": 0, "sadness": 2, "anticipation": 0, "surprise": 1, "anger": 0, "fear": 0, "disgust": 0, "trust": 0, "sentiment": -1 }, "avg_readers": { "joy": 0, "sadness": 2, "anticipation": 0, "surprise": 0, "anger": 0, "fear": 0, "disgust": 0, "trust": 0, "sentiment": -1 } } ``` ### Data Fields #### Ver. 1 - `sentence`: 投稿テキスト - `user_id`: ユーザー ID - `datetime`: 投稿日時 - `writer`: 主観 （書き手） - `joy`: 主観の喜びの感情 - `sadness`: 主観の悲しみの感情 - `anticipation`: 主観の期待の感情 - `surprise`: 主観の驚きの感情 - `anger`: 主観の怒りの感情 - `fear`: 主観の恐れの感情 - `disgust`: 主観の嫌悪の感情 - `trust`: 主観の信頼の感情 - `reader1`: 客観 A （読み手 A) - `joy`: 客観 A の喜びの感情 - `sadness`: 客観 A の悲しみの感情 - `anticipation`: 客観 A の期待の感情 - `surprise`: 客観 A の驚きの感情 - `anger`: 客観 A の怒りの感情 - `fear`: 客観 A の恐れの感情 - `disgust`: 客観 A の嫌悪の感情 - `trust`: 客観 A の信頼の感情 - `reader2`: 客観 B （読み手 B) - `joy`: 客観 B の喜びの感情 - `sadness`: 客観 B の悲しみの感情 - `anticipation`: 客観 B の期待の感情 - `surprise`: 客観 B の驚きの感情 - `anger`: 客観 B の怒りの感情 - `fear`: 客観 B の恐れの感情 - `disgust`: 客観 B の嫌悪の感情 - `trust`: 客観 B の信頼の感情 - `reader3`: 客観 C （読み手 C) - `joy`: 客観 C の喜びの感情 - `sadness`: 客観 C の悲しみの感情 - `anticipation`: 客観 C の期待の感情 - `surprise`: 客観 C の驚きの感情 - `anger`: 客観 C の怒りの感情 - `fear`: 客観 C の恐れの感情 - `disgust`: 客観 C の嫌悪の感情 - `trust`: 客観 C の信頼の感情 - `avg_readers` - `joy`: 客観 A, B, C 平均の喜びの感情 - `sadness`: 客観 A, B, C 平均の悲しみの感情 - `anticipation`: 客観 A, B, C 平均の期待の感情 - `surprise`: 客観 A, B, C 平均の驚きの感情 - `anger`: 客観 A, B, C 平均の怒りの感情 - `fear`: 客観 A, B, C 平均の恐れの感情 - `disgust`: 客観 A, B, C 平均の嫌悪の感情 - `trust`: 客観 A, B, C 平均の信頼の感情 #### Ver. 2 - `sentence`: 投稿テキスト - `user_id`: ユーザー ID - `datetime`: 投稿日時 - `writer`: 主観 （書き手） - `joy`: 主観の喜びの感情 - `sadness`: 主観の悲しみの感情 - `anticipation`: 主観の期待の感情 - `surprise`: 主観の驚きの感情 - `anger`: 主観の怒りの感情 - `fear`: 主観の恐れの感情 - `disgust`: 主観の嫌悪の感情 - `trust`: 主観の信頼の感情 - `sentiment`: 主観の感情極性 - `reader1`: 客観 A （読み手 A) - `joy`: 客観 A の喜びの感情 - `sadness`: 客観 A の悲しみの感情 - `anticipation`: 客観 A の期待の感情 - `surprise`: 客観 A の驚きの感情 - `anger`: 客観 A の怒りの感情 - `fear`: 客観 A の恐れの感情 - `disgust`: 客観 A の嫌悪の感情 - `trust`: 客観 A の信頼の感情 - `sentiment`: 客観 A の感情極性 - `reader2`: 客観 B （読み手 B) - `joy`: 客観 B の喜びの感情 - `sadness`: 客観 B の悲しみの感情 - `anticipation`: 客観 B の期待の感情 - `surprise`: 客観 B の驚きの感情 - `anger`: 客観 B の怒りの感情 - `fear`: 客観 B の恐れの感情 - `disgust`: 客観 B の嫌悪の感情 - `trust`: 客観 B の信頼の感情 - `sentiment`: 客観 B の感情極性 - `reader3`: 客観 C （読み手 C) - `joy`: 客観 C の喜びの感情 - `sadness`: 客観 C の悲しみの感情 - `anticipation`: 客観 C の期待の感情 - `surprise`: 客観 C の驚きの感情 - `anger`: 客観 C の怒りの感情 - `fear`: 客観 C の恐れの感情 - `disgust`: 客観 C の嫌悪の感情 - `trust`: 客観 C の信頼の感情 - `sentiment`: 客観 C の感情極性 - `avg_readers` - `joy`: 客観 A, B, C 平均の喜びの感情 - `sadness`: 客観 A, B, C 平均の悲しみの感情 - `anticipation`: 客観 A, B, C 平均の期待の感情 - `surprise`: 客観 A, B, C 平均の驚きの感情 - `anger`: 客観 A, B, C 平均の怒りの感情 - `fear`: 客観 A, B, C 平均の恐れの感情 - `disgust`: 客観 A, B, C 平均の嫌悪の感情 - `trust`: 客観 A, B, C 平均の信頼の感情 - `sentiment`: 客観 A, B, C 平均の感情極性 ### Data Splits | name | train | validation | test | |------|-------:|-----------:|------:| | ver1 | 40,000 | 1,200 | 2,000 | | ver2 | 30,000 | 2,500 | 2,500 | ## 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 From [the README](https://github.com/ids-cv/wrime/blob/master/README.en.md#licence) of the GitHub: - The dataset is available for research purposes only. - Redistribution of the dataset is prohibited. ### Citation Information ```bibtex @inproceedings{kajiwara-etal-2021-wrime, title = "{WRIME}: A New Dataset for Emotional Intensity Estimation with Subjective and Objective Annotations", author = "Kajiwara, Tomoyuki and Chu, Chenhui and Takemura, Noriko and Nakashima, Yuta and Nagahara, Hajime", booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jun, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.naacl-main.169", doi = "10.18653/v1/2021.naacl-main.169", pages = "2095--2104", abstract = "We annotate 17,000 SNS posts with both the writer{'}s subjective emotional intensity and the reader{'}s objective one to construct a Japanese emotion analysis dataset. In this study, we explore the difference between the emotional intensity of the writer and that of the readers with this dataset. We found that the reader cannot fully detect the emotions of the writer, especially anger and trust. In addition, experimental results in estimating the emotional intensity show that it is more difficult to estimate the writer{'}s subjective labels than the readers{'}. The large gap between the subjective and objective emotions imply the complexity of the mapping from a post to the subjective emotion intensities, which also leads to a lower performance with machine learning models.", } ``` ```bibtex @inproceedings{suzuki-etal-2022-japanese, title = "A {J}apanese Dataset for Subjective and Objective Sentiment Polarity Classification in Micro Blog Domain", author = "Suzuki, Haruya and Miyauchi, Yuto and Akiyama, Kazuki and Kajiwara, Tomoyuki and Ninomiya, Takashi and Takemura, Noriko and Nakashima, Yuta and Nagahara, Hajime", 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-1.759", pages = "7022--7028", abstract = "We annotate 35,000 SNS posts with both the writer{'}s subjective sentiment polarity labels and the reader{'}s objective ones to construct a Japanese sentiment analysis dataset. Our dataset includes intensity labels (\textit{none}, \textit{weak}, \textit{medium}, and \textit{strong}) for each of the eight basic emotions by Plutchik (\textit{joy}, \textit{sadness}, \textit{anticipation}, \textit{surprise}, \textit{anger}, \textit{fear}, \textit{disgust}, and \textit{trust}) as well as sentiment polarity labels (\textit{strong positive}, \textit{positive}, \textit{neutral}, \textit{negative}, and \textit{strong negative}). Previous studies on emotion analysis have studied the analysis of basic emotions and sentiment polarity independently. In other words, there are few corpora that are annotated with both basic emotions and sentiment polarity. Our dataset is the first large-scale corpus to annotate both of these emotion labels, and from both the writer{'}s and reader{'}s perspectives. In this paper, we analyze the relationship between basic emotion intensity and sentiment polarity on our dataset and report the results of benchmarking sentiment polarity classification.", } ``` ### Contributions Thanks to [@moguranosenshi](https://github.com/moguranosenshi) for creating this dataset.

dbrd

--- annotations_creators: - found language_creators: - found language: - nl license: - cc-by-nc-sa-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-generation - fill-mask - text-classification task_ids: - language-modeling - masked-language-modeling - sentiment-classification paperswithcode_id: dbrd pretty_name: DBRD dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': neg '1': pos config_name: plain_text splits: - name: train num_bytes: 29496333 num_examples: 20028 - name: test num_bytes: 3246243 num_examples: 2224 - name: unsupervised num_bytes: 152733031 num_examples: 96264 download_size: 79065872 dataset_size: 185475607 --- # Dataset Card for DBRD ## 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:** [Dutch Book Review Dataset (DBRD) homepage](https://benjaminvdb.github.io/DBRD) - **Repository:** https://github.com/benjaminvdb/DBRD - **Paper:** [The merits of Universal Language Model Fine-tuning for Small Datasets - a case with Dutch book reviews](https://arxiv.org/abs/1910.00896) - **Leaderboard:** - **Point of Contact:** [Benjamin van der Burgh](mailto:benjaminvdb@gmail.com) ### Dataset Summary The DBRD (pronounced *dee-bird*) dataset contains over 110k book reviews of which 22k have associated binary sentiment polarity labels. It is intended as a benchmark for sentiment classification in Dutch and was created due to a lack of annotated datasets in Dutch that are suitable for this task. ### Supported Tasks and Leaderboards - `text-generation`: The dataset can be used to train a model for sequence modeling, more specifically language modeling. - `text-classification`: The dataset can be used to train a model for text classification, more specifically sentiment classification, using the provided positive/negative sentiment polarity labels. ### Languages Non-Dutch reviews were filtered out using [langdetect](https://github.com/Mimino666/langdetect), and all reviews should therefore be in Dutch (nl). They are written by reviewers on [Hebban](https://www.hebban.nl), a Dutch website for book reviews. ## Dataset Structure ### Data Instances The dataset contains three subsets: train, test, and unsupervised. The `train` and `test` sets contain labels, while the `unsupervised` set doesn't (the label value is -1 for each instance in `unsupervised`). Here's an example of a positive review, indicated with a label value of `1`. ``` { 'label': 1, 'text': 'Super om te lezen hoe haar leven is vergaan.\nBijzonder dat ze zo openhartig is geweest.' } ``` ### Data Fields - `label`: either 0 (negative) or 1 (positive) in the supervised sets `train` and `test`. These are always -1 for the unsupervised set. - `text`: book review as a utf-8 encoded string. ### Data Splits The `train` and `test` sets were constructed by extracting all non-neutral reviews because we want to assign either a positive or negative polarity label to each instance. Furthermore, the positive (pos) and negative (neg) labels were balanced in both train and test sets. The remainder was added to the unsupervised set. | | Train | Test | Unsupervised | | ----- | ------ | ----- | ----------- | | # No. texts | 20028 | 2224 | 96264 | | % of total | 16.9% | 1.9% | 81.2% | ## Dataset Creation ### Curation Rationale This dataset was created due to a lack of annotated Dutch text that is suitable for sentiment classification. Non-Dutch texts were therefore removed, but other than that, no curation was done. ### Source Data The book reviews were taken from [Hebban](https://www.hebban.nl), a Dutch platform for book reviews. #### Initial Data Collection and Normalization The source code of the scraper and preprocessing process can be found in the [DBRD GitHub repository](https://github.com/benjaminvdb/DBRD). #### Who are the source language producers? The reviews are written by users of [Hebban](https://www.hebban.nl) and are of varying quality. Some are short, others long, and many contain spelling mistakes and other errors. ### Annotations Each book review was accompanied by a 1 to 5-star rating. The annotations are produced by mapping the user-provided ratings to either a positive or negative label. 1 and 2-star ratings are given the negative label `0` and 4 and 5-star ratings the positive label `1`. Reviews with a rating of 3 stars are considered neutral and left out of the `train`/`test` sets and added to the unsupervised set. #### Annotation process Users of [Hebban](https://www.hebban.nl) were unaware that their reviews would be used in the creation of this dataset. #### Who are the annotators? The annotators are the [Hebban](https://www.hebban.nl) users who wrote the book reviews associated with the annotation. Anyone can register on [Hebban](https://www.hebban.nl) and it's impossible to know the demographics of this group. ### Personal and Sensitive Information The book reviews and ratings are publicly available on [Hebban](https://www.hebban.nl) and no personal or otherwise sensitive information is contained in this dataset. ## Considerations for Using the Data ### Social Impact of Dataset While predicting sentiment of book reviews in itself is not that interesting, the value of this dataset lies in its usage for benchmarking models. The dataset contains some challenges that are common to outings on the internet, such as spelling mistakes and other errors. It is therefore very useful for validating models for their real-world performance. These datasets are abundant for English but are harder to find for Dutch, making them a valuable resource for ML tasks in this language. ### Discussion of Biases [More Information Needed] ### Other Known Limitations Reviews on [Hebban](https://www.hebban.nl) are usually written in Dutch, but some have been written in English and possibly in other languages. While we've done our best to filter out non-Dutch texts, it's hard to do this without errors. For example, some reviews are in multiple languages, and these might slip through. Also be aware that some commercial outings can appear in the text, making them different from other reviews and influencing your models. While this doesn't pose a major issue in most cases, we just wanted to mention it briefly. ## Additional Information ### Dataset Curators This dataset was created by [Benjamin van der Burgh](mailto:benjaminvdb@gmail.com), who was working at [Leiden Institute of Advanced Computer Science (LIACS)](https://liacs.leidenuniv.nl/) at the time. ### Licensing Information The dataset is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/). ### Citation Information Please use the following citation when making use of this dataset in your work. ``` @article{DBLP:journals/corr/abs-1910-00896, author = {Benjamin van der Burgh and Suzan Verberne}, title = {The merits of Universal Language Model Fine-tuning for Small Datasets - a case with Dutch book reviews}, journal = {CoRR}, volume = {abs/1910.00896}, year = {2019}, url = {http://arxiv.org/abs/1910.00896}, archivePrefix = {arXiv}, eprint = {1910.00896}, timestamp = {Fri, 04 Oct 2019 12:28:06 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1910-00896.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` ### Contributions Thanks to [@benjaminvdb](https://github.com/benjaminvdb) for adding this dataset.

pg19

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - apache-2.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-generation task_ids: - language-modeling paperswithcode_id: pg-19 pretty_name: PG-19 dataset_info: features: - name: short_book_title dtype: string - name: publication_date dtype: int32 - name: url dtype: string - name: text dtype: string splits: - name: train num_bytes: 11453688452 num_examples: 28602 - name: validation num_bytes: 17402295 num_examples: 50 - name: test num_bytes: 40482852 num_examples: 100 download_size: 11740397875 dataset_size: 11511573599 --- # Dataset Card for "pg19" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/deepmind/pg19](https://github.com/deepmind/pg19) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [Compressive Transformers for Long-Range Sequence Modelling](https://arxiv.org/abs/1911.05507) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 11.74 GB - **Size of the generated dataset:** 11.51 GB - **Total amount of disk used:** 23.25 GB ### Dataset Summary This repository contains the PG-19 language modeling benchmark. It includes a set of books extracted from the Project Gutenberg books library, that were published before 1919. It also contains metadata of book titles and publication dates. PG-19 is over double the size of the Billion Word benchmark and contains documents that are 20X longer, on average, than the WikiText long-range language modelling benchmark. Books are partitioned into a train, validation, and test set. Book metadata is stored in metadata.csv which contains (book_id, short_book_title, publication_date). Unlike prior benchmarks, we do not constrain the vocabulary size --- i.e. mapping rare words to an UNK token --- but instead release the data as an open-vocabulary benchmark. The only processing of the text that has been applied is the removal of boilerplate license text, and the mapping of offensive discriminatory words as specified by Ofcom to placeholder tokens. Users are free to model the data at the character-level, subword-level, or via any mechanism that can model an arbitrary string of text. To compare models we propose to continue measuring the word-level perplexity, by calculating the total likelihood of the dataset (via any chosen subword vocabulary or character-based scheme) divided by the number of tokens --- specified below in the dataset statistics table. One could use this dataset for benchmarking long-range language models, or use it to pre-train for other natural language processing tasks which require long-range reasoning, such as LAMBADA or NarrativeQA. We would not recommend using this dataset to train a general-purpose language model, e.g. for applications to a production-system dialogue agent, due to the dated linguistic style of old texts and the inherent biases present in historical writing. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 11.74 GB - **Size of the generated dataset:** 11.51 GB - **Total amount of disk used:** 23.25 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "publication_date": 1907, "short_book_title": "La Fiammetta by Giovanni Boccaccio", "text": "\"\\n\\n\\n\\nProduced by Ted Garvin, Dave Morgan and PG Distributed Proofreaders\\n\\n\\n\\n\\nLA FIAMMETTA\\n\\nBY\\n\\nGIOVANNI BOCCACCIO\\n...", "url": "http://www.gutenberg.org/ebooks/10006" } ``` ### Data Fields The data fields are the same among all splits. #### default - `short_book_title`: a `string` feature. - `publication_date`: a `int32` feature. - `url`: a `string` feature. - `text`: a `string` feature. ### Data Splits | name |train|validation|test| |-------|----:|---------:|---:| |default|28602| 50| 100| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information The dataset is licensed under [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0.html). ### Citation Information ``` @article{raecompressive2019, author = {Rae, Jack W and Potapenko, Anna and Jayakumar, Siddhant M and Hillier, Chloe and Lillicrap, Timothy P}, title = {Compressive Transformers for Long-Range Sequence Modelling}, journal = {arXiv preprint}, url = {https://arxiv.org/abs/1911.05507}, year = {2019}, } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@lucidrains](https://github.com/lucidrains), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

break_data

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text2text-generation task_ids: - open-domain-abstractive-qa paperswithcode_id: break pretty_name: BREAK dataset_info: - config_name: QDMR-high-level features: - name: question_id dtype: string - name: question_text dtype: string - name: decomposition dtype: string - name: operators dtype: string - name: split dtype: string splits: - name: test num_bytes: 482339 num_examples: 3195 - name: train num_bytes: 5148086 num_examples: 17503 - name: validation num_bytes: 914780 num_examples: 3130 download_size: 15971078 dataset_size: 6545205 - config_name: QDMR-high-level-lexicon features: - name: source dtype: string - name: allowed_tokens dtype: string splits: - name: test num_bytes: 4240755 num_examples: 3195 - name: train num_bytes: 23234518 num_examples: 17503 - name: validation num_bytes: 4158679 num_examples: 3130 download_size: 15971078 dataset_size: 31633952 - config_name: QDMR features: - name: question_id dtype: string - name: question_text dtype: string - name: decomposition dtype: string - name: operators dtype: string - name: split dtype: string splits: - name: test num_bytes: 900632 num_examples: 8069 - name: train num_bytes: 12790466 num_examples: 44321 - name: validation num_bytes: 2237472 num_examples: 7760 download_size: 15971078 dataset_size: 15928570 - config_name: QDMR-lexicon features: - name: source dtype: string - name: allowed_tokens dtype: string splits: - name: test num_bytes: 10331822 num_examples: 8069 - name: train num_bytes: 56913064 num_examples: 44321 - name: validation num_bytes: 9936933 num_examples: 7760 download_size: 15971078 dataset_size: 77181819 - config_name: logical-forms features: - name: question_id dtype: string - name: question_text dtype: string - name: decomposition dtype: string - name: operators dtype: string - name: split dtype: string - name: program dtype: string splits: - name: test num_bytes: 927038 num_examples: 8006 - name: train num_bytes: 19821676 num_examples: 44098 - name: validation num_bytes: 3504893 num_examples: 7719 download_size: 15971078 dataset_size: 24253607 --- # Dataset Card for "break_data" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/allenai/Break](https://github.com/allenai/Break) - **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:** 79.86 MB - **Size of the generated dataset:** 155.55 MB - **Total amount of disk used:** 235.39 MB ### Dataset Summary Break is a human annotated dataset of natural language questions and their Question Decomposition Meaning Representations (QDMRs). Break consists of 83,978 examples sampled from 10 question answering datasets over text, images and databases. This repository contains the Break dataset along with information on the exact data format. ### 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 #### QDMR - **Size of downloaded dataset files:** 15.97 MB - **Size of the generated dataset:** 15.93 MB - **Total amount of disk used:** 31.90 MB An example of 'validation' looks as follows. ``` { "decomposition": "return flights ;return #1 from denver ;return #2 to philadelphia ;return #3 if available", "operators": "['select', 'filter', 'filter', 'filter']", "question_id": "ATIS_dev_0", "question_text": "what flights are available tomorrow from denver to philadelphia ", "split": "dev" } ``` #### QDMR-high-level - **Size of downloaded dataset files:** 15.97 MB - **Size of the generated dataset:** 6.54 MB - **Total amount of disk used:** 22.51 MB An example of 'train' looks as follows. ``` { "decomposition": "return ground transportation ;return #1 which is available ;return #2 from the pittsburgh airport ;return #3 to downtown ;return the cost of #4", "operators": "['select', 'filter', 'filter', 'filter', 'project']", "question_id": "ATIS_dev_102", "question_text": "what ground transportation is available from the pittsburgh airport to downtown and how much does it cost ", "split": "dev" } ``` #### QDMR-high-level-lexicon - **Size of downloaded dataset files:** 15.97 MB - **Size of the generated dataset:** 31.64 MB - **Total amount of disk used:** 47.61 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "allowed_tokens": "\"['higher than', 'same as', 'what ', 'and ', 'than ', 'at most', 'he', 'distinct', 'House', 'two', 'at least', 'or ', 'date', 'o...", "source": "What office, also held by a member of the Maine House of Representatives, did James K. Polk hold before he was president?" } ``` #### QDMR-lexicon - **Size of downloaded dataset files:** 15.97 MB - **Size of the generated dataset:** 77.19 MB - **Total amount of disk used:** 93.16 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "allowed_tokens": "\"['higher than', 'same as', 'what ', 'and ', 'than ', 'at most', 'distinct', 'two', 'at least', 'or ', 'date', 'on ', '@@14@@', ...", "source": "what flights are available tomorrow from denver to philadelphia " } ``` #### logical-forms - **Size of downloaded dataset files:** 15.97 MB - **Size of the generated dataset:** 24.25 MB - **Total amount of disk used:** 40.22 MB An example of 'train' looks as follows. ``` { "decomposition": "return ground transportation ;return #1 which is available ;return #2 from the pittsburgh airport ;return #3 to downtown ;return the cost of #4", "operators": "['select', 'filter', 'filter', 'filter', 'project']", "program": "some program", "question_id": "ATIS_dev_102", "question_text": "what ground transportation is available from the pittsburgh airport to downtown and how much does it cost ", "split": "dev" } ``` ### Data Fields The data fields are the same among all splits. #### QDMR - `question_id`: a `string` feature. - `question_text`: a `string` feature. - `decomposition`: a `string` feature. - `operators`: a `string` feature. - `split`: a `string` feature. #### QDMR-high-level - `question_id`: a `string` feature. - `question_text`: a `string` feature. - `decomposition`: a `string` feature. - `operators`: a `string` feature. - `split`: a `string` feature. #### QDMR-high-level-lexicon - `source`: a `string` feature. - `allowed_tokens`: a `string` feature. #### QDMR-lexicon - `source`: a `string` feature. - `allowed_tokens`: a `string` feature. #### logical-forms - `question_id`: a `string` feature. - `question_text`: a `string` feature. - `decomposition`: a `string` feature. - `operators`: a `string` feature. - `split`: a `string` feature. - `program`: a `string` feature. ### Data Splits | name |train|validation|test| |-----------------------|----:|---------:|---:| |QDMR |44321| 7760|8069| |QDMR-high-level |17503| 3130|3195| |QDMR-high-level-lexicon|17503| 3130|3195| |QDMR-lexicon |44321| 7760|8069| |logical-forms |44098| 7719|8006| ## 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{Wolfson2020Break, title={Break It Down: A Question Understanding Benchmark}, author={Wolfson, Tomer and Geva, Mor and Gupta, Ankit and Gardner, Matt and Goldberg, Yoav and Deutch, Daniel and Berant, Jonathan}, journal={Transactions of the Association for Computational Linguistics}, year={2020}, } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf) for adding this dataset.