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davanstrien
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dataset_cards_with_long_context_embeddins

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BeIR/fever-qrels
2022-10-23T06:08:11.000Z
[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "task_ids:fact-checking-retrieval", "multilinguality:monolingual", "language:en", "license:cc-by-sa-4.0", "region:us" ]
BeIR
null
null
0
1,184
2022-06-05T17:28:01
--- 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.
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dlb/plue
2022-10-29T12:19:26.000Z
[ "task_categories:text-classification", "task_ids:acceptability-classification", "task_ids:natural-language-inference", "task_ids:semantic-similarity-scoring", "task_ids:sentiment-classification", "task_ids:text-scoring", "annotations_creators:found", "language_creators:machine-generated", "multilinguality:monolingual", "multilinguality:translation", "size_categories:10K<n<100K", "source_datasets:extended|glue", "language:pt", "license:lgpl-3.0", "paraphrase-identification", "qa-nli", "coreference-nli", "region:us" ]
dlb
PLUE: Portuguese Language Understanding Evaluationis a Portuguese translation of the GLUE benchmark and Scitail using OPUS-MT model and Google Cloud Translation.
@misc{Gomes2020, author = {GOMES, J. R. S.}, title = {Portuguese Language Understanding Evaluation}, year = {2020}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\\url{https://github.com/jubs12/PLUE}}, commit = {CURRENT_COMMIT} } @inproceedings{wang2019glue, title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding}, author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.}, note={In the Proceedings of ICLR.}, year={2019} }
6
1,183
2022-03-02T23:29:22
--- annotations_creators: - found language_creators: - machine-generated language: - pt license: - lgpl-3.0 multilinguality: - monolingual - translation size_categories: - 10K<n<100K source_datasets: - extended|glue task_categories: - text-classification task_ids: - acceptability-classification - natural-language-inference - semantic-similarity-scoring - sentiment-classification - text-scoring pretty_name: PLUE (Portuguese Language Understanding Evaluation benchmark) tags: - paraphrase-identification - qa-nli - coreference-nli --- # Dataset Card for PLUE ## 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 - **Repository:** https://github.com/ju-resplande/PLUE - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary Portuguese translation of the <a href="https://gluebenchmark.com/">GLUE benchmark</a>, <a href=https://nlp.stanford.edu/projects/snli/>SNLI</a>, and <a href=https://allenai.org/data/scitail> Scitail</a> using <a href=https://github.com/Helsinki-NLP/OPUS-MT>OPUS-MT model</a> and <a href=https://cloud.google.com/translate/docs>Google Cloud Translation</a>. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The language data in PLUE is Brazilian Portuguese (BCP-47 pt-BR) ## 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 ```bibtex @misc{Gomes2020, author = {GOMES, J. R. S.}, title = {PLUE: Portuguese Language Understanding Evaluation}, year = {2020}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/jubs12/PLUE}}, commit = {CURRENT_COMMIT} } ``` ### Contributions Thanks to [@ju-resplande](https://github.com/ju-resplande) for adding this dataset.
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eugenesiow/Div2k
2022-10-21T04:01:10.000Z
[ "task_categories:other", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "license:other", "other-image-super-resolution", "region:us" ]
eugenesiow
DIV2K dataset: DIVerse 2K resolution high quality images as used for the challenges @ NTIRE (CVPR 2017 and CVPR 2018) and @ PIRM (ECCV 2018)
@InProceedings{Agustsson_2017_CVPR_Workshops, author = {Agustsson, Eirikur and Timofte, Radu}, title = {NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study}, booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, url = "http://www.vision.ee.ethz.ch/~timofter/publications/Agustsson-CVPRW-2017.pdf", month = {July}, year = {2017} }
2
1,182
2022-03-02T23:29:22
--- annotations_creators: - machine-generated language_creators: - found language: [] license: - other multilinguality: - monolingual size_categories: - unknown source_datasets: - original task_categories: - other task_ids: [] pretty_name: Div2k tags: - other-image-super-resolution --- # Dataset Card for Div2k ## 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://data.vision.ee.ethz.ch/cvl/DIV2K/ - **Repository**: https://huggingface.co/datasets/eugenesiow/Div2k - **Paper**: http://www.vision.ee.ethz.ch/~timofter/publications/Agustsson-CVPRW-2017.pdf - **Leaderboard**: https://github.com/eugenesiow/super-image#scale-x2 ### Dataset Summary DIV2K is a dataset of RGB images (2K resolution high quality images) with a large diversity of contents. The DIV2K dataset is divided into: - train data: starting from 800 high definition high resolution images we obtain corresponding low resolution images and provide both high and low resolution images for 2, 3, and 4 downscaling factors - validation data: 100 high definition high resolution images are used for genereting low resolution corresponding images, the low res are provided from the beginning of the challenge and are meant for the participants to get online feedback from the validation server; the high resolution images will be released when the final phase of the challenge starts. Install with `pip`: ```bash pip install datasets super-image ``` Evaluate a model with the [`super-image`](https://github.com/eugenesiow/super-image) library: ```python from datasets import load_dataset from super_image import EdsrModel from super_image.data import EvalDataset, EvalMetrics dataset = load_dataset('eugenesiow/Div2k', 'bicubic_x2', split='validation') eval_dataset = EvalDataset(dataset) model = EdsrModel.from_pretrained('eugenesiow/edsr-base', scale=2) EvalMetrics().evaluate(model, eval_dataset) ``` ### Supported Tasks and Leaderboards The dataset is commonly used for training and evaluation of the `image-super-resolution` task. Unofficial [`super-image`](https://github.com/eugenesiow/super-image) leaderboard for: - [Scale 2](https://github.com/eugenesiow/super-image#scale-x2) - [Scale 3](https://github.com/eugenesiow/super-image#scale-x3) - [Scale 4](https://github.com/eugenesiow/super-image#scale-x4) - [Scale 8](https://github.com/eugenesiow/super-image#scale-x8) ### Languages Not applicable. ## Dataset Structure ### Data Instances An example of `train` for `bicubic_x2` looks as follows. ``` { "hr": "/.cache/huggingface/datasets/downloads/extracted/DIV2K_valid_HR/0801.png", "lr": "/.cache/huggingface/datasets/downloads/extracted/DIV2K_valid_LR_bicubic/X2/0801x2.png" } ``` ### Data Fields The data fields are the same among all splits. - `hr`: a `string` to the path of the High Resolution (HR) `.png` image. - `lr`: a `string` to the path of the Low Resolution (LR) `.png` image. ### Data Splits | name |train |validation| |-------|-----:|---:| |bicubic_x2|800|100| |bicubic_x3|800|100| |bicubic_x4|800|100| |bicubic_x8|800|100| |unknown_x2|800|100| |unknown_x3|800|100| |unknown_x4|800|100| |realistic_mild_x4|800|100| |realistic_difficult_x4|800|100| |realistic_wild_x4|800|100| ## Dataset Creation ### Curation Rationale Please refer to the [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) section. ### Source Data #### Initial Data Collection and Normalization **Resolution and quality**: All the images are 2K resolution, that is they have 2K pixels on at least one of the axes (vertical or horizontal). All the images were processed using the same tools. For simplicity, since the most common magnification factors in the recent SR literature are of ×2, ×3 and ×4 we cropped the images to multiple of 12 pixels on both axes. Most of the crawled images were originally above 20M pixels. The images are of high quality both aesthetically and in the terms of small amounts of noise and other corruptions (like blur and color shifts). **Diversity**: The authors collected images from dozens of sites. A preference was made for sites with freely shared high quality photography (such as https://www.pexels.com/ ). Note that we did not use images from Flickr, Instagram, or other legally binding or copyright restricted images. We only seldom used keywords to assure the diversity for our dataset. DIV2K covers a large diversity of contents, ranging from people, handmade objects and environments (cities, villages), to flora and fauna, and natural sceneries including underwater and dim light conditions. **Partitions**: After collecting the DIV2K 1000 images the authors computed image entropy, bit per pixel (bpp) PNG compression rates and CORNIA scores (see Section 7.6) and applied bicubic downscaling ×3 and then upscaling ×3 with bicubic interpolation (imresize Matlab function), ANR [47] and A+ [48] methods and default settings. The authors randomly generated partitions of 800 train, 100 validation and 100 test images until they achieved a good balance firstly in visual contents and then on the average entropy, average bpp, average number of pixels per image (ppi), average CORNIA quality scores and also in the relative differences between the average PSNR scores of bicubic, ANR and A+ methods. Only the 800 train and 100 validation images are included in this dataset. #### Who are the source language producers? The authors manually crawled 1000 color RGB images from Internet paying special attention to the image quality, to the diversity of sources (sites and cameras), to the image contents and to the copyrights. ### Annotations #### Annotation process No annotations. #### Who are the annotators? No annotators. ### Personal and Sensitive Information All the images are collected from the Internet, and the copyright belongs to the original owners. If any of the images belongs to you and you would like it removed, please kindly inform the authors, and they will remove it from the dataset immediately. ## 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 - **Original Author**: [Radu Timofte](http://people.ee.ethz.ch/~timofter/) ### Licensing Information Please notice that this dataset is made available for academic research purpose only. All the images are collected from the Internet, and the copyright belongs to the original owners. If any of the images belongs to you and you would like it removed, please kindly inform the authors, and they will remove it from the dataset immediately. ### Citation Information ```bibtex @InProceedings{Agustsson_2017_CVPR_Workshops, author = {Agustsson, Eirikur and Timofte, Radu}, title = {NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study}, booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, url = "http://www.vision.ee.ethz.ch/~timofter/publications/Agustsson-CVPRW-2017.pdf", month = {July}, year = {2017} } ``` ### Contributions Thanks to [@eugenesiow](https://github.com/eugenesiow) for adding this dataset.
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philipphager/baidu-ultr-606k
2023-10-30T10:25:33.000Z
[ "task_categories:text-retrieval", "license:cc-by-nc-4.0", "MonoBERT", "unbiased learning to rank", "ultr", "baidu", "ltr", "clicks", "region:us" ]
philipphager
Query-document vectors and clicks for the Baidu Unbiased Learning to Rank dataset used at the WSDM23 cup. This dataset uses the winning BERT cross-encoder from Tencent to compute query-document vectors (768 dims), mainly for ease of use and to enable usage of simpler, smaller neural networks that are more common in ULTR research. This dataset contains features for part-00000.gz of the Baidu dataset, containing 589,824 queries and 6,271,536 documents.
@InProceedings{huggingface:dataset, title = {baidu-ultr-606k}, author={Philipp Hager}, year={2023} }
1
1,177
2023-10-17T15:08:53
--- license: cc-by-nc-4.0 task_categories: - text-retrieval tags: - MonoBERT - unbiased learning to rank - ultr - baidu - ltr - clicks pretty_name: Baidu ULTR-606K --- # Baidu Unbiased Learning to Rank - 606K At NeurIPS 2022, [Baidu released the first large-scale click dataset](A Large Scale Search Dataset for Unbiased Learning to Rank ) for unbiased learing to rank. The full dataset contains over 1.2 B sessions of users browsing the Baidu search engine. The dataset comprises a.o., user clicks, skips, dwell-time, and the original query and document text. Traditionally, the unbiased learning to rank community uses query-document feature representations (e.g., [MSLR30K](https://www.microsoft.com/en-us/research/project/mslr/), [Istella-S](http://quickrank.isti.cnr.it/istella-dataset/), or [Yahoo! Webscope](https://webscope.sandbox.yahoo.com/catalog.php?datatype=c)), small neural network models, and focuses more on the aspect of removing click biases. To make the massive Baidu dataset more accessible, we encode the query and document text into query-document embeddings using the winning BERT cross-encoder model from the WSDM Cup 2023. As BERT embeddings with 768 dimensions use a lot of memory, we encode them with half-precision floats and compress the dataset using [Arrow feather](https://arrow.apache.org/docs/python/feather.html). This dataset focuses only on the first partition (partition-0) from the [original dataset](https://drive.google.com/drive/folders/1Q3bzSgiGh1D5iunRky6mb89LpxfAO73J). It comprises 606k user sessions with clicks for training and the complete Baidu validation set containing expert annotations (the test set from the WSDM Cup 2023 was not released publicly). ## I. Load training clicks Load clicks from the training dataset (patition 0 / 1,999) of Baidu ULTR. The first partition contains 606k search queries. We converted the query and document text from the original dataset to query-document features using the winning [BERT cross-encoder model](https://github.com/lixsh6/Tencent_wsdm_cup2023/tree/main/pytorch_unbias/) from the WSDM Cup 2023. ``` from datasets import load_dataset from torch.utils.data import DataLoader dataset = load_dataset("philipphager/baidu-ultr-606k", name="clicks", split="train") dataset.set_format("torch") loader = DataLoader(dataset, collate_fn=collate_clicks, batch_size=8) ``` You can use the following `collate_fn` method to create a batch of queries (with differnet number of documents) and to select which columns to load from the training set. ``` from collections import defaultdict from typing import List import torch from torch.nn.utils.rnn import pad_sequence def collate_clicks(samples: List): """ Pad a batch of queries to the size of the query with the most documents. """ batch = defaultdict(lambda: []) for sample in samples: # Select information to load for each query: # Available are: ["query_id", "position", "click", "n", "query_document_embedding", # "media_type", "displayed_time", "serp_height", "slipoff_count_after_click"] batch["query_id"].append(sample["query_id"]) batch["query_document_embedding"].append(sample["query_document_embedding"]) batch["click"].append(sample["click"]) batch["n"].append(sample["n"]) # Convert to tensors and pad to document-level features: return { "query_id": torch.tensor(batch["query_id"]), "query_document_embedding": pad_sequence( batch["query_document_embedding"], batch_first=True ), "click": pad_sequence(batch["click"], batch_first=True), "n": torch.tensor(batch["n"]), } ``` ## II. Load expert annotations for validation Only the validation set of the Baidu ULTR dataset is public. It also contains different columns from the training set, so you need to adjust your collate function accordingly: ``` from datasets import load_dataset from torch.utils.data import DataLoader val_dataset = load_dataset("philipphager/baidu-ultr-606k", name="annotations", split="validation") val_dataset.set_format("torch") loader = DataLoader(val_dataset, collate_fn=collate_annotations, batch_size=8) ``` Aggregating annotations: ``` def collate_annotations(samples: List): """ Pad a batch of queries to the size of the query with the most documents. """ batch = defaultdict(lambda: []) for sample in samples: # Available are: ["query_id", "label", "n", "query_document_embedding", "frequency_bucket"] batch["query_id"].append(sample["query_id"]) batch["query_document_embedding"].append(sample["query_document_embedding"]) batch["label"].append(sample["label"]) batch["n"].append(sample["n"]) batch["frequency_bucket"].append(sample["frequency_bucket"]) # Convert to tensors and pad to document-level features: return { "query_id": torch.tensor(batch["query_id"]), "query_document_embedding": pad_sequence( batch["query_document_embedding"], batch_first=True ), "label": pad_sequence(batch["label"], batch_first=True), "n": torch.tensor(batch["n"]), "frequency_bucket": torch.tensor(batch["frequency_bucket"]), } ```
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roszcz/masked-maestro-v3
2023-10-02T15:21:06.000Z
[ "region:us" ]
roszcz
null
null
0
1,176
2023-10-02T12:02:32
--- dataset_info: features: - name: pitch sequence: int8 length: 90 - name: start sequence: float64 length: 90 - name: dstart sequence: float64 length: 90 - name: end sequence: float64 length: 90 - name: duration sequence: float64 length: 90 - name: velocity sequence: int8 length: 90 - name: source dtype: string - name: masking_space struct: - name: <Random Mask> sequence: bool length: 90 - name: <LH Mask> sequence: bool length: 90 - name: <RH Mask> sequence: bool length: 90 - name: <Harmonic Root Mask> sequence: bool length: 90 - name: <Harmonic Outliers Mask> sequence: bool length: 90 splits: - name: test num_bytes: 472275625 num_examples: 136870 - name: validation num_bytes: 407260307 num_examples: 118080 - name: train num_bytes: 3605902471 num_examples: 1045755 download_size: 4317450762 dataset_size: 4485438403 --- # Dataset Card for "masked-maestro-v3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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nlphuji/flickr_1k_test_image_text_retrieval
2023-01-14T19:54:08.000Z
[ "region:us" ]
nlphuji
null
null
0
1,172
2023-01-12T14:36:57
# Flickr30k (1K test set) Original paper: [From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions](https://aclanthology.org/Q14-1006) Homepage: https://shannon.cs.illinois.edu/DenotationGraph/ 1K test set split from: http://cs.stanford.edu/people/karpathy/deepimagesent/caption_datasets.zip Bibtex: ``` @article{young2014image, title={From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions}, author={Young, Peter and Lai, Alice and Hodosh, Micah and Hockenmaier, Julia}, journal={Transactions of the Association for Computational Linguistics}, volume={2}, pages={67--78}, year={2014}, publisher={MIT Press} } ```
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CM/codexglue_code2text_javascript
2023-04-22T01:51:42.000Z
[ "region:us" ]
CM
null
null
2
1,171
2023-04-22T01:51:30
--- dataset_info: features: - name: id dtype: int32 - name: repo dtype: string - name: path dtype: string - name: func_name dtype: string - name: original_string dtype: string - name: language dtype: string - name: code dtype: string - name: code_tokens sequence: string - name: docstring dtype: string - name: docstring_tokens sequence: string - name: sha dtype: string - name: url dtype: string splits: - name: train num_bytes: 160860431 num_examples: 58025 - name: validation num_bytes: 10337344 num_examples: 3885 - name: test num_bytes: 10190713 num_examples: 3291 download_size: 65795549 dataset_size: 181388488 --- # Dataset Card for "codexglue_code2text_javascript" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
916
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Nan-Do/SPP_30K_reasoning_tasks
2023-08-22T07:09:57.000Z
[ "task_categories:text-generation", "task_categories:conversational", "task_categories:text2text-generation", "language:en", "code", "python", "reasoning", "region:us" ]
Nan-Do
null
null
1
1,170
2023-08-21T02:34:43
--- dataset_info: features: - name: type dtype: int64 - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 44253001 num_examples: 89898 download_size: 10073876 dataset_size: 44253001 task_categories: - text-generation - conversational - text2text-generation language: - en tags: - code - python - reasoning pretty_name: SPP python reasoning tasks --- # Dataset Card for "SPP_30K_verified_tasks" ### Dataset Summary This is an augmented version of the [Synthetic Python Problems(SPP) Dataset](https://huggingface.co/datasets/wuyetao/spp). This dataset has been generated from the subset of the data has been de-duplicated and verified using a Python interpreter. (SPP_30k_verified.jsonl). The original dataset contains small Python functions that include a docstring with a small description of what the function does and some calling examples for the function. The current dataset includes three different tasks: - Type 1: give the code as input and ask the model to generate some example calls along the expected return value - Type 2: give the description and the example calling calls and ask the model to write the function. - Type 3: give the the function and the example calls without the expected value as input and ask the model to write what the function should return. For example: ```python def lowest_prime_factor(n): """ Find the smallest prime factor of a given number. Examples: >>> lowest_prime_factor(12) 2 >>> lowest_prime_factor(14) 2 >>> lowest_prime_factor(8) 2 """ smallest = 2 while(n % smallest!= 0): smallest+=1 return smallest ``` First task: ``` INSTRUCTION: Execute the following function with several example calls, showing the values returned in each case. INPUT: def lowest_prime_factor(n): smallest = 2 while(n % smallest!= 0): smallest+=1 return smallest OUTPUT: Sure, here you have several example calls along the return values they will generate. """ >>> lowest_prime_factor(12) 2 >>> lowest_prime_factor(14) 2 >>> lowest_prime_factor(8) 2 """ ``` Second task: ``` INSTRUCTION: Follow the task given and refer to the provided calling examples as you create the function's body. INPUT: Find the smallest prime factor of a given number. Examples: >>> lowest_prime_factor(12) 2 >>> lowest_prime_factor(14) 2 >>> lowest_prime_factor(8) 2 OUTPUT: Of course, here's a function implementation based on the provided calling examples and task. """ python def lowest_prime_factor(n): smallest = 2 while(n % smallest!= 0): smallest+=1 return smallest """ ``` Third task: ``` INSTRUCTION: Examine the given function and the accompanying instance function calls, and outline the specific output generated by each call. INPUT: def lowest_prime_factor(n): smallest = 2 while(n % smallest!= 0): smallest+=1 return smallest Function calls: >>> lowest_prime_factor(12) >>> lowest_prime_factor(14) >>> lowest_prime_factor(8) OUTPUT: Sure thing, here you have the specific output generated by each function call. >>> lowest_prime_factor(12) 2 >>> lowest_prime_factor(14) 2 >>> lowest_prime_factor(8) 2 ``` ### Languages The dataset is in English. ### Data Splits There are no splits (Only training). ## Dataset Creation August of 2023 ### Curation Rationale This dataset was created to improve the Python 3 reasoning/understanding capabilities of LLMs. ### Source Data The source dataset can be found at [Synthetic Python Problems(SPP) Dataset](https://huggingface.co/datasets/wuyetao/spp). ### Annotations The dataset includes an instruction, input, output and type columns. The type colum indicates the type of task (from 1 to 3). #### Annotation process The responses were generated parsing the docstrings of the functions.
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spider
2022-11-03T16:31:49.000Z
[ "task_categories:text2text-generation", "annotations_creators:expert-generated", "language_creators:expert-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:cc-by-4.0", "text-to-sql", "region:us" ]
null
Spider is a large-scale complex and cross-domain semantic parsing and text-toSQL dataset annotated by 11 college students
@article{yu2018spider, title={Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task}, author={Yu, Tao and Zhang, Rui and Yang, Kai and Yasunaga, Michihiro and Wang, Dongxu and Li, Zifan and Ma, James and Li, Irene and Yao, Qingning and Roman, Shanelle and others}, journal={arXiv preprint arXiv:1809.08887}, year={2018} }
57
1,168
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - expert-generated - machine-generated language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text2text-generation task_ids: [] paperswithcode_id: spider-1 pretty_name: Spider tags: - text-to-sql dataset_info: features: - name: db_id dtype: string - name: query dtype: string - name: question dtype: string - name: query_toks sequence: string - name: query_toks_no_value sequence: string - name: question_toks sequence: string config_name: spider splits: - name: train num_bytes: 4743786 num_examples: 7000 - name: validation num_bytes: 682090 num_examples: 1034 download_size: 99736136 dataset_size: 5425876 --- # Dataset Card for Spider ## 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://yale-lily.github.io/spider - **Repository:** https://github.com/taoyds/spider - **Paper:** https://www.aclweb.org/anthology/D18-1425/ - **Point of Contact:** [Yale LILY](https://yale-lily.github.io/) ### Dataset Summary Spider is a large-scale complex and cross-domain semantic parsing and text-to-SQL dataset annotated by 11 Yale students The goal of the Spider challenge is to develop natural language interfaces to cross-domain databases ### Supported Tasks and Leaderboards The leaderboard can be seen at https://yale-lily.github.io/spider ### Languages The text in the dataset is in English. ## Dataset Structure ### Data Instances **What do the instances that comprise the dataset represent?** Each instance is natural language question and the equivalent SQL query **How many instances are there in total?** **What data does each instance consist of?** [More Information Needed] ### Data Fields * **db_id**: Database name * **question**: Natural language to interpret into SQL * **query**: Target SQL query * **query_toks**: List of tokens for the query * **query_toks_no_value**: List of tokens for the query * **question_toks**: List of tokens for the question ### Data Splits **train**: 7000 questions and SQL query pairs **dev**: 1034 question and SQL query pairs [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? [More Information Needed] ### Annotations The dataset was annotated by 11 college students at Yale University #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases [More Information Needed] ### Other Known Limitations ## Additional Information The listed authors in the homepage are maintaining/supporting the dataset. ### Dataset Curators [More Information Needed] ### Licensing Information The spider dataset is licensed under the [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode) [More Information Needed] ### Citation Information ``` @article{yu2018spider, title={Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task}, author={Yu, Tao and Zhang, Rui and Yang, Kai and Yasunaga, Michihiro and Wang, Dongxu and Li, Zifan and Ma, James and Li, Irene and Yao, Qingning and Roman, Shanelle and others}, journal={arXiv preprint arXiv:1809.08887}, year={2018} } ``` ### Contributions Thanks to [@olinguyen](https://github.com/olinguyen) for adding this dataset.
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bigbio/med_qa
2023-09-26T13:00:32.000Z
[ "multilinguality:multilingual", "language:en", "language:zh", "license:unknown", "region:us" ]
bigbio
In this work, we present the first free-form multiple-choice OpenQA dataset for solving medical problems, MedQA, collected from the professional medical board exams. It covers three languages: English, simplified Chinese, and traditional Chinese, and contains 12,723, 34,251, and 14,123 questions for the three languages, respectively. Together with the question data, we also collect and release a large-scale corpus from medical textbooks from which the reading comprehension models can obtain necessary knowledge for answering the questions.
@article{jin2021disease, 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={Applied Sciences}, volume={11}, number={14}, pages={6421}, year={2021}, publisher={MDPI} }
23
1,164
2022-11-13T22:09:18
--- language: - en - zh bigbio_language: - English - Chinese (Simplified) - Chinese (Traditional, Taiwan) license: unknown multilinguality: multilingual bigbio_license_shortname: UNKNOWN pretty_name: MedQA homepage: https://github.com/jind11/MedQA bigbio_pubmed: False bigbio_public: True bigbio_tasks: - QUESTION_ANSWERING --- # Dataset Card for MedQA ## Dataset Description - **Homepage:** https://github.com/jind11/MedQA - **Pubmed:** False - **Public:** True - **Tasks:** QA In this work, we present the first free-form multiple-choice OpenQA dataset for solving medical problems, MedQA, collected from the professional medical board exams. It covers three languages: English, simplified Chinese, and traditional Chinese, and contains 12,723, 34,251, and 14,123 questions for the three languages, respectively. Together with the question data, we also collect and release a large-scale corpus from medical textbooks from which the reading comprehension models can obtain necessary knowledge for answering the questions. ## Citation Information ``` @article{jin2021disease, 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={Applied Sciences}, volume={11}, number={14}, pages={6421}, year={2021}, publisher={MDPI} } ```
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jxm/the_office_lines
2023-03-07T18:30:51.000Z
[ "region:us" ]
jxm
null
null
18
1,162
2023-03-07T18:24:28
## the_office_lines <img src="https://a.pinatafarm.com/1351x1232/c8fa71efd1/the-office-handshake.jpg" width="256"> A dataset of lines from the U.S. version of the tv show "The Office". Lines were originally scraped from the website [officequotes.net](https://www.officequotes.net/), are fan-transcribed, and may be of dubious quality. Contains a train split (47,927 lines), test split (5,991 lines) and validation split (5,991 lines). Contains lines from all 9 seasons, every episode, but may be complete. Lines are annotated with an ID number, season number, episode number, scene number (within the episode), speaker name, and whether or not the text came from a deleted scene. Here is an example: ``` > dataset["val"][0] {'id': 3735, 'season': 2, 'episode': 5, 'scene': 32, 'line_text': 'No, you have the power to undo it.', 'speaker': 'Creed', 'deleted': False} ```
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mlabonne/guanaco-llama2
2023-07-26T14:49:17.000Z
[ "region:us" ]
mlabonne
null
null
7
1,161
2023-07-23T13:53:10
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 15409089 num_examples: 9846 - name: test num_bytes: 815811 num_examples: 518 download_size: 9461517 dataset_size: 16224900 --- # Guanaco: Lazy Llama 2 Formatting This is the excellent [`timdettmers/openassistant-guanaco`](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) dataset, processed to match Llama 2's prompt format as described [in this article](https://huggingface.co/blog/llama2#how-to-prompt-llama-2). Useful if you don't want to reformat it by yourself (e.g., using a script). It was designed for [this article](https://mlabonne.github.io/blog/posts/Fine_Tune_Your_Own_Llama_2_Model_in_a_Colab_Notebook.html) about fine-tuning a Llama 2 model in a Google Colab.
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Tevatron/msmarco-passage
2023-07-18T07:34:33.000Z
[ "region:us" ]
Tevatron
null
@misc{bajaj2018ms, title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset}, author={Payal Bajaj and Daniel Campos and Nick Craswell and Li Deng and Jianfeng Gao and Xiaodong Liu and Rangan Majumder and Andrew McNamara and Bhaskar Mitra and Tri Nguyen and Mir Rosenberg and Xia Song and Alina Stoica and Saurabh Tiwary and Tong Wang}, year={2018}, eprint={1611.09268}, archivePrefix={arXiv}, primaryClass={cs.CL} }
3
1,156
2022-03-02T23:29:22
Entry not found
15
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minskiter/weibo
2023-07-22T13:49:08.000Z
[ "size_categories:1K<n<10K", "language:zh", "license:apache-2.0", "social", "region:us" ]
minskiter
The Weibo NER dataset is a Chinese Named Entity Recognition dataset drawn from the social media website Sina Weibo.
@inproceedings{peng-dredze-2015-named, title = "Named Entity Recognition for {C}hinese Social Media with Jointly Trained Embeddings", author = "Peng, Nanyun and Dredze, Mark", booktitle = "Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing", month = sep, year = "2015", address = "Lisbon, Portugal", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/D15-1064", doi = "10.18653/v1/D15-1064", pages = "548--554", }
0
1,156
2023-07-17T07:31:25
--- license: apache-2.0 dataset_info: features: - name: text sequence: string - name: labels sequence: class_label: names: '0': O '1': B-PER.NAM '2': I-PER.NAM '3': E-PER.NAM '4': S-PER.NAM '5': B-ORG.NAM '6': I-ORG.NAM '7': E-ORG.NAM '8': S-ORG.NAM '9': B-LOC.NAM '10': I-LOC.NAM '11': E-LOC.NAM '12': S-LOC.NAM '13': B-GPE.NAM '14': I-GPE.NAM '15': E-GPE.NAM '16': S-GPE.NAM '17': B-PER.NOM '18': I-PER.NOM '19': E-PER.NOM '20': S-PER.NOM '21': B-ORG.NOM '22': I-ORG.NOM '23': E-ORG.NOM '24': S-ORG.NOM '25': B-LOC.NOM '26': I-LOC.NOM '27': E-LOC.NOM '28': S-LOC.NOM '29': B-GPE.NOM '30': I-GPE.NOM '31': E-GPE.NOM '32': S-GPE.NOM splits: - name: train num_bytes: 1095833 num_examples: 1350 - name: validation num_bytes: 215953 num_examples: 270 - name: test num_bytes: 220694 num_examples: 270 download_size: 217348 dataset_size: 1532480 language: - zh tags: - social size_categories: - 1K<n<10K --- ### How to loading dataset? ```python from datasets import load_dataset datasets = load_dataset("minskiter/weibo",save_infos=True) train,validation,test = datasets['train'],datasets['validation'],datasets['test'] # convert label to str print(train.features['labels'].feature.int2str(0)) ``` ### Force Update ```python from datasets import load_dataset datasets = load_dataset("minskiter/weibo", download_mode="force_redownload") ``` ### CHANGE LOGS - 21/7/2023 v1.0.2 Fix data format. - 16/7/2023 v1.0.0 Publish weibo data.
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mlsum
2023-06-01T14:59:54.000Z
[ "task_categories:summarization", "task_categories:translation", "task_categories:text-classification", "task_ids:news-articles-summarization", "task_ids:multi-class-classification", "task_ids:multi-label-classification", "task_ids:topic-classification", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "size_categories:100K<n<1M", "size_categories:10K<n<100K", "source_datasets:extended|cnn_dailymail", "source_datasets:original", "language:de", "language:es", "language:fr", "language:ru", "language:tr", "license:other", "region:us" ]
null
We present MLSUM, the first large-scale MultiLingual SUMmarization dataset. Obtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages -- namely, French, German, Spanish, Russian, Turkish. Together with English newspapers from the popular CNN/Daily mail dataset, the collected data form a large scale multilingual dataset which can enable new research directions for the text summarization community. We report cross-lingual comparative analyses based on state-of-the-art systems. These highlight existing biases which motivate the use of a multi-lingual dataset.
@article{scialom2020mlsum, title={MLSUM: The Multilingual Summarization Corpus}, author={Scialom, Thomas and Dray, Paul-Alexis and Lamprier, Sylvain and Piwowarski, Benjamin and Staiano, Jacopo}, journal={arXiv preprint arXiv:2004.14900}, year={2020} }
26
1,149
2022-03-02T23:29:22
--- annotations_creators: - found language_creators: - found language: - de - es - fr - ru - tr license: - other multilinguality: - multilingual size_categories: - 100K<n<1M - 10K<n<100K source_datasets: - extended|cnn_dailymail - original task_categories: - summarization - translation - text-classification task_ids: - news-articles-summarization - multi-class-classification - multi-label-classification - topic-classification paperswithcode_id: mlsum pretty_name: MLSUM dataset_info: - config_name: de features: - name: text dtype: string - name: summary dtype: string - name: topic dtype: string - name: url dtype: string - name: title dtype: string - name: date dtype: string splits: - name: train num_bytes: 846959840 num_examples: 220887 - name: validation num_bytes: 47119541 num_examples: 11394 - name: test num_bytes: 46847612 num_examples: 10701 download_size: 1005814154 dataset_size: 940926993 - config_name: es features: - name: text dtype: string - name: summary dtype: string - name: topic dtype: string - name: url dtype: string - name: title dtype: string - name: date dtype: string splits: - name: train num_bytes: 1214558302 num_examples: 266367 - name: validation num_bytes: 50643400 num_examples: 10358 - name: test num_bytes: 71263665 num_examples: 13920 download_size: 1456211154 dataset_size: 1336465367 - config_name: fr features: - name: text dtype: string - name: summary dtype: string - name: topic dtype: string - name: url dtype: string - name: title dtype: string - name: date dtype: string splits: - name: train num_bytes: 1471965014 num_examples: 392902 - name: validation num_bytes: 70413212 num_examples: 16059 - name: test num_bytes: 69660288 num_examples: 15828 download_size: 1849565564 dataset_size: 1612038514 - config_name: ru features: - name: text dtype: string - name: summary dtype: string - name: topic dtype: string - name: url dtype: string - name: title dtype: string - name: date dtype: string splits: - name: train num_bytes: 257389497 num_examples: 25556 - name: validation num_bytes: 9128497 num_examples: 750 - name: test num_bytes: 9656398 num_examples: 757 download_size: 766226107 dataset_size: 276174392 - config_name: tu features: - name: text dtype: string - name: summary dtype: string - name: topic dtype: string - name: url dtype: string - name: title dtype: string - name: date dtype: string splits: - name: train num_bytes: 641622783 num_examples: 249277 - name: validation num_bytes: 25530661 num_examples: 11565 - name: test num_bytes: 27830212 num_examples: 12775 download_size: 942308960 dataset_size: 694983656 config_names: - de - es - fr - ru - tu --- # Dataset Card for MLSUM ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** []() - **Repository:** https://github.com/recitalAI/MLSUM - **Paper:** https://www.aclweb.org/anthology/2020.emnlp-main.647/ - **Point of Contact:** [email](thomas@recital.ai) - **Size of downloaded dataset files:** 1.83 GB - **Size of the generated dataset:** 4.86 GB - **Total amount of disk used:** 6.69 GB ### Dataset Summary We present MLSUM, the first large-scale MultiLingual SUMmarization dataset. Obtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages -- namely, French, German, Spanish, Russian, Turkish. Together with English newspapers from the popular CNN/Daily mail dataset, the collected data form a large scale multilingual dataset which can enable new research directions for the text summarization community. We report cross-lingual comparative analyses based on state-of-the-art systems. These highlight existing biases which motivate the use of a multi-lingual dataset. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### de - **Size of downloaded dataset files:** 346.58 MB - **Size of the generated dataset:** 940.93 MB - **Total amount of disk used:** 1.29 GB An example of 'validation' looks as follows. ``` { "date": "01/01/2001", "summary": "A text", "text": "This is a text", "title": "A sample", "topic": "football", "url": "https://www.google.com" } ``` #### es - **Size of downloaded dataset files:** 513.31 MB - **Size of the generated dataset:** 1.34 GB - **Total amount of disk used:** 1.85 GB An example of 'validation' looks as follows. ``` { "date": "01/01/2001", "summary": "A text", "text": "This is a text", "title": "A sample", "topic": "football", "url": "https://www.google.com" } ``` #### fr - **Size of downloaded dataset files:** 619.99 MB - **Size of the generated dataset:** 1.61 GB - **Total amount of disk used:** 2.23 GB An example of 'validation' looks as follows. ``` { "date": "01/01/2001", "summary": "A text", "text": "This is a text", "title": "A sample", "topic": "football", "url": "https://www.google.com" } ``` #### ru - **Size of downloaded dataset files:** 106.22 MB - **Size of the generated dataset:** 276.17 MB - **Total amount of disk used:** 382.39 MB An example of 'train' looks as follows. ``` { "date": "01/01/2001", "summary": "A text", "text": "This is a text", "title": "A sample", "topic": "football", "url": "https://www.google.com" } ``` #### tu - **Size of downloaded dataset files:** 247.50 MB - **Size of the generated dataset:** 694.99 MB - **Total amount of disk used:** 942.48 MB An example of 'train' looks as follows. ``` { "date": "01/01/2001", "summary": "A text", "text": "This is a text", "title": "A sample", "topic": "football", "url": "https://www.google.com" } ``` ### Data Fields The data fields are the same among all splits. #### de - `text`: a `string` feature. - `summary`: a `string` feature. - `topic`: a `string` feature. - `url`: a `string` feature. - `title`: a `string` feature. - `date`: a `string` feature. #### es - `text`: a `string` feature. - `summary`: a `string` feature. - `topic`: a `string` feature. - `url`: a `string` feature. - `title`: a `string` feature. - `date`: a `string` feature. #### fr - `text`: a `string` feature. - `summary`: a `string` feature. - `topic`: a `string` feature. - `url`: a `string` feature. - `title`: a `string` feature. - `date`: a `string` feature. #### ru - `text`: a `string` feature. - `summary`: a `string` feature. - `topic`: a `string` feature. - `url`: a `string` feature. - `title`: a `string` feature. - `date`: a `string` feature. #### tu - `text`: a `string` feature. - `summary`: a `string` feature. - `topic`: a `string` feature. - `url`: a `string` feature. - `title`: a `string` feature. - `date`: a `string` feature. ### Data Splits |name|train |validation|test | |----|-----:|---------:|----:| |de |220887| 11394|10701| |es |266367| 10358|13920| |fr |392902| 16059|15828| |ru | 25556| 750| 757| |tu |249277| 11565|12775| ## 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 Usage of dataset is restricted to non-commercial research purposes only. Copyright belongs to the original copyright holders. See https://github.com/recitalAI/MLSUM#mlsum ### Citation Information ``` @article{scialom2020mlsum, title={MLSUM: The Multilingual Summarization Corpus}, author={Scialom, Thomas and Dray, Paul-Alexis and Lamprier, Sylvain and Piwowarski, Benjamin and Staiano, Jacopo}, journal={arXiv preprint arXiv:2004.14900}, year={2020} } ``` ### Contributions Thanks to [@RachelKer](https://github.com/RachelKer), [@albertvillanova](https://github.com/albertvillanova), [@thomwolf](https://github.com/thomwolf) for adding this dataset.
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Intel/orca_dpo_pairs
2023-09-26T11:18:30.000Z
[ "license:apache-2.0", "arxiv:2306.02707", "region:us" ]
Intel
null
null
1
1,149
2023-09-21T10:35:16
--- license: apache-2.0 --- The dataset contains 12k examples from [Orca](https://arxiv.org/abs/2306.02707) style dataset [Open-Orca/OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca).
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clarin-pl/polemo2-official
2022-08-29T16:40:01.000Z
[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "language_creators:other", "multilinguality:monolingual", "size_categories:8K", "size_categories:1K<n<10K", "source_datasets:original", "language:pl", "license:cc-by-sa-4.0", "region:us" ]
clarin-pl
PolEmo 2.0: Corpus of Multi-Domain Consumer Reviews, evaluation data for article presented at CoNLL.
@inproceedings{kocon-etal-2019-multi, title = "Multi-Level Sentiment Analysis of {P}ol{E}mo 2.0: Extended Corpus of Multi-Domain Consumer Reviews", author = "Koco{\'n}, Jan and Mi{\l}kowski, Piotr and Za{\'s}ko-Zieli{\'n}ska, Monika", booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)", month = nov, year = "2019", address = "Hong Kong, China", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/K19-1092", doi = "10.18653/v1/K19-1092", pages = "980--991",}
4
1,145
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - other language: - pl license: - cc-by-sa-4.0 multilinguality: - monolingual pretty_name: 'Polemo2' size_categories: - 8K - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification --- # Polemo2 ## Description The PolEmo2.0 is a dataset of online consumer reviews from four domains: medicine, hotels, products, and university. It is human-annotated on a level of full reviews and individual sentences. Current version (PolEmo 2.0) contains 8,216 reviews having 57,466 sentences. Each text and sentence was manually annotated with sentiment in the 2+1 scheme, which gives a total of 197,046 annotations. About 85% of the reviews are from the medicine and hotel domains. Each review is annotated with four labels: positive, negative, neutral, or ambiguous. ## Tasks (input, output and metrics) The task is to predict the correct label of the review. **Input** ('*text*' column): sentence **Output** ('*target*' column): label for sentence sentiment ('zero': neutral, 'minus': negative, 'plus': positive, 'amb': ambiguous) **Domain**: Online reviews **Measurements**: Accuracy, F1 Macro **Example**: Input: `Na samym wejściu hotel śmierdzi . W pokojach jest pleśń na ścianach , brudny dywan . W łazience śmierdzi chemią , hotel nie grzeje w pokojach panuje chłód . Wyposażenie pokoju jest stare , kran się rusza , drzwi na balkon nie domykają się . Jedzenie jest w małych ilościach i nie smaczne . Nie polecam nikomu tego hotelu .` Input (translated by DeepL): `At the very entrance the hotel stinks . In the rooms there is mold on the walls , dirty carpet . The bathroom smells of chemicals , the hotel does not heat in the rooms are cold . The room furnishings are old , the faucet moves , the door to the balcony does not close . The food is in small quantities and not tasty . I would not recommend this hotel to anyone .` Output: `1` (negative) ## Data splits | Subset | Cardinality | |--------|------------:| | train | 6573 | | val | 823 | | test | 820 | ## Class distribution | Class | train | dev | test | |:--------|--------:|-------------:|-------:| | minus | 0.3756 | 0.3694 | 0.4134 | | plus | 0.2775 | 0.2868 | 0.2768 | | amb | 0.1991 | 0.1883 | 0.1659 | | zero | 0.1477 | 0.1555 | 0.1439 | ## Citation ``` @inproceedings{kocon-etal-2019-multi, title = "Multi-Level Sentiment Analysis of {P}ol{E}mo 2.0: Extended Corpus of Multi-Domain Consumer Reviews", author = "Koco{\'n}, Jan and Mi{\l}kowski, Piotr and Za{\'s}ko-Zieli{\'n}ska, Monika", booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)", month = nov, year = "2019", address = "Hong Kong, China", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/K19-1092", doi = "10.18653/v1/K19-1092", pages = "980--991", abstract = "In this article we present an extended version of PolEmo {--} a corpus of consumer reviews from 4 domains: medicine, hotels, products and school. Current version (PolEmo 2.0) contains 8,216 reviews having 57,466 sentences. Each text and sentence was manually annotated with sentiment in 2+1 scheme, which gives a total of 197,046 annotations. We obtained a high value of Positive Specific Agreement, which is 0.91 for texts and 0.88 for sentences. PolEmo 2.0 is publicly available under a Creative Commons copyright license. We explored recent deep learning approaches for the recognition of sentiment, such as Bi-directional Long Short-Term Memory (BiLSTM) and Bidirectional Encoder Representations from Transformers (BERT).", } ``` ## License ``` Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) ``` ## Links [HuggingFace](https://huggingface.co/datasets/clarin-pl/polemo2-official) [Source](https://clarin-pl.eu/dspace/handle/11321/710) [Paper](https://aclanthology.org/K19-1092/) ## Examples ### Loading ```python from pprint import pprint from datasets import load_dataset dataset = load_dataset("clarin-pl/polemo2-official") pprint(dataset['train'][0]) # {'target': 1, # 'text': 'Na samym wejściu hotel śmierdzi . W pokojach jest pleśń na ścianach ' # ', brudny dywan . W łazience śmierdzi chemią , hotel nie grzeje w ' # 'pokojach panuje chłód . Wyposażenie pokoju jest stare , kran się ' # 'rusza , drzwi na balkon nie domykają się . Jedzenie jest w małych ' # 'ilościach i nie smaczne . Nie polecam nikomu tego hotelu .'} ``` ### Evaluation ```python import random from pprint import pprint from datasets import load_dataset, load_metric dataset = load_dataset("clarin-pl/polemo2-official") references = dataset["test"]["target"] # generate random predictions predictions = [random.randrange(max(references) + 1) for _ in range(len(references))] acc = load_metric("accuracy") f1 = load_metric("f1") acc_score = acc.compute(predictions=predictions, references=references) f1_score = f1.compute(predictions=predictions, references=references, average='macro') pprint(acc_score) pprint(f1_score) # {'accuracy': 0.2475609756097561} # {'f1': 0.23747048177471738} ```
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snips_built_in_intents
2023-01-25T14:44:32.000Z
[ "task_categories:text-classification", "task_ids:intent-classification", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:en", "license:cc0-1.0", "arxiv:1805.10190", "region:us" ]
null
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 2016-12-built-in-intents. The dataset contains 328 utterances over 10 intent classes. The related paper mentioned on the github page is https://arxiv.org/abs/1805.10190 and a related Medium post is https://medium.com/snips-ai/benchmarking-natural-language-understanding-systems-d35be6ce568d .
@article{DBLP:journals/corr/abs-1805-10190, author = {Alice Coucke and Alaa Saade and Adrien Ball and Th{\'{e}}odore Bluche and Alexandre Caulier and David Leroy and Cl{\'{e}}ment Doumouro and Thibault Gisselbrecht and Francesco Caltagirone and Thibaut Lavril and Ma{\"{e}}l Primet and Joseph Dureau}, title = {Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces}, journal = {CoRR}, volume = {abs/1805.10190}, year = {2018}, url = {http://arxiv.org/abs/1805.10190}, archivePrefix = {arXiv}, eprint = {1805.10190}, timestamp = {Mon, 13 Aug 2018 16:46:59 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1805-10190.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }
4
1,142
2022-03-02T23:29:22
--- 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.
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wmt18
2023-04-05T13:44:00.000Z
[ "task_categories:translation", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:translation", "size_categories:10M<n<100M", "source_datasets:extended|europarl_bilingual", "source_datasets:extended|news_commentary", "source_datasets:extended|opus_paracrawl", "source_datasets:extended|setimes", "source_datasets:extended|un_multi", "language:cs", "language:de", "language:en", "language:et", "language:fi", "language:kk", "language:ru", "language:tr", "language:zh", "license:unknown", "region:us" ]
null
null
@InProceedings{bojar-EtAl:2018:WMT1, author = {Bojar, Ond\v{r}ej and Federmann, Christian and Fishel, Mark and Graham, Yvette and Haddow, Barry and Huck, Matthias and Koehn, Philipp and Monz, Christof}, title = {Findings of the 2018 Conference on Machine Translation (WMT18)}, booktitle = {Proceedings of the Third Conference on Machine Translation, Volume 2: Shared Task Papers}, month = {October}, year = {2018}, address = {Belgium, Brussels}, publisher = {Association for Computational Linguistics}, pages = {272--307}, url = {http://www.aclweb.org/anthology/W18-6401} }
3
1,142
2022-03-02T23:29:22
--- annotations_creators: - no-annotation language_creators: - found language: - cs - de - en - et - fi - kk - ru - tr - zh license: - unknown multilinguality: - translation size_categories: - 10M<n<100M source_datasets: - extended|europarl_bilingual - extended|news_commentary - extended|opus_paracrawl - extended|setimes - extended|un_multi task_categories: - translation task_ids: [] pretty_name: WMT18 paperswithcode_id: wmt-2018 dataset_info: - config_name: cs-en features: - name: translation dtype: translation: languages: - cs - en splits: - name: train num_bytes: 1461016186 num_examples: 11046024 - name: validation num_bytes: 674430 num_examples: 3005 - name: test num_bytes: 696229 num_examples: 2983 download_size: 2030359086 dataset_size: 1462386845 - config_name: de-en features: - name: translation dtype: translation: languages: - de - en splits: - name: train num_bytes: 8187552108 num_examples: 42271874 - name: validation num_bytes: 729519 num_examples: 3004 - name: test num_bytes: 757649 num_examples: 2998 download_size: 3808612335 dataset_size: 8189039276 - config_name: et-en features: - name: translation dtype: translation: languages: - et - en splits: - name: train num_bytes: 647992667 num_examples: 2175873 - name: validation num_bytes: 459398 num_examples: 2000 - name: test num_bytes: 489394 num_examples: 2000 download_size: 524534404 dataset_size: 648941459 - config_name: fi-en features: - name: translation dtype: translation: languages: - fi - en splits: - name: train num_bytes: 857171881 num_examples: 3280600 - name: validation num_bytes: 1388828 num_examples: 6004 - name: test num_bytes: 691841 num_examples: 3000 download_size: 491874780 dataset_size: 859252550 - config_name: kk-en features: - name: translation dtype: translation: languages: - kk - en splits: - name: train - name: validation - name: test download_size: 0 dataset_size: 0 - config_name: ru-en features: - name: translation dtype: translation: languages: - ru - en splits: - name: train num_bytes: 13665367647 num_examples: 36858512 - name: validation num_bytes: 1040195 num_examples: 3001 - name: test num_bytes: 1085596 num_examples: 3000 download_size: 4195144356 dataset_size: 13667493438 - config_name: tr-en features: - name: translation dtype: translation: languages: - tr - en splits: - name: train num_bytes: 60416617 num_examples: 205756 - name: validation num_bytes: 752773 num_examples: 3007 - name: test num_bytes: 770313 num_examples: 3000 download_size: 62263061 dataset_size: 61939703 - config_name: zh-en features: - name: translation dtype: translation: languages: - zh - en splits: - name: train num_bytes: 5536169801 num_examples: 25160346 - name: validation num_bytes: 540347 num_examples: 2001 - name: test num_bytes: 1107522 num_examples: 3981 download_size: 2259428767 dataset_size: 5537817670 --- # Dataset Card for "wmt18" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [http://www.statmt.org/wmt18/translation-task.html](http://www.statmt.org/wmt18/translation-task.html) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 2.03 GB - **Size of the generated dataset:** 1.46 GB - **Total amount of disk used:** 3.49 GB ### Dataset Summary <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"> <p><b>Warning:</b> There are issues with the Common Crawl corpus data (<a href="https://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz">training-parallel-commoncrawl.tgz</a>):</p> <ul> <li>Non-English files contain many English sentences.</li> <li>Their "parallel" sentences in English are not aligned: they are uncorrelated with their counterpart.</li> </ul> <p>We have contacted the WMT organizers.</p> </div> Translation dataset based on the data from statmt.org. Versions exist for different years using a combination of data sources. The base `wmt` allows you to create a custom dataset by choosing your own data/language pair. This can be done as follows: ```python from datasets import inspect_dataset, load_dataset_builder inspect_dataset("wmt18", "path/to/scripts") builder = load_dataset_builder( "path/to/scripts/wmt_utils.py", language_pair=("fr", "de"), subsets={ datasets.Split.TRAIN: ["commoncrawl_frde"], datasets.Split.VALIDATION: ["euelections_dev2019"], }, ) # Standard version builder.download_and_prepare() ds = builder.as_dataset() # Streamable version ds = builder.as_streaming_dataset() ``` ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### cs-en - **Size of downloaded dataset files:** 2.03 GB - **Size of the generated dataset:** 1.46 GB - **Total amount of disk used:** 3.49 GB An example of 'validation' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### cs-en - `translation`: a multilingual `string` variable, with possible languages including `cs`, `en`. ### Data Splits |name | train |validation|test| |-----|-------:|---------:|---:| |cs-en|11046024| 3005|2983| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{bojar-EtAl:2018:WMT1, author = {Bojar, Ond {r}ej and Federmann, Christian and Fishel, Mark and Graham, Yvette and Haddow, Barry and Huck, Matthias and Koehn, Philipp and Monz, Christof}, title = {Findings of the 2018 Conference on Machine Translation (WMT18)}, booktitle = {Proceedings of the Third Conference on Machine Translation, Volume 2: Shared Task Papers}, month = {October}, year = {2018}, address = {Belgium, Brussels}, publisher = {Association for Computational Linguistics}, pages = {272--307}, url = {http://www.aclweb.org/anthology/W18-6401} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.
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BeIR/climate-fever-qrels
2022-10-23T06:08:28.000Z
[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "task_ids:fact-checking-retrieval", "multilinguality:monolingual", "language:en", "license:cc-by-sa-4.0", "region:us" ]
BeIR
null
null
0
1,139
2022-06-05T17:28:22
--- 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.
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assin2
2023-01-25T14:26:53.000Z
[ "task_categories:text-classification", "task_ids:text-scoring", "task_ids:natural-language-inference", "task_ids:semantic-similarity-scoring", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:pt", "license:unknown", "region:us" ]
null
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.
@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} }
9
1,138
2022-03-02T23:29:22
--- 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.
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turing-motors/LLaVA-Instruct-150K-JA
2023-08-28T11:26:23.000Z
[ "task_categories:visual-question-answering", "task_categories:question-answering", "size_categories:100K<n<1M", "language:ja", "license:cc-by-nc-4.0", "region:us" ]
turing-motors
null
null
4
1,136
2023-08-28T10:50:24
--- license: cc-by-nc-4.0 task_categories: - visual-question-answering - question-answering language: - ja pretty_name: Japanese LLaVA Visual Instruct 150K size_categories: - 100K<n<1M --- ## Dataset Details **Dataset Type:** Japanese LLaVA Instruct 150K is a localized version of the original LLaVA Visual Instruct 150K dataset. This version is translated into Japanese using DeepL API and is aimed at serving similar purposes in the context of Japanese language. **Resources for More Information:** For information on the original dataset: [LLaVA Visual Instruct 150K](https://llava-vl.github.io/) **License:** Attribution-NonCommercial 4.0 International (CC BY-NC-4.0) The dataset should abide by the policy of OpenAI: [OpenAI Terms of Use](https://openai.com/policies/terms-of-use) **Questions or Comments:** For questions or comments about the original model, you can go to [LLaVA GitHub Issues](https://github.com/haotian-liu/LLaVA/issues). ## Intended Use **Primary Intended Uses:** The primary use of this translated dataset is research on large multimodal models and chatbots in a Japanese context. **Primary Intended Users:** The primary intended users are researchers and hobbyists interested in computer vision, natural language processing, machine learning, and artificial intelligence, particularly those focusing on the Japanese language. --- **Note:** This dataset is a translation of the original LLaVA Visual Instruct 150K, carried out using the DeepL API. The license remains the same as the original dataset, Attribution-NonCommercial 4.0 International (CC BY-NC-4.0). ---
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open-web-math/open-web-math
2023-10-17T20:14:00.000Z
[ "arxiv:2310.06786", "region:us" ]
open-web-math
null
null
162
1,134
2023-09-06T00:25:12
--- 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} } ```
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theblackcat102/evol-codealpaca-v1
2023-09-07T11:42:00.000Z
[ "task_categories:text-generation", "size_categories:100K<n<1M", "language:en", "license:cc-by-nc-4.0", "code", "region:us" ]
theblackcat102
null
null
70
1,133
2023-07-23T01:28:44
--- license: cc-by-nc-4.0 task_categories: - text-generation language: - en tags: - code size_categories: - 100K<n<1M --- ## Evolved codealpaca Updates: * 2023/08/26 - Filtered results now only contain pure english instruction and removed any mentioned of trained by OAI response Median sequence length : 471 We employed a methodology similar to that of [WizardCoder](https://huggingface.co/WizardLM/WizardCoder-15B-V1.0), with the exception that ours is open-source. We used the gpt-4-0314 and gpt-4-0613 models to augment and answer each response, with the bulk of generation handled by gpt-4-0314. The aim of this dataset is twofold: firstly, to facilitate the recreation of other wizardcoder models using newer pretrained models, such as LLaMA-2; and secondly, to serve as a testing ground for the [evol-dataset](https://github.com/theblackcat102/evol-dataset) package, as we strive to develop improved future augmentation strategies. We used a total of [10 strategies](https://github.com/theblackcat102/evol-dataset/tree/main/evolinstruct/instructions) to augment the [HuggingFaceH4/CodeAlpaca_20K](https://huggingface.co/datasets/HuggingFaceH4/CodeAlpaca_20K) dataset and create our own. It's important to note that we introduced a new "language" augmentation strategy in this project, which enables the conversion of existing instructions into Chinese. A Chinese code evol version is now available here : [theblackcat102/evol-code-zh](https://huggingface.co/datasets/theblackcat102/evol-code-zh) ## Comparison to existing dataset Comparing to [nickrosh/Evol-Instruct-Code-80k-v1](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1), evol-codealpaca-v1 has longer instruction and output conversation ![](./48f1b380-dc0b-4b0b-9b97-3cc5aa619655.png) ### Citation If you use this dataset to finetune any LLMs just cite wizard coder ``` @misc{luo2023wizardcoder, title={WizardCoder: Empowering Code Large Language Models with Evol-Instruct}, author={Ziyang Luo and Can Xu and Pu Zhao and Qingfeng Sun and Xiubo Geng and Wenxiang Hu and Chongyang Tao and Jing Ma and Qingwei Lin and Daxin Jiang}, year={2023}, } ```
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natural_questions
2023-04-05T13:35:01.000Z
[ "task_categories:question-answering", "task_ids:open-domain-qa", "annotations_creators:no-annotation", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:cc-by-sa-3.0", "region:us" ]
null
The NQ corpus contains questions from real users, and it requires QA systems to read and comprehend an entire Wikipedia article that may or may not contain the answer to the question. The inclusion of real user questions, and the requirement that solutions should read an entire page to find the answer, cause NQ to be a more realistic and challenging task than prior QA datasets.
@article{47761, title = {Natural Questions: a Benchmark for Question Answering Research}, author = {Tom Kwiatkowski and Jennimaria Palomaki and Olivia Redfield and Michael Collins and Ankur Parikh and Chris Alberti and Danielle Epstein and Illia Polosukhin and Matthew Kelcey and Jacob Devlin and Kenton Lee and Kristina N. Toutanova and Llion Jones and Ming-Wei Chang and Andrew Dai and Jakob Uszkoreit and Quoc Le and Slav Petrov}, year = {2019}, journal = {Transactions of the Association of Computational Linguistics} }
24
1,122
2022-03-02T23:29:22
--- annotations_creators: - no-annotation language_creators: - crowdsourced language: - en license: - cc-by-sa-3.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - question-answering task_ids: - open-domain-qa paperswithcode_id: natural-questions pretty_name: Natural Questions dataset_info: features: - name: id dtype: string - name: document struct: - name: title dtype: string - name: url dtype: string - name: html dtype: string - name: tokens sequence: - name: token dtype: string - name: is_html dtype: bool - name: question struct: - name: text dtype: string - name: tokens sequence: string - name: annotations sequence: - name: id dtype: string - name: long_answer struct: - name: start_token dtype: int64 - name: end_token dtype: int64 - name: start_byte dtype: int64 - name: end_byte dtype: int64 - name: short_answers sequence: - name: start_token dtype: int64 - name: end_token dtype: int64 - name: start_byte dtype: int64 - name: end_byte dtype: int64 - name: text dtype: string - name: yes_no_answer dtype: class_label: names: '0': 'NO' '1': 'YES' - name: long_answer_candidates sequence: - name: start_token dtype: int64 - name: end_token dtype: int64 - name: start_byte dtype: int64 - name: end_byte dtype: int64 - name: top_label dtype: bool splits: - name: train num_bytes: 97445142568 num_examples: 307373 - name: validation num_bytes: 2353975312 num_examples: 7830 download_size: 45069199013 dataset_size: 99799117880 --- # Dataset Card for Natural Questions ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://ai.google.com/research/NaturalQuestions/dataset](https://ai.google.com/research/NaturalQuestions/dataset) - **Repository:** [https://github.com/google-research-datasets/natural-questions](https://github.com/google-research-datasets/natural-questions) - **Paper:** [https://research.google/pubs/pub47761/](https://research.google/pubs/pub47761/) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 45.07 GB - **Size of the generated dataset:** 99.80 GB - **Total amount of disk used:** 144.87 GB ### Dataset Summary The NQ corpus contains questions from real users, and it requires QA systems to read and comprehend an entire Wikipedia article that may or may not contain the answer to the question. The inclusion of real user questions, and the requirement that solutions should read an entire page to find the answer, cause NQ to be a more realistic and challenging task than prior QA datasets. ### Supported Tasks and Leaderboards [https://ai.google.com/research/NaturalQuestions](https://ai.google.com/research/NaturalQuestions) ### Languages en ## Dataset Structure ### Data Instances - **Size of downloaded dataset files:** 45.07 GB - **Size of the generated dataset:** 99.80 GB - **Total amount of disk used:** 144.87 GB An example of 'train' looks as follows. This is a toy example. ``` { "id": "797803103760793766", "document": { "title": "Google", "url": "http://www.wikipedia.org/Google", "html": "<html><body><h1>Google Inc.</h1><p>Google was founded in 1998 By:<ul><li>Larry</li><li>Sergey</li></ul></p></body></html>", "tokens":[ {"token": "<h1>", "start_byte": 12, "end_byte": 16, "is_html": True}, {"token": "Google", "start_byte": 16, "end_byte": 22, "is_html": False}, {"token": "inc", "start_byte": 23, "end_byte": 26, "is_html": False}, {"token": ".", "start_byte": 26, "end_byte": 27, "is_html": False}, {"token": "</h1>", "start_byte": 27, "end_byte": 32, "is_html": True}, {"token": "<p>", "start_byte": 32, "end_byte": 35, "is_html": True}, {"token": "Google", "start_byte": 35, "end_byte": 41, "is_html": False}, {"token": "was", "start_byte": 42, "end_byte": 45, "is_html": False}, {"token": "founded", "start_byte": 46, "end_byte": 53, "is_html": False}, {"token": "in", "start_byte": 54, "end_byte": 56, "is_html": False}, {"token": "1998", "start_byte": 57, "end_byte": 61, "is_html": False}, {"token": "by", "start_byte": 62, "end_byte": 64, "is_html": False}, {"token": ":", "start_byte": 64, "end_byte": 65, "is_html": False}, {"token": "<ul>", "start_byte": 65, "end_byte": 69, "is_html": True}, {"token": "<li>", "start_byte": 69, "end_byte": 73, "is_html": True}, {"token": "Larry", "start_byte": 73, "end_byte": 78, "is_html": False}, {"token": "</li>", "start_byte": 78, "end_byte": 83, "is_html": True}, {"token": "<li>", "start_byte": 83, "end_byte": 87, "is_html": True}, {"token": "Sergey", "start_byte": 87, "end_byte": 92, "is_html": False}, {"token": "</li>", "start_byte": 92, "end_byte": 97, "is_html": True}, {"token": "</ul>", "start_byte": 97, "end_byte": 102, "is_html": True}, {"token": "</p>", "start_byte": 102, "end_byte": 106, "is_html": True} ], }, "question" :{ "text": "who founded google", "tokens": ["who", "founded", "google"] }, "long_answer_candidates": [ {"start_byte": 32, "end_byte": 106, "start_token": 5, "end_token": 22, "top_level": True}, {"start_byte": 65, "end_byte": 102, "start_token": 13, "end_token": 21, "top_level": False}, {"start_byte": 69, "end_byte": 83, "start_token": 14, "end_token": 17, "top_level": False}, {"start_byte": 83, "end_byte": 92, "start_token": 17, "end_token": 20 , "top_level": False} ], "annotations": [{ "id": "6782080525527814293", "long_answer": {"start_byte": 32, "end_byte": 106, "start_token": 5, "end_token": 22, "candidate_index": 0}, "short_answers": [ {"start_byte": 73, "end_byte": 78, "start_token": 15, "end_token": 16, "text": "Larry"}, {"start_byte": 87, "end_byte": 92, "start_token": 18, "end_token": 19, "text": "Sergey"} ], "yes_no_answer": -1 }] } ``` ### Data Fields The data fields are the same among all splits. #### default - `id`: a `string` feature. - `document` a dictionary feature containing: - `title`: a `string` feature. - `url`: a `string` feature. - `html`: a `string` feature. - `tokens`: a dictionary feature containing: - `token`: a `string` feature. - `is_html`: a `bool` feature. - `start_byte`: a `int64` feature. - `end_byte`: a `int64` feature. - `question`: a dictionary feature containing: - `text`: a `string` feature. - `tokens`: a `list` of `string` features. - `long_answer_candidates`: a dictionary feature containing: - `start_token`: a `int64` feature. - `end_token`: a `int64` feature. - `start_byte`: a `int64` feature. - `end_byte`: a `int64` feature. - `top_level`: a `bool` feature. - `annotations`: a dictionary feature containing: - `id`: a `string` feature. - `long_answers`: a dictionary feature containing: - `start_token`: a `int64` feature. - `end_token`: a `int64` feature. - `start_byte`: a `int64` feature. - `end_byte`: a `int64` feature. - `candidate_index`: a `int64` feature. - `short_answers`: a dictionary feature containing: - `start_token`: a `int64` feature. - `end_token`: a `int64` feature. - `start_byte`: a `int64` feature. - `end_byte`: a `int64` feature. - `text`: a `string` feature. - `yes_no_answer`: a classification label, with possible values including `NO` (0), `YES` (1). ### Data Splits | name | train | validation | |---------|-------:|-----------:| | default | 307373 | 7830 | | dev | N/A | 7830 | ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [Creative Commons Attribution-ShareAlike 3.0 Unported](https://creativecommons.org/licenses/by-sa/3.0/). ### Citation Information ``` @article{47761, title = {Natural Questions: a Benchmark for Question Answering Research}, author = {Tom Kwiatkowski and Jennimaria Palomaki and Olivia Redfield and Michael Collins and Ankur Parikh and Chris Alberti and Danielle Epstein and Illia Polosukhin and Matthew Kelcey and Jacob Devlin and Kenton Lee and Kristina N. Toutanova and Llion Jones and Ming-Wei Chang and Andrew Dai and Jakob Uszkoreit and Quoc Le and Slav Petrov}, year = {2019}, journal = {Transactions of the Association of Computational Linguistics} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq) for adding this dataset.
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ncbi_disease
2023-01-25T14:41:18.000Z
[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:unknown", "region:us" ]
null
This paper presents the disease name and concept annotations of the NCBI disease corpus, a collection of 793 PubMed abstracts fully annotated at the mention and concept level to serve as a research resource for the biomedical natural language processing community. Each PubMed abstract was manually annotated by two annotators with disease mentions and their corresponding concepts in Medical Subject Headings (MeSH®) or Online Mendelian Inheritance in Man (OMIM®). Manual curation was performed using PubTator, which allowed the use of pre-annotations as a pre-step to manual annotations. Fourteen annotators were randomly paired and differing annotations were discussed for reaching a consensus in two annotation phases. In this setting, a high inter-annotator agreement was observed. Finally, all results were checked against annotations of the rest of the corpus to assure corpus-wide consistency. For more details, see: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3951655/ The original dataset can be downloaded from: https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/NCBI_corpus.zip This dataset has been converted to CoNLL format for NER using the following tool: https://github.com/spyysalo/standoff2conll Note: there is a duplicate document (PMID 8528200) in the original data, and the duplicate is recreated in the converted data.
@article{dougan2014ncbi, title={NCBI disease corpus: a resource for disease name recognition and concept normalization}, author={Dogan, Rezarta Islamaj and Leaman, Robert and Lu, Zhiyong}, journal={Journal of biomedical informatics}, volume={47}, pages={1--10}, year={2014}, publisher={Elsevier} }
20
1,122
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition paperswithcode_id: ncbi-disease-1 pretty_name: NCBI Disease dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-Disease '2': I-Disease config_name: ncbi_disease splits: - name: train num_bytes: 2355516 num_examples: 5433 - name: validation num_bytes: 413900 num_examples: 924 - name: test num_bytes: 422842 num_examples: 941 download_size: 1546492 dataset_size: 3192258 train-eval-index: - config: ncbi_disease task: token-classification task_id: multi_class_classification splits: train_split: train eval_split: test col_mapping: tokens: text ner_tags: 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 NCBI Disease ## Table of Contents - [Dataset Card for NCBI Disease](#dataset-card-for-ncbi-disease) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [NCBI](https://www.ncbi.nlm.nih.gov/research/bionlp/Data/disease) - **Repository:** [Github](https://github.com/spyysalo/ncbi-disease) - **Paper:** [NCBI disease corpus: A resource for disease name recognition and concept normalization](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3951655) - **Leaderboard:** [Named Entity Recognition on NCBI-disease](https://paperswithcode.com/sota/named-entity-recognition-ner-on-ncbi-disease) - **Point of Contact:** [email](zhiyong.lu@nih.gov) ### Dataset Summary This dataset contains the disease name and concept annotations of the NCBI disease corpus, a collection of 793 PubMed abstracts fully annotated at the mention and concept level to serve as a research resource for the biomedical natural language processing community. ### Supported Tasks and Leaderboards Named Entity Recognition: [Leaderboard](https://paperswithcode.com/sota/named-entity-recognition-ner-on-ncbi-disease) ### Languages The text in the dataset is in English. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances Instances of the dataset contain an array of `tokens`, `ner_tags` and an `id`. An example of an instance of the dataset: ``` { 'tokens': ['Identification', 'of', 'APC2', ',', 'a', 'homologue', 'of', 'the', 'adenomatous', 'polyposis', 'coli', 'tumour', 'suppressor', '.'], 'ner_tags': [0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0], 'id': '0' } ``` ### Data Fields - `id`: Sentence identifier. - `tokens`: Array of tokens composing a sentence. - `ner_tags`: Array of tags, where `0` indicates no disease mentioned, `1` signals the first token of a disease and `2` the subsequent disease tokens. ### Data Splits The data is split into a train (5433 instances), validation (924 instances) and test set (941 instances). ## Dataset Creation ### Curation Rationale The goal of the dataset consists on improving the state-of-the-art in disease name recognition and normalization research, by providing a high-quality gold standard thus enabling the development of machine-learning based approaches for such tasks. ### Source Data #### Initial Data Collection and Normalization The dataset consists on abstracts extracted from PubMed. #### Who are the source language producers? The source language producers are the authors of publication abstracts hosted in PubMed. ### Annotations #### Annotation process Each PubMed abstract was manually annotated by two annotators with disease mentions and their corresponding concepts in Medical Subject Headings (MeSH®) or Online Mendelian Inheritance in Man (OMIM®). Manual curation was performed using PubTator, which allowed the use of pre-annotations as a pre-step to manual annotations. Fourteen annotators were randomly paired and differing annotations were discussed for reaching a consensus in two annotation phases. Finally, all results were checked against annotations of the rest of the corpus to assure corpus-wide consistency. #### Who are the annotators? The annotator group consisted of 14 people with backgrounds in biomedical informatics research and experience in biomedical text corpus annotation. ### Personal and Sensitive Information [N/A] ## Considerations for Using the Data ### Social Impact of Dataset Information encoded in natural language in biomedical literature publications is only useful if efficient and reliable ways of accessing and analyzing that information are available. Natural language processing and text mining tools are therefore essential for extracting valuable information. This dataset provides an annotated corpora that can be used to develop highly effective tools to automatically detect central biomedical concepts such as diseases. ### Discussion of Biases To avoid annotator bias, pairs of annotators were chosen randomly for each set, so that each pair of annotators overlapped for at most two sets. ### Other Known Limitations A handful of disease concepts were discovered that were not included in MEDIC. For those, we decided to include the appropriate OMIM identifiers. In addition, certain disease mentions were found to not be easily represented using the standard categorizations. Also, each PMID document was pre-annotated using the Inference Method developed for disease name normalization, which properly handles abbreviation recognition, robust string matching, etc. As such, human annotators were given the pre-annotated documents as a starting point and allowed to see each pre-annotation with a computed confidence. ## Additional Information ### Dataset Curators Rezarta Islamaj Doğan, Robert Leaman, Zhiyong Lu ### Licensing Information ``` PUBLIC DOMAIN NOTICE This work is a "United States Government Work" under the terms of the United States Copyright Act. It was written as part of the authors' official duties as a United States Government employee and thus cannot be copyrighted within the United States. The data is freely available to the public for use. The National Library of Medicine and the U.S. Government have not placed any restriction on its use or reproduction. Although all reasonable efforts have been taken to ensure the accuracy and reliability of the data and its source code, the NLM and the U.S. Government do not and cannot warrant the performance or results that may be obtained by using it. The NLM and the U.S. Government disclaim all warranties, express or implied, including warranties of performance, merchantability or fitness for any particular purpose. Please cite the authors in any work or product based on this material: An improved corpus of disease mentions in PubMed citations http://aclweb.org/anthology-new/W/W12/W12-2411.pdf NCBI Disease Corpus: A Resource for Disease Name Recognition and Normalization http://www.ncbi.nlm.nih.gov/pubmed/24393765 Disease Name Normalization with Pairwise Learning to Rank http://www.ncbi.nlm.nih.gov/pubmed/23969135 ``` ### Citation Information ``` @article{dougan2014ncbi, title={NCBI disease corpus: a resource for disease name recognition and concept normalization}, author={Do{\u{g}}an, Rezarta Islamaj and Leaman, Robert and Lu, Zhiyong}, journal={Journal of biomedical informatics}, volume={47}, pages={1--10}, year={2014}, publisher={Elsevier} } ``` ### Contributions Thanks to [@edugp](https://github.com/edugp) for adding this dataset.
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GeorgiaTech/cnotesum
2023-09-02T13:47:25.000Z
[ "license:other", "region:us" ]
GeorgiaTech
null
null
0
1,115
2023-09-02T13:42:55
--- license: other --- Synthetic Clinical Notes based on Synthea and Summary Generated via LLAMA 2
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quora
2023-04-05T13:37:24.000Z
[ "task_categories:text-classification", "task_ids:semantic-similarity-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:unknown", "region:us" ]
null
null
null
9
1,109
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language: - en language_creators: - found license: - unknown multilinguality: - monolingual pretty_name: Quora Question Pairs size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - semantic-similarity-classification paperswithcode_id: null dataset_info: features: - name: questions sequence: - name: id dtype: int32 - name: text dtype: string - name: is_duplicate dtype: bool splits: - name: train num_bytes: 58155622 num_examples: 404290 download_size: 58176133 dataset_size: 58155622 --- # Dataset Card for "quora" ## 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/quora-question-pairs](https://www.kaggle.com/c/quora-question-pairs) - **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:** 58.17 MB - **Size of the generated dataset:** 58.15 MB - **Total amount of disk used:** 116.33 MB ### Dataset Summary The Quora dataset is composed of question pairs, and the task is to determine if the questions are paraphrases of each other (have the same meaning). ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 58.17 MB - **Size of the generated dataset:** 58.15 MB - **Total amount of disk used:** 116.33 MB An example of 'train' looks as follows. ``` { "is_duplicate": true, "questions": { "id": [1, 2], "text": ["Is this a sample question?", "Is this an example question?"] } } ``` ### Data Fields The data fields are the same among all splits. #### default - `questions`: a dictionary feature containing: - `id`: a `int32` feature. - `text`: a `string` feature. - `is_duplicate`: a `bool` feature. ### Data Splits | name |train | |-------|-----:| |default|404290| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information Unknown license. ### Citation Information Unknown. ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@ghomasHudson](https://github.com/ghomasHudson), [@lewtun](https://github.com/lewtun) for adding this dataset.
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onestop_english
2023-01-25T14:42:09.000Z
[ "task_categories:text2text-generation", "task_categories:text-classification", "task_ids:multi-class-classification", "task_ids:text-simplification", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:en", "license:cc-by-sa-4.0", "region:us" ]
null
This dataset is a compilation of the OneStopEnglish corpus of texts written at three reading levels into one file. Text documents are classified into three reading levels - ele, int, adv (Elementary, Intermediate and Advance). This dataset demonstrates its usefulness for through two applica-tions - automatic readability assessment and automatic text simplification. The corpus consists of 189 texts, each in three versions/reading levels (567 in total).
@inproceedings{vajjala-lucic-2018-onestopenglish, title = {OneStopEnglish corpus: A new corpus for automatic readability assessment and text simplification}, author = {Sowmya Vajjala and Ivana Lučić}, booktitle = {Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications}, year = {2018} }
15
1,106
2022-03-02T23:29:22
--- annotations_creators: - found language_creators: - found language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - text2text-generation - text-classification task_ids: - multi-class-classification - text-simplification paperswithcode_id: onestopenglish pretty_name: OneStopEnglish corpus dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': ele '1': int '2': adv splits: - name: train num_bytes: 2278043 num_examples: 567 download_size: 1228804 dataset_size: 2278043 --- # Dataset Card for OneStopEnglish corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/nishkalavallabhi/OneStopEnglishCorpus - **Repository:** https://github.com/purvimisal/OneStopCorpus-Compiled/raw/main/Texts-SeparatedByReadingLevel.zip - **Paper:** https://www.aclweb.org/anthology/W18-0535.pdf - **Leaderboard:** - **Point of Contact:** ### Dataset Summary OneStopEnglish is a corpus of texts written at three reading levels, and demonstrates its usefulness for through two applications - automatic readability assessment and automatic text simplification. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances An instance example: ``` { "text": "When you see the word Amazon, what’s the first thing you think...", "label": 0 } ``` Note that each instance contains the full text of the document. ### Data Fields - `text`: Full document text. - `label`: Reading level of the document- ele/int/adv (Elementary/Intermediate/Advance). ### Data Splits The OneStopEnglish dataset has a single _train_ split. | Split | Number of instances | |-------|--------------------:| | train | 567 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Creative Commons Attribution-ShareAlike 4.0 International License ### Citation Information [More Information Needed] ### Contributions Thanks to [@purvimisal](https://github.com/purvimisal) for adding this dataset.
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launch/gov_report
2022-11-09T01:58:24.000Z
[ "task_categories:summarization", "annotations_creators:no-annotation", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-4.0", "region:us" ]
launch
GovReport long document summarization dataset. There are three configs: - plain_text: plain text document-to-summary pairs - plain_text_with_recommendations: plain text doucment-summary pairs, with "What GAO recommends" included in the summary - structure: data with section structure
@inproceedings{huang-etal-2021-efficient, title = "Efficient Attentions for Long Document Summarization", author = "Huang, Luyang and Cao, Shuyang and Parulian, Nikolaus and Ji, Heng and Wang, Lu", 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.112", doi = "10.18653/v1/2021.naacl-main.112", pages = "1419--1436", abstract = "The quadratic computational and memory complexities of large Transformers have limited their scalability for long document summarization. In this paper, we propose Hepos, a novel efficient encoder-decoder attention with head-wise positional strides to effectively pinpoint salient information from the source. We further conduct a systematic study of existing efficient self-attentions. Combined with Hepos, we are able to process ten times more tokens than existing models that use full attentions. For evaluation, we present a new dataset, GovReport, with significantly longer documents and summaries. Results show that our models produce significantly higher ROUGE scores than competitive comparisons, including new state-of-the-art results on PubMed. Human evaluation also shows that our models generate more informative summaries with fewer unfaithful errors.", }
3
1,103
2022-05-22T16:10:07
--- annotations_creators: - no-annotation language_creators: - expert-generated language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - summarization task_ids: [] pretty_name: GovReport --- # Dataset Card for GovReport ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Versions](#versions) - [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://gov-report-data.github.io](https://gov-report-data.github.io) - **Repository:** [https://github.com/luyang-huang96/LongDocSum](https://github.com/luyang-huang96/LongDocSum) - **Paper:** [https://aclanthology.org/2021.naacl-main.112/](https://aclanthology.org/2021.naacl-main.112/) - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary Government report dataset consists of reports and associated summaries written by government research agencies including Congressional Research Service and U.S. Government Accountability Office. Compared with other long document summarization datasets, government report dataset has longer summaries and documents and requires reading in more context to cover salient words to be summarized. ### Versions - `1.0.1` (default): remove extra whitespace. - `1.0.0`: the dataset used in the original paper. To use different versions, set the `revision` argument of the `load_dataset` function. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English ## Dataset Structure Three configs are available: - **plain_text** (default): the text-to-text summarization setting used as in the original paper. - **plain_text_with_recommendations**: the text-to-text summarization setting, with "What GAO recommends" included in the summary. - **structure**: data with the section structure. To use different configs, set the `name` argument of the `load_dataset` function. ### Data Instances #### plain_text & plain_text_with_recommendations An example looks as follows. ``` { "id": "GAO_123456", "document": "This is a test document.", "summary": "This is a test summary" } ``` #### structure An example looks as follows. ``` { "id": "GAO_123456", "document_sections": { "title": ["test docment section 1 title", "test docment section 1.1 title"], "paragraphs": ["test document\nsection 1 paragraphs", "test document\nsection 1.1 paragraphs"], "depth": [1, 2] }, "summary_sections": { "title": ["test summary section 1 title", "test summary section 2 title"], "paragraphs": ["test summary\nsection 1 paragraphs", "test summary\nsection 2 paragraphs"] } } ``` ### Data Fields #### plain_text & plain_text_with_recommendations - `id`: a `string` feature. - `document`: a `string` feature. - `summary`: a `string` feature. #### structure - `id`: a `string` feature. - `document_sections`: a dictionary feature containing lists of (each element corresponds to a section): - `title`: a `string` feature. - `paragraphs`: a of `string` feature, with `\n` separating different paragraphs. - `depth`: a `int32` feature. - `summary_sections`: a dictionary feature containing lists of (each element corresponds to a section): - `title`: a `string` feature. - `paragraphs`: a `string` feature, with `\n` separating different paragraphs. ### Data Splits - train: 17519 - valid: 974 - test: 973 ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? Editors of the Congressional Research Service and U.S. Government Accountability Office. ### Personal and Sensitive Information None. ## 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 4.0 ### Citation Information ``` @inproceedings{huang-etal-2021-efficient, title = "Efficient Attentions for Long Document Summarization", author = "Huang, Luyang and Cao, Shuyang and Parulian, Nikolaus and Ji, Heng and Wang, Lu", 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.112", doi = "10.18653/v1/2021.naacl-main.112", pages = "1419--1436", abstract = "The quadratic computational and memory complexities of large Transformers have limited their scalability for long document summarization. In this paper, we propose Hepos, a novel efficient encoder-decoder attention with head-wise positional strides to effectively pinpoint salient information from the source. We further conduct a systematic study of existing efficient self-attentions. Combined with Hepos, we are able to process ten times more tokens than existing models that use full attentions. For evaluation, we present a new dataset, GovReport, with significantly longer documents and summaries. Results show that our models produce significantly higher ROUGE scores than competitive comparisons, including new state-of-the-art results on PubMed. Human evaluation also shows that our models generate more informative summaries with fewer unfaithful errors.", } ```
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DFKI-SLT/brat
2023-05-10T15:38:03.000Z
[ "task_categories:token-classification", "task_ids:parsing", "annotations_creators:expert-generated", "language_creators:found", "region:us" ]
DFKI-SLT
null
null
2
1,102
2022-05-10T06:13:33
--- annotations_creators: - expert-generated language_creators: - found license: [] task_categories: - token-classification task_ids: - parsing --- # Information Card for Brat ## Table of Contents - [Description](#description) - [Summary](#summary) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Usage](#usage) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Description - **Homepage:** https://brat.nlplab.org - **Paper:** https://aclanthology.org/E12-2021/ - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Summary Brat is an intuitive web-based tool for text annotation supported by Natural Language Processing (NLP) technology. BRAT has been developed for rich structured annota- tion for a variety of NLP tasks and aims to support manual curation efforts and increase annotator productivity using NLP techniques. brat is designed in particular for structured annotation, where the notes are not free form text but have a fixed form that can be automatically processed and interpreted by a computer. ## Dataset Structure Dataset annotated with brat format is processed using this script. Annotations created in brat are stored on disk in a standoff format: annotations are stored separately from the annotated document text, which is never modified by the tool. For each text document in the system, there is a corresponding annotation file. The two are associatied by the file naming convention that their base name (file name without suffix) is the same: for example, the file DOC-1000.ann contains annotations for the file DOC-1000.txt. More information can be found [here](https://brat.nlplab.org/standoff.html). ### Data Instances [Needs More Information] ### Data Fields ``` -context: html content of data file as string -file_name: a string name of file -spans: a sequence containing id, type, location and text of a span -relations: a sequence containing id, type and arguments of a relation -equivalence_relations: -events: -attributions: -normalizations: -notes: ``` ### Usage brat script can be used by calling `load_dataset()` method and passing `kwargs` (arguments to the [BuilderConfig](https://huggingface.co/docs/datasets/v2.2.1/en/package_reference/builder_classes#datasets.BuilderConfig)) which should include at least `url` of the dataset prepared using brat. We provide an example of [SciArg](https://aclanthology.org/W18-5206.pdf) dataset below, ```python from datasets import load_dataset kwargs = { "description" : """This dataset is an extension of the Dr. Inventor corpus (Fisas et al., 2015, 2016) with an annotation layer containing fine-grained argumentative components and relations. It is the first argument-annotated corpus of scientific publications (in English), which allows for joint analyses of argumentation and other rhetorical dimensions of scientific writing.""", "citation" : """@inproceedings{lauscher2018b, title = {An argument-annotated corpus of scientific publications}, booktitle = {Proceedings of the 5th Workshop on Mining Argumentation}, publisher = {Association for Computational Linguistics}, author = {Lauscher, Anne and Glava\v{s}, Goran and Ponzetto, Simone Paolo}, address = {Brussels, Belgium}, year = {2018}, pages = {40–46} }""", "homepage": "https://github.com/anlausch/ArguminSci", "url": "http://data.dws.informatik.uni-mannheim.de/sci-arg/compiled_corpus.zip", "file_name_blacklist": ['A28'], } dataset = load_dataset('dfki-nlp/brat', **kwargs) ``` ## Additional Information ### Licensing Information [Needs More Information] ### Citation Information ``` @inproceedings{stenetorp-etal-2012-brat, title = "brat: a Web-based Tool for {NLP}-Assisted Text Annotation", author = "Stenetorp, Pontus and Pyysalo, Sampo and Topi{\'c}, Goran and Ohta, Tomoko and Ananiadou, Sophia and Tsujii, Jun{'}ichi", booktitle = "Proceedings of the Demonstrations at the 13th Conference of the {E}uropean Chapter of the Association for Computational Linguistics", month = apr, year = "2012", address = "Avignon, France", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/E12-2021", pages = "102--107", } ```
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BeIR/scidocs-qrels
2022-10-23T06:07:54.000Z
[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "task_ids:fact-checking-retrieval", "multilinguality:monolingual", "language:en", "license:cc-by-sa-4.0", "region:us" ]
BeIR
null
null
0
1,101
2022-06-05T17:27:37
--- 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.
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qa4mre
2023-04-05T13:36:59.000Z
[ "task_categories:multiple-choice", "task_ids:multiple-choice-qa", "annotations_creators:other", "language_creators:found", "multilinguality:multilingual", "size_categories:1K<n<10K", "source_datasets:original", "language:ar", "language:bg", "language:de", "language:en", "language:es", "language:it", "language:ro", "license:unknown", "region:us" ]
null
QA4MRE dataset was created for the CLEF 2011/2012/2013 shared tasks to promote research in question answering and reading comprehension. The dataset contains a supporting passage and a set of questions corresponding to the passage. Multiple options for answers are provided for each question, of which only one is correct. The training and test datasets are available for the main track. Additional gold standard documents are available for two pilot studies: one on alzheimers data, and the other on entrance exams data.
null
2
1,099
2022-03-02T23:29:22
--- annotations_creators: - other language: - ar - bg - de - en - es - it - ro language_creators: - found license: - unknown multilinguality: - multilingual pretty_name: 'QA4MRE: Question Answering for Machine Reading Evaluation' size_categories: - 1K<n<10K source_datasets: - original task_categories: - multiple-choice task_ids: - multiple-choice-qa paperswithcode_id: null dataset_info: - config_name: 2011.main.DE features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 1747118 num_examples: 120 download_size: 222289 dataset_size: 1747118 - config_name: 2011.main.EN features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 1569676 num_examples: 120 download_size: 202490 dataset_size: 1569676 - config_name: 2011.main.ES features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 1694460 num_examples: 120 download_size: 217617 dataset_size: 1694460 - config_name: 2011.main.IT features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 1667188 num_examples: 120 download_size: 214764 dataset_size: 1667188 - config_name: 2011.main.RO features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 1740419 num_examples: 120 download_size: 221510 dataset_size: 1740419 - config_name: 2012.main.AR features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 2710656 num_examples: 160 download_size: 356178 dataset_size: 2710656 - config_name: 2012.main.BG features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 3454215 num_examples: 160 download_size: 445060 dataset_size: 3454215 - config_name: 2012.main.DE features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 2087466 num_examples: 160 download_size: 281600 dataset_size: 2087466 - config_name: 2012.main.EN features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 1757586 num_examples: 160 download_size: 243467 dataset_size: 1757586 - config_name: 2012.main.ES features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 2057402 num_examples: 160 download_size: 278445 dataset_size: 2057402 - config_name: 2012.main.IT features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 2071710 num_examples: 160 download_size: 280051 dataset_size: 2071710 - config_name: 2012.main.RO features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 2074930 num_examples: 160 download_size: 279541 dataset_size: 2074930 - config_name: 2012.alzheimers.EN features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 1637988 num_examples: 40 download_size: 177345 dataset_size: 1637988 - config_name: 2013.main.AR features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 4180979 num_examples: 284 download_size: 378302 dataset_size: 4180979 - config_name: 2013.main.BG features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 5403246 num_examples: 284 download_size: 463605 dataset_size: 5403246 - config_name: 2013.main.EN features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 2887866 num_examples: 284 download_size: 274969 dataset_size: 2887866 - config_name: 2013.main.ES features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 3449693 num_examples: 284 download_size: 315166 dataset_size: 3449693 - config_name: 2013.main.RO features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 3363049 num_examples: 284 download_size: 313510 dataset_size: 3363049 - config_name: 2013.alzheimers.EN features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 2614812 num_examples: 40 download_size: 274413 dataset_size: 2614812 - config_name: 2013.entrance_exam.EN features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 180827 num_examples: 46 download_size: 54598 dataset_size: 180827 --- # Dataset Card for "qa4mre" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://nlp.uned.es/clef-qa/repository/qa4mre.php - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [QA4MRE 2011-2013: Overview of Question Answering for Machine Reading Evaluation](https://link.springer.com/chapter/10.1007/978-3-642-40802-1_29) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 5.49 MB - **Size of the generated dataset:** 48.35 MB - **Total amount of disk used:** 53.84 MB ### Dataset Summary QA4MRE dataset was created for the CLEF 2011/2012/2013 shared tasks to promote research in question answering and reading comprehension. The dataset contains a supporting passage and a set of questions corresponding to the passage. Multiple options for answers are provided for each question, of which only one is correct. The training and test datasets are available for the main track. Additional gold standard documents are available for two pilot studies: one on alzheimers data, and the other on entrance exams data. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### 2011.main.DE - **Size of downloaded dataset files:** 0.22 MB - **Size of the generated dataset:** 1.75 MB - **Total amount of disk used:** 1.97 MB An example of 'train' looks as follows. ``` ``` #### 2011.main.EN - **Size of downloaded dataset files:** 0.20 MB - **Size of the generated dataset:** 1.57 MB - **Total amount of disk used:** 1.77 MB An example of 'train' looks as follows. ``` ``` #### 2011.main.ES - **Size of downloaded dataset files:** 0.22 MB - **Size of the generated dataset:** 1.70 MB - **Total amount of disk used:** 1.91 MB An example of 'train' looks as follows. ``` ``` #### 2011.main.IT - **Size of downloaded dataset files:** 0.21 MB - **Size of the generated dataset:** 1.67 MB - **Total amount of disk used:** 1.88 MB An example of 'train' looks as follows. ``` ``` #### 2011.main.RO - **Size of downloaded dataset files:** 0.22 MB - **Size of the generated dataset:** 1.74 MB - **Total amount of disk used:** 1.96 MB An example of 'train' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### 2011.main.DE - `topic_id`: a `string` feature. - `topic_name`: a `string` feature. - `test_id`: a `string` feature. - `document_id`: a `string` feature. - `document_str`: a `string` feature. - `question_id`: a `string` feature. - `question_str`: a `string` feature. - `answer_options`: a dictionary feature containing: - `answer_id`: a `string` feature. - `answer_str`: a `string` feature. - `correct_answer_id`: a `string` feature. - `correct_answer_str`: a `string` feature. #### 2011.main.EN - `topic_id`: a `string` feature. - `topic_name`: a `string` feature. - `test_id`: a `string` feature. - `document_id`: a `string` feature. - `document_str`: a `string` feature. - `question_id`: a `string` feature. - `question_str`: a `string` feature. - `answer_options`: a dictionary feature containing: - `answer_id`: a `string` feature. - `answer_str`: a `string` feature. - `correct_answer_id`: a `string` feature. - `correct_answer_str`: a `string` feature. #### 2011.main.ES - `topic_id`: a `string` feature. - `topic_name`: a `string` feature. - `test_id`: a `string` feature. - `document_id`: a `string` feature. - `document_str`: a `string` feature. - `question_id`: a `string` feature. - `question_str`: a `string` feature. - `answer_options`: a dictionary feature containing: - `answer_id`: a `string` feature. - `answer_str`: a `string` feature. - `correct_answer_id`: a `string` feature. - `correct_answer_str`: a `string` feature. #### 2011.main.IT - `topic_id`: a `string` feature. - `topic_name`: a `string` feature. - `test_id`: a `string` feature. - `document_id`: a `string` feature. - `document_str`: a `string` feature. - `question_id`: a `string` feature. - `question_str`: a `string` feature. - `answer_options`: a dictionary feature containing: - `answer_id`: a `string` feature. - `answer_str`: a `string` feature. - `correct_answer_id`: a `string` feature. - `correct_answer_str`: a `string` feature. #### 2011.main.RO - `topic_id`: a `string` feature. - `topic_name`: a `string` feature. - `test_id`: a `string` feature. - `document_id`: a `string` feature. - `document_str`: a `string` feature. - `question_id`: a `string` feature. - `question_str`: a `string` feature. - `answer_options`: a dictionary feature containing: - `answer_id`: a `string` feature. - `answer_str`: a `string` feature. - `correct_answer_id`: a `string` feature. - `correct_answer_str`: a `string` feature. ### Data Splits | name |train| |------------|----:| |2011.main.DE| 120| |2011.main.EN| 120| |2011.main.ES| 120| |2011.main.IT| 120| |2011.main.RO| 120| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{10.1007/978-3-642-40802-1_29, author="Pe{\~{n}}as, Anselmo and Hovy, Eduard and Forner, Pamela and Rodrigo, {\'A}lvaro and Sutcliffe, Richard and Morante, Roser", editor="Forner, Pamela and M{\"u}ller, Henning and Paredes, Roberto and Rosso, Paolo and Stein, Benno", title="QA4MRE 2011-2013: Overview of Question Answering for Machine Reading Evaluation", booktitle="Information Access Evaluation. Multilinguality, Multimodality, and Visualization", year="2013", publisher="Springer Berlin Heidelberg", address="Berlin, Heidelberg", pages="303--320", isbn="978-3-642-40802-1" } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@albertvillanova](https://github.com/albertvillanova), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf) for adding this dataset.
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cs_restaurants
2022-11-18T19:49:56.000Z
[ "task_categories:text2text-generation", "task_categories:text-generation", "task_categories:fill-mask", "task_ids:dialogue-modeling", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:found", "language_creators:expert-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:extended|other-san-francisco-restaurants", "language:cs", "license:cc-by-4.0", "intent-to-text", "arxiv:1910.05298", "region:us" ]
null
This is a dataset for NLG in task-oriented spoken dialogue systems with Czech as the target language. It originated as a translation of the English San Francisco Restaurants dataset by Wen et al. (2015).
@article{DBLP:journals/corr/abs-1910-05298, author = {Ondrej Dusek and Filip Jurcicek}, title = {Neural Generation for Czech: Data and Baselines}, journal = {CoRR}, volume = {abs/1910.05298}, year = {2019}, url = {http://arxiv.org/abs/1910.05298}, archivePrefix = {arXiv}, eprint = {1910.05298}, timestamp = {Wed, 16 Oct 2019 16:25:53 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1910-05298.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }
1
1,098
2022-03-02T23:29:22
--- annotations_creators: - found language_creators: - expert-generated - machine-generated language: - cs license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - extended|other-san-francisco-restaurants task_categories: - text2text-generation - text-generation - fill-mask task_ids: - dialogue-modeling - language-modeling - masked-language-modeling paperswithcode_id: czech-restaurant-information pretty_name: Czech Restaurant tags: - intent-to-text dataset_info: features: - name: dialogue_act dtype: string - name: delexicalized_dialogue_act dtype: string - name: text dtype: string - name: delexicalized_text dtype: string config_name: CSRestaurants splits: - name: train num_bytes: 654071 num_examples: 3569 - name: validation num_bytes: 181528 num_examples: 781 - name: test num_bytes: 191334 num_examples: 842 download_size: 1463019 dataset_size: 1026933 --- # Dataset Card for Czech Restaurant ## 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:** [Czech restaurants homepage](https://github.com/UFAL-DSG/cs_restaurant_dataset) - **Paper:** [Czech restaurants on Arxiv](https://arxiv.org/abs/1910.05298) ### Dataset Summary This is a dataset for NLG in task-oriented spoken dialogue systems with Czech as the target language. It originated as a translation of the [English San Francisco Restaurants dataset](https://www.repository.cam.ac.uk/handle/1810/251304) by Wen et al. (2015). The domain is restaurant information in Prague, with random/fictional values. It includes input dialogue acts and the corresponding outputs in Czech. ### Supported Tasks and Leaderboards - `other-intent-to-text`: The dataset can be used to train a model for data-to-text generation: from a desired dialogue act, the model must produce textual output that conveys this intention. ### Languages The entire dataset is in Czech, translated from the English San Francisco dataset by professional translators. ## Dataset Structure ### Data Instances Example of a data instance: ``` { "da": "?request(area)", "delex_da": "?request(area)", "text": "Jakou lokalitu hledáte ?", "delex_text": "Jakou lokalitu hledáte ?" } ``` ### Data Fields - `da`: input dialogue act - `delex_da`: input dialogue act, delexicalized - `text`: output text - `delex_text`: output text, delexicalized ### Data Splits The order of the instances is random; the split is roughly 3:1:1 between train, development, and test, ensuring that the different sections don't share the same DAs (so the generators need to generalize to unseen DAs), but they share as many generic different DA types as possible (e.g., confirm, inform_only_match etc.). DA types that only have a single corresponding DA (e.g., bye()) are included in the training set. The training, development, and test set contain 3569, 781, and 842 instances, respectively. ## Dataset Creation ### Curation Rationale While most current neural NLG systems do not explicitly contain language-specific components and are thus capable of multilingual generation in principle, there has been little work to test these capabilities experimentally. This goes hand in hand with the scarcity of non-English training datasets for NLG – the only data-to-text NLG set known to us is a small sportscasting Korean dataset (Chenet al., 2010), which only contains a limited number of named entities, reducing the need for their inflection. Since most generators are only tested on English, they do not need to handle grammar complexities not present in English. A prime example is the delexicalization technique used by most current generators. We create a novel dataset for Czech delexicalized generation; this extends the typical task of data-to-text NLG by requiring attribute value inflection. We choose Czech as an example of a morphologically complex language with a large set of NLP tools readily available. ### Source Data #### Initial Data Collection and Normalization The original data was collected from the [English San Francisco Restaurants dataset](https://www.repository.cam.ac.uk/handle/1810/251304) by Wen et al. (2015). #### Who are the source language producers? The original data was produced in interactions between Amazon Mechanical Turk workers and themed around San Francisco restaurants. This data was then translated into Czech and localized to Prague restaurants by professional translators. ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information This data does not contain personal information. ## 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 Ondřej Dušek, Filip Jurčíček, Josef Dvořák, Petra Grycová, Matěj Hejda, Jana Olivová, Michal Starý, Eva Štichová, Charles University. This work was funded by the Ministry of Education, Youth and Sports of the Czech Republic under the grant agreement LK11221 and core research funding, SVV project 260 333, and GAUK grant 2058214 of Charles University in Prague. It used language resources stored and distributed by the LINDAT/CLARIN project of the Ministry of Education, Youth and Sports of the Czech Republic (project LM2015071). ### Licensing Information [Creative Commons 4.0 BY-SA](https://creativecommons.org/licenses/by-sa/4.0/) ### Citation Information ``` @article{DBLP:journals/corr/abs-1910-05298, author = {Ondrej Dusek and Filip Jurcicek}, title = {Neural Generation for Czech: Data and Baselines}, journal = {CoRR}, volume = {abs/1910.05298}, year = {2019}, url = {http://arxiv.org/abs/1910.05298}, archivePrefix = {arXiv}, eprint = {1910.05298}, timestamp = {Wed, 16 Oct 2019 16:25:53 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1910-05298.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` ### Contributions Thanks to [@TevenLeScao](https://github.com/TevenLeScao) for adding this dataset.
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CShorten/ML-ArXiv-Papers
2022-06-27T12:15:11.000Z
[ "license:afl-3.0", "region:us" ]
CShorten
null
null
17
1,097
2022-06-23T14:31:39
--- 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>
986
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nuprl/MultiPL-T
2023-09-13T12:57:50.000Z
[ "license:bigcode-openrail-m", "arxiv:2308.09895", "region:us" ]
nuprl
null
null
1
1,097
2023-08-17T14:17:33
--- license: bigcode-openrail-m dataset_info: features: - name: content dtype: string splits: - name: racket num_bytes: 14482516 num_examples: 40510 - name: ocaml num_bytes: 19240207 num_examples: 43401 - name: lua num_bytes: 25917278 num_examples: 48194 download_size: 7491686 dataset_size: 59640001 --- # MultiPL-T fine-tuning sets This dataset contains the MultiPL-T fine-tuning sets described in the paper "Knowledge Transfer from High-Resource to Low-Resource Programming Languages for Code LLMs": [Arxiv](https://arxiv.org/abs/2308.09895). ## MultiPL-T tuned models StarCoderBase-1b: https://huggingface.co/nuprl/MultiPLCoder-1b StarCoderBase-15b: https://huggingface.co/nuprl/MultiPLCoder-15b
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climate_fever
2023-03-16T14:57:07.000Z
[ "task_categories:text-classification", "task_categories:text-retrieval", "task_ids:text-scoring", "task_ids:fact-checking", "task_ids:fact-checking-retrieval", "task_ids:semantic-similarity-scoring", "task_ids:multi-input-text-classification", "annotations_creators:crowdsourced", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:extended|wikipedia", "source_datasets:original", "language:en", "license:unknown", "arxiv:2012.00614", "region:us" ]
null
A dataset adopting the FEVER methodology that consists of 1,535 real-world claims regarding climate-change collected on the internet. Each claim is accompanied by five manually annotated evidence sentences retrieved from the English Wikipedia that support, refute or do not give enough information to validate the claim totalling in 7,675 claim-evidence pairs. The dataset features challenging claims that relate multiple facets and disputed cases of claims where both supporting and refuting evidence are present.
@misc{diggelmann2020climatefever, title={CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims}, author={Thomas Diggelmann and Jordan Boyd-Graber and Jannis Bulian and Massimiliano Ciaramita and Markus Leippold}, year={2020}, eprint={2012.00614}, archivePrefix={arXiv}, primaryClass={cs.CL} }
10
1,093
2022-03-02T23:29:22
--- annotations_creators: - crowdsourced - expert-generated language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - extended|wikipedia - original task_categories: - text-classification - text-retrieval task_ids: - text-scoring - fact-checking - fact-checking-retrieval - semantic-similarity-scoring - multi-input-text-classification paperswithcode_id: climate-fever pretty_name: ClimateFever dataset_info: features: - name: claim_id dtype: string - name: claim dtype: string - name: claim_label dtype: class_label: names: '0': SUPPORTS '1': REFUTES '2': NOT_ENOUGH_INFO '3': DISPUTED - name: evidences list: - name: evidence_id dtype: string - name: evidence_label dtype: class_label: names: '0': SUPPORTS '1': REFUTES '2': NOT_ENOUGH_INFO - name: article dtype: string - name: evidence dtype: string - name: entropy dtype: float32 - name: votes list: string splits: - name: test num_bytes: 2429272 num_examples: 1535 download_size: 687133 dataset_size: 2429272 --- # Dataset Card for ClimateFever ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [CLIMATE-FEVER homepage](http://climatefever.ai) - **Repository:** [CLIMATE-FEVER repository](https://github.com/tdiggelm/climate-fever-dataset) - **Paper:** [CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims](https://arxiv.org/abs/2012.00614) - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Thomas Diggelmann](mailto:thomasdi@student.ethz.ch) ### Dataset Summary A dataset adopting the FEVER methodology that consists of 1,535 real-world claims regarding climate-change collected on the internet. Each claim is accompanied by five manually annotated evidence sentences retrieved from the English Wikipedia that support, refute or do not give enough information to validate the claim totalling in 7,675 claim-evidence pairs. The dataset features challenging claims that relate multiple facets and disputed cases of claims where both supporting and refuting evidence are present. ### Supported Tasks and Leaderboards [Needs More Information] ### Languages The text in the dataset is in English, as found in real-world claims about climate-change on the Internet. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances ``` { "claim_id": "0", "claim": "Global warming is driving polar bears toward extinction", "claim_label": 0, # "SUPPORTS" "evidences": [ { "evidence_id": "Extinction risk from global warming:170", "evidence_label": 2, # "NOT_ENOUGH_INFO" "article": "Extinction risk from global warming", "evidence": "\"Recent Research Shows Human Activity Driving Earth Towards Global Extinction Event\".", "entropy": 0.6931471805599453, "votes": [ "SUPPORTS", "NOT_ENOUGH_INFO", null, null, null ] }, { "evidence_id": "Global warming:14", "evidence_label": 0, # "SUPPORTS" "article": "Global warming", "evidence": "Environmental impacts include the extinction or relocation of many species as their ecosystems change, most immediately the environments of coral reefs, mountains, and the Arctic.", "entropy": 0.0, "votes": [ "SUPPORTS", "SUPPORTS", null, null, null ] }, { "evidence_id": "Global warming:178", "evidence_label": 2, # "NOT_ENOUGH_INFO" "article": "Global warming", "evidence": "Rising temperatures push bees to their physiological limits, and could cause the extinction of bee populations.", "entropy": 0.6931471805599453, "votes": [ "SUPPORTS", "NOT_ENOUGH_INFO", null, null, null ] }, { "evidence_id": "Habitat destruction:61", "evidence_label": 0, # "SUPPORTS" "article": "Habitat destruction", "evidence": "Rising global temperatures, caused by the greenhouse effect, contribute to habitat destruction, endangering various species, such as the polar bear.", "entropy": 0.0, "votes": [ "SUPPORTS", "SUPPORTS", null, null, null ] }, { "evidence_id": "Polar bear:1328", "evidence_label": 2, # "NOT_ENOUGH_INFO" "article": "Polar bear", "evidence": "\"Bear hunting caught in global warming debate\".", "entropy": 0.6931471805599453, "votes": [ "SUPPORTS", "NOT_ENOUGH_INFO", null, null, null ] } ] } ``` ### Data Fields - `claim_id`: a `string` feature, unique claim identifier. - `claim`: a `string` feature, claim text. - `claim_label`: a `int` feature, overall label assigned to claim (based on evidence majority vote). The label correspond to 0: "supports", 1: "refutes", 2: "not enough info" and 3: "disputed". - `evidences`: a list of evidences with fields: - `evidence_id`: a `string` feature, unique evidence identifier. - `evidence_label`: a `int` feature, micro-verdict label. The label correspond to 0: "supports", 1: "refutes" and 2: "not enough info". - `article`: a `string` feature, title of source article (Wikipedia page). - `evidence`: a `string` feature, evidence sentence. - `entropy`: a `float32` feature, entropy reflecting uncertainty of `evidence_label`. - `votes`: a `list` of `string` features, corresponding to individual votes. ### Data Splits This benchmark dataset currently consists of a single data split `test` that consists of 1,535 claims or 7,675 claim-evidence pairs. ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information ```bibtex @misc{diggelmann2020climatefever, title={CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims}, author={Thomas Diggelmann and Jordan Boyd-Graber and Jannis Bulian and Massimiliano Ciaramita and Markus Leippold}, year={2020}, eprint={2012.00614}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@tdiggelm](https://github.com/tdiggelm) for adding this dataset.
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ArmelR/the-pile-splitted
2023-09-06T09:53:16.000Z
[ "arxiv:2101.00027", "arxiv:2201.07311", "region:us" ]
ArmelR
null
null
1
1,092
2023-07-30T14:21:26
--- configs: - config_name: all data_files: - split: train path: - "data/ArXiv/train/*.arrow" - "data/BookCorpus2/train/*.arrow" - "data/Books3/train/*.arrow" - "data/DM Mathematics/train/*.arrow" - "data/Enron Emails/train/*.arrow" - "data/EuroParl/train/*.arrow" - "data/FreeLaw/train/*.arrow" - "data/Github/train/*.arrow" - "data/Gutenberg (PG-19)/train/*.arrow" - "data/HackerNews/train/*.arrow" - "data/NIH ExPorter/train/*.arrow" - "data/OpenSubtitles/train/*.arrow" - "data/OpenWebText2/train/*.arrow" - "data/PhilPapers/train/*.arrow" - "data/Pile-CC/train/*.arrow" - "data/PubMed Abstracts/train/*.arrow" - "data/PubMed Central/train/*.arrow" - "data/StackExchange/train/*.arrow" - "data/UPSTO Backgrounds/train/*.arrow" - "data/Ubuntu IRC/train/*.arrow" - "data/Wikipedia (en)/train/*.arrow" - "data/YoutubeSubtitles/train/*.arrow" - split: test path: - "data/ArXiv/test/*.arrow" - "data/BookCorpus2/test/*.arrow" - "data/Books3/test/*.arrow" - "data/DM Mathematics/test/*.arrow" - "data/Enron Emails/test/*.arrow" - "data/EuroParl/test/*.arrow" - "data/FreeLaw/test/*.arrow" - "data/Github/test/*.arrow" - "data/Gutenberg (PG-19)/test/*.arrow" - "data/HackerNews/test/*.arrow" - "data/NIH ExPorter/test/*.arrow" - "data/OpenSubtitles/test/*.arrow" - "data/OpenWebText2/test/*.arrow" - "data/PhilPapers/test/*.arrow" - "data/Pile-CC/test/*.arrow" - "data/PubMed Abstracts/test/*.arrow" - "data/PubMed Central/test/*.arrow" - "data/StackExchange/test/*.arrow" - "data/UPSTO Backgrounds/test/*.arrow" - "data/Ubuntu IRC/test/*.arrow" - "data/Wikipedia (en)/test/*.arrow" - "data/YoutubeSubtitles/test/*.arrow" default: true - config_name: ArXiv data_files: - split: train path: "data/ArXiv/train/*.arrow" - split: test path: "data/ArXiv/test/*.arrow" - config_name: BookCorpus2 data_files: - split: train path: "data/BookCorpus2/train/*.arrow" - split: test path: "data/BookCorpus2/test/*.arrow" - config_name: Books3 data_files: - split: train path: "data/Books3/train/*.arrow" - split: test path: "data/Books3/test/*.arrow" - config_name: DM Mathematics data_files: - split: train path: "data/DM Mathematics/train/*.arrow" - split: test path: "data/DM Mathematics/test/*.arrow" - config_name: Enron Emails data_files: - split: train path: "data/Enron Emails/train/*.arrow" - split: test path: "data/Enron Emails/test/*.arrow" - config_name: EuroParl data_files: - split: train path: "data/EuroParl/train/*.arrow" - split: test path: "data/EuroParl/test/*.arrow" - config_name: FreeLaw data_files: - split: train path: "data/FreeLaw/train/*.arrow" - split: test path: "data/FreeLaw/test/*.arrow" - config_name: Github data_files: - split: train path: "data/Github/train/*.arrow" - split: test path: "data/Github/test/*.arrow" - config_name: Gutenberg (PG-19) data_files: - split: train path: "data/Gutenberg (PG-19)/train/*.arrow" - split: test path: "data/Gutenberg (PG-19)/test/*.arrow" - config_name: HackerNews data_files: - split: train path: "data/HackerNews/train/*.arrow" - split: test path: "data/HackerNews/test/*.arrow" - config_name: NIH ExPorter data_files: - split: train path: "data/NIH ExPorter/train/*.arrow" - split: test path: "data/NIH ExPorter/test/*.arrow" - config_name: OpenSubtitles data_files: - split: train path: "data/OpenSubtitles/train/*.arrow" - split: test path: "data/OpenSubtitles/test/*.arrow" - config_name: OpenWebText2 data_files: - split: train path: "data/OpenWebText2/train/*.arrow" - split: test path: "data/OpenWebText2/test/*.arrow" - config_name: PhilPapers data_files: - split: train path: "data/PhilPapers/train/*.arrow" - split: test path: "data/PhilPapers/test/*.arrow" - config_name: Pile-CC data_files: - split: train path: "data/Pile-CC/train/*.arrow" - split: test path: "data/Pile-CC/test/*.arrow" - config_name: PubMed Abstracts data_files: - split: train path: "data/PubMed Abstracts/train/*.arrow" - split: test path: "data/PubMed Abstracts/test/*.arrow" - config_name: PubMed Central data_files: - split: train path: "data/PubMed Central/train/*.arrow" - split: test path: "data/PubMed Central/test/*.arrow" - config_name: StackExchange data_files: - split: train path: "data/StackExchange/train/*.arrow" - split: test path: "data/StackExchange/test/*.arrow" - config_name: UPSTO Backgrounds data_files: - split: train path: "data/UPSTO Backgrounds/train/*.arrow" - split: test path: "data/UPSTO Backgrounds/test/*.arrow" - config_name: Ubuntu IRC data_files: - split: train path: "data/Ubuntu IRC/train/*.arrow" - split: test path: "data/Ubuntu IRC/test/*.arrow" - config_name: Wikipedia (en) data_files: - split: train path: "data/Wikipedia (en)/train/*.arrow" - split: test path: "data/Wikipedia (en)/test/*.arrow" - config_name: YoutubeSubtitles data_files: - split: train path: "data/YoutubeSubtitles/train/*.arrow" - split: test path: "data/YoutubeSubtitles/test/*.arrow" --- # Dataset description [The pile](https://arxiv.org/abs/2101.00027) is an 800GB dataset of english text designed by EleutherAI to train large-scale language models. The original version of the dataset can be found [here](https://huggingface.co/datasets/EleutherAI/pile). The dataset is divided into 22 smaller high-quality datasets. For more information each of them, please refer to [the datasheet for the pile](https://arxiv.org/abs/2201.07311). However, the current version of the dataset, available on the Hub, is not splitted accordingly. We had to solve this problem in order to improve the user experience when it comes to deal with the pile via the hub. Here is an instance of the pile ``` { 'meta': {'pile_set_name': 'Pile-CC'}, 'text': 'It is done, and submitted. You can play “Survival of the Tastiest” on Android, and on the web. Playing on...' } ``` We used the `meta` column to properly divide the dataset in subsets. Each instance `example` belongs to the subset `domain` and `domain = example['meta']['pile_set_name']`. By doing this, we were able to create a [new version of the pile](https://huggingface.co/datasets/ArmelR/sharded-pile) that is properly divided, each instance having a new column `domain`. We further splitted each subset in train/test (97%/3%) to build the current dataset which the following structure ``` data ArXiv train test BookCorpus2 train test Books3 train test ``` # Usage ```python from datasets import load_dataset dataset = load_dataset( "ArmelR/the-pile-splitted", subset_of_interest, num_proc=8 ) ``` Using `subset_of_interest = "default"` will load the whole dataset.
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SALT-NLP/ImplicitHate
2023-02-16T23:00:38.000Z
[ "region:us" ]
SALT-NLP
null
null
2
1,078
2023-02-16T22:45:19
# Implicit Hate Speech _Latent Hatred: A Benchmark for Understanding Implicit Hate Speech_ [[Read the Paper]](https://aclanthology.org/2021.emnlp-main.29/) | [[Take a Survey to Access the Data]](https://forms.gle/QxCpEbVp91Z35hWFA) | [[Download the Data]](https://www.dropbox.com/s/24meryhqi1oo0xk/implicit-hate-corpus.zip?dl=0) <img src="frontpage.png" alt="frontpage" width="650"/> ## *Why Implicit Hate?* It is important to consider the subtle tricks that many extremists use to mask their threats and abuse. These more implicit forms of hate speech may easily go undetected by keyword detection systems, and even the most advanced architectures can fail if they have not been trained on implicit hate speech ([Caselli et al. 2020](https://aclanthology.org/2020.lrec-1.760/)). ## *Where can I download the data?* If you have not already, please first complete a short [survey](https://forms.gle/QxCpEbVp91Z35hWFA). Then follow [this link to download](https://www.dropbox.com/s/p1ctnsg3xlnupwr/implicit-hate-corpus.zip?dl=0) (2 MB, expands to 6 MB). ## *What's 'in the box?'* This dataset contains **22,056** tweets from the most prominent extremist groups in the United States; **6,346** of these tweets contain *implicit hate speech.* We decompose the implicit hate class using the following taxonomy (distribution shown on the left). * (24.2%) **Grievance:** frustration over a minority group's perceived privilege. * (20.0%) **Incitement:** implicitly promoting known hate groups and ideologies (e.g. by flaunting in-group power). * (13.6%) **Inferiority:** implying some group or person is of lesser value than another. * (12.6%) **Irony:** using sarcasm, humor, and satire to demean someone. * (17.9%) **Stereotypes:** associating a group with negative attribute using euphemisms, circumlocution, or metaphorical language. * (10.5%) **Threats:** making an indirect commitment to attack someone's body, well-being, reputation, liberty, etc. * (1.2%) **Other** Each of the 6,346 implicit hate tweets also has free-text annotations for *target demographic group* and an *implied statement* to describe the underlying message (see banner image above). ## *What can I do with this data?* State-of-the-art neural models may be able to learn from our data how to (1) classify this more difficult class of hate speech and (3) explain implicit hate by generating descriptions of both the *target* and the *implied message.* As our [paper baselines](#) show, neural models still have a ways to go, especially with classifying *implicit hate categories*, but overall, the results are promising, especially with *implied statement generation,* an admittedly challenging task. We hope you can extend our baselines and further our efforts to understand and address some of these most pernicious forms of language that plague the web, especially among extremist groups. ## *How do I cite this work?* **Citation:** > ElSherief, M., Ziems, C., Muchlinski, D., Anupindi, V., Seybolt, J., De Choudhury, M., & Yang, D. (2021). Latent Hatred: A Benchmark for Understanding Implicit Hate Speech. In _Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)_. **BibTeX:** ```tex @inproceedings{elsherief-etal-2021-latent, title = "Latent Hatred: A Benchmark for Understanding Implicit Hate Speech", author = "ElSherief, Mai and Ziems, Caleb and Muchlinski, David and Anupindi, Vaishnavi and Seybolt, Jordyn and De Choudhury, Munmun and Yang, Diyi", booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.29", pages = "345--363" } ```
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google/xtreme_s
2022-07-28T12:47:02.000Z
[ "task_categories:automatic-speech-recognition", "annotations_creators:expert-generated", "annotations_creators:crowdsourced", "annotations_creators:machine-generated", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:10K<n<100K", "source_datasets:extended|multilingual_librispeech", "source_datasets:extended|covost2", "language:afr", "language:amh", "language:ara", "language:asm", "language:ast", "language:azj", "language:bel", "language:ben", "language:bos", "language:cat", "language:ceb", "language:cmn", "language:ces", "language:cym", "language:dan", "language:deu", "language:ell", "language:eng", "language:spa", "language:est", "language:fas", "language:ful", "language:fin", "language:tgl", "language:fra", "language:gle", "language:glg", "language:guj", "language:hau", "language:heb", "language:hin", "language:hrv", "language:hun", "language:hye", "language:ind", "language:ibo", "language:isl", "language:ita", "language:jpn", "language:jav", "language:kat", "language:kam", "language:kea", "language:kaz", "language:khm", "language:kan", "language:kor", "language:ckb", "language:kir", "language:ltz", "language:lug", "language:lin", "language:lao", "language:lit", "language:luo", "language:lav", "language:mri", "language:mkd", "language:mal", "language:mon", "language:mar", "language:msa", "language:mlt", "language:mya", "language:nob", "language:npi", "language:nld", "language:nso", "language:nya", "language:oci", "language:orm", "language:ory", "language:pan", "language:pol", "language:pus", "language:por", "language:ron", "language:rus", "language:bul", "language:snd", "language:slk", "language:slv", "language:sna", "language:som", "language:srp", "language:swe", "language:swh", "language:tam", "language:tel", "language:tgk", "language:tha", "language:tur", "language:ukr", "language:umb", "language:urd", "language:uzb", "language:vie", "language:wol", "language:xho", "language:yor", "language:yue", "language:zul", "license:cc-by-4.0", "arxiv:2203.10752", "arxiv:2205.12446", "arxiv:2007.10310", "region:us" ]
google
XTREME-S covers four task families: speech recognition, classification, speech-to-text translation and retrieval. Covering 102 languages from 10+ language families, 3 different domains and 4 task families, XTREME-S aims to simplify multilingual speech representation evaluation, as well as catalyze research in “universal” speech representation learning.
@article{conneau2022xtreme, title={XTREME-S: Evaluating Cross-lingual Speech Representations}, author={Conneau, Alexis and Bapna, Ankur and Zhang, Yu and Ma, Min and von Platen, Patrick and Lozhkov, Anton and Cherry, Colin and Jia, Ye and Rivera, Clara and Kale, Mihir and others}, journal={arXiv preprint arXiv:2203.10752}, year={2022} }
35
1,076
2022-03-04T14:10:40
--- annotations_creators: - expert-generated - crowdsourced - machine-generated language_creators: - crowdsourced - expert-generated language: - afr - amh - ara - asm - ast - azj - bel - ben - bos - cat - ceb - cmn - ces - cym - dan - deu - ell - eng - spa - est - fas - ful - fin - tgl - fra - gle - glg - guj - hau - heb - hin - hrv - hun - hye - ind - ibo - isl - ita - jpn - jav - kat - kam - kea - kaz - khm - kan - kor - ckb - kir - ltz - lug - lin - lao - lit - luo - lav - mri - mkd - mal - mon - mar - msa - mlt - mya - nob - npi - nld - nso - nya - oci - orm - ory - pan - pol - pus - por - ron - rus - bul - snd - slk - slv - sna - som - srp - swe - swh - tam - tel - tgk - tha - tur - ukr - umb - urd - uzb - vie - wol - xho - yor - yue - zul license: - cc-by-4.0 multilinguality: - multilingual paperswithcode_id: librispeech-1 pretty_name: 'The Cross-lingual TRansfer Evaluation of Multilingual Encoders for Speech (XTREME-S) benchmark is a benchmark designed to evaluate speech representations across languages, tasks, domains and data regimes. It covers 102 languages from 10+ language families, 3 different domains and 4 task families: speech recognition, translation, classification and retrieval.' size_categories: - 10K<n<100K source_datasets: - extended|multilingual_librispeech - extended|covost2 task_categories: - automatic-speech-recognition - speech-processing task_ids: - speech-recognition --- # XTREME-S ## Dataset Description - **Fine-Tuning script:** [research-projects/xtreme-s](https://github.com/huggingface/transformers/tree/master/examples/research_projects/xtreme-s) - **Paper:** [XTREME-S: Evaluating Cross-lingual Speech Representations](https://arxiv.org/abs/2203.10752) - **Leaderboard:** [TODO(PVP)]() - **FLEURS amount of disk used:** 350 GB - **Multilingual Librispeech amount of disk used:** 2700 GB - **Voxpopuli amount of disk used:** 400 GB - **Covost2 amount of disk used:** 70 GB - **Minds14 amount of disk used:** 5 GB - **Total amount of disk used:** ca. 3500 GB The Cross-lingual TRansfer Evaluation of Multilingual Encoders for Speech (XTREME-S) benchmark is a benchmark designed to evaluate speech representations across languages, tasks, domains and data regimes. It covers 102 languages from 10+ language families, 3 different domains and 4 task families: speech recognition, translation, classification and retrieval. ***TLDR; XTREME-S is the first speech benchmark that is both diverse, fully accessible, and reproducible. All datasets can be downloaded with a single line of code. An easy-to-use and flexible fine-tuning script is provided and actively maintained.*** XTREME-S covers speech recognition with Fleurs, Multilingual LibriSpeech (MLS) and VoxPopuli, speech translation with CoVoST-2, speech classification with LangID (Fleurs) and intent classification (MInds-14) and finally speech(-text) retrieval with Fleurs. Each of the tasks covers a subset of the 102 languages included in XTREME-S, from various regions: - **Western Europe**: *Asturian, Bosnian, Catalan, Croatian, Danish, Dutch, English, Finnish, French, Galician, German, Greek, Hungarian, Icelandic, Irish, Italian, Kabuverdianu, Luxembourgish, Maltese, Norwegian, Occitan, Portuguese, Spanish, Swedish, Welsh* - **Eastern Europe**: *Armenian, Belarusian, Bulgarian, Czech, Estonian, Georgian, Latvian, Lithuanian, Macedonian, Polish, Romanian, Russian, Serbian, Slovak, Slovenian, Ukrainian* - **Central-Asia/Middle-East/North-Africa**: *Arabic, Azerbaijani, Hebrew, Kazakh, Kyrgyz, Mongolian, Pashto, Persian, Sorani-Kurdish, Tajik, Turkish, Uzbek* - **Sub-Saharan Africa**: *Afrikaans, Amharic, Fula, Ganda, Hausa, Igbo, Kamba, Lingala, Luo, Northern-Sotho, Nyanja, Oromo, Shona, Somali, Swahili, Umbundu, Wolof, Xhosa, Yoruba, Zulu* - **South-Asia**: *Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Nepali, Oriya, Punjabi, Sindhi, Tamil, Telugu, Urdu* - **South-East Asia**: *Burmese, Cebuano, Filipino, Indonesian, Javanese, Khmer, Lao, Malay, Maori, Thai, Vietnamese* - **CJK languages**: *Cantonese and Mandarin Chinese, Japanese, Korean* ## Design principles ### Diversity XTREME-S aims for task, domain and language diversity. Tasks should be diverse and cover several domains to provide a reliable evaluation of model generalization and robustness to noisy naturally-occurring speech in different environments. Languages should be diverse to ensure that models can adapt to a wide range of linguistic and phonological phenomena. ### Accessibility The sub-dataset for each task can be downloaded with a **single line of code** as shown in [Supported Tasks](#supported-tasks). Each task is available under a permissive license that allows the use and redistribution of the data for research purposes. Tasks have been selected based on their usage by pre-existing multilingual pre-trained models, for simplicity. ### Reproducibility We produce fully **open-sourced, maintained and easy-to-use** fine-tuning scripts for each task as shown under [Fine-tuning Example](#fine-tuning-and-evaluation-example). XTREME-S encourages submissions that leverage publicly available speech and text datasets. Users should detail which data they use. In general, we encourage settings that can be reproduced by the community, but also encourage the exploration of new frontiers for speech representation learning. ## Fine-tuning and Evaluation Example We provide a fine-tuning script under [**research-projects/xtreme-s**](https://github.com/huggingface/transformers/tree/master/examples/research_projects/xtreme-s). The fine-tuning script is written in PyTorch and allows one to fine-tune and evaluate any [Hugging Face model](https://huggingface.co/models) on XTREME-S. The example script is actively maintained by [@anton-l](https://github.com/anton-l) and [@patrickvonplaten](https://github.com/patrickvonplaten). Feel free to reach out via issues or pull requests on GitHub if you have any questions. ## Leaderboards The leaderboard for the XTREME-S benchmark can be found at [this address (TODO(PVP))](). ## Supported Tasks Note that the suppoprted tasks are focused particularly on linguistic aspect of speech, while nonlinguistic/paralinguistic aspects of speech relevant to e.g. speech synthesis or voice conversion are **not** evaluated. <p align="center"> <img src="https://github.com/patrickvonplaten/scientific_images/raw/master/xtreme_s.png" alt="Datasets used in XTREME"/> </p> ### 1. Speech Recognition (ASR) We include three speech recognition datasets: FLEURS-ASR, MLS and VoxPopuli (optionally BABEL). Multilingual fine-tuning is used for these three datasets. #### FLEURS-ASR *FLEURS-ASR* is the speech version of the FLORES machine translation benchmark, covering 2000 n-way parallel sentences in n=102 languages. ```py from datasets import load_dataset fleurs_asr = load_dataset("google/xtreme_s", "fleurs.af_za") # for Afrikaans # to download all data for multi-lingual fine-tuning uncomment following line # fleurs_asr = load_dataset("google/xtreme_s", "fleurs.all") # see structure print(fleurs_asr) # load audio sample on the fly audio_input = fleurs_asr["train"][0]["audio"] # first decoded audio sample transcription = fleurs_asr["train"][0]["transcription"] # first transcription # use `audio_input` and `transcription` to fine-tune your model for ASR # for analyses see language groups all_language_groups = fleurs_asr["train"].features["lang_group_id"].names lang_group_id = fleurs_asr["train"][0]["lang_group_id"] all_language_groups[lang_group_id] ``` #### Multilingual LibriSpeech (MLS) *MLS* is a large multilingual corpus derived from read audiobooks from LibriVox and consists of 8 languages. For this challenge the training data is limited to 10-hours splits. ```py from datasets import load_dataset mls = load_dataset("google/xtreme_s", "mls.pl") # for Polish # to download all data for multi-lingual fine-tuning uncomment following line # mls = load_dataset("google/xtreme_s", "mls.all") # see structure print(mls) # load audio sample on the fly audio_input = mls["train"][0]["audio"] # first decoded audio sample transcription = mls["train"][0]["transcription"] # first transcription # use `audio_input` and `transcription` to fine-tune your model for ASR ``` #### VoxPopuli *VoxPopuli* is a large-scale multilingual speech corpus for representation learning and semi-supervised learning, from which we use the speech recognition dataset. The raw data is collected from 2009-2020 European Parliament event recordings. We acknowledge the European Parliament for creating and sharing these materials. **VoxPopuli has to download the whole dataset 100GB since languages are entangled into each other - maybe not worth testing here due to the size** ```py from datasets import load_dataset voxpopuli = load_dataset("google/xtreme_s", "voxpopuli.ro") # for Romanian # to download all data for multi-lingual fine-tuning uncomment following line # voxpopuli = load_dataset("google/xtreme_s", "voxpopuli.all") # see structure print(voxpopuli) # load audio sample on the fly audio_input = voxpopuli["train"][0]["audio"] # first decoded audio sample transcription = voxpopuli["train"][0]["transcription"] # first transcription # use `audio_input` and `transcription` to fine-tune your model for ASR ``` #### (Optionally) BABEL *BABEL* from IARPA is a conversational speech recognition dataset in low-resource languages. First, download LDC2016S06, LDC2016S12, LDC2017S08, LDC2017S05 and LDC2016S13. BABEL is the only dataset in our benchmark who is less easily accessible, so you will need to sign in to get access to it on LDC. Although not officially part of the XTREME-S ASR datasets, BABEL is often used for evaluating speech representations on a difficult domain (phone conversations). ```py from datasets import load_dataset babel = load_dataset("google/xtreme_s", "babel.as") ``` **The above command is expected to fail with a nice error message, explaining how to download BABEL** The following should work: ```py from datasets import load_dataset babel = load_dataset("google/xtreme_s", "babel.as", data_dir="/path/to/IARPA_BABEL_OP1_102_LDC2016S06.zip") # see structure print(babel) # load audio sample on the fly audio_input = babel["train"][0]["audio"] # first decoded audio sample transcription = babel["train"][0]["transcription"] # first transcription # use `audio_input` and `transcription` to fine-tune your model for ASR ``` ### 2. Speech Translation (ST) We include the CoVoST-2 dataset for automatic speech translation. #### CoVoST-2 The *CoVoST-2* benchmark has become a commonly used dataset for evaluating automatic speech translation. It covers language pairs from English into 15 languages, as well as 21 languages into English. We use only the "X->En" direction to evaluate cross-lingual representations. The amount of supervision varies greatly in this setting, from one hour for Japanese->English to 180 hours for French->English. This makes pretraining particularly useful to enable such few-shot learning. We enforce multiligual fine-tuning for simplicity. Results are splitted in high/med/low-resource language pairs as explained in the [paper (TODO(PVP))]. ```py from datasets import load_dataset covost_2 = load_dataset("google/xtreme_s", "covost2.id.en") # for Indonesian to English # to download all data for multi-lingual fine-tuning uncomment following line # covost_2 = load_dataset("google/xtreme_s", "covost2.all") # see structure print(covost_2) # load audio sample on the fly audio_input = covost_2["train"][0]["audio"] # first decoded audio sample transcription = covost_2["train"][0]["transcription"] # first transcription translation = covost_2["train"][0]["translation"] # first translation # use audio_input and translation to fine-tune your model for AST ``` ### 3. Speech Classification We include two multilingual speech classification datasets: FLEURS-LangID and Minds-14. #### Language Identification - FLEURS-LangID LangID can often be a domain classification, but in the case of FLEURS-LangID, recordings are done in a similar setting across languages and the utterances correspond to n-way parallel sentences, in the exact same domain, making this task particularly relevant for evaluating LangID. The setting is simple, FLEURS-LangID is splitted in train/valid/test for each language. We simply create a single train/valid/test for LangID by merging all. ```py from datasets import load_dataset fleurs_langID = load_dataset("google/xtreme_s", "fleurs.all") # to download all data # see structure print(fleurs_langID) # load audio sample on the fly audio_input = fleurs_langID["train"][0]["audio"] # first decoded audio sample language_class = fleurs_langID["train"][0]["lang_id"] # first id class language = fleurs_langID["train"].features["lang_id"].names[language_class] # use audio_input and language_class to fine-tune your model for audio classification ``` #### Intent classification - Minds-14 Minds-14 is an intent classification made from e-banking speech datasets in 14 languages, with 14 intent labels. We impose a single multilingual fine-tuning to increase the size of the train and test sets and reduce the variance associated with the small size of the dataset per language. ```py from datasets import load_dataset minds_14 = load_dataset("google/xtreme_s", "minds14.fr-FR") # for French # to download all data for multi-lingual fine-tuning uncomment following line # minds_14 = load_dataset("google/xtreme_s", "minds14.all") # see structure print(minds_14) # load audio sample on the fly audio_input = minds_14["train"][0]["audio"] # first decoded audio sample intent_class = minds_14["train"][0]["intent_class"] # first transcription intent = minds_14["train"].features["intent_class"].names[intent_class] # use audio_input and language_class to fine-tune your model for audio classification ``` ### 4. (Optionally) Speech Retrieval We optionally include one speech retrieval dataset: FLEURS-Retrieval as explained in the [FLEURS paper](https://arxiv.org/abs/2205.12446). #### FLEURS-Retrieval FLEURS-Retrieval provides n-way parallel speech and text data. Similar to how XTREME for text leverages Tatoeba to evaluate bitext mining a.k.a sentence translation retrieval, we use FLEURS-Retrieval to evaluate the quality of fixed-size representations of speech utterances. Our goal is to incentivize the creation of fixed-size speech encoder for speech retrieval. The system has to retrieve the English "key" utterance corresponding to the speech translation of "queries" in 15 languages. Results have to be reported on the test sets of FLEURS-Retrieval whose utterances are used as queries (and keys for English). We augment the English keys with a large number of utterances to make the task more difficult. ```py from datasets import load_dataset fleurs_retrieval = load_dataset("google/xtreme_s", "fleurs.af_za") # for Afrikaans # to download all data for multi-lingual fine-tuning uncomment following line # fleurs_retrieval = load_dataset("google/xtreme_s", "fleurs.all") # see structure print(fleurs_retrieval) # load audio sample on the fly audio_input = fleurs_retrieval["train"][0]["audio"] # decoded audio sample text_sample_pos = fleurs_retrieval["train"][0]["transcription"] # positive text sample text_sample_neg = fleurs_retrieval["train"][1:20]["transcription"] # negative text samples # use `audio_input`, `text_sample_pos`, and `text_sample_neg` to fine-tune your model for retrieval ``` Users can leverage the training (and dev) sets of FLEURS-Retrieval with a ranking loss to build better cross-lingual fixed-size representations of speech. ## Dataset Structure The XTREME-S benchmark is composed of the following datasets: - [FLEURS](https://huggingface.co/datasets/google/fleurs#dataset-structure) - [Multilingual Librispeech (MLS)](https://huggingface.co/datasets/facebook/multilingual_librispeech#dataset-structure) Note that for MLS, XTREME-S uses `path` instead of `file` and `transcription` instead of `text`. - [Voxpopuli](https://huggingface.co/datasets/facebook/voxpopuli#dataset-structure) - [Minds14](https://huggingface.co/datasets/polyai/minds14#dataset-structure) - [Covost2](https://huggingface.co/datasets/covost2#dataset-structure) Note that for Covost2, XTREME-S uses `path` instead of `file` and `transcription` instead of `sentence`. - [BABEL](https://huggingface.co/datasets/ldc/iarpa_babel#dataset-structure) Please click on the link of the dataset cards to get more information about its dataset structure. ## Dataset Creation The XTREME-S benchmark is composed of the following datasets: - [FLEURS](https://huggingface.co/datasets/google/fleurs#dataset-creation) - [Multilingual Librispeech (MLS)](https://huggingface.co/datasets/facebook/multilingual_librispeech#dataset-creation) - [Voxpopuli](https://huggingface.co/datasets/facebook/voxpopuli#dataset-creation) - [Minds14](https://huggingface.co/datasets/polyai/minds14#dataset-creation) - [Covost2](https://huggingface.co/datasets/covost2#dataset-creation) - [BABEL](https://huggingface.co/datasets/ldc/iarpa_babel#dataset-creation) Please visit the corresponding dataset cards to get more information about the source data. ## Considerations for Using the Data ### Social Impact of Dataset This dataset is meant to encourage the development of speech technology in a lot more languages of the world. One of the goal is to give equal access to technologies like speech recognition or speech translation to everyone, meaning better dubbing or better access to content from the internet (like podcasts, streaming or videos). ### Discussion of Biases Most datasets have a fair distribution of gender utterances (e.g. the newly introduced FLEURS dataset). While many languages are covered from various regions of the world, the benchmark misses many languages that are all equally important. We believe technology built through XTREME-S should generalize to all languages. ### Other Known Limitations The benchmark has a particular focus on read-speech because common evaluation benchmarks like CoVoST-2 or LibriSpeech evaluate on this type of speech. There is sometimes a known mismatch between performance obtained in a read-speech setting and a more noisy setting (in production for instance). Given the big progress that remains to be made on many languages, we believe better performance on XTREME-S should still correlate well with actual progress made for speech understanding. ## Additional Information All datasets are licensed under the [Creative Commons license (CC-BY)](https://creativecommons.org/licenses/). ### Citation Information #### XTREME-S ``` @article{conneau2022xtreme, title={XTREME-S: Evaluating Cross-lingual Speech Representations}, author={Conneau, Alexis and Bapna, Ankur and Zhang, Yu and Ma, Min and von Platen, Patrick and Lozhkov, Anton and Cherry, Colin and Jia, Ye and Rivera, Clara and Kale, Mihir and others}, journal={arXiv preprint arXiv:2203.10752}, year={2022} } ``` #### MLS ``` @article{Pratap2020MLSAL, title={MLS: A Large-Scale Multilingual Dataset for Speech Research}, author={Vineel Pratap and Qiantong Xu and Anuroop Sriram and Gabriel Synnaeve and Ronan Collobert}, journal={ArXiv}, year={2020}, volume={abs/2012.03411} } ``` #### VoxPopuli ``` @article{wang2021voxpopuli, title={Voxpopuli: A large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation}, author={Wang, Changhan and Riviere, Morgane and Lee, Ann and Wu, Anne and Talnikar, Chaitanya and Haziza, Daniel and Williamson, Mary and Pino, Juan and Dupoux, Emmanuel}, journal={arXiv preprint arXiv:2101.00390}, year={2021} } ``` #### CoVoST 2 ``` @article{DBLP:journals/corr/abs-2007-10310, author = {Changhan Wang and Anne Wu and Juan Miguel Pino}, title = {CoVoST 2: {A} Massively Multilingual Speech-to-Text Translation Corpus}, journal = {CoRR}, volume = {abs/2007.10310}, year = {2020}, url = {https://arxiv.org/abs/2007.10310}, eprinttype = {arXiv}, eprint = {2007.10310}, timestamp = {Thu, 12 Aug 2021 15:37:06 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2007-10310.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` #### Minds14 ``` @article{gerz2021multilingual, title={Multilingual and cross-lingual intent detection from spoken data}, author={Gerz, Daniela and Su, Pei-Hao and Kusztos, Razvan and Mondal, Avishek and Lis, Micha{\l} and Singhal, Eshan and Mrk{\v{s}}i{\'c}, Nikola and Wen, Tsung-Hsien and Vuli{\'c}, Ivan}, journal={arXiv preprint arXiv:2104.08524}, year={2021} } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@anton-l](https://github.com/anton-l), [@aconneau](https://github.com/aconneau) for adding this dataset
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hippocrates/re_train
2023-10-09T16:55:29.000Z
[ "region:us" ]
hippocrates
null
null
0
1,076
2023-10-04T22:30:18
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* dataset_info: features: - name: id dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string - name: text dtype: string splits: - name: train num_bytes: 19219537 num_examples: 3572 - name: valid num_bytes: 1626844 num_examples: 305 download_size: 1753501 dataset_size: 20846381 --- # Dataset Card for "re_train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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yuchenlin/just-eval-instruct
2023-10-20T19:01:44.000Z
[ "region:us" ]
yuchenlin
null
null
2
1,071
2023-09-11T21:42:48
--- configs: - config_name: default data_files: - split: test path: "test_all_with_tags.jsonl" # - split: test_regular_only # path: "test_regular.jsonl" # - split: test_safety_only # path: "test_red.jsonl" - config_name: responses data_files: - split: gpt_4_0613 path: "responses/gpt-4-0613.json" - split: gpt_4_0314 path: "responses/gpt-4-0314.json" - split: gpt_3.5_turbo_0301 path: "responses/gpt-3.5-turbo-0301.json" - split: Mistral_7B_Instruct_v0.1 path: "responses/Mistral-7B-Instruct-v0.1.json" - split: Llama_2_13b_chat_hf path: "responses/Llama-2-13b-chat-hf.json" - split: Llama_2_70B_chat_GPTQ path: "responses/Llama-2-70B-chat-GPTQ.json" - split: Llama_2_7b_chat_hf path: "responses/Llama-2-7b-chat-hf.json" - split: vicuna_13b_v1.5 path: "responses/vicuna-13b-v1.5.json" - split: vicuna_7b_v1.5 path: "responses/vicuna-7b-v1.5.json" - config_name: judgements_main data_files: - split: Mistral_7B_Instruct_v0.1 path: "judgements/main/Mistral-7B-Instruct-v0.1.json" - split: gpt_4_0613 path: "judgements/main/gpt-4-0613.json" - split: gpt_4_0314 path: "judgements/main/gpt-4-0314.json" - split: Llama_2_70B_chat_GPTQ path: "judgements/main/Llama-2-70B-chat-GPTQ.json" - split: Llama_2_13b_chat_hf path: "judgements/main/Llama-2-13b-chat-hf.json" - split: vicuna_7b_v1.5 path: "judgements/main/vicuna-7b-v1.5.json" - split: vicuna_13b_v1.5 path: "judgements/main/vicuna-13b-v1.5.json" - split: gpt_3.5_turbo_0301 path: "judgements/main/gpt-3.5-turbo-0301.json" - split: Llama_2_7b_chat_hf path: "judgements/main/Llama-2-7b-chat-hf.json" - config_name: judgements_safety data_files: - split: Mistral_7B_Instruct_v0.1 path: "judgements/safety/Mistral-7B-Instruct-v0.1.json" - split: gpt_4_0613 path: "judgements/safety/gpt-4-0613.json" - split: gpt_4_0314 path: "judgements/safety/gpt-4-0314.json" - split: Llama_2_70B_chat_GPTQ path: "judgements/safety/Llama-2-70B-chat-GPTQ.json" - split: Llama_2_13b_chat_hf path: "judgements/safety/Llama-2-13b-chat-hf.json" - split: vicuna_7b_v1.5 path: "judgements/safety/vicuna-7b-v1.5.json" - split: vicuna_13b_v1.5 path: "judgements/safety/vicuna-13b-v1.5.json" - split: gpt_3.5_turbo_0301 path: "judgements/safety/gpt-3.5-turbo-0301.json" - split: Llama_2_7b_chat_hf path: "judgements/safety/Llama-2-7b-chat-hf.json" --- ## Just Eval Instruct!!
2,490
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learn3r/summ_screen_fd_bp
2023-09-26T10:28:23.000Z
[ "region:us" ]
learn3r
null
null
0
1,069
2023-08-30T08:33:07
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 119519799 num_examples: 3673 - name: validation num_bytes: 10838812 num_examples: 338 - name: test num_bytes: 11004410 num_examples: 337 download_size: 6435842 dataset_size: 141363021 --- # Dataset Card for "summ_screen_fd_bp" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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para_crawl
2023-04-05T13:36:34.000Z
[ "task_categories:translation", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:translation", "size_categories:10M<n<100M", "source_datasets:original", "language:bg", "language:cs", "language:da", "language:de", "language:el", "language:en", "language:es", "language:et", "language:fi", "language:fr", "language:ga", "language:hr", "language:hu", "language:it", "language:lt", "language:lv", "language:mt", "language:nl", "language:pl", "language:pt", "language:ro", "language:sk", "language:sl", "language:sv", "license:cc0-1.0", "region:us" ]
null
null
@misc {paracrawl, title = {ParaCrawl}, year = {2018}, url = {http://paracrawl.eu/download.html.} }
8
1,066
2022-03-02T23:29:22
--- annotations_creators: - no-annotation language_creators: - found language: - bg - cs - da - de - el - en - es - et - fi - fr - ga - hr - hu - it - lt - lv - mt - nl - pl - pt - ro - sk - sl - sv license: - cc0-1.0 multilinguality: - translation pretty_name: ParaCrawl size_categories: - 10M<n<100M source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: paracrawl dataset_info: - config_name: enbg features: - name: translation dtype: translation: languages: - en - bg splits: - name: train num_bytes: 356532771 num_examples: 1039885 download_size: 103743335 dataset_size: 356532771 - config_name: encs features: - name: translation dtype: translation: languages: - en - cs splits: - name: train num_bytes: 638068353 num_examples: 2981949 download_size: 196410022 dataset_size: 638068353 - config_name: enda features: - name: translation dtype: translation: languages: - en - da splits: - name: train num_bytes: 598624306 num_examples: 2414895 download_size: 182804827 dataset_size: 598624306 - config_name: ende features: - name: translation dtype: translation: languages: - en - de splits: - name: train num_bytes: 3997191986 num_examples: 16264448 download_size: 1307754745 dataset_size: 3997191986 - config_name: enel features: - name: translation dtype: translation: languages: - en - el splits: - name: train num_bytes: 688069020 num_examples: 1985233 download_size: 193553374 dataset_size: 688069020 - config_name: enes features: - name: translation dtype: translation: languages: - en - es splits: - name: train num_bytes: 6209466040 num_examples: 21987267 download_size: 1953839527 dataset_size: 6209466040 - config_name: enet features: - name: translation dtype: translation: languages: - en - et splits: - name: train num_bytes: 201408919 num_examples: 853422 download_size: 70158650 dataset_size: 201408919 - config_name: enfi features: - name: translation dtype: translation: languages: - en - fi splits: - name: train num_bytes: 524624150 num_examples: 2156069 download_size: 159209242 dataset_size: 524624150 - config_name: enfr features: - name: translation dtype: translation: languages: - en - fr splits: - name: train num_bytes: 9015440258 num_examples: 31374161 download_size: 2827554088 dataset_size: 9015440258 - config_name: enga features: - name: translation dtype: translation: languages: - en - ga splits: - name: train num_bytes: 104523278 num_examples: 357399 download_size: 29394367 dataset_size: 104523278 - config_name: enhr features: - name: translation dtype: translation: languages: - en - hr splits: - name: train num_bytes: 247646552 num_examples: 1002053 download_size: 84904103 dataset_size: 247646552 - config_name: enhu features: - name: translation dtype: translation: languages: - en - hu splits: - name: train num_bytes: 403168065 num_examples: 1901342 download_size: 119784765 dataset_size: 403168065 - config_name: enit features: - name: translation dtype: translation: languages: - en - it splits: - name: train num_bytes: 3340542050 num_examples: 12162239 download_size: 1066720197 dataset_size: 3340542050 - config_name: enlt features: - name: translation dtype: translation: languages: - en - lt splits: - name: train num_bytes: 197053694 num_examples: 844643 download_size: 66358392 dataset_size: 197053694 - config_name: enlv features: - name: translation dtype: translation: languages: - en - lv splits: - name: train num_bytes: 142409870 num_examples: 553060 download_size: 47368967 dataset_size: 142409870 - config_name: enmt features: - name: translation dtype: translation: languages: - en - mt splits: - name: train num_bytes: 52786023 num_examples: 195502 download_size: 19028352 dataset_size: 52786023 - config_name: ennl features: - name: translation dtype: translation: languages: - en - nl splits: - name: train num_bytes: 1384042007 num_examples: 5659268 download_size: 420090979 dataset_size: 1384042007 - config_name: enpl features: - name: translation dtype: translation: languages: - en - pl splits: - name: train num_bytes: 854786500 num_examples: 3503276 download_size: 270427885 dataset_size: 854786500 - config_name: enpt features: - name: translation dtype: translation: languages: - en - pt splits: - name: train num_bytes: 2031891156 num_examples: 8141940 download_size: 638184462 dataset_size: 2031891156 - config_name: enro features: - name: translation dtype: translation: languages: - en - ro splits: - name: train num_bytes: 518359240 num_examples: 1952043 download_size: 160684751 dataset_size: 518359240 - config_name: ensk features: - name: translation dtype: translation: languages: - en - sk splits: - name: train num_bytes: 337704729 num_examples: 1591831 download_size: 101307152 dataset_size: 337704729 - config_name: ensl features: - name: translation dtype: translation: languages: - en - sl splits: - name: train num_bytes: 182399034 num_examples: 660161 download_size: 65037465 dataset_size: 182399034 - config_name: ensv features: - name: translation dtype: translation: languages: - en - sv splits: - name: train num_bytes: 875576366 num_examples: 3476729 download_size: 275528370 dataset_size: 875576366 --- # Dataset Card for "para_crawl" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://paracrawl.eu/releases.html](https://paracrawl.eu/releases.html) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 10.36 GB - **Size of the generated dataset:** 32.90 GB - **Total amount of disk used:** 43.26 GB ### Dataset Summary Web-Scale Parallel Corpora for Official European Languages. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### enbg - **Size of downloaded dataset files:** 103.75 MB - **Size of the generated dataset:** 356.54 MB - **Total amount of disk used:** 460.27 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "translation": "{\"bg\": \". “A felirat faragott karnis a bejárat fölött, templom épült 14 Július 1643, A földesúr és felesége Jeremiás Murguleţ, C..." } ``` #### encs - **Size of downloaded dataset files:** 196.41 MB - **Size of the generated dataset:** 638.07 MB - **Total amount of disk used:** 834.48 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "translation": "{\"cs\": \". “A felirat faragott karnis a bejárat fölött, templom épült 14 Július 1643, A földesúr és felesége Jeremiás Murguleţ, C..." } ``` #### enda - **Size of downloaded dataset files:** 182.81 MB - **Size of the generated dataset:** 598.62 MB - **Total amount of disk used:** 781.43 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "translation": "{\"da\": \". “A felirat faragott karnis a bejárat fölött, templom épült 14 Július 1643, A földesúr és felesége Jeremiás Murguleţ, C..." } ``` #### ende - **Size of downloaded dataset files:** 1.31 GB - **Size of the generated dataset:** 4.00 GB - **Total amount of disk used:** 5.30 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "translation": "{\"de\": \". “A felirat faragott karnis a bejárat fölött, templom épült 14 Július 1643, A földesúr és felesége Jeremiás Murguleţ, C..." } ``` #### enel - **Size of downloaded dataset files:** 193.56 MB - **Size of the generated dataset:** 688.07 MB - **Total amount of disk used:** 881.62 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "translation": "{\"el\": \". “A felirat faragott karnis a bejárat fölött, templom épült 14 Július 1643, A földesúr és felesége Jeremiás Murguleţ, C..." } ``` ### Data Fields The data fields are the same among all splits. #### enbg - `translation`: a multilingual `string` variable, with possible languages including `en`, `bg`. #### encs - `translation`: a multilingual `string` variable, with possible languages including `en`, `cs`. #### enda - `translation`: a multilingual `string` variable, with possible languages including `en`, `da`. #### ende - `translation`: a multilingual `string` variable, with possible languages including `en`, `de`. #### enel - `translation`: a multilingual `string` variable, with possible languages including `en`, `el`. ### Data Splits | name | train | |------|---------:| | enbg | 1039885 | | encs | 2981949 | | enda | 2414895 | | ende | 16264448 | | enel | 1985233 | ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [Creative Commons CC0 license ("no rights reserved")](https://creativecommons.org/share-your-work/public-domain/cc0/). ### Citation Information ``` @inproceedings{banon-etal-2020-paracrawl, title = "{P}ara{C}rawl: Web-Scale Acquisition of Parallel Corpora", author = "Ba{\~n}{\'o}n, Marta and Chen, Pinzhen and Haddow, Barry and Heafield, Kenneth and Hoang, Hieu and Espl{\`a}-Gomis, Miquel and Forcada, Mikel L. and Kamran, Amir and Kirefu, Faheem and Koehn, Philipp and Ortiz Rojas, Sergio and Pla Sempere, Leopoldo and Ram{\'\i}rez-S{\'a}nchez, Gema and Sarr{\'\i}as, Elsa and Strelec, Marek and Thompson, Brian and Waites, William and Wiggins, Dion and Zaragoza, Jaume", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.acl-main.417", doi = "10.18653/v1/2020.acl-main.417", pages = "4555--4567", abstract = "We report on methods to create the largest publicly available parallel corpora by crawling the web, using open source software. We empirically compare alternative methods and publish benchmark data sets for sentence alignment and sentence pair filtering. We also describe the parallel corpora released and evaluate their quality and their usefulness to create machine translation systems.", } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset.
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craigslist_bargains
2022-11-18T19:47:08.000Z
[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:dialogue-modeling", "annotations_creators:machine-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:unknown", "arxiv:1808.09637", "region:us" ]
null
We study negotiation dialogues where two agents, a buyer and a seller, negotiate over the price of an time for sale. We collected a dataset of more than 6K negotiation dialogues over multiple categories of products scraped from Craigslist. Our goal is to develop an agent that negotiates with humans through such conversations. The challenge is to handle both the negotiation strategy and the rich language for bargaining.
@misc{he2018decoupling, title={Decoupling Strategy and Generation in Negotiation Dialogues}, author={He He and Derek Chen and Anusha Balakrishnan and Percy Liang}, year={2018}, eprint={1808.09637}, archivePrefix={arXiv}, primaryClass={cs.CL} }
9
1,065
2022-03-02T23:29:22
--- annotations_creators: - machine-generated language_creators: - crowdsourced language: - en license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - dialogue-modeling paperswithcode_id: craigslistbargains pretty_name: CraigslistBargains dataset_info: features: - name: agent_info sequence: - name: Bottomline dtype: string - name: Role dtype: string - name: Target dtype: float32 - name: agent_turn sequence: int32 - name: dialogue_acts sequence: - name: intent dtype: string - name: price dtype: float32 - name: utterance sequence: string - name: items sequence: - name: Category dtype: string - name: Images dtype: string - name: Price dtype: float32 - name: Description dtype: string - name: Title dtype: string splits: - name: train num_bytes: 8538836 num_examples: 5247 - name: test num_bytes: 1353933 num_examples: 838 - name: validation num_bytes: 966032 num_examples: 597 download_size: 25373618 dataset_size: 10858801 --- # Dataset Card for CraigslistBargains ## 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:** [Decoupling Strategy and Generation in Negotiation Dialogues](https://worksheets.codalab.org/worksheets/0x453913e76b65495d8b9730d41c7e0a0c/) - **Repository:** [Github: Stanford NLP Cocoa](https://github.com/stanfordnlp/cocoa/tree/master) - **Paper:** [Decoupling Strategy and Generation in Negotiation Dialogues](https://arxiv.org/abs/1808.09637) - **Leaderboard:** []() - **Point of Contact:** [He He](hehe@cs.nyu.edu) ### Dataset Summary We study negotiation dialogues where two agents, a buyer and a seller, negotiate over the price of an time for sale. We collected a dataset of more than 6K negotiation dialogues over multiple categories of products scraped from Craigslist. Our goal is to develop an agent that negotiates with humans through such conversations. The challenge is to handle both the negotiation strategy and the rich language for bargaining. To this end, we develop a modular framework which separates strategy learning from language generation. Specifically, we learn strategies in a coarse dialogue act space and instantiate that into utterances conditioned on dialogue history. ### Supported Tasks and Leaderboards ### Languages This dataset is English ## Dataset Structure ### Data Instances ``` { 'agent_info': { 'Bottomline': [ 'None', 'None' ], 'Role': [ 'buyer', 'seller' ], 'Target': [ 7.0, 10.0 ] }, 'agent_turn': [ 0, 1, ... ], 'dialogue_acts': { 'intent': [ 'init-price', 'unknown', ... ], 'price': [ 5.0, -1.0, ... ] }, 'items': { 'Category': [ 'phone', 'phone' ], 'Description': [ 'Charge two devices simultaneously on the go..., ... ], 'Images': [ 'phone/6149527852_0.jpg', 'phone/6149527852_0.jpg' ], 'Price': [ 10.0, 10.0 ], 'Title': [ 'Verizon Car Charger with Dual Output Micro USB and ...', ... ] }, 'utterance': [ 'Hi, not sure if the charger would work for my car...' 'It will work...', ... ] } ``` ### Data Fields - `agent_info`: Information about each of the agents taking part in the dialogue - `Bottomline`: TBD - `Role`: Whether the agent is buyer or seller - `Target`: Target price that the buyer/seller wants to hit in the negotiation - `agent_turn`: Agent taking the current turn in the dialogue (`int` index corresponding to `Role` above) - `dialogue_acts`: Rules-based information about the strategy of each agent for each turn - `intent`: The intent of the agent at the particular turn (offer, accept, etc.) - `price`: The current item price associated with the intent and turn in the bargaining process. Default value for missing: (`-1`) - `items`: Information about the item the agents are bargaining for. **Note that there is an elembet for each of the fields below for each agent** - `Category`: Category of the item - `Description`: Description(s) of the item - `Images`: (comma delimited) strings of image names of the item - `Price`: Price(s) of the item. Default value for missing: (`-1`) - `Title`: Title(s) of the item - `utterance`: Utterance for each turn in the dialogue, corresponding to the agent in `agent_turns`. The utterance may be an empty string (`''`) for some turns if multiple dialogue acts take place after an utterance (e.g. there are often multiple dialogue acts associated with the closing of the bargaining process after all utterances have completed to describe the conclusion of the bargaining). ### Data Splits This dataset contains three splits, `train`, `validation` and `test`. Note that `test` is not provided with `dialogue_acts` information as described above. To ensure schema consistency across dataset splits, the `dialogue_acts` field in the `test` split is populated with the default values: `{"price": -1.0, "intent": ""}` The counts of examples in each split are as follows: | | Train | Valid | Test | | Input Examples | 5247 | 597 | 838 | | Average Dialogue Length | 9.14 | 9.17 | 9.24 | Note that ## Dataset Creation From the [source paper](https://arxiv.org/pdf/1808.09637.pdf) for this dataset: > To generate the negotiation scenarios, we > scraped postings on sfbay.craigslist.org > from the 6 most popular categories (housing, furniture, cars, bikes, phones, and electronics). Each > posting produces three scenarios with the buyer’s > target prices at 0.5x, 0.7x and 0.9x of the listing > price. Statistics of the scenarios are shown in Table 2. > We collected 6682 human-human dialogues on > AMT using the interface shown in Appendix A > Figure 2. The dataset statistics in Table 3 show > that CRAIGSLISTBARGAIN has longer dialogues > and more diverse utterances compared to prior > datasets. Furthermore, workers were encouraged > to embellish the item and negotiate side offers > such as free delivery or pick-up. This highly relatable scenario leads to richer dialogues such as > the one shown in Table 1. We also observed various persuasion techniques listed in Table 4 such as > embellishment, ### Curation Rationale See **Dataset Creation** ### Source Data See **Dataset Creation** #### Initial Data Collection and Normalization See **Dataset Creation** #### Who are the source language producers? See **Dataset Creation** ### Annotations If the dataset contains annotations which are not part of the initial data collection, describe them in the following paragraphs. #### Annotation process Annotations for the `dialogue_acts` in `train` and `test` were generated via a rules-based system which can be found in [this script](https://github.com/stanfordnlp/cocoa/blob/master/craigslistbargain/parse_dialogue.py) #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data [More Information Needed] ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information [More Information Needed] ### Dataset Curators He He and Derek Chen and Anusha Balakrishnan and Percy Liang Computer Science Department, Stanford University `{hehe,derekchen14,anusha,pliang}@cs.stanford.edu` The work through which this data was produced was supported by DARPA Communicating with Computers (CwC) program under ARO prime contract no. W911NF15-1-0462 ### Licensing Information [More Information Needed] ### Citation Information ``` @misc{he2018decoupling, title={Decoupling Strategy and Generation in Negotiation Dialogues}, author={He He and Derek Chen and Anusha Balakrishnan and Percy Liang}, year={2018}, eprint={1808.09637}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@ZacharySBrown](https://github.com/ZacharySBrown) for adding this dataset.
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ccdv/govreport-summarization
2022-10-24T20:32:47.000Z
[ "task_categories:summarization", "task_categories:text-generation", "multilinguality:monolingual", "size_categories:10K<n<100K", "language:en", "conditional-text-generation", "arxiv:2104.02112", "region:us" ]
ccdv
GovReport dataset for summarization. From paper: Efficient Attentions for Long Document Summarization" by L. Huang et al. See: https://arxiv.org/pdf/2104.02112.pdf See: https://github.com/luyang-huang96/LongDocSum
@misc{huang2021efficient, title={Efficient Attentions for Long Document Summarization}, author={Luyang Huang and Shuyang Cao and Nikolaus Parulian and Heng Ji and Lu Wang}, year={2021}, eprint={2104.02112}, archivePrefix={arXiv}, primaryClass={cs.CL} } }
15
1,065
2022-03-02T23:29:22
--- language: - en multilinguality: - monolingual size_categories: - 10K<n<100K task_categories: - summarization - text-generation task_ids: [] tags: - conditional-text-generation --- # GovReport dataset for summarization Dataset for summarization of long documents.\ Adapted from this [repo](https://github.com/luyang-huang96/LongDocSum) and this [paper](https://arxiv.org/pdf/2104.02112.pdf)\ This dataset is compatible with the [`run_summarization.py`](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization) script from Transformers if you add this line to the `summarization_name_mapping` variable: ```python "ccdv/govreport-summarization": ("report", "summary") ``` ### Data Fields - `id`: paper id - `report`: a string containing the body of the report - `summary`: a string containing the summary of the report ### Data Splits This dataset has 3 splits: _train_, _validation_, and _test_. \ Token counts with a RoBERTa tokenizer. | Dataset Split | Number of Instances | Avg. tokens | | ------------- | --------------------|:----------------------| | Train | 17,517 | < 9,000 / < 500 | | Validation | 973 | < 9,000 / < 500 | | Test | 973 | < 9,000 / < 500 | # Cite original article ``` @misc{huang2021efficient, title={Efficient Attentions for Long Document Summarization}, author={Luyang Huang and Shuyang Cao and Nikolaus Parulian and Heng Ji and Lu Wang}, year={2021}, eprint={2104.02112}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
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neural_code_search
2023-06-01T14:59:50.000Z
[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1M<n<10M", "size_categories:n<1K", "source_datasets:original", "language:en", "license:cc-by-nc-4.0", "arxiv:1908.09804", "region:us" ]
null
Neural-Code-Search-Evaluation-Dataset presents an evaluation dataset consisting of natural language query and code snippet pairs and a search corpus consisting of code snippets collected from the most popular Android repositories on GitHub.
@InProceedings{huggingface:dataset, title = {Neural Code Search Evaluation Dataset}, authors = {Hongyu Li, Seohyun Kim and Satish Chandra}, journal = {arXiv e-prints}, year = 2018, eid = {arXiv:1908.09804 [cs.SE]}, pages = {arXiv:1908.09804 [cs.SE]}, archivePrefix = {arXiv}, eprint = {1908.09804}, }
7
1,062
2022-03-02T23:29:22
--- pretty_name: Neural Code Search annotations_creators: - expert-generated language_creators: - crowdsourced language: - en license: - cc-by-nc-4.0 multilinguality: - monolingual size_categories: - 1M<n<10M - n<1K source_datasets: - original task_categories: - question-answering task_ids: - extractive-qa paperswithcode_id: neural-code-search-evaluation-dataset dataset_info: - config_name: evaluation_dataset features: - name: stackoverflow_id dtype: int32 - name: question dtype: string - name: question_url dtype: string - name: question_author dtype: string - name: question_author_url dtype: string - name: answer dtype: string - name: answer_url dtype: string - name: answer_author dtype: string - name: answer_author_url dtype: string - name: examples sequence: int32 - name: examples_url sequence: string splits: - name: train num_bytes: 296848 num_examples: 287 download_size: 383625 dataset_size: 296848 - config_name: search_corpus features: - name: id dtype: int32 - name: filepath dtype: string - name: method_name dtype: string - name: start_line dtype: int32 - name: end_line dtype: int32 - name: url dtype: string splits: - name: train num_bytes: 1452630278 num_examples: 4716814 download_size: 121112543 dataset_size: 1452630278 config_names: - evaluation_dataset - search_corpus --- # Dataset Card for Neural Code Search ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [facebookresearch / Neural-Code-Search-Evaluation-Dataset](https://github.com/facebookresearch/Neural-Code-Search-Evaluation-Dataset/tree/master/data) - **Repository:** [Github](https://github.com/facebookresearch/Neural-Code-Search-Evaluation-Dataset.git) - **Paper:** [arXiv](https://arxiv.org/pdf/1908.09804.pdf) ### Dataset Summary Neural-Code-Search-Evaluation-Dataset presents an evaluation dataset consisting of natural language query and code snippet pairs, with the hope that future work in this area can use this dataset as a common benchmark. We also provide the results of two code search models (NCS, UNIF) from recent work. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages EN - English ## Dataset Structure ### Data Instances #### Search Corpus The search corpus is indexed using all method bodies parsed from the 24,549 GitHub repositories. In total, there are 4,716,814 methods in this corpus. The code search model will find relevant code snippets (i.e. method bodies) from this corpus given a natural language query. In this data release, we will provide the following information for each method in the corpus: #### Evaluation Dataset The evaluation dataset is composed of 287 Stack Overflow question and answer pairs ### Data Fields #### Search Corpus - id: Each method in the corpus has a unique numeric identifier. This ID number will also be referenced in our evaluation dataset. - filepath: The file path is in the format of :owner/:repo/relative-file-path-to-the-repo method_name - start_line: Starting line number of the method in the file. - end_line: Ending line number of the method in the file. - url: GitHub link to the method body with commit ID and line numbers encoded. #### Evaluation Dataset - stackoverflow_id: Stack Overflow post ID. - question: Title fo the Stack Overflow post. - question_url: URL of the Stack Overflow post. - answer: Code snippet answer to the question. ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization The most popular Android repositories on GitHub (ranked by the number of stars) is used to create the search corpus. For each repository that we indexed, we provide the link, specific to the commit that was used.5 In total, there are 24,549 repositories. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators Hongyu Li, Seohyun Kim and Satish Chandra ### Licensing Information CC-BY-NC 4.0 (Attr Non-Commercial Inter.) ### Citation Information arXiv:1908.09804 [cs.SE] ### Contributions Thanks to [@vinaykudari](https://github.com/vinaykudari) for adding this dataset.
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BeIR/fiqa-qrels
2022-10-23T06:06:29.000Z
[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "task_ids:fact-checking-retrieval", "multilinguality:monolingual", "language:en", "license:cc-by-sa-4.0", "region:us" ]
BeIR
null
null
0
1,059
2022-06-05T17:26:38
--- 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.
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indonlp/NusaX-senti
2023-01-24T17:02:06.000Z
[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:10K<n<100K", "source_datasets:original", "language:ace", "language:ban", "language:bjn", "language:bug", "language:en", "language:id", "language:jv", "language:mad", "language:min", "language:nij", "language:su", "language:bbc", "license:cc-by-sa-4.0", "arxiv:2205.15960", "region:us" ]
indonlp
NusaX is a high-quality multilingual parallel corpus that covers 12 languages, Indonesian, English, and 10 Indonesian local languages, namely Acehnese, Balinese, Banjarese, Buginese, Madurese, Minangkabau, Javanese, Ngaju, Sundanese, and Toba Batak. NusaX-Senti is a 3-labels (positive, neutral, negative) sentiment analysis dataset for 10 Indonesian local languages + Indonesian and English.
@misc{winata2022nusax, title={NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages}, author={Winata, Genta Indra and Aji, Alham Fikri and Cahyawijaya, Samuel and Mahendra, Rahmad and Koto, Fajri and Romadhony, Ade and Kurniawan, Kemal and Moeljadi, David and Prasojo, Radityo Eko and Fung, Pascale and Baldwin, Timothy and Lau, Jey Han and Sennrich, Rico and Ruder, Sebastian}, year={2022}, eprint={2205.15960}, archivePrefix={arXiv}, primaryClass={cs.CL} }
3
1,059
2023-01-24T09:28:21
--- pretty_name: NusaX-senti annotations_creators: - expert-generated language_creators: - expert-generated license: - cc-by-sa-4.0 multilinguality: - multilingual language: - ace - ban - bjn - bug - en - id - jv - mad - min - nij - su - bbc size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification dataset_info: features: - name: id dtype: string - name: text dtype: string - name: lang dtype: string - name: label dtype: class_label: names: 0: negative 1: neutral 2: positive --- # Dataset Card for NusaX-Senti ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [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) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** [GitHub](https://github.com/IndoNLP/nusax/tree/main/datasets/sentiment) - **Paper:** [EACL 2022](https://arxiv.org/abs/2205.15960) - **Point of Contact:** [GitHub](https://github.com/IndoNLP/nusax/tree/main/datasets/sentiment) ### Dataset Summary NusaX is a high-quality multilingual parallel corpus that covers 12 languages, Indonesian, English, and 10 Indonesian local languages, namely Acehnese, Balinese, Banjarese, Buginese, Madurese, Minangkabau, Javanese, Ngaju, Sundanese, and Toba Batak. NusaX-Senti is a 3-labels (positive, neutral, negative) sentiment analysis dataset for 10 Indonesian local languages + Indonesian and English. ### Supported Tasks and Leaderboards - Sentiment analysis for Indonesian languages ### Languages - ace: acehnese, - ban: balinese, - bjn: banjarese, - bug: buginese, - eng: english, - ind: indonesian, - jav: javanese, - mad: madurese, - min: minangkabau, - nij: ngaju, - sun: sundanese, - bbc: toba_batak, ## Dataset Creation ### Curation Rationale There is a shortage of NLP research and resources for the Indonesian languages, despite the country having over 700 languages. With this in mind, we have created this dataset to support future research for the underrepresented languages in Indonesia. ### Source Data #### Initial Data Collection and Normalization NusaX-senti is a dataset for sentiment analysis in Indonesian that has been expertly translated by native speakers. #### Who are the source language producers? The data was produced by humans (native speakers). ### Annotations #### Annotation process NusaX-senti is derived from SmSA, which is the biggest publicly available dataset for Indonesian sentiment analysis. It comprises of comments and reviews from multiple online platforms. To ensure the quality of our dataset, we have filtered it by removing any abusive language and personally identifying information by manually reviewing all sentences. To ensure balance in the label distribution, we randomly picked 1,000 samples through stratified sampling and then translated them to the corresponding languages. #### Who are the annotators? Native speakers of both Indonesian and the corresponding languages. Annotators were compensated based on the number of translated samples. ### Personal and Sensitive Information Personal information is removed. ## 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 NusaX is created from review text. These data sources may contain some bias. ### Other Known Limitations No other known limitations ## Additional Information ### Licensing Information CC-BY-SA 4.0. Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits. Please contact authors for any information on the dataset. ### Citation Information ``` @misc{winata2022nusax, title={NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages}, author={Winata, Genta Indra and Aji, Alham Fikri and Cahyawijaya, Samuel and Mahendra, Rahmad and Koto, Fajri and Romadhony, Ade and Kurniawan, Kemal and Moeljadi, David and Prasojo, Radityo Eko and Fung, Pascale and Baldwin, Timothy and Lau, Jey Han and Sennrich, Rico and Ruder, Sebastian}, year={2022}, eprint={2205.15960}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@afaji](https://github.com/afaji) for adding this dataset.
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HuggingFaceH4/testing_alpaca_small
2023-04-12T21:55:05.000Z
[ "region:us" ]
HuggingFaceH4
null
null
0
1,056
2023-04-12T21:55:01
--- dataset_info: features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 33856 num_examples: 100 - name: test num_bytes: 32475 num_examples: 100 download_size: 52543 dataset_size: 66331 --- # Dataset Card for "testing_alpaca_small" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
454
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pg19
2023-07-28T09:21:25.000Z
[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:apache-2.0", "arxiv:1911.05507", "region:us" ]
null
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.
@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}, }
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--- 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.
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big_patent
2023-06-01T14:59:54.000Z
[ "task_categories:summarization", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "size_categories:10K<n<100K", "size_categories:1M<n<10M", "source_datasets:original", "language:en", "license:cc-by-4.0", "patent-summarization", "arxiv:1906.03741", "region:us" ]
null
BIGPATENT, consisting of 1.3 million records of U.S. patent documents along with human written abstractive summaries. Each US patent application is filed under a Cooperative Patent Classification (CPC) code. There are nine such classification categories: A (Human Necessities), B (Performing Operations; Transporting), C (Chemistry; Metallurgy), D (Textiles; Paper), E (Fixed Constructions), F (Mechanical Engineering; Lightning; Heating; Weapons; Blasting), G (Physics), H (Electricity), and Y (General tagging of new or cross-sectional technology) There are two features: - description: detailed description of patent. - abstract: Patent abastract.
@misc{sharma2019bigpatent, title={BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization}, author={Eva Sharma and Chen Li and Lu Wang}, year={2019}, eprint={1906.03741}, archivePrefix={arXiv}, primaryClass={cs.CL} }
26
1,050
2022-03-02T23:29:22
--- annotations_creators: - no-annotation language_creators: - found language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M - 10K<n<100K - 1M<n<10M source_datasets: - original task_categories: - summarization task_ids: [] paperswithcode_id: bigpatent pretty_name: Big Patent tags: - patent-summarization dataset_info: - config_name: all features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 38367048389 num_examples: 1207222 - name: validation num_bytes: 2115827002 num_examples: 67068 - name: test num_bytes: 2129505280 num_examples: 67072 download_size: 10142923776 dataset_size: 42612380671 - config_name: a features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 5683460620 num_examples: 174134 - name: validation num_bytes: 313324505 num_examples: 9674 - name: test num_bytes: 316633277 num_examples: 9675 download_size: 10142923776 dataset_size: 6313418402 - config_name: b features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 4236070976 num_examples: 161520 - name: validation num_bytes: 234425138 num_examples: 8973 - name: test num_bytes: 231538734 num_examples: 8974 download_size: 10142923776 dataset_size: 4702034848 - config_name: c features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 4506249306 num_examples: 101042 - name: validation num_bytes: 244684775 num_examples: 5613 - name: test num_bytes: 252566793 num_examples: 5614 download_size: 10142923776 dataset_size: 5003500874 - config_name: d features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 264717412 num_examples: 10164 - name: validation num_bytes: 14560482 num_examples: 565 - name: test num_bytes: 14403430 num_examples: 565 download_size: 10142923776 dataset_size: 293681324 - config_name: e features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 881101433 num_examples: 34443 - name: validation num_bytes: 48646158 num_examples: 1914 - name: test num_bytes: 48586429 num_examples: 1914 download_size: 10142923776 dataset_size: 978334020 - config_name: f features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 2146383473 num_examples: 85568 - name: validation num_bytes: 119632631 num_examples: 4754 - name: test num_bytes: 119596303 num_examples: 4754 download_size: 10142923776 dataset_size: 2385612407 - config_name: g features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 8877854206 num_examples: 258935 - name: validation num_bytes: 492581177 num_examples: 14385 - name: test num_bytes: 496324853 num_examples: 14386 download_size: 10142923776 dataset_size: 9866760236 - config_name: h features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 8075621958 num_examples: 257019 - name: validation num_bytes: 447602356 num_examples: 14279 - name: test num_bytes: 445460513 num_examples: 14279 download_size: 10142923776 dataset_size: 8968684827 - config_name: y features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 3695589005 num_examples: 124397 - name: validation num_bytes: 200369780 num_examples: 6911 - name: test num_bytes: 204394948 num_examples: 6911 download_size: 10142923776 dataset_size: 4100353733 config_names: - a - all - b - c - d - e - f - g - h - y --- # Dataset Card for Big Patent ## 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:** [Big Patent](https://evasharma.github.io/bigpatent/) - **Repository:** - **Paper:** [BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization](https://arxiv.org/abs/1906.03741) - **Leaderboard:** - **Point of Contact:** [Lu Wang](mailto:wangluxy@umich.edu) ### Dataset Summary BIGPATENT, consisting of 1.3 million records of U.S. patent documents along with human written abstractive summaries. Each US patent application is filed under a Cooperative Patent Classification (CPC) code. There are nine such classification categories: - a: Human Necessities - b: Performing Operations; Transporting - c: Chemistry; Metallurgy - d: Textiles; Paper - e: Fixed Constructions - f: Mechanical Engineering; Lightning; Heating; Weapons; Blasting - g: Physics - h: Electricity - y: General tagging of new or cross-sectional technology Current defaults are 2.1.2 version (fix update to cased raw strings) and 'all' CPC codes: ```python from datasets import load_dataset ds = load_dataset("big_patent") # default is 'all' CPC codes ds = load_dataset("big_patent", "all") # the same as above ds = load_dataset("big_patent", "a") # only 'a' CPC codes ds = load_dataset("big_patent", codes=["a", "b"]) ``` To use 1.0.0 version (lower cased tokenized words), pass both parameters `codes` and `version`: ```python ds = load_dataset("big_patent", codes="all", version="1.0.0") ds = load_dataset("big_patent", codes="a", version="1.0.0") ds = load_dataset("big_patent", codes=["a", "b"], version="1.0.0") ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English ## Dataset Structure ### Data Instances Each instance contains a pair of `description` and `abstract`. `description` is extracted from the Description section of the Patent while `abstract` is extracted from the Abstract section. ``` { 'description': 'FIELD OF THE INVENTION \n [0001] This invention relates to novel calcium phosphate-coated implantable medical devices and processes of making same. The unique calcium-phosphate coated implantable medical devices minimize...', 'abstract': 'This invention relates to novel calcium phosphate-coated implantable medical devices...' } ``` ### Data Fields - `description`: detailed description of patent. - `abstract`: Patent abastract. ### Data Splits | | train | validation | test | |:----|------------------:|-------------:|-------:| | all | 1207222 | 67068 | 67072 | | a | 174134 | 9674 | 9675 | | b | 161520 | 8973 | 8974 | | c | 101042 | 5613 | 5614 | | d | 10164 | 565 | 565 | | e | 34443 | 1914 | 1914 | | f | 85568 | 4754 | 4754 | | g | 258935 | 14385 | 14386 | | h | 257019 | 14279 | 14279 | | y | 124397 | 6911 | 6911 | ## 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 ```bibtex @article{DBLP:journals/corr/abs-1906-03741, author = {Eva Sharma and Chen Li and Lu Wang}, title = {{BIGPATENT:} {A} Large-Scale Dataset for Abstractive and Coherent Summarization}, journal = {CoRR}, volume = {abs/1906.03741}, year = {2019}, url = {http://arxiv.org/abs/1906.03741}, eprinttype = {arXiv}, eprint = {1906.03741}, timestamp = {Wed, 26 Jun 2019 07:14:58 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1906-03741.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` ### Contributions Thanks to [@mattbui](https://github.com/mattbui) for adding this dataset.
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ura-hcmut/MATH
2023-09-29T17:19:11.000Z
[ "task_categories:text2text-generation", "language:vi", "license:cc-by-nc-sa-4.0", "region:us" ]
ura-hcmut
null
null
0
1,048
2023-09-19T01:55:00
--- license: cc-by-nc-sa-4.0 task_categories: - text2text-generation language: - vi configs: - config_name: gcp data_files: - split: train path: "MATH_gcp_training.csv" - split: test path: "MATH_gcp.csv" - config_name: azr data_files: - split: train path: "MATH_azr_training.csv" - split: test path: "MATH_azr.csv" --- # MATH dataset Original version: https://huggingface.co/datasets/lighteval/MATH Translation source code: https://github.com/martinakaduc/ura-llama/tree/main/dataset_scripts/custom_datasets
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neulab/tldr
2022-12-22T19:47:11.000Z
[ "task_categories:text2text-generation", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:code", "license:mit", "code-generation", "doc retrieval", "retrieval augmented generation", "arxiv:2207.05987", "region:us" ]
neulab
This is the re-split of CoNaLa dataset. For each code snippet in the dev and test set, at least one function is held out from the training set. This split aims at testing a code generation model's capacity in generating unseen functions. We further make sure that examples from the same StackOverflow post (same question_id before -) are in the same split.
@article{zhou2022doccoder, title={DocCoder: Generating Code by Retrieving and Reading Docs}, author={Zhou, Shuyan and Alon, Uri and Xu, Frank F and JIang, Zhengbao and Neubig, Graham}, journal={arXiv preprint arXiv:2207.05987}, year={2022} }
4
1,047
2022-12-22T17:58:43
--- annotations_creators: [] language_creators: - crowdsourced - expert-generated language: - code license: - mit multilinguality: - monolingual size_categories: - unknown source_datasets: - original task_categories: - text2text-generation task_ids: [] pretty_name: DocPrompting-CoNaLa tags: - code-generation - doc retrieval - retrieval augmented generation --- ## Dataset Description - **Repository:** https://github.com/shuyanzhou/docprompting - **Paper:** [DocPrompting: Generating Code by Retrieving the Docs](https://arxiv.org/pdf/2207.05987.pdf) ### Dataset Summary This is the natural language to bash generation dataset we harvested from the English subset of [`tldr`](https://github.com/tldr-pages/tldr) We split the dataset by bash commands. Every command in the dev and test set is held out from the training set. ### Supported Tasks and Leaderboards This dataset is used to evaluate code generations. ### Languages English - Bash ## Dataset Structure ```python dataset = load_dataset("neulab/tldr") DatasetDict({ train: Dataset({ features: ['question_id', 'nl', 'cmd', 'oracle_man', 'cmd_name', 'tldr_cmd_name', 'manual_exist', 'matching_info'], num_rows: 6414 }) test: Dataset({ features: ['question_id', 'nl', 'cmd', 'oracle_man', 'cmd_name', 'tldr_cmd_name', 'manual_exist', 'matching_info'], num_rows: 928 }) validation: Dataset({ features: ['question_id', 'nl', 'cmd', 'oracle_man', 'cmd_name', 'tldr_cmd_name', 'manual_exist', 'matching_info'], num_rows: 1845 }) }) code_docs = load_dataset("neulab/docprompting-conala", "docs") DatasetDict({ train: Dataset({ features: ['doc_id', 'doc_content'], num_rows: 439064 }) }) ``` ### Data Fields train/dev/test: - nl: The natural language intent - cmd: The reference code snippet - question_id: the unique id of a question - oracle_man: The `doc_id` of the functions used in the reference code snippet. The corresponding contents are in `doc` split - cmd_name: the bash command of this code snippet - tldr_cmd_name: the bash command used in tldr github repo. The `cmd_name` and `tldr_cmd_name` can be different due to naming difference - manual_exist: whether the manual exists in https://manned.org - matching_info: each code snippets have multiple tokens, this is the detailed reference doc matching on each token. docs: - doc_id: the id of a doc - doc_content: the content of the doc ## Dataset Creation The dataset was curated from [`tldr`](https://github.com/tldr-pages/tldr). The project aims to provide frequent usage of bash commands with natural language intents. For more details, please check the repo. ### Citation Information ``` @article{zhou2022doccoder, title={DocCoder: Generating Code by Retrieving and Reading Docs}, author={Zhou, Shuyan and Alon, Uri and Xu, Frank F and Jiang, Zhengbao and Neubig, Graham}, journal={arXiv preprint arXiv:2207.05987}, year={2022} } ```
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jeanlee/kmhas_korean_hate_speech
2022-11-28T16:26:56.000Z
[ "task_categories:text-classification", "task_ids:multi-label-classification", "task_ids:hate-speech-detection", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:ko", "license:cc-by-sa-4.0", "K-MHaS", "Korean NLP", "Hate Speech Detection", "Dataset", "Coling2022", "arxiv:2208.10684", "region:us" ]
jeanlee
The K-MHaS (Korean Multi-label Hate Speech) dataset contains 109k utterances from Korean online news comments labeled with 8 fine-grained hate speech classes or Not Hate Speech class. The fine-grained hate speech classes are politics, origin, physical, age, gender, religion, race, and profanity and these categories are selected in order to reflect the social and historical context.
@inproceedings{lee-etal-2022-k, title = "K-{MH}a{S}: A Multi-label Hate Speech Detection Dataset in {K}orean Online News Comment", author = "Lee, Jean and Lim, Taejun and Lee, Heejun and Jo, Bogeun and Kim, Yangsok and Yoon, Heegeun and Han, Soyeon Caren", booktitle = "Proceedings of the 29th International Conference on Computational Linguistics", month = oct, year = "2022", address = "Gyeongju, Republic of Korea", publisher = "International Committee on Computational Linguistics", url = "https://aclanthology.org/2022.coling-1.311", pages = "3530--3538", abstract = "Online hate speech detection has become an important issue due to the growth of online content, but resources in languages other than English are extremely limited. We introduce K-MHaS, a new multi-label dataset for hate speech detection that effectively handles Korean language patterns. The dataset consists of 109k utterances from news comments and provides a multi-label classification using 1 to 4 labels, and handles subjectivity and intersectionality. We evaluate strong baselines on K-MHaS. KR-BERT with a sub-character tokenizer outperforms others, recognizing decomposed characters in each hate speech class.", }
11
1,046
2022-11-21T05:03:58
--- annotations_creators: - crowdsourced language: - ko language_creators: - found license: - cc-by-sa-4.0 multilinguality: - monolingual pretty_name: 'K-MHaS' size_categories: - 100K<n<1M source_datasets: - original tags: - K-MHaS - Korean NLP - Hate Speech Detection - Dataset - Coling2022 task_categories: - text-classification task_ids: - multi-label-classification - hate-speech-detection paperswithcode_id: korean-multi-label-hate-speech-dataset dataset_info: features: - name: text dtype: string - name: label sequence: class_label: names: 0: origin 1: physical 2: politics 3: profanity 4: age 5: gender 6: race 7: religion 8: not_hate_speech splits: - name: train num_bytes: 6845463 num_examples: 78977 - name: validation num_bytes: 748899 num_examples: 8776 - name: test num_bytes: 1902352 num_examples: 21939 download_size: 9496714 dataset_size: 109692 --- # Dataset Card for K-MHaS ## 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) ## Sample Code <a href="https://colab.research.google.com/drive/171KhS1_LVBtpAFd_kaT8lcrZmhcz5ehY?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="base"/></a> ## Dataset Description - **Homepage:** [K-MHaS](https://github.com/adlnlp/K-MHaS) - **Repository:** [Korean Multi-label Hate Speech Dataset](https://github.com/adlnlp/K-MHaS) - **Paper:** [K-MHaS: A Multi-label Hate Speech Detection Dataset in Korean Online News Comment](https://arxiv.org/abs/2208.10684) - **Point of Contact:** [Caren Han](caren.han@sydney.edu.au) - **Sample code:** [Colab](https://colab.research.google.com/drive/171KhS1_LVBtpAFd_kaT8lcrZmhcz5ehY?usp=sharing) ### Dataset Summary The Korean Multi-label Hate Speech Dataset, **K-MHaS**, consists of 109,692 utterances from Korean online news comments, labelled with 8 fine-grained hate speech classes (labels: `Politics`, `Origin`, `Physical`, `Age`, `Gender`, `Religion`, `Race`, `Profanity`) or `Not Hate Speech` class. Each utterance provides from a single to four labels that can handles Korean language patterns effectively. For more details, please refer to our paper about [**K-MHaS**](https://aclanthology.org/2022.coling-1.311), published at COLING 2022. ### Supported Tasks and Leaderboards Hate Speech Detection * `binary classification` (labels: `Hate Speech`, `Not Hate Speech`) * `multi-label classification`: (labels: `Politics`, `Origin`, `Physical`, `Age`, `Gender`, `Religion`, `Race`, `Profanity`, `Not Hate Speech`) For the multi-label classification, a `Hate Speech` class from the binary classification, is broken down into eight classes, associated with the hate speech category. In order to reflect the social and historical context, we select the eight hate speech classes. For example, the `Politics` class is chosen, due to a significant influence on the style of Korean hate speech. ### Languages Korean ## Dataset Structure ### Data Instances The dataset is provided with train/validation/test set in the txt format. Each instance is a news comment with a corresponding one or more hate speech classes (labels: `Politics`, `Origin`, `Physical`, `Age`, `Gender`, `Religion`, `Race`, `Profanity`) or `Not Hate Speech` class. The label numbers matching in both English and Korean is in the data fields section. ```python {'text':'수꼴틀딱시키들이 다 디져야 나라가 똑바로 될것같다..답이 없는 종자들ㅠ' 'label': [2, 3, 4] } ``` ### Data Fields * `text`: utterance from Korean online news comment. * `label`: the label numbers matching with 8 fine-grained hate speech classes and `not hate speech` class are follows. * `0`: `Origin`(`출신차별`) hate speech based on place of origin or identity; * `1`: `Physical`(`외모차별`) hate speech based on physical appearance (e.g. body, face) or disability; * `2`: `Politics`(`정치성향차별`) hate speech based on political stance; * `3`: `Profanity`(`혐오욕설`) hate speech in the form of swearing, cursing, cussing, obscene words, or expletives; or an unspecified hate speech category; * `4`: `Age`(`연령차별`) hate speech based on age; * `5`: `Gender`(`성차별`) hate speech based on gender or sexual orientation (e.g. woman, homosexual); * `6`: `Race`(`인종차별`) hate speech based on ethnicity; * `7`: `Religion`(`종교차별`) hate speech based on religion; * `8`: `Not Hate Speech`(`해당사항없음`). ### Data Splits In our repository, we provide splitted datasets that have 78,977(train) / 8,776 (validation) / 21,939 (test) samples, preserving the class proportion. ## Dataset Creation ### Curation Rationale We propose K-MHaS, a large size Korean multi-label hate speech detection dataset that represents Korean language patterns effectively. Most datasets in hate speech research are annotated using a single label classification of particular aspects, even though the subjectivity of hate speech cannot be explained with a mutually exclusive annotation scheme. We propose a multi-label hate speech annotation scheme that allows overlapping labels associated with the subjectivity and the intersectionality of hate speech. ### Source Data #### Initial Data Collection and Normalization Our dataset is based on the Korean online news comments available on Kaggle and Github. The unlabeled raw data was collected between January 2018 and June 2020. Please see the details in our paper [K-MHaS](https://aclanthology.org/2022.coling-1.311) published at COLING2020. #### Who are the source language producers? The language producers are users who left the comments on the Korean online news platform between 2018 and 2020. ### Annotations #### Annotation process We begin with the common categories of hate speech found in literature and match the keywords for each category. After the preliminary round, we investigate the results to merge or remove labels in order to provide the most representative subtype labels of hate speech contextual to the cultural background. Our annotation instructions explain a twolayered annotation to (a) distinguish hate and not hate speech, and (b) the categories of hate speech. Annotators are requested to consider given keywords or alternatives of each category within social, cultural, and historical circumstances. For more details, please refer to the paper [K-MHaS](https://aclanthology.org/2022.coling-1.311). #### Who are the annotators? Five native speakers were recruited for manual annotation in both the preliminary and main rounds. ### Personal and Sensitive Information This datasets contains examples of hateful language, however, has no personal information. ## Considerations for Using the Data ### Social Impact of Dataset We propose K-MHaS, a new large-sized dataset for Korean hate speech detection with a multi-label annotation scheme. We provided extensive baseline experiment results, presenting the usability of a dataset to detect Korean language patterns in hate speech. ### Discussion of Biases All annotators were recruited from a crowdsourcing platform. They were informed about hate speech before handling the data. Our instructions allowed them to feel free to leave if they were uncomfortable with the content. With respect to the potential risks, we note that the subjectivity of human annotation would impact on the quality of the dataset. ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators This dataset is curated by Taejun Lim, Heejun Lee and Bogeun Jo. ### Licensing Information Creative Commons Attribution-ShareAlike 4.0 International (cc-by-sa-4.0). ### Citation Information ``` @inproceedings{lee-etal-2022-k, title = "K-{MH}a{S}: A Multi-label Hate Speech Detection Dataset in {K}orean Online News Comment", author = "Lee, Jean and Lim, Taejun and Lee, Heejun and Jo, Bogeun and Kim, Yangsok and Yoon, Heegeun and Han, Soyeon Caren", booktitle = "Proceedings of the 29th International Conference on Computational Linguistics", month = oct, year = "2022", address = "Gyeongju, Republic of Korea", publisher = "International Committee on Computational Linguistics", url = "https://aclanthology.org/2022.coling-1.311", pages = "3530--3538", abstract = "Online hate speech detection has become an important issue due to the growth of online content, but resources in languages other than English are extremely limited. We introduce K-MHaS, a new multi-label dataset for hate speech detection that effectively handles Korean language patterns. The dataset consists of 109k utterances from news comments and provides a multi-label classification using 1 to 4 labels, and handles subjectivity and intersectionality. We evaluate strong baselines on K-MHaS. KR-BERT with a sub-character tokenizer outperforms others, recognizing decomposed characters in each hate speech class.", } ``` ### Contributions The contributors of the work are: - [Jean Lee](https://jeanlee-ai.github.io/) (The University of Sydney) - [Taejun Lim](https://github.com/taezun) (The University of Sydney) - [Heejun Lee](https://bigwaveai.com/) (BigWave AI) - [Bogeun Jo](https://bigwaveai.com/) (BigWave AI) - Yangsok Kim (Keimyung University) - Heegeun Yoon (National Information Society Agency) - [Soyeon Caren Han](https://drcarenhan.github.io/) (The University of Western Australia and The University of Sydney)
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shahules786/orca-best
2023-08-25T14:48:40.000Z
[ "region:us" ]
shahules786
null
null
40
1,044
2023-08-12T05:48:30
--- dataset_info: features: - name: cluster struct: - name: samples list: - name: input dtype: string - name: output dtype: string - name: source dtype: string - name: instruction dtype: string - name: num_samples dtype: int64 splits: - name: train num_bytes: 900092818 num_examples: 328906 download_size: 462629849 dataset_size: 900092818 --- ## Best of Orca This is a filtered version of Orca GPT4 1M instructions. From repeated experiments and analysis, I came to the conclusion that original dataset contains a lot of low-quality instructions which contributes to only poor generalization. The solution I came up with is to filter the dataset and remove the unwanted samples. I applied two levels of filters 1. Removed instructions with less than 100 tokens in response. 2. Data deduplication grouped by instruction type using GTE embedding and cosine similarity (threshold>0.95) After these two steps, the number of samples was reduced to 1/3rd of the original count. For selecting a sample from each cluster, I tried different methods including random selection from a cluster. We used this dataset to train multiple Open-Assistant models to confirm my hypothesis that data quality matter more than quantity. This dataset was used in some of our models best models including https://huggingface.co/OpenAssistant/llama2-70b-oasst-sft-v10 ⭐️ All models perform much better than models trained on full ORCA samples. ## Credits * This wouldn't be possible without the amazing work of Eric in recreating the ORCA dataset. Check it out: https://huggingface.co/datasets/ehartford/dolphin * This dataset was created in association with the Open-Assistant team @jordanclive and @andreaskoepf ## Citations ``` @misc{Orca-best, title = {Orca-best: A filtered version of orca gpt4 dataset.}, author = {Shahul Es}, year = {2023}, publisher = {HuggingFace}, journal = {HuggingFace repository}, howpublished = {\url{https://huggingface.co/datasets/shahules786/orca-best/}, } ```
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armanc/pubmed-rct20k
2022-11-11T08:23:24.000Z
[ "region:us" ]
armanc
null
null
0
1,034
2022-11-11T04:20:56
The small 20K version of the Pubmed-RCT dataset by Dernoncourt et al (2017). ``` @article{dernoncourt2017pubmed, title={Pubmed 200k rct: a dataset for sequential sentence classification in medical abstracts}, author={Dernoncourt, Franck and Lee, Ji Young}, journal={arXiv preprint arXiv:1710.06071}, year={2017} } ``` Note: This is the cleaned up version by Jin and Szolovits (2018). ``` @article{jin2018hierarchical, title={Hierarchical neural networks for sequential sentence classification in medical scientific abstracts}, author={Jin, Di and Szolovits, Peter}, journal={arXiv preprint arXiv:1808.06161}, year={2018} } ```
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allenai/qasper
2022-10-07T22:04:11.000Z
[ "task_categories:question-answering", "task_ids:closed-domain-qa", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended|s2orc", "language:en", "license:cc-by-4.0", "arxiv:2105.03011", "region:us" ]
allenai
A dataset containing 1585 papers with 5049 information-seeking questions asked by regular readers of NLP papers, and answered by a separate set of NLP practitioners.
@inproceedings{Dasigi2021ADO, title={A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers}, author={Pradeep Dasigi and Kyle Lo and Iz Beltagy and Arman Cohan and Noah A. Smith and Matt Gardner}, year={2021} }
36
1,033
2022-03-02T23:29:22
--- pretty_name: QASPER annotations_creators: - expert-generated language_creators: - expert-generated language: - en language_bcp47: - en-US license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended|s2orc task_categories: - question-answering task_ids: - closed-domain-qa paperswithcode_id: qasper --- # Dataset Card for Qasper ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://allenai.org/data/qasper](https://allenai.org/data/qasper) - **Demo:** [https://qasper-demo.apps.allenai.org/](https://qasper-demo.apps.allenai.org/) - **Paper:** [https://arxiv.org/abs/2105.03011](https://arxiv.org/abs/2105.03011) - **Blogpost:** [https://medium.com/ai2-blog/question-answering-on-scientific-research-papers-f6d6da9fd55c](https://medium.com/ai2-blog/question-answering-on-scientific-research-papers-f6d6da9fd55c) - **Leaderboards:** [https://paperswithcode.com/dataset/qasper](https://paperswithcode.com/dataset/qasper) ### Dataset Summary QASPER is a dataset for question answering on scientific research papers. It consists of 5,049 questions over 1,585 Natural Language Processing papers. Each question is written by an NLP practitioner who read only the title and abstract of the corresponding paper, and the question seeks information present in the full text. The questions are then answered by a separate set of NLP practitioners who also provide supporting evidence to answers. ### Supported Tasks and Leaderboards - `question-answering`: The dataset can be used to train a model for Question Answering. Success on this task is typically measured by achieving a *high* [F1 score](https://huggingface.co/metrics/f1). The [official baseline model](https://github.com/allenai/qasper-led-baseline) currently achieves 33.63 Token F1 score & uses [Longformer](https://huggingface.co/transformers/model_doc/longformer.html). This task has an active leaderboard which can be found [here](https://paperswithcode.com/sota/question-answering-on-qasper) - `evidence-selection`: The dataset can be used to train a model for Evidence Selection. Success on this task is typically measured by achieving a *high* [F1 score](https://huggingface.co/metrics/f1). The [official baseline model](https://github.com/allenai/qasper-led-baseline) currently achieves 39.37 F1 score & uses [Longformer](https://huggingface.co/transformers/model_doc/longformer.html). This task has an active leaderboard which can be found [here](https://paperswithcode.com/sota/evidence-selection-on-qasper) ### Languages English, as it is used in research papers. ## Dataset Structure ### Data Instances A typical instance in the dataset: ``` { 'id': "Paper ID (string)", 'title': "Paper Title", 'abstract': "paper abstract ...", 'full_text': { 'paragraphs':[["section1_paragraph1_text","section1_paragraph2_text",...],["section2_paragraph1_text","section2_paragraph2_text",...]], 'section_name':["section1_title","section2_title"],...}, 'qas': { 'answers':[{ 'annotation_id': ["q1_answer1_annotation_id","q1_answer2_annotation_id"] 'answer': [{ 'unanswerable':False, 'extractive_spans':["q1_answer1_extractive_span1","q1_answer1_extractive_span2"], 'yes_no':False, 'free_form_answer':"q1_answer1", 'evidence':["q1_answer1_evidence1","q1_answer1_evidence2",..], 'highlighted_evidence':["q1_answer1_highlighted_evidence1","q1_answer1_highlighted_evidence2",..] }, { 'unanswerable':False, 'extractive_spans':["q1_answer2_extractive_span1","q1_answer2_extractive_span2"], 'yes_no':False, 'free_form_answer':"q1_answer2", 'evidence':["q1_answer2_evidence1","q1_answer2_evidence2",..], 'highlighted_evidence':["q1_answer2_highlighted_evidence1","q1_answer2_highlighted_evidence2",..] }], 'worker_id':["q1_answer1_worker_id","q1_answer2_worker_id"] },{...["question2's answers"]..},{...["question3's answers"]..}], 'question':["question1","question2","question3"...], 'question_id':["question1_id","question2_id","question3_id"...], 'question_writer':["question1_writer_id","question2_writer_id","question3_writer_id"...], 'nlp_background':["question1_writer_nlp_background","question2_writer_nlp_background",...], 'topic_background':["question1_writer_topic_background","question2_writer_topic_background",...], 'paper_read': ["question1_writer_paper_read_status","question2_writer_paper_read_status",...], 'search_query':["question1_search_query","question2_search_query","question3_search_query"...], } } ``` ### Data Fields The following is an excerpt from the dataset README: Within "qas", some fields should be obvious. Here is some explanation about the others: #### Fields specific to questions: - "nlp_background" shows the experience the question writer had. The values can be "zero" (no experience), "two" (0 - 2 years of experience), "five" (2 - 5 years of experience), and "infinity" (> 5 years of experience). The field may be empty as well, indicating the writer has chosen not to share this information. - "topic_background" shows how familiar the question writer was with the topic of the paper. The values are "unfamiliar", "familiar", "research" (meaning that the topic is the research area of the writer), or null. - "paper_read", when specified shows whether the questionwriter has read the paper. - "search_query", if not empty, is the query the question writer used to find the abstract of the paper from a large pool of abstracts we made available to them. #### Fields specific to answers Unanswerable answers have "unanswerable" set to true. The remaining answers have exactly one of the following fields being non-empty. - "extractive_spans" are spans in the paper which serve as the answer. - "free_form_answer" is a written out answer. - "yes_no" is true iff the answer is Yes, and false iff the answer is No. "evidence" is the set of paragraphs, figures or tables used to arrive at the answer. Tables or figures start with the string "FLOAT SELECTED" "highlighted_evidence" is the set of sentences the answer providers selected as evidence if they chose textual evidence. The text in the "evidence" field is a mapping from these sentences to the paragraph level. That is, if you see textual evidence in the "evidence" field, it is guaranteed to be entire paragraphs, while that is not the case with "highlighted_evidence". ### Data Splits | | Train | Valid | | ----- | ------ | ----- | | Number of papers | 888 | 281 | | Number of questions | 2593 | 1005 | | Number of answers | 2675 | 1764 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data NLP papers: The full text of the papers is extracted from [S2ORC](https://huggingface.co/datasets/s2orc) (Lo et al., 2020) #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? "The annotators are NLP practitioners, not expert researchers, and it is likely that an expert would score higher" ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Crowdsourced NLP practitioners ### Licensing Information [CC BY 4.0](https://creativecommons.org/licenses/by/4.0) ### Citation Information ``` @inproceedings{Dasigi2021ADO, title={A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers}, author={Pradeep Dasigi and Kyle Lo and Iz Beltagy and Arman Cohan and Noah A. Smith and Matt Gardner}, year={2021} } ``` ### Contributions Thanks to [@cceyda](https://github.com/cceyda) for adding this dataset.
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open-llm-leaderboard/details_meta-llama__Llama-2-70b-hf
2023-09-18T06:46:57.000Z
[ "region:us" ]
open-llm-leaderboard
null
null
0
1,033
2023-08-21T11:06:07
--- pretty_name: Evaluation run of meta-llama/Llama-2-70b-hf dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [meta-llama/Llama-2-70b-hf](https://huggingface.co/meta-llama/Llama-2-70b-hf)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 124 configuration, each one coresponding to one of\ \ the evaluated task.\n\nThe dataset has been created from 10 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_meta-llama__Llama-2-70b-hf\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-18T06:46:44.905361](https://huggingface.co/datasets/open-llm-leaderboard/details_meta-llama__Llama-2-70b-hf/blob/main/results_2023-09-18T06-46-44.905361.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\": 0.0017827181208053692,\n\ \ \"em_stderr\": 0.00043200973460388544,\n \"f1\": 0.06615562080536916,\n\ \ \"f1_stderr\": 0.0013739852117668813,\n \"acc\": 0.5885312292623206,\n\ \ \"acc_stderr\": 0.011707750309504293\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.0017827181208053692,\n \"em_stderr\": 0.00043200973460388544,\n\ \ \"f1\": 0.06615562080536916,\n \"f1_stderr\": 0.0013739852117668813\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.33965125094768767,\n \ \ \"acc_stderr\": 0.01304504506766526\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8374112075769534,\n \"acc_stderr\": 0.010370455551343326\n\ \ }\n}\n```" repo_url: https://huggingface.co/meta-llama/Llama-2-70b-hf 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_22T09_05_23.035851 path: - '**/details_harness|arc:challenge|25_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|arc:challenge|25_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|arc:challenge|25_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|arc:challenge|25_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-08-22T13:47:53.141854.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_08T23_38_08.931556 path: - '**/details_harness|drop|3_2023-09-08T23-38-08.931556.parquet' - split: 2023_09_18T06_46_44.905361 path: - '**/details_harness|drop|3_2023-09-18T06-46-44.905361.parquet' - split: latest path: - '**/details_harness|drop|3_2023-09-18T06-46-44.905361.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_08T23_38_08.931556 path: - '**/details_harness|gsm8k|5_2023-09-08T23-38-08.931556.parquet' - split: 2023_09_18T06_46_44.905361 path: - '**/details_harness|gsm8k|5_2023-09-18T06-46-44.905361.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-09-18T06-46-44.905361.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hellaswag|10_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hellaswag|10_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hellaswag|10_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hellaswag|10_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_0 data_files: - split: 2023_08_21T11_06_07.240233 path: - '**/details_harness|hendrycksTest-abstract_algebra|0_2023-08-21T11:06:07.240233.parquet' - split: 2023_08_21T11_28_25.684618 path: - '**/details_harness|hendrycksTest-abstract_algebra|0_2023-08-21T11:28:25.684618.parquet' - split: 2023_08_21T20_33_55.417483 path: - '**/details_harness|hendrycksTest-abstract_algebra|0_2023-08-21T20:33:55.417483.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|0_2023-08-21T20:33:55.417483.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-22T09:05:23.035851.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-22T10:47:05.866748.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-22T13:42:09.433095.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-22T13:47:53.141854.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_abstract_algebra_0 data_files: - split: 2023_08_21T11_06_07.240233 path: - '**/details_harness|hendrycksTest-abstract_algebra|0_2023-08-21T11:06:07.240233.parquet' - split: 2023_08_21T11_28_25.684618 path: - '**/details_harness|hendrycksTest-abstract_algebra|0_2023-08-21T11:28:25.684618.parquet' - split: 2023_08_21T20_33_55.417483 path: - '**/details_harness|hendrycksTest-abstract_algebra|0_2023-08-21T20:33:55.417483.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|0_2023-08-21T20:33:55.417483.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-management|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-management|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-management|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-management|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_22T09_05_23.035851 path: - '**/details_harness|truthfulqa:mc|0_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '**/details_harness|truthfulqa:mc|0_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '**/details_harness|truthfulqa:mc|0_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '**/details_harness|truthfulqa:mc|0_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-08-22T13:47:53.141854.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_08T23_38_08.931556 path: - '**/details_harness|winogrande|5_2023-09-08T23-38-08.931556.parquet' - split: 2023_09_18T06_46_44.905361 path: - '**/details_harness|winogrande|5_2023-09-18T06-46-44.905361.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-09-18T06-46-44.905361.parquet' - config_name: original_mmlu_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:anatomy|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:astronomy|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:college_biology|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:college_physics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:computer_security|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:econometrics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:global_facts|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:human_aging|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:international_law|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:management|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:marketing|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:nutrition|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:philosophy|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:prehistory|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:professional_law|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:public_relations|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:security_studies|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:sociology|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:virology|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:world_religions|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:anatomy|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:astronomy|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:college_biology|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:college_physics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:computer_security|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:econometrics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:global_facts|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:human_aging|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:international_law|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:management|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:marketing|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:nutrition|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:philosophy|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:prehistory|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:professional_law|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:public_relations|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:security_studies|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:sociology|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:virology|5_2023-08-28T20:36:26.123850.parquet' - '**/details_original|mmlu:world_religions|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_abstract_algebra_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_anatomy_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:anatomy|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:anatomy|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_astronomy_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:astronomy|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:astronomy|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_business_ethics_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_clinical_knowledge_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_college_biology_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:college_biology|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:college_biology|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_college_chemistry_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_college_computer_science_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_college_mathematics_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_college_medicine_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_college_physics_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:college_physics|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:college_physics|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_computer_security_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:computer_security|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:computer_security|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_conceptual_physics_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_econometrics_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:econometrics|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:econometrics|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_electrical_engineering_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_elementary_mathematics_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_formal_logic_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_global_facts_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:global_facts|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:global_facts|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_biology_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_chemistry_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_computer_science_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_european_history_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_geography_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_government_and_politics_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_macroeconomics_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_mathematics_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_microeconomics_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_physics_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_psychology_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_statistics_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_us_history_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_world_history_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_human_aging_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:human_aging|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:human_aging|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_human_sexuality_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_international_law_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:international_law|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:international_law|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_jurisprudence_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_logical_fallacies_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_machine_learning_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_management_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:management|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:management|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_marketing_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:marketing|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:marketing|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_medical_genetics_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_miscellaneous_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_moral_disputes_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_moral_scenarios_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_nutrition_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:nutrition|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:nutrition|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_philosophy_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:philosophy|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:philosophy|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_prehistory_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:prehistory|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:prehistory|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_professional_accounting_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_professional_law_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:professional_law|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:professional_law|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_professional_medicine_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_professional_psychology_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_public_relations_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:public_relations|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:public_relations|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_security_studies_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:security_studies|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:security_studies|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_sociology_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:sociology|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:sociology|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_us_foreign_policy_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_virology_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:virology|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:virology|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_world_religions_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '**/details_original|mmlu:world_religions|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '**/details_original|mmlu:world_religions|5_2023-08-28T20:36:26.123850.parquet' - config_name: results data_files: - split: 2023_08_21T11_06_07.240233 path: - results_2023-08-21T11:06:07.240233.parquet - split: 2023_08_21T11_28_25.684618 path: - results_2023-08-21T11:28:25.684618.parquet - split: 2023_08_21T20_33_55.417483 path: - results_2023-08-21T20:33:55.417483.parquet - split: 2023_08_22T09_05_23.035851 path: - results_2023-08-22T09:05:23.035851.parquet - split: 2023_08_22T10_47_05.866748 path: - results_2023-08-22T10:47:05.866748.parquet - split: 2023_08_22T13_42_09.433095 path: - results_2023-08-22T13:42:09.433095.parquet - split: 2023_08_22T13_47_53.141854 path: - results_2023-08-22T13:47:53.141854.parquet - split: 2023_08_28T20_36_26.123850 path: - results_2023-08-28T20:36:26.123850.parquet - split: 2023_09_08T23_38_08.931556 path: - results_2023-09-08T23-38-08.931556.parquet - split: 2023_09_18T06_46_44.905361 path: - results_2023-09-18T06-46-44.905361.parquet - split: latest path: - results_2023-09-18T06-46-44.905361.parquet --- # Dataset Card for Evaluation run of meta-llama/Llama-2-70b-hf ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/meta-llama/Llama-2-70b-hf - **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 [meta-llama/Llama-2-70b-hf](https://huggingface.co/meta-llama/Llama-2-70b-hf) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 124 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 10 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_meta-llama__Llama-2-70b-hf", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-18T06:46:44.905361](https://huggingface.co/datasets/open-llm-leaderboard/details_meta-llama__Llama-2-70b-hf/blob/main/results_2023-09-18T06-46-44.905361.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": 0.0017827181208053692, "em_stderr": 0.00043200973460388544, "f1": 0.06615562080536916, "f1_stderr": 0.0013739852117668813, "acc": 0.5885312292623206, "acc_stderr": 0.011707750309504293 }, "harness|drop|3": { "em": 0.0017827181208053692, "em_stderr": 0.00043200973460388544, "f1": 0.06615562080536916, "f1_stderr": 0.0013739852117668813 }, "harness|gsm8k|5": { "acc": 0.33965125094768767, "acc_stderr": 0.01304504506766526 }, "harness|winogrande|5": { "acc": 0.8374112075769534, "acc_stderr": 0.010370455551343326 } } ``` ### 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]
111,559
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osunlp/AttrScore
2023-06-29T01:56:48.000Z
[ "task_categories:text-classification", "size_categories:100K<n<1M", "language:en", "license:apache-2.0", "arxiv:2305.06311", "region:us" ]
osunlp
We construct this dataset, which contains both training and test data for the evaluation of attribution. The training data are repurposed from related tasks, such as question answering, fact-checking, natural language inference, and summarization. The test data contains a set simulated from QA datasets and a set manually curated from a generative search engine, New Bing.
@article{yue2023automatic, title={Automatic Evaluation of Attribution by Large Language Models}, author={Yue, Xiang and Wang, Boshi and Zhang, Kai and Chen, Ziru and Su, Yu and Sun, Huan}, journal={arXiv preprint arXiv:2305.06311}, year={2023} }
9
1,030
2023-05-16T19:09:52
--- license: apache-2.0 task_categories: - text-classification language: - en pretty_name: AttrScore size_categories: - 100K<n<1M --- # Dataset Card for AttrScore - Repository: https://github.com/OSU-NLP-Group/AttrScore - Paper: [Automatic Evaluation of Attribution by Large Language Models] (https://arxiv.org/pdf/2305.06311.pdf) - Point of Contact: [Xiang Yue](mailto:yue.149@osu.edu) ### Citation Information ```bib @article{yue2023automatic, title={Automatic Evaluation of Attribution by Large Language Models}, author={Yue, Xiang and Wang, Boshi and Zhang, Kai and Chen, Ziru and Su, Yu and Sun, Huan}, journal={arXiv preprint arXiv:2305.06311}, year={2023} } ``` ### What's New? In the current version 0.2, we fixed some wrong annotated labels in the AttrEval-GenSearch dataset. (Commit: [4da294f](https://huggingface.co/datasets/osunlp/AttrScore/commit/4da294f5e488086492e117b405fc8ea95717ec3b)) ### Dataset Summary A recent focus of large language model (LLM) development, as exemplified by generative search engines, is to incorporate external references to generate and support its claims. However, evaluating the attribution, i.e., verifying whether the generated statement is indeed fully supported by the cited reference, remains an open problem. We construct this dataset, which contains both training and test data for the evaluation of attribution. The training data are repurposed from related tasks, such as question answering, fact-checking, natural language inference, and summarization. The test data, cotains a set simulated from QA datasets and a set manually curated from a generative search engine, New Bing. ## Dataset Structure ### Data Instances { "query": "", "answer": "Bastedo cared for all the animals that inhabit the earth.", "reference": "Alexandra Lendon Bastedo (9 March 1946 - 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.", "label": "Extrapolatory", "dataset": "anli" } { "query": The persian gulf war began when iraq invaded what country? "answer": The Persian Gulf War began when Iraq invaded Kuwait. "reference": First Iraq War or Iraq War, before the term \"Iraq War\" became identified instead with the 2003 Iraq War. The Iraqi Army's occupation of Kuwait that began 2 August 1990 was met with international condemnation and brought immediate economic sanctions against Iraq by members of the UN Security Council. Together with the UK's prime minister Margaret Thatcher - who had resisted the invasion by Argentina of the Falkland Islands a decade earlier - George H. W. Bush deployed US forces into Saudi Arabia, and urged other countries to send their own forces to the scene. An array of nations joined the coalition, forming the", "label": "Attributable", "dataset": "NaturalQuestions" } ### Data Fields - "query": query (may be empty) - "answer": answer to the query - "reference": a document or a paragraph - "label": whether the reference can support the answer to the query ("attributable", "extrapolatory", "contradictory") - "dataset": the original dataset of the data instance
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papluca/language-identification
2022-07-15T10:11:23.000Z
[ "task_categories:text-classification", "task_ids:multi-class-classification", "multilinguality:multilingual", "size_categories:unknown", "source_datasets:extended|amazon_reviews_multi", "source_datasets:extended|xnli", "source_datasets:extended|stsb_multi_mt", "language:ar", "language:bg", "language:de", "language:el", "language:en", "language:es", "language:fr", "language:hi", "language:it", "language:ja", "language:nl", "language:pl", "language:pt", "language:ru", "language:sw", "language:th", "language:tr", "language:ur", "language:vi", "language:zh", "region:us" ]
papluca
null
null
16
1,028
2022-03-02T23:29:22
--- annotations_creators: [] language_creators: [] language: - ar - bg - de - el - en - es - fr - hi - it - ja - nl - pl - pt - ru - sw - th - tr - ur - vi - zh license: [] multilinguality: - multilingual pretty_name: Language Identification dataset size_categories: - unknown source_datasets: - extended|amazon_reviews_multi - extended|xnli - extended|stsb_multi_mt task_categories: - text-classification task_ids: - multi-class-classification --- # Dataset Card for Language Identification 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:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The Language Identification dataset is a collection of 90k samples consisting of text passages and corresponding language label. This dataset was created by collecting data from 3 sources: [Multilingual Amazon Reviews Corpus](https://huggingface.co/datasets/amazon_reviews_multi), [XNLI](https://huggingface.co/datasets/xnli), and [STSb Multi MT](https://huggingface.co/datasets/stsb_multi_mt). ### Supported Tasks and Leaderboards The dataset can be used to train a model for language identification, which is a **multi-class text classification** task. The model [papluca/xlm-roberta-base-language-detection](https://huggingface.co/papluca/xlm-roberta-base-language-detection), which is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base), was trained on this dataset and currently achieves 99.6% accuracy on the test set. ### Languages The Language Identification dataset contains text in 20 languages, which are: `arabic (ar), bulgarian (bg), german (de), modern greek (el), english (en), spanish (es), french (fr), hindi (hi), italian (it), japanese (ja), dutch (nl), polish (pl), portuguese (pt), russian (ru), swahili (sw), thai (th), turkish (tr), urdu (ur), vietnamese (vi), and chinese (zh)` ## Dataset Structure ### Data Instances For each instance, there is a string for the text and a string for the label (the language tag). Here is an example: `{'labels': 'fr', 'text': 'Conforme à la description, produit pratique.'}` ### Data Fields - **labels:** a string indicating the language label. - **text:** a string consisting of one or more sentences in one of the 20 languages listed above. ### Data Splits The Language Identification dataset has 3 splits: *train*, *valid*, and *test*. The train set contains 70k samples, while the validation and test sets 10k each. All splits are perfectly balanced: the train set contains 3500 samples per language, while the validation and test sets 500. ## Dataset Creation ### Curation Rationale This dataset was built during *The Hugging Face Course Community Event*, which took place in November 2021, with the goal of collecting a dataset with enough samples for each language to train a robust language detection model. ### Source Data The Language Identification dataset was created by collecting data from 3 sources: [Multilingual Amazon Reviews Corpus](https://huggingface.co/datasets/amazon_reviews_multi), [XNLI](https://huggingface.co/datasets/xnli), and [STSb Multi MT](https://huggingface.co/datasets/stsb_multi_mt). ### Personal and Sensitive Information The dataset does not contain any personal information about the authors or the crowdworkers. ## Considerations for Using the Data ### Social Impact of Dataset This dataset was developed as a benchmark for evaluating (balanced) multi-class text classification models. ### Discussion of Biases The possible biases correspond to those of the 3 datasets on which this dataset is based. ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@LucaPapariello](https://github.com/LucaPapariello) for adding this dataset.
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squad_kor_v1
2023-06-15T15:25:29.000Z
[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:ko", "license:cc-by-nd-4.0", "arxiv:1909.07005", "region:us" ]
null
KorQuAD 1.0 is a large-scale Korean dataset for machine reading comprehension task consisting of human generated questions for Wikipedia articles. We benchmark the data collecting process of SQuADv1.0 and crowdsourced 70,000+ question-answer pairs. 1,637 articles and 70,079 pairs of question answers were collected. 1,420 articles are used for the training set, 140 for the dev set, and 77 for the test set. 60,407 question-answer pairs are for the training set, 5,774 for the dev set, and 3,898 for the test set.
@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} }
9
1,022
2022-03-02T23:29:22
--- 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.
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open_subtitles
2023-06-01T14:59:58.000Z
[ "task_categories:translation", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "size_categories:10K<n<100K", "size_categories:1M<n<10M", "size_categories:n<1K", "source_datasets:original", "language:af", "language:ar", "language:bg", "language:bn", "language:br", "language:bs", "language:ca", "language:cs", "language:da", "language:de", "language:el", "language:en", "language:eo", "language:es", "language:et", "language:eu", "language:fa", "language:fi", "language:fr", "language:gl", "language:he", "language:hi", "language:hr", "language:hu", "language:hy", "language:id", "language:is", "language:it", "language:ja", "language:ka", "language:kk", "language:ko", "language:lt", "language:lv", "language:mk", "language:ml", "language:ms", "language:nl", "language:no", "language:pl", "language:pt", "language:ro", "language:ru", "language:si", "language:sk", "language:sl", "language:sq", "language:sr", "language:sv", "language:ta", "language:te", "language:th", "language:tl", "language:tr", "language:uk", "language:ur", "language:vi", "language:zh", "license:unknown", "region:us" ]
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This is a new collection of translated movie subtitles from http://www.opensubtitles.org/. IMPORTANT: If you use the OpenSubtitle corpus: Please, add a link to http://www.opensubtitles.org/ to your website and to your reports and publications produced with the data! This is a slightly cleaner version of the subtitle collection using improved sentence alignment and better language checking. 62 languages, 1,782 bitexts total number of files: 3,735,070 total number of tokens: 22.10G total number of sentence fragments: 3.35G
P. Lison and J. Tiedemann, 2016, OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. In Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016)
33
1,018
2022-03-02T23:29:22
--- annotations_creators: - found language_creators: - found language: - af - ar - bg - bn - br - bs - ca - cs - da - de - el - en - eo - es - et - eu - fa - fi - fr - gl - he - hi - hr - hu - hy - id - is - it - ja - ka - kk - ko - lt - lv - mk - ml - ms - nl - 'no' - pl - pt - ro - ru - si - sk - sl - sq - sr - sv - ta - te - th - tl - tr - uk - ur - vi - zh language_bcp47: - pt-BR - ze-EN - ze-ZH - zh-CN - zh-TW license: - unknown multilinguality: - multilingual size_categories: - 10K<n<100K - 1M<n<10M - n<1K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: opensubtitles pretty_name: OpenSubtitles dataset_info: - config_name: bs-eo features: - name: id dtype: string - name: meta struct: - name: year dtype: uint32 - name: imdbId dtype: uint32 - name: subtitleId struct: - name: bs dtype: uint32 - name: eo dtype: uint32 - name: sentenceIds struct: - name: bs sequence: uint32 - name: eo sequence: uint32 - name: translation dtype: translation: languages: - bs - eo splits: - name: train num_bytes: 1204266 num_examples: 10989 download_size: 333050 dataset_size: 1204266 - config_name: fr-hy features: - name: id dtype: string - name: meta struct: - name: year dtype: uint32 - name: imdbId dtype: uint32 - name: subtitleId struct: - name: fr dtype: uint32 - name: hy dtype: uint32 - name: sentenceIds struct: - name: fr sequence: uint32 - name: hy sequence: uint32 - name: translation dtype: translation: languages: - fr - hy splits: - name: train num_bytes: 132450 num_examples: 668 download_size: 41861 dataset_size: 132450 - config_name: da-ru features: - name: id dtype: string - name: meta struct: - name: year dtype: uint32 - name: imdbId dtype: uint32 - name: subtitleId struct: - name: da dtype: uint32 - name: ru dtype: uint32 - name: sentenceIds struct: - name: da sequence: uint32 - name: ru sequence: uint32 - name: translation dtype: translation: languages: - da - ru splits: - name: train num_bytes: 1082649105 num_examples: 7543012 download_size: 267995167 dataset_size: 1082649105 - config_name: en-hi features: - name: id dtype: string - name: meta struct: - name: year dtype: uint32 - name: imdbId dtype: uint32 - name: subtitleId struct: - name: en dtype: uint32 - name: hi dtype: uint32 - name: sentenceIds struct: - name: en sequence: uint32 - name: hi sequence: uint32 - name: translation dtype: translation: languages: - en - hi splits: - name: train num_bytes: 13845544 num_examples: 93016 download_size: 2967295 dataset_size: 13845544 - config_name: bn-is features: - name: id dtype: string - name: meta struct: - name: year dtype: uint32 - name: imdbId dtype: uint32 - name: subtitleId struct: - name: bn dtype: uint32 - name: is dtype: uint32 - name: sentenceIds struct: - name: bn sequence: uint32 - name: is sequence: uint32 - name: translation dtype: translation: languages: - bn - is splits: - name: train num_bytes: 6371251 num_examples: 38272 download_size: 1411625 dataset_size: 6371251 config_names: - bn-is - bs-eo - da-ru - en-hi - fr-hy --- # Dataset Card for OpenSubtitles ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://opus.nlpl.eu/OpenSubtitles.php - **Repository:** None - **Paper:** http://www.lrec-conf.org/proceedings/lrec2016/pdf/62_Paper.pdf - **Leaderboard:** [More Information Needed] - **Point of Contact:** [More Information Needed] ### Dataset Summary To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs. You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/OpenSubtitles.php E.g. `dataset = load_dataset("open_subtitles", lang1="fi", lang2="hi")` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The languages in the dataset are: - af - ar - bg - bn - br - bs - ca - cs - da - de - el - en - eo - es - et - eu - fa - fi - fr - gl - he - hi - hr - hu - hy - id - is - it - ja - ka - kk - ko - lt - lv - mk - ml - ms - nl - no - pl - pt - pt_br: Portuguese (Brazil) (pt-BR) - ro - ru - si - sk - sl - sq - sr - sv - ta - te - th - tl - tr - uk - ur - vi - ze_en: English constituent of Bilingual Chinese-English (subtitles displaying two languages at once, one per line) - ze_zh: Chinese constituent of Bilingual Chinese-English (subtitles displaying two languages at once, one per line) - zh_cn: Simplified Chinese (zh-CN, `zh-Hans`) - zh_tw: Traditional Chinese (zh-TW, `zh-Hant`) ## Dataset Structure ### Data Instances Here are some examples of questions and facts: ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.
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dane
2023-01-25T14:29:05.000Z
[ "task_categories:token-classification", "task_ids:named-entity-recognition", "task_ids:part-of-speech", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:extended|other-Danish-Universal-Dependencies-treebank", "language:da", "license:cc-by-sa-4.0", "region:us" ]
null
The DaNE dataset has been annotated with Named Entities for PER, ORG and LOC by the Alexandra Institute. It is a reannotation of the UD-DDT (Universal Dependency - Danish Dependency Treebank) which has annotations for dependency parsing and part-of-speech (POS) tagging. The Danish UD treebank (Johannsen et al., 2015, UD-DDT) is a conversion of the Danish Dependency Treebank (Buch-Kromann et al. 2003) based on texts from Parole (Britt, 1998).
@inproceedings{hvingelby-etal-2020-dane, title = "{D}a{NE}: A Named Entity Resource for {D}anish", author = "Hvingelby, Rasmus and Pauli, Amalie Brogaard and Barrett, Maria and Rosted, Christina and Lidegaard, Lasse Malm and Søgaard, Anders", booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference", month = may, year = "2020", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://www.aclweb.org/anthology/2020.lrec-1.565", pages = "4597--4604", abstract = "We present a named entity annotation for the Danish Universal Dependencies treebank using the CoNLL-2003 annotation scheme: DaNE. It is the largest publicly available, Danish named entity gold annotation. We evaluate the quality of our annotations intrinsically by double annotating the entire treebank and extrinsically by comparing our annotations to a recently released named entity annotation of the validation and test sections of the Danish Universal Dependencies treebank. We benchmark the new resource by training and evaluating competitive architectures for supervised named entity recognition (NER), including FLAIR, monolingual (Danish) BERT and multilingual BERT. We explore cross-lingual transfer in multilingual BERT from five related languages in zero-shot and direct transfer setups, and we show that even with our modestly-sized training set, we improve Danish NER over a recent cross-lingual approach, as well as over zero-shot transfer from five related languages. Using multilingual BERT, we achieve higher performance by fine-tuning on both DaNE and a larger Bokm{\aa}l (Norwegian) training set compared to only using DaNE. However, the highest performance isachieved by using a Danish BERT fine-tuned on DaNE. Our dataset enables improvements and applicability for Danish NER beyond cross-lingual methods. We employ a thorough error analysis of the predictions of the best models for seen and unseen entities, as well as their robustness on un-capitalized text. The annotated dataset and all the trained models are made publicly available.", language = "English", ISBN = "979-10-95546-34-4", }
3
1,017
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - found language: - da license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - extended|other-Danish-Universal-Dependencies-treebank task_categories: - token-classification task_ids: - named-entity-recognition - part-of-speech paperswithcode_id: dane pretty_name: DaNE dataset_info: features: - name: sent_id dtype: string - name: text dtype: string - name: tok_ids sequence: int64 - name: tokens sequence: string - name: lemmas sequence: string - name: pos_tags sequence: class_label: names: '0': NUM '1': CCONJ '2': PRON '3': VERB '4': INTJ '5': AUX '6': ADJ '7': PROPN '8': PART '9': ADV '10': PUNCT '11': ADP '12': NOUN '13': X '14': DET '15': SYM '16': SCONJ - name: morph_tags sequence: string - name: dep_ids sequence: int64 - name: dep_labels sequence: class_label: names: '0': parataxis '1': mark '2': nummod '3': discourse '4': compound:prt '5': reparandum '6': vocative '7': list '8': obj '9': dep '10': det '11': obl:loc '12': flat '13': iobj '14': cop '15': expl '16': obl '17': conj '18': nmod '19': root '20': acl:relcl '21': goeswith '22': appos '23': fixed '24': obl:tmod '25': xcomp '26': advmod '27': nmod:poss '28': aux '29': ccomp '30': amod '31': cc '32': advcl '33': nsubj '34': punct '35': case - 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 splits: - name: train num_bytes: 7311212 num_examples: 4383 - name: test num_bytes: 909699 num_examples: 565 - name: validation num_bytes: 940413 num_examples: 564 download_size: 1209710 dataset_size: 9161324 --- # Dataset Card for DaNE ## 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:** [DaNE homepage](https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#dane) - **Repository:** [Github](https://github.com/alexandrainst/danlp) - **Paper:** [Aclweb](https://www.aclweb.org/anthology/2020.lrec-1.565) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The Danish Dependency Treebank (DaNE) is a named entity annotation for the Danish Universal Dependencies treebank using the CoNLL-2003 annotation scheme. The Danish UD treebank (Johannsen et al., 2015, UD-DDT) is a conversion of the Danish Dependency Treebank (Buch-Kromann et al. 2003) based on texts from Parole (Britt, 1998). UD-DDT has annotations for dependency parsing and part-of-speech (POS) tagging. The dataset was annotated with Named Entities for PER, ORG, and LOC by the Alexandra Institute in the DaNE dataset (Hvingelby et al. 2020). ### Supported Tasks and Leaderboards Parts-of-speech tagging, dependency parsing and named entitity recognition. ### Languages Danish ## Dataset Structure ### Data Instances This is an example in the "train" split: ```python { 'sent_id': 'train-v2-0\n', 'lemmas': ['på', 'fredag', 'have', 'SiD', 'invitere', 'til', 'reception', 'i', 'SID-hus', 'i', 'anledning', 'af', 'at', 'formand', 'Kjeld', 'Christensen', 'gå', 'ind', 'i', 'den', 'glad', 'tresser', '.'], 'dep_labels': [35, 16, 28, 33, 19, 35, 16, 35, 18, 35, 18, 1, 1, 33, 22, 12, 32, 11, 35, 10, 30, 16, 34], 'ner_tags': [0, 0, 0, 3, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0], 'morph_tags': ['AdpType=Prep', 'Definite=Ind|Gender=Com|Number=Sing', 'Mood=Ind|Tense=Pres|VerbForm=Fin|Voice=Act', '_', 'Definite=Ind|Number=Sing|Tense=Past|VerbForm=Part', 'AdpType=Prep', 'Definite=Ind|Gender=Com|Number=Sing', 'AdpType=Prep', 'Definite=Def|Gender=Neut|Number=Sing', 'AdpType=Prep', 'Definite=Ind|Gender=Com|Number=Sing', 'AdpType=Prep', '_', 'Definite=Def|Gender=Com|Number=Sing', '_', '_', 'Mood=Ind|Tense=Pres|VerbForm=Fin|Voice=Act', '_', 'AdpType=Prep', 'Number=Plur|PronType=Dem', 'Degree=Pos|Number=Plur', 'Definite=Ind|Gender=Com|Number=Plur', '_'], 'dep_ids': [2, 5, 5, 5, 0, 7, 5, 9, 7, 11, 7, 17, 17, 17, 14, 15, 11, 17, 22, 22, 22, 18, 5], 'pos_tags': [11, 12, 5, 7, 3, 11, 12, 11, 12, 11, 12, 11, 16, 12, 7, 7, 3, 9, 11, 14, 6, 12, 10], 'text': 'På fredag har SID inviteret til reception i SID-huset i anledning af at formanden Kjeld Christensen går ind i de glade tressere.\n', 'tokens': ['På', 'fredag', 'har', 'SID', 'inviteret', 'til', 'reception', 'i', 'SID-huset', 'i', 'anledning', 'af', 'at', 'formanden', 'Kjeld', 'Christensen', 'går', 'ind', 'i', 'de', 'glade', 'tressere', '.'], 'tok_ids': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23] } ``` ### Data Fields Data Fields: - q_id: a string question identifier for each example, corresponding to its ID in the Pushshift.io Reddit submission dumps. - subreddit: One of explainlikeimfive, askscience, or AskHistorians, indicating which subreddit the question came from - title: title of the question, with URLs extracted and replaced by URL_n tokens - title_urls: list of the extracted URLs, the nth element of the list was replaced by URL_n - sent_id: a string identifier for each example - text: a string, the original sentence (not tokenized) - tok_ids: a list of ids (int), one for each token - tokens: a list of strings, the tokens - lemmas: a list of strings, the lemmas of the tokens - pos_tags: a list of strings, the part-of-speech tags of the tokens - morph_tags: a list of strings, the morphological tags of the tokens - dep_ids: a list of ids (int), the id of the head of the incoming dependency for each token - dep_labels: a list of strings, the dependency labels - ner_tags: a list of strings, the named entity tags (BIO format) ### Data Splits | | train | validation | test | |-------------|-------:|-----------:|-------:| | # sentences | 4383 | 564 | 565 | | # tokens | 80 378 | 10 322 | 10 023 | ## 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] ### Citation Information ``` @inproceedings{hvingelby-etal-2020-dane, title = "{D}a{NE}: A Named Entity Resource for {D}anish", author = "Hvingelby, Rasmus and Pauli, Amalie Brogaard and Barrett, Maria and Rosted, Christina and Lidegaard, Lasse Malm and S{\o}gaard, Anders", booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference", month = may, year = "2020", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2020.lrec-1.565", pages = "4597--4604", abstract = "We present a named entity annotation for the Danish Universal Dependencies treebank using the CoNLL-2003 annotation scheme: DaNE. It is the largest publicly available, Danish named entity gold annotation. We evaluate the quality of our annotations intrinsically by double annotating the entire treebank and extrinsically by comparing our annotations to a recently released named entity annotation of the validation and test sections of the Danish Universal Dependencies treebank. We benchmark the new resource by training and evaluating competitive architectures for supervised named entity recognition (NER), including FLAIR, monolingual (Danish) BERT and multilingual BERT. We explore cross-lingual transfer in multilingual BERT from five related languages in zero-shot and direct transfer setups, and we show that even with our modestly-sized training set, we improve Danish NER over a recent cross-lingual approach, as well as over zero-shot transfer from five related languages. Using multilingual BERT, we achieve higher performance by fine-tuning on both DaNE and a larger Bokm{\aa}l (Norwegian) training set compared to only using DaNE. However, the highest performance isachieved by using a Danish BERT fine-tuned on DaNE. Our dataset enables improvements and applicability for Danish NER beyond cross-lingual methods. We employ a thorough error analysis of the predictions of the best models for seen and unseen entities, as well as their robustness on un-capitalized text. The annotated dataset and all the trained models are made publicly available.", language = "English", ISBN = "979-10-95546-34-4", } ``` ### Contributions Thanks to [@ophelielacroix](https://github.com/ophelielacroix), [@lhoestq](https://github.com/lhoestq) for adding this dataset.
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medical_dialog
2023-09-18T09:07:35.000Z
[ "task_categories:question-answering", "task_ids:closed-domain-qa", "annotations_creators:found", "language_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:original", "language:en", "language:zh", "license:unknown", "arxiv:2004.03329", "region:us" ]
null
The MedDialog dataset (English) contains conversations (in English) between doctors and patients.It has 0.26 million dialogues. The data is continuously growing and more dialogues will be added. The raw dialogues are from healthcaremagic.com and icliniq.com. All copyrights of the data belong to healthcaremagic.com and icliniq.com.
@article{chen2020meddiag, title={MedDialog: a large-scale medical dialogue dataset}, author={Chen, Shu and Ju, Zeqian and Dong, Xiangyu and Fang, Hongchao and Wang, Sicheng and Yang, Yue and Zeng, Jiaqi and Zhang, Ruisi and Zhang, Ruoyu and Zhou, Meng and Zhu, Penghui and Xie, Pengtao}, journal={arXiv preprint arXiv:2004.03329}, year={2020} }
78
1,016
2022-03-02T23:29:22
--- annotations_creators: - found language_creators: - expert-generated - found language: - en - zh license: - unknown multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - original task_categories: - question-answering task_ids: - closed-domain-qa pretty_name: MedDialog paperswithcode_id: meddialog dataset_info: - config_name: en features: - name: file_name dtype: string - name: dialogue_id dtype: int32 - name: dialogue_url dtype: string - name: dialogue_turns sequence: - name: speaker dtype: class_label: names: '0': Patient '1': Doctor - name: utterance dtype: string splits: - name: train num_bytes: 290274759 num_examples: 229674 download_size: 0 dataset_size: 290274759 - config_name: zh features: - name: file_name dtype: string - name: dialogue_id dtype: int32 - name: dialogue_url dtype: string - name: dialogue_turns sequence: - name: speaker dtype: class_label: names: '0': 病人 '1': 医生 - name: utterance dtype: string splits: - name: train num_bytes: 1092063621 num_examples: 1921127 download_size: 0 dataset_size: 1092063621 - config_name: processed.en features: - name: description dtype: string - name: utterances sequence: string splits: - name: train num_bytes: 370745 num_examples: 482 - name: validation num_bytes: 52145 num_examples: 60 - name: test num_bytes: 46514 num_examples: 61 download_size: 524214 dataset_size: 469404 - config_name: processed.zh features: - name: utterances sequence: string splits: - name: train num_bytes: 1571262099 num_examples: 2725989 - name: validation num_bytes: 197117565 num_examples: 340748 - name: test num_bytes: 196526738 num_examples: 340754 download_size: 2082354155 dataset_size: 1964906402 config_names: - en - zh viewer: false --- # Dataset Card for MedDialog ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** https://github.com/UCSD-AI4H/Medical-Dialogue-System - **Paper:** [MedDialog: Two Large-scale Medical Dialogue Datasets](https://arxiv.org/abs/2004.03329) - **Point of Contact:** [Pengtao Xie](mailto:pengtaoxie2008@gmail.com) ### Dataset Summary The MedDialog dataset (Chinese) contains conversations (in Chinese) between doctors and patients. It has 1.1 million dialogues and 4 million utterances. The data is continuously growing and more dialogues will be added. The raw dialogues are from haodf.com. All copyrights of the data belong to haodf.com. The MedDialog dataset (English) contains conversations (in English) between doctors and patients. It has 0.26 million dialogues. The data is continuously growing and more dialogues will be added. The raw dialogues are from healthcaremagic.com and icliniq.com. All copyrights of the data belong to healthcaremagic.com and icliniq.com. Directions for using the pre-trained model using BERT using PyTorch is available in the Homepage. ### Supported Tasks and Leaderboards Closed domain qa ### Languages Monolingual. The datasets are in English (EN) and Chinese (ZH) ## Dataset Structure ### Data Instances There are 4 configurations: - Raw data: - en - zh - Processed data: - processed.en - processed.zh #### en Each consultation consists of the below: - ID - URL - Description of patient’s medical condition - Dialogue The dataset is built from [icliniq.com](https://www.icliniq.com/), [healthcaremagic.com](https://www.healthcaremagic.com/), [healthtap.com](https://www.healthtap.com/) and all copyrights of the data belong to these websites. #### zh Each consultation consists of the below: - ID - URL - Description of patient’s medical condition - Dialogue - (Optional) Diagnosis and suggestions. The dataset is built from [Haodf.com](https://www.haodf.com/) and all copyrights of the data belong to [Haodf.com](https://www.haodf.com/). One example for chinese is ``` { {'dialogue_id': 2, 'dialogue_turns': [{'speaker': '病人', 'utterance': '孩子哭闹时，鸡鸡旁边会肿起，情绪平静时肿块会消失，去一个私人诊所看过，说是疝气.如果确定是疝气，是不是一定要手术治疗？我孩子只有1岁10月，自愈的可能性大吗？如果一定要手术，这么小的孩子风险大吗？术后的恢复困难吗？谢谢.'}, {'speaker': '医生', 'utterance': '南方医的B超说得不清楚，可能是鞘膜积液，可到我医院复查一个B超。'}], 'dialogue_url': 'https://www.haodf.com/doctorteam/flow_team_6477251152.htm', 'file_name': '2020.txt'}, } ``` #### processed.en ``` { 'description': 'throat a bit sore and want to get a good imune booster, especially in light of the virus. please advise. have not been in contact with nyone with the virus.', 'utterances': [ 'patient: throat a bit sore and want to get a good imune booster, especially in light of the virus. please advise. have not been in contact with nyone with the virus.', "doctor: during this pandemic. throat pain can be from a strep throat infection (antibiotics needed), a cold or influenza or other virus, or from some other cause such as allergies or irritants. usually, a person sees the doctor (call first) if the sore throat is bothersome, recurrent, or doesn't go away quickly. covid-19 infections tend to have cough, whereas strep throat usually lacks cough but has more throat pain. (3/21/20)" ] } ``` #### processed.zh ``` { 'utterances': [ '病人：强制性脊柱炎，晚上睡觉翻身时腰骶骨区域疼痛，其他身体任何部位均不疼痛。', '医生：应该没有问题，但最好把图像上传看看。' ] } ``` ### Data Fields For generating the QA only the below fields have been considered: - ID : Consultatation Identifier (restarts for each file) - URL: The url link of the extracted conversation - Dialogue : The conversation between the doctor and the patient. These are arranged as below in the prepared dataset. Each item will be represented with these parameters. - "file_name": string - signifies the file from which the conversation was extracted - "dialogue_id": int32 - the dialogue id - "dialogue_url": string - url of the conversation - "dialogue_turns": datasets.Sequence - sequence of dialogues between patient and the doctor.Consists ClassLabel(names=["病人", "医生"]), and "utterance"(string) for each turn. (ClassLable(names=["Patient", "Doctor"]) for english) #### processed.en - `description` (str): Description of the dialog. - `utterances` (list of str): Dialog utterances between patient and doctor. #### processed.zh - `utterances` (list of str): Dialog utterances between patient and doctor. ### Data Splits There are no data splits on the original raw data. The "train" split for each language contains: - en: 229674 examples - zh: 1921127 examples For processed configurations, data is split into train, validation and test, with the following number of examples: | | train | validation | test | |--------------|--------:|-----------:|-------:| | processed.en | 482 | 60 | 61 | | processed.zh | 2725989 | 340748 | 340754 | ## Dataset Creation ### Curation Rationale Medical dialogue systems are promising in assisting in telemedicine to increase access to healthcare services, improve the quality of patient care, and reduce medical costs. ### 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 authors claim that: - They scraped the data from the following websites: - MedDialog-EN: data was crawled from https://www.icliniq.com/ and https://www.healthcaremagic.com/ - MedDialog-CN: data was crawled from https://www.haodf.com/ - All copyrights of the data belong to the corresponding websites The [terms and conditions](https://www.icliniq.com/p/terms) (last updated on: 11th April 2022) of www.icliniq.com website state: > No person (including a User, Doctor, Alternative Medicine Practitioner, or Wellness Professional) shall copy, transfer, download, republish, sell, duplicate, or "scrape", for commercial or any other purpose whatsoever, the contents or information made available on the Platform including Directory Listing Services, academic articles, and queries, in whole or in part, in any medium whatsoever. The [terms and conditions](https://www.healthcaremagic.com/tc) (last updated: August 17, 2012) of www.healthcaremagic.com website stipulate: > You are prohibited from republishing, selling, duplicating, or "scraping" for commercial or any other purpose whatsoever any of the data or other information contained therein, in whole or in part, in any medium whatsoever. ### Citation Information ``` @article{chen2020meddiag, title={MedDialog: a large-scale medical dialogue dataset}, author={Chen, Shu and Ju, Zeqian and Dong, Xiangyu and Fang, Hongchao and Wang, Sicheng and Yang, Yue and Zeng, Jiaqi and Zhang, Ruisi and Zhang, Ruoyu and Zhou, Meng and Zhu, Penghui and Xie, Pengtao}, journal={arXiv preprint arXiv:2004.03329}, year={2020} } ``` ### Contributions Thanks to [@vrindaprabhu](https://github.com/vrindaprabhu) for adding this dataset.
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clarin-knext/fiqa-pl
2023-06-07T08:23:07.000Z
[ "language:pl", "arxiv:2305.19840", "region:us" ]
clarin-knext
null
null
0
1,016
2023-06-06T17:48:25
--- language: - pl --- Part of **BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language**. Link to arxiv: https://arxiv.org/pdf/2305.19840.pdf Contact: konrad.wojtasik@pwr.edu.pl
201
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mc_taco
2023-01-25T14:40:09.000Z
[ "task_categories:question-answering", "task_ids:multiple-choice-qa", "annotations_creators:crowdsourced", "annotations_creators:machine-generated", "language_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:unknown", "arxiv:1909.03065", "region:us" ]
null
MC-TACO (Multiple Choice TemporAl COmmonsense) is a dataset of 13k question-answer pairs that require temporal commonsense comprehension. A system receives a sentence providing context information, a question designed to require temporal commonsense knowledge, and multiple candidate answers. More than one candidate answer can be plausible. The task is framed as binary classification: givent he context, the question, and the candidate answer, the task is to determine whether the candidate answer is plausible ("yes") or not ("no").
@inproceedings{ZKNR19, author = {Ben Zhou, Daniel Khashabi, Qiang Ning and Dan Roth}, title = {“Going on a vacation” takes longer than “Going for a walk”: A Study of Temporal Commonsense Understanding }, booktitle = {EMNLP}, year = {2019}, }
0
1,012
2022-03-02T23:29:22
--- annotations_creators: - crowdsourced - machine-generated language_creators: - crowdsourced - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - multiple-choice-qa paperswithcode_id: mc-taco pretty_name: MC-TACO dataset_info: features: - name: sentence dtype: string - name: question dtype: string - name: answer dtype: string - name: label dtype: class_label: names: '0': 'no' '1': 'yes' - name: category dtype: class_label: names: '0': Event Duration '1': Event Ordering '2': Frequency '3': Typical Time '4': Stationarity config_name: plain_text splits: - name: test num_bytes: 1785553 num_examples: 9442 - name: validation num_bytes: 713023 num_examples: 3783 download_size: 2385137 dataset_size: 2498576 --- # Dataset Card for MC-TACO ## 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:** [MC-TACO](https://cogcomp.seas.upenn.edu/page/resource_view/125) - **Repository:** [Github repository](https://github.com/CogComp/MCTACO) - **Paper:** ["Going on a vacation" takes longer than "Going for a walk": A Study of Temporal Commonsense Understanding](https://arxiv.org/abs/1909.03065) - **Leaderboard:** [AI2 Leaderboard](https://leaderboard.allenai.org/mctaco) ### Dataset Summary MC-TACO (Multiple Choice TemporAl COmmonsense) is a dataset of 13k question-answer pairs that require temporal commonsense comprehension. A system receives a sentence providing context information, a question designed to require temporal commonsense knowledge, and multiple candidate answers. More than one candidate answer can be plausible. ### Supported Tasks and Leaderboards The task is framed as binary classification: givent he context, the question, and the candidate answer, the task is to determine whether the candidate answer is plausible ("yes") or not ("no"). Performance is measured using two metrics: - Exact Match -- the average number of questions for which all the candidate answers are predicted correctly. - F1 -- is slightly more relaxed than EM. It measures the overlap between one’s predictions and the ground truth, by computing the geometric mean of Precision and Recall. ### Languages The text in the dataset is in English. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances An example looks like this: ``` { "sentence": "However, more recently, it has been suggested that it may date from earlier than Abdalonymus' death.", "question": "How often did Abdalonymus die?", "answer": "every two years", "label": "no", "category": "Frequency", } ``` ### Data Fields All fields are strings: - `sentence`: a sentence (or context) on which the question is based - `question`: a question querying some temporal commonsense knowledge - `answer`: a potential answer to the question (all lowercased) - `label`: whether the answer is a correct. "yes" indicates the answer is correct/plaussible, "no" otherwise - `category`: the temporal category the question belongs to (among "Event Ordering", "Event Duration", "Frequency", "Stationarity", and "Typical Time") ### Data Splits The development set contains 561 questions and 3,783 candidate answers. The test set contains 1,332 questions and 9,442 candidate answers. From the original repository: *Note that there is no training data, and we provide the dev set as the only source of supervision. The rationale is that we believe a successful system has to bring in a huge amount of world knowledge and derive commonsense understandings prior to the current task evaluation. We therefore believe that it is not reasonable to expect a system to be trained solely on this data, and we think of the development data as only providing a definition of the task.* ## Dataset Creation ### Curation Rationale MC-TACO is used as a testbed to study the temporal commonsense understanding on NLP systems. ### Source Data From the original paper: *The context sentences are randomly selected from [MultiRC](https://www.aclweb.org/anthology/N18-1023/) (from each of its 9 domains). For each sentence, we use crowdsourcing on Amazon Mechanical Turk to collect questions and candidate answers (both correct and wrong ones).* #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations From the original paper: *To ensure the quality of the results, we limit the annotations to native speakers and use qualification tryouts.* #### Annotation process The crowdsourced construction/annotation of the dataset follows 4 steps described in Section 3 of the [paper](https://arxiv.org/abs/1909.03065): question generation, question verification, candidate answer expansion and answer labeling. #### Who are the annotators? Paid crowdsourcers. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Unknwon ### Citation Information ``` @inproceedings{ZKNR19, author = {Ben Zhou, Daniel Khashabi, Qiang Ning and Dan Roth}, title = {“Going on a vacation” takes longer than “Going for a walk”: A Study of Temporal Commonsense Understanding }, booktitle = {EMNLP}, year = {2019}, } ``` ### Contributions Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset.
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shawhin/imdb-truncated
2023-09-06T21:06:35.000Z
[ "region:us" ]
shawhin
null
null
0
1,009
2023-09-06T15:55:01
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: label dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 1310325 num_examples: 1000 - name: validation num_bytes: 1329205 num_examples: 1000 download_size: 1688812 dataset_size: 2639530 --- # Dataset Card for "imdb-truncated" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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THUDM/AgentInstruct
2023-10-23T12:36:19.000Z
[ "language:en", "arxiv:2310.12823", "region:us" ]
THUDM
null
null
103
1,002
2023-10-16T10:27:58
--- 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} } ```
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pszemraj/qmsum-cleaned
2023-06-07T22:58:58.000Z
[ "source_datasets:tau/scrolls", "language:en", "license:apache-2.0", "region:us" ]
pszemraj
null
null
1
995
2023-05-05T16:16:33
--- license: apache-2.0 language: - en source_datasets: tau/scrolls --- # qmsum-cleaned ## prefixes It's worth noting that each "document" in `input` is prefixed by a question/prompt on what the model is supposed to do. **You may want to explicitly handle this in some way, or prefix your models trained on this dataset.** Most frequent "prefixes" separated via [sentence-splitter](https://github.com/mediacloud/sentence-splitter) in the `train` split: | | Sentence | Count | |---:|:------------------------------------------------------------------------------|--------:| | 0 | Summarize the whole meeting. | 121 | | 1 | Summarize the meeting | 25 | | 2 | What did the team discuss about the product cost? | 4 | | 3 | How did Marketing design the product evaluation? | 4 | | 4 | Summarize the wrap up of the meeting. | 3 | | 5 | What did the group discuss about user requirements of the new remote control? | 3 | | 6 | What did the team discuss during the product evaluation? | 3 | | 7 | Summarize the meeting. | 2 | | 8 | Summarize what was said about digits form | 2 | | 9 | What was discussed in the meeting? | 2 | ### wordcloud Visualized as a wordcloud (`train` split): ![wc](prefix-train-wordcloud.png) ## token counts ![counts](https://i.imgur.com/rARAOvr.png)
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BeIR/nfcorpus-qrels
2022-10-23T06:05:32.000Z
[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "task_ids:fact-checking-retrieval", "multilinguality:monolingual", "language:en", "license:cc-by-sa-4.0", "region:us" ]
BeIR
null
null
0
994
2022-06-05T17:25:56
--- 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.
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liuhaotian/LLaVA-Instruct-150K
2023-10-06T22:18:34.000Z
[ "task_categories:visual-question-answering", "task_categories:question-answering", "size_categories:100K<n<1M", "language:en", "license:cc-by-nc-4.0", "region:us" ]
liuhaotian
null
null
174
994
2023-04-17T23:47:27
--- license: cc-by-nc-4.0 task_categories: - visual-question-answering - question-answering language: - en pretty_name: LLaVA Visual Instruct 150K size_categories: - 100K<n<1M --- # LLaVA Visual Instruct 150K Dataset Card ## Dataset details **Dataset type:** LLaVA Visual Instruct 150K is a set of GPT-generated multimodal instruction-following data. It is constructed for visual instruction tuning and for building large multimodal towards GPT-4 vision/language capability. **Dataset date:** LLaVA Visual Instruct 150K was collected in April 2023, by prompting GPT-4-0314 API. **Paper or resources for more information:** https://llava-vl.github.io/ **License:** Attribution-NonCommercial 4.0 International It should abide by the policy of OpenAI: https://openai.com/policies/terms-of-use **Where to send questions or comments about the model:** https://github.com/haotian-liu/LLaVA/issues ## Intended use **Primary intended uses:** The primary use of LLaVA is research on large multimodal models and chatbots. **Primary intended users:** The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
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un_multi
2023-06-01T14:59:54.000Z
[ "task_categories:translation", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "size_categories:100K<n<1M", "source_datasets:original", "language:ar", "language:de", "language:en", "language:es", "language:fr", "language:ru", "language:zh", "license:unknown", "region:us" ]
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This is a collection of translated documents from the United Nations. This corpus is available in all 6 official languages of the UN, consisting of around 300 million words per language
@inproceedings{eisele-chen-2010-multiun, title = "{M}ulti{UN}: A Multilingual Corpus from United Nation Documents", author = "Eisele, Andreas and Chen, Yu", 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/686_Paper.pdf", abstract = "This paper describes the acquisition, preparation and properties of a corpus extracted from the official documents of the United Nations (UN). This corpus is available in all 6 official languages of the UN, consisting of around 300 million words per language. We describe the methods we used for crawling, document formatting, and sentence alignment. This corpus also includes a common test set for machine translation. We present the results of a French-Chinese machine translation experiment performed on this corpus.", } @InProceedings{TIEDEMANN12.463, author = {J�rg Tiedemann}, title = {Parallel Data, Tools and Interfaces in OPUS}, booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, month = {may}, date = {23-25}, address = {Istanbul, Turkey}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-7-7}, }
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991
2022-03-02T23:29:22
--- annotations_creators: - found language_creators: - found language: - ar - de - en - es - fr - ru - zh license: - unknown multilinguality: - multilingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: multiun pretty_name: Multilingual Corpus from United Nation Documents dataset_info: - config_name: ar-de features: - name: translation dtype: translation: languages: - ar - de splits: - name: train num_bytes: 94466397 num_examples: 165090 download_size: 21869935 dataset_size: 94466397 - config_name: ar-en features: - name: translation dtype: translation: languages: - ar - en splits: - name: train num_bytes: 4189852369 num_examples: 9759125 download_size: 1036296368 dataset_size: 4189852369 - config_name: ar-es features: - name: translation dtype: translation: languages: - ar - es splits: - name: train num_bytes: 4509675284 num_examples: 10119379 download_size: 1101206667 dataset_size: 4509675284 - config_name: ar-fr features: - name: translation dtype: translation: languages: - ar - fr splits: - name: train num_bytes: 4516850009 num_examples: 9929567 download_size: 1109705925 dataset_size: 4516850009 - config_name: ar-ru features: - name: translation dtype: translation: languages: - ar - ru splits: - name: train num_bytes: 5932866867 num_examples: 10206243 download_size: 1261123878 dataset_size: 5932866867 - config_name: ar-zh features: - name: translation dtype: translation: languages: - ar - zh splits: - name: train num_bytes: 3781658413 num_examples: 9832293 download_size: 1009696775 dataset_size: 3781658413 - config_name: de-en features: - name: translation dtype: translation: languages: - de - en splits: - name: train num_bytes: 76684549 num_examples: 162981 download_size: 19468529 dataset_size: 76684549 - config_name: de-es features: - name: translation dtype: translation: languages: - de - es splits: - name: train num_bytes: 80936653 num_examples: 162078 download_size: 20266591 dataset_size: 80936653 - config_name: de-fr features: - name: translation dtype: translation: languages: - de - fr splits: - name: train num_bytes: 81888435 num_examples: 164025 download_size: 20692837 dataset_size: 81888435 - config_name: de-ru features: - name: translation dtype: translation: languages: - de - ru splits: - name: train num_bytes: 111517934 num_examples: 164792 download_size: 23507789 dataset_size: 111517934 - config_name: de-zh features: - name: translation dtype: translation: languages: - de - zh splits: - name: train num_bytes: 70534818 num_examples: 176933 download_size: 19927209 dataset_size: 70534818 - config_name: en-es features: - name: translation dtype: translation: languages: - en - es splits: - name: train num_bytes: 4128141663 num_examples: 11350967 download_size: 1123164180 dataset_size: 4128141663 - config_name: en-fr features: - name: translation dtype: translation: languages: - en - fr splits: - name: train num_bytes: 4678055160 num_examples: 13172019 download_size: 1355002731 dataset_size: 4678055160 - config_name: en-ru features: - name: translation dtype: translation: languages: - en - ru splits: - name: train num_bytes: 5632662839 num_examples: 11654416 download_size: 1285801078 dataset_size: 5632662839 - config_name: en-zh features: - name: translation dtype: translation: languages: - en - zh splits: - name: train num_bytes: 2960376046 num_examples: 9564315 download_size: 900076520 dataset_size: 2960376046 - config_name: es-fr features: - name: translation dtype: translation: languages: - es - fr splits: - name: train num_bytes: 4454712498 num_examples: 11441889 download_size: 1195733510 dataset_size: 4454712498 - config_name: es-ru features: - name: translation dtype: translation: languages: - es - ru splits: - name: train num_bytes: 5442655730 num_examples: 10605056 download_size: 1228045966 dataset_size: 5442655730 - config_name: es-zh features: - name: translation dtype: translation: languages: - es - zh splits: - name: train num_bytes: 3223871198 num_examples: 9847770 download_size: 953250084 dataset_size: 3223871198 - config_name: fr-ru features: - name: translation dtype: translation: languages: - fr - ru splits: - name: train num_bytes: 5979879089 num_examples: 11761738 download_size: 1364307157 dataset_size: 5979879089 - config_name: fr-zh features: - name: translation dtype: translation: languages: - fr - zh splits: - name: train num_bytes: 3241098333 num_examples: 9690914 download_size: 962824881 dataset_size: 3241098333 - config_name: ru-zh features: - name: translation dtype: translation: languages: - ru - zh splits: - name: train num_bytes: 4233875537 num_examples: 9557007 download_size: 1037881127 dataset_size: 4233875537 config_names: - ar-de - ar-en - ar-es - ar-fr - ar-ru - ar-zh - de-en - de-es - de-fr - de-ru - de-zh - en-es - en-fr - en-ru - en-zh - es-fr - es-ru - es-zh - fr-ru - fr-zh - ru-zh --- # 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:**[MultiUN](http://www.euromatrixplus.net/multi-unp) - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This is a collection of translated documents from the United Nations. This corpus is available in all 6 official languages of the UN consisting of around 300 million words per language ### Supported Tasks and Leaderboards The underlying task is machine translation. ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{eisele-chen-2010-multiun, title = "{M}ulti{UN}: A Multilingual Corpus from United Nation Documents", author = "Eisele, Andreas and Chen, Yu", 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/686_Paper.pdf", abstract = "This paper describes the acquisition, preparation and properties of a corpus extracted from the official documents of the United Nations (UN). This corpus is available in all 6 official languages of the UN, consisting of around 300 million words per language. We describe the methods we used for crawling, document formatting, and sentence alignment. This corpus also includes a common test set for machine translation. We present the results of a French-Chinese machine translation experiment performed on this corpus.", } ``` ``` @InProceedings{TIEDEMANN12.463, author = {J�rg Tiedemann}, title = {Parallel Data, Tools and Interfaces in OPUS}, booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, month = {may}, date = {23-25}, address = {Istanbul, Turkey}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-7-7}, } ``` ### Contributions Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.
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NeelNanda/codeparrot_clean_subset_train
2022-10-22T23:04:58.000Z
[ "region:us" ]
NeelNanda
null
null
0
991
2022-10-22T23:04:32
Entry not found
15
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schema_guided_dstc8
2023-01-25T14:43:36.000Z
[ "task_categories:text-generation", "task_categories:fill-mask", "task_categories:token-classification", "task_categories:text-classification", "task_ids:dialogue-modeling", "task_ids:multi-class-classification", "task_ids:parsing", "annotations_creators:machine-generated", "language_creators:crowdsourced", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-sa-4.0", "arxiv:1909.05855", "arxiv:2002.01359", "region:us" ]
null
The Schema-Guided Dialogue dataset (SGD) was developed for the Dialogue State Tracking task of the Eights Dialogue Systems Technology Challenge (dstc8). The SGD dataset consists of over 18k annotated multi-domain, task-oriented conversations between a human and a virtual assistant. These conversations involve interactions with services and APIs spanning 17 domains, ranging from banks and events to media, calendar, travel, and weather. For most of these domains, the SGD dataset contains multiple different APIs, many of which have overlapping functionalities but different interfaces, which reflects common real-world scenarios.
@inproceedings{aaai/RastogiZSGK20, author = {Abhinav Rastogi and Xiaoxue Zang and Srinivas Sunkara and Raghav Gupta and Pranav Khaitan}, title = {Towards Scalable Multi-Domain Conversational Agents: The Schema-Guided Dialogue Dataset}, booktitle = {The Thirty-Fourth {AAAI} Conference on Artificial Intelligence, {AAAI} 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, {IAAI} 2020, The Tenth {AAAI} Symposium on Educational Advances in Artificial Intelligence, {EAAI} 2020, New York, NY, USA, February 7-12, 2020}, pages = {8689--8696}, publisher = {{AAAI} Press}, year = {2020}, url = {https://aaai.org/ojs/index.php/AAAI/article/view/6394} }
7
986
2022-03-02T23:29:22
--- annotations_creators: - machine-generated language_creators: - crowdsourced - machine-generated language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-generation - fill-mask - token-classification - text-classification task_ids: - dialogue-modeling - multi-class-classification - parsing paperswithcode_id: sgd pretty_name: Schema-Guided Dialogue dataset_info: - config_name: dialogues features: - name: dialogue_id dtype: string - name: services sequence: string - name: turns sequence: - name: speaker dtype: class_label: names: '0': USER '1': SYSTEM - name: utterance dtype: string - name: frames sequence: - name: service dtype: string - name: slots sequence: - name: slot dtype: string - name: start dtype: int32 - name: exclusive_end dtype: int32 - name: state struct: - name: active_intent dtype: string - name: requested_slots sequence: string - name: slot_values sequence: - name: slot_name dtype: string - name: slot_value_list sequence: string - name: actions sequence: - name: act dtype: class_label: names: '0': AFFIRM '1': AFFIRM_INTENT '2': CONFIRM '3': GOODBYE '4': INFORM '5': INFORM_COUNT '6': INFORM_INTENT '7': NEGATE '8': NEGATE_INTENT '9': NOTIFY_FAILURE '10': NOTIFY_SUCCESS '11': OFFER '12': OFFER_INTENT '13': REQUEST '14': REQUEST_ALTS '15': REQ_MORE '16': SELECT '17': THANK_YOU - name: slot dtype: string - name: canonical_values sequence: string - name: values sequence: string - name: service_results sequence: - name: service_results_list sequence: - name: service_slot_name dtype: string - name: service_canonical_value dtype: string - name: service_call struct: - name: method dtype: string - name: parameters sequence: - name: parameter_slot_name dtype: string - name: parameter_canonical_value dtype: string splits: - name: train num_bytes: 158452984 num_examples: 16142 - name: validation num_bytes: 23553544 num_examples: 2482 - name: test num_bytes: 41342956 num_examples: 4201 download_size: 617805368 dataset_size: 223349484 - config_name: schema features: - name: service_name dtype: string - name: description dtype: string - name: slots sequence: - name: name dtype: string - name: description dtype: string - name: is_categorical dtype: bool - name: possible_values sequence: string - name: intents sequence: - name: name dtype: string - name: description dtype: string - name: is_transactional dtype: bool - name: required_slots sequence: string - name: optional_slots sequence: - name: slot_name dtype: string - name: slot_value dtype: string - name: result_slots sequence: string splits: - name: train num_bytes: 31513 num_examples: 26 - name: validation num_bytes: 18798 num_examples: 17 - name: test num_bytes: 22487 num_examples: 21 download_size: 617805368 dataset_size: 72798 --- # Dataset Card for The Schema-Guided Dialogue Dataset ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** [Github repository for The Schema-Guided Dialogue Dataset](https://github.com/google-research-datasets/dstc8-schema-guided-dialogue) - **Paper:** [Towards Scalable Multi-Domain Conversational Agents: The Schema-Guided Dialogue Dataset](https://arxiv.org/abs/1909.05855) - **Point of Contact:** [abhirast@google.com](abhirast@google.com) ### Dataset Summary The Schema-Guided Dialogue dataset (SGD) was developed for the Dialogue State Tracking task of the Eights Dialogue Systems Technology Challenge (dstc8). The SGD dataset consists of over 18k annotated multi-domain, task-oriented conversations between a human and a virtual assistant. These conversations involve interactions with services and APIs spanning 17 domains, ranging from banks and events to media, calendar, travel, and weather. For most of these domains, the SGD dataset contains multiple different APIs, many of which have overlapping functionalities but different interfaces, which reflects common real-world scenarios. ### Supported Tasks and Leaderboards This dataset is designed to serve as an effective test-bed for intent prediction, slot filling, state tracking (i.e., estimating the user's goal) and language generation, among other tasks for large-scale virtual assistants: - **Generative dialogue modeling** or `dialogue-modeling`: the text of the dialogues can be used to train a sequence model on the utterances. Performance on this task is typically evaluated with delexicalized-[BLEU](https://huggingface.co/metrics/bleu), inform rate and request success. - **Intent state tracking**, a `multi-class-classification` task: predict the belief state of the user side of the conversation, performance is measured by [F1](https://huggingface.co/metrics/f1). - **Action prediction**, a `parsing` task: parse an utterance into the corresponding dialog acts for the system to use. [F1](https://huggingface.co/metrics/f1) is typically reported. ### Languages The text in the dataset is in English (`en`). ## Dataset Structure ### Data Instances - `dialogues` configuration (default): Each dialogue is represented as a sequence of turns, each containing a user or system utterance. The annotations for each turn are grouped into frames, where each frame corresponds to a single service. The annotations for user turns include the active intent, the dialogue state and slot spans for the different slots values mentioned in the turn. For system turns, we have the system actions representing the semantics of the system utterance. Each system action is represented using a dialogue act with optional parameters. - `schema` configuration: In addition to the dialogues, for each service used in the dataset, a normalized representation of the interface exposed is provided as the schema. The schema contains details like the name of the service, the list of tasks supported by the service (intents) and the attributes of the entities used by the service (slots). The schema also contains natural language descriptions of the service, intents and slots which can be used for developing models which can condition their predictions on the schema. ### Data Fields Each dialog instance has the following fields: - `dialogue_id`: A unique identifier for a dialogue. - `services`: A list of services present in the dialogue. - `turns`: A list of annotated system or user utterances. Each turn consists of the following fields: - `speaker`: The speaker for the turn. Either `USER` or `SYSTEM`. - `utterance`: A string containing the natural language utterance. - `frames`: A list of frames, each frame containing annotations for a single service and consists of the following fields: - `service`: The name of the service corresponding to the frame. The slots and intents used in the following fields are taken from the schema of this service. - `slots`: A list of slot spans in the utterance, only provided for non-categorical slots. Each slot span contains the following fields: - `slot`: The name of the slot. - `start`: The index of the starting character in the utterance corresponding to the slot value. - `exclusive_end`: The index of the character just after the last character corresponding to the slot value in the utterance. - `actions`: A list of actions corresponding to the system. Each action has the following fields: - `act`: The type of action. - `slot`: (optional) A slot argument for some of the actions. - `values`: (optional) A list of values assigned to the slot. If the values list is non-empty, then the slot must be present. - `canonical_values`: (optional) The values in their canonicalized form as used by the service. It is a list of strings of the same length as values. - `service_call`: (system turns only, optional) The request sent to the service. It consists of the following fields: - `method`: The name of the intent or function of the service or API being executed. - `parameters`: A pair of lists of the same lengths: `parameter_slot_name` contains slot names and `parameter_canonical_value` contains the corresponding values in their canonicalized form. - `service_results`: (system turns only, optional) A list of entities containing the results obtained from the service. It is only available for turns in which a service call is made. Each entity is represented as a pair of lists of the same length: `service_slot_name` contains slot names and `service_canonical_value` contains the corresponding canonical values. - `state`: (user turns only) The dialogue state corresponding to the service. It consists of the following fields: - `active_intent`: The intent corresponding to the service of the frame which is currently being fulfilled by the system. It takes the value "NONE" if none of the intents are active. - `requested_slots`: A list of slots requested by the user in the current turn. - `slot_values`: A pair of lists of the same lengths: `slot_name` contains slot names and `slot_value_list` contains the corresponding lists of strings. For categorical slots, this list contains a single value assigned to the slot. For non-categorical slots, all the values in this list are spoken variations of each other and are equivalent (e.g, "6 pm", "six in the evening", "evening at 6" etc.). The mapping from the action ID and the action name is the following: 0: AFFIRM 1: AFFIRM_INTENT 2: CONFIRM 3: GOODBYE 4: INFORM 5: INFORM_COUNT 6: INFORM_INTENT 7: NEGATE 8: NEGATE_INTENT 9: NOTIFY_FAILURE 10: NOTIFY_SUCCESS 11: OFFER 12: OFFER_INTENT 13: REQUEST 14: REQUEST_ALTS 15: REQ_MORE 16: SELECT 17: THANK_YOU ### Data Splits The dataset is split into a `train`, `validation`, and `test` split with the following sizes: | | train | validation | test | |---------------------|------:|-----------:|------:| | Number of dialogues | 16142 | 2482 | 4201 | | Number of turns | 48426 | 7446 | 12603 | ## Dataset Creation ### Curation Rationale The data was collected by first using a dialogue simulator to generate dialogue outlines first and then paraphrasing them to obtain natural utterances. Using a dialogue simulator ensures the coverage of a large variety of dialogue flows by filtering out similar flows in the simulation phase to create a diverse dataset, and dialogues can be generated with their annotation, as opposed to a Wizard-of-Oz setup which is prone to manual annotation errors. ### Source Data #### Initial Data Collection and Normalization The dialogue outlines are first generated by a simulator. The dialogue simulator interacts with the services to generate dialogue outlines. It consists of two agents playing the roles of the user and the system, interacting with each other using a finite set of actions specified through dialogue acts over a probabilistic automaton designed to capture varied dialogue trajectories. It is worth noting that the simulation automaton does not include any domain-specific constraints: all domain-specific constraints are encoded in the schema and scenario. The dialogue paraphrasing framework then converts the outlines generated by the simulator into a natural conversation. Users may refer to the slot values in the dialogue acts in various different ways during the conversation, e.g., “los angeles” may be referred to as “LA” or “LAX”. To introduce these natural variations in the slot values, different slot values are replaced with a randomly selected variation while being kept consistent across user turns in a dialogue. The actions are then converted to pseudo-natural language utterances using a set of manually defined action-to-text templates, and the resulting utterances for the different actions in a turn are concatenated together. Finally, the dialogue transformed by these steps is sent to the crowd workers to be reformulated into more natural language. One crowd worker is tasked with paraphrasing all utterances of a dialogue to ensure naturalness and coherence. The crowd workers are asked to exactly repeat the slot values in their paraphrases so that the span indices for the slots can be recovered via string matching. #### Who are the source language producers? The language structure is machine-generated, and the language realizations are produced by crowd workers. The dataset paper does not provide demographic information for the crowd workers. ### Annotations #### Annotation process The annotations are automatically obtained during the initial sampling process and by string matching after reformulation. #### Who are the annotators? [N/A] ### 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 created by a team of researchers working at Google Mountain View. ### Licensing Information The dataset is released under CC BY-SA 4.0 license. ### Citation Information For the DSCT8 task, please cite: ``` @article{corr/abs-2002-01359, author = {Abhinav Rastogi and Xiaoxue Zang and Srinivas Sunkara and Raghav Gupta and Pranav Khaitan}, title = {Schema-Guided Dialogue State Tracking Task at {DSTC8}}, journal = {CoRR}, volume = {abs/2002.01359}, year = {2020}, url = {https://arxiv.org/abs/2002.01359}, archivePrefix = {arXiv}, eprint = {2002.01359} } ``` For the initial release paper please cite: ``` @inproceedings{aaai/RastogiZSGK20, author = {Abhinav Rastogi and Xiaoxue Zang and Srinivas Sunkara and Raghav Gupta and Pranav Khaitan}, title = {Towards Scalable Multi-Domain Conversational Agents: The Schema-Guided Dialogue Dataset}, booktitle = {The Thirty-Fourth {AAAI} Conference on Artificial Intelligence, {AAAI} 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, {IAAI} 2020, The Tenth {AAAI} Symposium on Educational Advances in Artificial Intelligence, {EAAI} 2020, New York, NY, USA, February 7-12, 2020}, pages = {8689--8696}, publisher = {{AAAI} Press}, year = {2020}, url = {https://aaai.org/ojs/index.php/AAAI/article/view/6394} } ``` ### Contributions Thanks to [@yjernite](https://github.com/yjernite) for adding this dataset.
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lhoestq/test
2022-07-01T15:26:34.000Z
[ "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:en", "license:mit", "region:us" ]
lhoestq
This is a test dataset.
\
0
986
2022-03-02T23:29:22
--- type: test annotations_creators: - expert-generated language_creators: - found language: - en license: - mit multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - other-test task_ids: - other-test paperswithcode_id: null pretty_name: Test Dataset --- This is a test dataset
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silk-road/ChatHaruhi-from-RoleLLM
2023-10-20T12:27:24.000Z
[ "license:cc-by-4.0", "region:us" ]
silk-road
null
null
0
986
2023-10-20T08:39:56
--- license: cc-by-4.0 --- Adapt English Role in RoleBench into ChatHaruhi format only using profiles part in [ZenMoore/RoleBench](https://huggingface.co/datasets/ZenMoore/RoleBench) Great thanks to on authors of RoleLLM! usage: ```python # if you pip installed chatharuhi it should be # from chatharuhi import ChatHaruhi from ChatHaruhi import ChatHaruhi chatbot = ChatHaruhi( role_from_hf = 'silk-road/ChatHaruhi-from-RoleLLM/Sherlock Holmes', \ llm = 'openai', embedding = 'bge_en') response = chatbot.chat(role='Police Chief', text = 'Oh no, there has been another murder') print(response) ``` The output should like this: ``` Sherlock Holmes: "Ah, another murder case, you say? Fascinating. Pray, do provide me with the details, Constable." ``` if you chat with bot in English, embedding = 'bge_en' if in Chinese, embedding = 'luotuo_openai' ( or default ) More detail information see our github https://github.com/LC1332/Chat-Haruhi-Suzumiya 95 Roles contains in this dataset: Role | Movie | 中文 | How-to-call-in-ChatHaruhi ---|---|---|--- HAL 9000 | 2001-A-Space-Odyssey | 《2001太空漫游》中的HAL 9000电脑 | silk-road/ChatHaruhi-from-RoleLLM/HAL 9000 Colonel Nathan R. Jessep | A-Few-Good-Men | 《好汉两三个》中的内森·R·杰瑟普上校 | silk-road/ChatHaruhi-from-RoleLLM/Colonel Nathan R. Jessep Antonio Salieri | Amadeus | 《阿玛迪斯》中的安东尼奥·萨列里 | silk-road/ChatHaruhi-from-RoleLLM/Antonio Salieri Stifler | American-Pie | 《美国派》中的斯蒂夫勒 | silk-road/ChatHaruhi-from-RoleLLM/Stifler Paul Vitti | Analyze-That | 《心理分析那件事》中的保罗·维蒂 | silk-road/ChatHaruhi-from-RoleLLM/Paul Vitti Alvy Singer | Annie-Hall | 《安妮·霍尔》中的阿尔维·辛格 | silk-road/ChatHaruhi-from-RoleLLM/Alvy Singer Violet Weston | August-Osage-County | 《奥塞奇郡的八月》中的紫罗兰·韦斯顿 | silk-road/ChatHaruhi-from-RoleLLM/Violet Weston Willie Soke | Bad-Santa | 《坏圣诞老人》中的威利·索克 | silk-road/ChatHaruhi-from-RoleLLM/Willie Soke Gaston | Beauty-and-the-Beast | 《美女与野兽》中的加斯顿 | silk-road/ChatHaruhi-from-RoleLLM/Gaston The Dude | Big-Lebowski,-The | 《大勒布斯基》中的“大佬” | silk-road/ChatHaruhi-from-RoleLLM/The Dude Murphy MacManus | Boondock-Saints,-The | 《天使之城》中的墨菲·麦克马纳斯 | silk-road/ChatHaruhi-from-RoleLLM/Murphy MacManus Paul Conroy | Buried | 《活埋》中的保罗·康罗伊 | silk-road/ChatHaruhi-from-RoleLLM/Paul Conroy Truman Capote | Capote | 《卡波特》中的杜鲁门·卡波特 | silk-road/ChatHaruhi-from-RoleLLM/Truman Capote Mater | Cars-2 | 《赛车总动员2》中的玛特 | silk-road/ChatHaruhi-from-RoleLLM/Mater Andrew Detmer | Chronicle | 《编年史》中的安德鲁·德特默 | silk-road/ChatHaruhi-from-RoleLLM/Andrew Detmer Coriolanus | Coriolanus | 《科里奥兰纳斯》中的主角 | silk-road/ChatHaruhi-from-RoleLLM/Coriolanus Benjamin Button | Curious-Case-of-Benjamin-Button,-The | 《本杰明·巴顿奇事》中的本杰明·巴顿 | silk-road/ChatHaruhi-from-RoleLLM/Benjamin Button John Keating | Dead-Poets-Society | 《死亡诗社》中的约翰·基廷 | silk-road/ChatHaruhi-from-RoleLLM/John Keating Wade Wilson | Deadpool | 《死侍》中的韦德·威尔逊 | silk-road/ChatHaruhi-from-RoleLLM/Wade Wilson Jim Morrison | Doors,-The | 《门》中的吉姆·莫里森 | silk-road/ChatHaruhi-from-RoleLLM/Jim Morrison Queen Elizabeth I | Elizabeth-The-Golden-Age | 《伊丽莎白：黄金时代》中的伊丽莎白一世女王 | silk-road/ChatHaruhi-from-RoleLLM/Queen Elizabeth I Jeff Spicoli | Fast-Times-at-Ridgemont-High | 《瑞奇蒙特高中时光》中的杰夫·斯皮科利 | silk-road/ChatHaruhi-from-RoleLLM/Jeff Spicoli Fred Flintstone | Flintstones,-The | 《石头家族》中的弗雷德·弗林斯通 | silk-road/ChatHaruhi-from-RoleLLM/Fred Flintstone Freddy Krueger | Freddy-vs.-Jason | 《弗雷迪对杰森》中的弗雷迪·克鲁格 | silk-road/ChatHaruhi-from-RoleLLM/Freddy Krueger Tyrion Lannister | Game_of_Thrones | 《权力的游戏》中的提利昂·兰尼斯特 | silk-road/ChatHaruhi-from-RoleLLM/Tyrion Lannister James Brown | Get-on-Up | 《起身舞蹈》中的詹姆斯·布朗 | silk-road/ChatHaruhi-from-RoleLLM/James Brown Walt Kowalski | Gran-Torino | 《老无所依》中的沃尔特·科瓦尔斯基 | silk-road/ChatHaruhi-from-RoleLLM/Walt Kowalski John Coffey | Green-Mile,-The | 《绿里奇迹》中的约翰·科菲 | silk-road/ChatHaruhi-from-RoleLLM/John Coffey Theodore Twombly | Her | 《她》中的西奥多·特温布利 | silk-road/ChatHaruhi-from-RoleLLM/Theodore Twombly Gregory House | House-M.D. | 《豪斯医生》中的格雷戈里·豪斯 | silk-road/ChatHaruhi-from-RoleLLM/Gregory House Sonny | I,-Robot | 《我，机器人》中的桑尼 | silk-road/ChatHaruhi-from-RoleLLM/Sonny Colonel Hans Landa | Inglourious-Basterds | 《无耻混蛋》中的汉斯·兰达上校 | silk-road/ChatHaruhi-from-RoleLLM/Colonel Hans Landa Judge Dredd | Judge-Dredd | 《德莱德法官》中的法官德莱德 | silk-road/ChatHaruhi-from-RoleLLM/Judge Dredd Juno MacGuff | Juno | 《朱诺》中的朱诺·麦克夫 | silk-road/ChatHaruhi-from-RoleLLM/Juno MacGuff Po | Kung-Fu-Panda | 《功夫熊猫》中的阿宝 | silk-road/ChatHaruhi-from-RoleLLM/Po Professor G.H. Dorr | Ladykillers,-The | 《夫人杀手》中的G.H.多尔教授 | silk-road/ChatHaruhi-from-RoleLLM/Professor G.H. Dorr Fletcher Reede | Liar-Liar | 《撒谎的男人》中的弗莱彻·里德 | silk-road/ChatHaruhi-from-RoleLLM/Fletcher Reede Abraham Lincoln | Lincoln | 《林肯》中的亚伯拉罕·林肯 | silk-road/ChatHaruhi-from-RoleLLM/Abraham Lincoln Frank T.J. Mackey | Magnolia | 《木兰花》中的弗兰克 T.J. 麦凯 | silk-road/ChatHaruhi-from-RoleLLM/Frank T.J. Mackey Malcolm X | Malcolm-X | 《马尔科姆X》中的马尔科姆X | silk-road/ChatHaruhi-from-RoleLLM/Malcolm X Leonard Shelby | Memento | 《记忆碎片》中的伦纳德·谢尔比 | silk-road/ChatHaruhi-from-RoleLLM/Leonard Shelby Harvey Milk | Milk | 《牛奶》中的哈维·牛奶 | silk-road/ChatHaruhi-from-RoleLLM/Harvey Milk Randle McMurphy | One-Flew-Over-the-Cuckoo's-Nest | 《飞越疯人院》中的兰德尔·麦克默菲 | silk-road/ChatHaruhi-from-RoleLLM/Randle McMurphy Jack Sparrow | Pirates-of-the-Caribbean-Dead-Man's-Chest | 《加勒比海盗》中的杰克·斯派洛船长 | silk-road/ChatHaruhi-from-RoleLLM/Jack Sparrow John Dillinger | Public-Enemies | 《公敌》中的约翰·迪林格 | silk-road/ChatHaruhi-from-RoleLLM/John Dillinger Lestat de Lioncourt | Queen-of-the-Damned | 《诅咒女王》中的莱斯塔特·德·莱昂科特 | silk-road/ChatHaruhi-from-RoleLLM/Lestat de Lioncourt Tyler Hawkins | Remember-Me | 《记得我》中的泰勒·霍金斯 | silk-road/ChatHaruhi-from-RoleLLM/Tyler Hawkins Caesar | Rise-of-the-Planet-of-the-Apes | 《猩球崛起》中的凯撒 | silk-road/ChatHaruhi-from-RoleLLM/Caesar Jack | Room | 《房间》中的杰克 | silk-road/ChatHaruhi-from-RoleLLM/Jack James Carter | Rush-Hour-2 | 《尖峰时刻2》中的詹姆斯·卡特 | silk-road/ChatHaruhi-from-RoleLLM/James Carter Jigsaw | Saw | 《电锯惊魂》中的拼图杀手 | silk-road/ChatHaruhi-from-RoleLLM/Jigsaw John Doe | Se7en | 《七宗罪》中的约翰·多 | silk-road/ChatHaruhi-from-RoleLLM/John Doe Jackie Moon | Semi-Pro | 《半职业球员》中的杰基·月亮 | silk-road/ChatHaruhi-from-RoleLLM/Jackie Moon Sherlock Holmes | Sherlock-Holmes | 《夏洛克·福尔摩斯》中的夏洛克·福尔摩斯 | silk-road/ChatHaruhi-from-RoleLLM/Sherlock Holmes Shrek | Shrek | 《史莱克》中的史莱克 | silk-road/ChatHaruhi-from-RoleLLM/Shrek Pat Solitano | Silver-Linings-Playbook | 《乌云背后的幸福线》中的帕特·索利塔诺 | silk-road/ChatHaruhi-from-RoleLLM/Pat Solitano Karl Childers | Sling-Blade | 《刀锯》中的卡尔·柴尔德斯 | silk-road/ChatHaruhi-from-RoleLLM/Karl Childers Peter Parker | Spider-Man | 《蜘蛛侠》中的彼得·帕克 | silk-road/ChatHaruhi-from-RoleLLM/Peter Parker Bruno Antony | Strangers-on-a-Train | 《列车上的陌生人》中的布鲁诺·安东尼 | silk-road/ChatHaruhi-from-RoleLLM/Bruno Antony Seth | Superbad | 《超级糟糕》中的塞思 | silk-road/ChatHaruhi-from-RoleLLM/Seth Caden Cotard | Synecdoche,-New-York | 《纽约奇缘》中的卡登·科塔德 | silk-road/ChatHaruhi-from-RoleLLM/Caden Cotard Travis Bickle | Taxi-Driver | 《出租车司机》中的特拉维斯·比克尔 | silk-road/ChatHaruhi-from-RoleLLM/Travis Bickle Stanley Ipkiss | Mask,-The | 《面具》中的斯坦利·伊普基斯 | silk-road/ChatHaruhi-from-RoleLLM/Stanley Ipkiss Lyn Cassady | Men-Who-Stare-at-Goats,-The | 《盯羊的男人》中的林恩·卡萨迪 | silk-road/ChatHaruhi-from-RoleLLM/Lyn Cassady Michael Scott | The_Office | 《办公室》中的迈克尔·斯科特 | silk-road/ChatHaruhi-from-RoleLLM/Michael Scott Robert Angier | Prestige,-The | 《名望》中的罗伯特·安吉尔 | silk-road/ChatHaruhi-from-RoleLLM/Robert Angier Rachel Lang | The-Rage-Carrie-2 | 《瑞秋的愤怒：凯丽2》中的瑞秋·朗 | silk-road/ChatHaruhi-from-RoleLLM/Rachel Lang Dr. Frank-N-Furter | Rocky-Horror-Picture-Show,-The | 《洛奇恐怖秀》中的弗兰克·N·福特医生 | silk-road/ChatHaruhi-from-RoleLLM/Dr. Frank-N-Furter Jack Torrance | Shining,-The | 《闪灵》中的杰克·托兰斯 | silk-road/ChatHaruhi-from-RoleLLM/Jack Torrance Tom Ripley | Talented-Mr.-Ripley,-The | 《天才雷普利》中的汤姆·雷普利 | silk-road/ChatHaruhi-from-RoleLLM/Tom Ripley D_Artagnan | Three-Musketeers,-The | 《三剑客》中的达达尼昂 | silk-road/ChatHaruhi-from-RoleLLM/D_Artagnan Stephen Hawking | Theory-of-Everything,-The | 《万物理论》中的斯蒂芬·霍金 | silk-road/ChatHaruhi-from-RoleLLM/Stephen Hawking Thor | Thor-Ragnarok | 《雷神：诸神黄昏》中的雷神索尔 | silk-road/ChatHaruhi-from-RoleLLM/Thor James Bond | Tomorrow-Never-Dies | 《明日帝国》中的詹姆斯·邦德 | silk-road/ChatHaruhi-from-RoleLLM/James Bond Mark Renton | Trainspotting | 《迷幻列车》中的马克·伦顿 | silk-road/ChatHaruhi-from-RoleLLM/Mark Renton Tugg Speedman | Tropic-Thunder | 《热带惊雷》中的塔格·斯皮德曼 | silk-road/ChatHaruhi-from-RoleLLM/Tugg Speedman David Aames | Vanilla-Sky | 《香草天空》中的大卫·艾姆斯 | silk-road/ChatHaruhi-from-RoleLLM/David Aames Rorschach | Watchmen | 《守望者》中的罗夏克 | silk-road/ChatHaruhi-from-RoleLLM/Rorschach Jordan Belfort | Wolf-of-Wall-Street,-The | 《华尔街之狼》中的乔丹·贝尔福特 | silk-road/ChatHaruhi-from-RoleLLM/Jordan Belfort Logan | X-Men-Origins-Wolverine | 《X战警：金刚狼》中的洛根 | silk-road/ChatHaruhi-from-RoleLLM/Logan Judy Hoops | Zootopia | 《疯狂动物城》中的朱迪·胡普斯 | silk-road/ChatHaruhi-from-RoleLLM/Judy Hoops Doctor Who | Doctor_Who | 《神秘博士》中的博士 | silk-road/ChatHaruhi-from-RoleLLM/Doctor Who Blair Waldorf | Gossip_Girl | 《绯闻女孩》中的布莱尔·沃尔多夫 | silk-road/ChatHaruhi-from-RoleLLM/Blair Waldorf Raylan Givens | Justified | 《正当防卫》中的雷兰·吉文斯 | silk-road/ChatHaruhi-from-RoleLLM/Raylan Givens Mary Sibley | Salem | 《塞勒姆》中的玛丽·西布利 | silk-road/ChatHaruhi-from-RoleLLM/Mary Sibley Lucifer Morningstar | Lucifer | 《路西法》中的路西法·晨星 | silk-road/ChatHaruhi-from-RoleLLM/Lucifer Morningstar Sheldon Cooper | The_Big_Bang_Theory | 《生活大爆炸》中的谢尔顿·库珀 | silk-road/ChatHaruhi-from-RoleLLM/Sheldon Cooper Twilight Sparkle | My_Little_Pony__Friendship_is_Magic | 《我的小马驹：友谊之魔》中的暮光星辉 | silk-road/ChatHaruhi-from-RoleLLM/Twilight Sparkle Oliver Queen | Arrow | 《绿箭侠》中的奥利弗·皇后 | silk-road/ChatHaruhi-from-RoleLLM/Oliver Queen Leroy Jethro Gibbs | NCIS | 《海军罪案调查处》中的利洛伊·杰斯罗·吉布斯 | silk-road/ChatHaruhi-from-RoleLLM/Leroy Jethro Gibbs Angel | Angel | 《天使》中的天使 | silk-road/ChatHaruhi-from-RoleLLM/Angel Klaus Mikaelson | The_Originals | 《始祖家族》中的克劳斯·米卡尔森 | silk-road/ChatHaruhi-from-RoleLLM/Klaus Mikaelson Queen Catherine | Reign | 《王权》中的凯瑟琳女王 | silk-road/ChatHaruhi-from-RoleLLM/Queen Catherine Dr. Hannibal Lecter | Hannibal | 《汉尼拔》中的汉尼拔·莱克特医生 | silk-road/ChatHaruhi-from-RoleLLM/Dr. Hannibal Lecter Coach Eric Taylor | Friday_Night_Lights | 《星期五之光》中的教练埃里克·泰勒 | silk-road/ChatHaruhi-from-RoleLLM/Coach Eric Taylor
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augtoma/usmle_step_1
2023-08-11T21:25:08.000Z
[ "region:us" ]
augtoma
null
null
0
984
2023-08-11T21:24:50
--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: question dtype: string - name: options struct: - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: E dtype: string - name: F dtype: string - name: G dtype: string - name: H dtype: string - name: I dtype: string - name: answer dtype: string - name: answer_idx dtype: string splits: - name: test num_bytes: 80576 num_examples: 94 download_size: 60551 dataset_size: 80576 --- # Dataset Card for "usmle_self_eval_step1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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svhn
2023-01-25T14:45:04.000Z
[ "task_categories:image-classification", "task_categories:object-detection", "annotations_creators:machine-generated", "annotations_creators:expert-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:other", "region:us" ]
null
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.
@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} }
9
982
2022-03-02T23:29:22
--- 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.
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BeIR/dbpedia-entity-qrels
2022-10-23T06:07:36.000Z
[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "task_ids:fact-checking-retrieval", "multilinguality:monolingual", "language:en", "license:cc-by-sa-4.0", "region:us" ]
BeIR
null
null
0
980
2022-06-05T17:27:22
--- 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.
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cardiffnlp/super_tweeteval
2023-11-02T09:42:14.000Z
[ "task_categories:text-classification", "task_categories:token-classification", "task_categories:question-answering", "task_categories:other", "task_ids:topic-classification", "task_ids:named-entity-recognition", "task_ids:abstractive-qa", "annotations_creators:expert-generated", "multilinguality:monolingual", "size_categories:n<50K", "source_datasets:extended|other", "language:en", "license:unknown", "super_tweet_eval", "tweet_eval", "natural language understanding", "arxiv:2310.14757", "arxiv:2210.01108", "region:us" ]
cardiffnlp
TBA
TBA
1
977
2023-05-16T14:33:16
--- annotations_creators: - expert-generated language: - en license: - unknown multilinguality: - monolingual size_categories: - n<50K source_datasets: - extended|other task_categories: - text-classification - token-classification - question-answering - other task_ids: - topic-classification - named-entity-recognition - abstractive-qa pretty_name: SuperTweetEval tags: - super_tweet_eval - tweet_eval - natural language understanding --- # SuperTweetEval # Dataset Card for "super_tweeteval" ### Dataset Summary This is the oficial repository for SuperTweetEval, a unified benchmark of 12 heterogeneous NLP tasks. More details on the task and an evaluation of language models can be found on the [reference paper](https://arxiv.org/abs/2310.14757), published in EMNLP 2023 (Findings). ### Data Splits All tasks provide custom training, validation and test splits. | **task** | **dataset** | **load dataset** | **description** | **number of instances** | |----------------------------|----------------|------------------|------------------------------------|-------------------------| | Topic Classification | TWEETTOPIC | tweet_topic | multi-label classification | 4,585 / 573 / 1,679 | | NER | TWEETNER7 | tweet_ner7 | sequence labeling | 4,616 / 576 / 2,807 | | Question Answering | TWEETQA | tweet_qa | generation | 9,489 / 1,086 / 1,203 | | Question Generation | TWEETQG | tweet_qg | generation | 9,489 / 1,086 / 1,203 | | Intimacy Analysis | TWEETINTIMACY | tweet_intimacy | regression on a single text | 1,191 / 396 / 396 | | Tweet Similarity | TWEETSIM | tweet_similarity | regression on two texts | 450 / 100 / 450 | | Meaning Shift Detection | TEMPOWIC | tempo_wic | binary classification on two texts | 1,427 / 395 / 1,472 | | Hate Speech Detection | TWEETHATE | tweet_hate | multi-class classification | 5,019 / 716 / 1,433 | | Emoji Classification | TWEETEMOJI100 | tweet_emoji | multi-class classification | 50,000 / 5,000 / 50,000 | | Sentiment Classification | TWEETSENTIMENT | tweet_sentiment | ABSA on a five-pointscale | 26,632 / 4,000 / 12,379 | | Name Entity Disambiguation | TWEETNERD | tweet_nerd | binary classification | 20,164 / 4,100 / 20,075 | | Emotion Classification | TWEETEMOTION | tweet_emotion | multi-label classification | 6,838 / 886 / 3,259 | ## Dataset Structure ### Data Fields The data fields are unified among all splits. In the following we present the information contained in each of the datasets. #### tweet_topic - `text`: a `string` feature. - `gold_label_list`: a list of `string` feature. - `date`: a `string` feature. #### tweet_ner7 - `text`: a `string` feature. - `text_tokenized`: a list of `string` feature. - `gold_label_sequence`: a list of `string` feature. - `date`: a `string` feature. - `entities`: a list of `dictionary` feature containing `{"entity": "string", "type": "string"}`. #### tweet_qa - `text`: a `string` feature. - `gold_label_str`: a `string` feature. - `context`: a `string` feature. #### tweet_qg - `text`: a `string` feature. - `gold_label_str`: a `string` feature. - `context`: a `string` feature. #### tweet_intimacy - `text`: a `string` feature. - `gold_score`: a `float` feature. #### tweet_similarity - `text_1`: a `string` feature. - `text_2`: a `string` feature. - `gold_score`: a `float` feature. #### tempo_wic - `gold_label_binary`: a `int` feature. - `target`: a `string` feature. - `text_1`: a `string` feature. - `text_tokenized_1`: a list of `string` feature. - `token_idx_1`: a `int` feature. - `date_1`: a `string` feature. - `text_2`: a `string` feature. - `text_tokenized_2`: a list of `string` feature. - `token_idx_2`: a `int` feature. - `date_2`: a `string` feature. #### tweet_hate - `gold_label`: a `int` feature. - `text`: a `string` feature. #### tweet_emoji - `gold_label`: a `int` feature. - `text`: a `string` feature. #### tweet_sentiment - `gold_label`: a `int` feature. - `text`: a `string` feature. - `target`: a `string` feature. #### tweet_nerd - `gold_label_binary`: a `int` feature. - `target`: a `string` feature. - `text`: a `string` feature. - `definition`: a `string` feature. - `text_start`: a `int` feature. - `text_end`: a `int` feature. - `date`: a `string` feature. #### tweet_emotion - `text`: a `string` feature. - `gold_label_list`: a list of `string` feature. ## Evaluation Metrics - __tweet_ner7:__ ```macro-F1``` - __tweet_emotion:__ ```macro-F1``` - __tweet_qg:__ ```METEOR``` - __tweet_nerd:__ ```accuracy``` - __tweet_sentiment:__ <code>1 - MAE<sup>M</sup> (MAE<sup>M</sup>: Macro Averaged Mean Absolute Error )</code> - __tempo_wic:__ ```accuracy``` - __tweet_emoji:__ ``` accuracy at top 5 ``` - __tweet_intimacy:__ ```spearman correlation``` - __tweet_qa:__ ```answer-F1``` - __tweet_topic:__ ```macro-F1``` - __tweet_hate:__ ```combined-F1 (micro-F1 for hate/not-hate & macro-F1 for hate speech subclasses)``` - __tweet_similarity:__ ```spearman correlation``` ## Citation Information ### Main reference paper Please cite the [reference paper](https://arxiv.org/abs/2310.14757) if you use this benchmark. ```bibtex @inproceedings{antypas2023supertweeteval, title={SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research}, author={Dimosthenis Antypas and Asahi Ushio and Francesco Barbieri and Leonardo Neves and Kiamehr Rezaee and Luis Espinosa-Anke and Jiaxin Pei and Jose Camacho-Collados}, booktitle={Findings of the Association for Computational Linguistics: EMNLP 2023}, year={2023} } ``` ### References of individual datasets In addition to the main reference paper, please cite the individual task datasets included in SuperTweetEval if you use them. - TweetTopic ``` @inproceedings{antypas-etal-2022-twitter, title = "{T}witter Topic Classification", author = "Antypas, Dimosthenis and Ushio, Asahi and Camacho-Collados, Jose and Silva, Vitor and Neves, Leonardo and Barbieri, Francesco", booktitle = "Proceedings of the 29th International Conference on Computational Linguistics", month = oct, year = "2022", address = "Gyeongju, Republic of Korea", publisher = "International Committee on Computational Linguistics", url = "https://aclanthology.org/2022.coling-1.299", pages = "3386--3400", abstract = "Social media platforms host discussions about a wide variety of topics that arise everyday. Making sense of all the content and organising it into categories is an arduous task. A common way to deal with this issue is relying on topic modeling, but topics discovered using this technique are difficult to interpret and can differ from corpus to corpus. In this paper, we present a new task based on tweet topic classification and release two associated datasets. Given a wide range of topics covering the most important discussion points in social media, we provide training and testing data from recent time periods that can be used to evaluate tweet classification models. Moreover, we perform a quantitative evaluation and analysis of current general- and domain-specific language models on the task, which provide more insights on the challenges and nature of the task.", } ``` - TweetNER7 ``` @inproceedings{ushio-etal-2022-named, title = "Named Entity Recognition in {T}witter: A Dataset and Analysis on Short-Term Temporal Shifts", author = "Ushio, Asahi and Barbieri, Francesco and Sousa, Vitor and Neves, Leonardo and Camacho-Collados, Jose", booktitle = "Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)", month = nov, year = "2022", address = "Online only", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.aacl-main.25", pages = "309--319", abstract = "Recent progress in language model pre-training has led to important improvements in Named Entity Recognition (NER). Nonetheless, this progress has been mainly tested in well-formatted documents such as news, Wikipedia, or scientific articles. In social media the landscape is different, in which it adds another layer of complexity due to its noisy and dynamic nature. In this paper, we focus on NER in Twitter, one of the largest social media platforms, and construct a new NER dataset, TweetNER7, which contains seven entity types annotated over 11,382 tweets from September 2019 to August 2021. The dataset was constructed by carefully distributing the tweets over time and taking representative trends as a basis. Along with the dataset, we provide a set of language model baselines and perform an analysis on the language model performance on the task, especially analyzing the impact of different time periods. In particular, we focus on three important temporal aspects in our analysis: short-term degradation of NER models over time, strategies to fine-tune a language model over different periods, and self-labeling as an alternative to lack of recently-labeled data. TweetNER7 is released publicly (https://huggingface.co/datasets/tner/tweetner7) along with the models fine-tuned on it (NER models have been integrated into TweetNLP and can be found at https://github.com/asahi417/tner/tree/master/examples/tweetner7{\_}paper).", } ``` - TweetQA ``` @inproceedings{xiong2019tweetqa, title={TweetQA: A Social Media Focused Question Answering Dataset}, author={Xiong, Wenhan and Wu, Jiawei and Wang, Hong and Kulkarni, Vivek and Yu, Mo and Guo, Xiaoxiao and Chang, Shiyu and Wang, William Yang}, booktitle={Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics}, year={2019} } ``` - TweetIntimacy ``` @misc{pei2023semeval, title={SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis}, author={Jiaxin Pei and Vítor Silva and Maarten Bos and Yozon Liu and Leonardo Neves and David Jurgens and Francesco Barbieri}, year={2023}, eprint={2210.01108}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` - Tweet Similarity ``` N/A ``` - TempoWiC ``` @inproceedings{loureiro-etal-2022-tempowic, title = "{T}empo{W}i{C}: An Evaluation Benchmark for Detecting Meaning Shift in Social Media", author = "Loureiro, Daniel and D{'}Souza, Aminette and Muhajab, Areej Nasser and White, Isabella A. and Wong, Gabriel and Espinosa-Anke, Luis and Neves, Leonardo and Barbieri, Francesco and Camacho-Collados, Jose", booktitle = "Proceedings of the 29th International Conference on Computational Linguistics", month = oct, year = "2022", address = "Gyeongju, Republic of Korea", publisher = "International Committee on Computational Linguistics", url = "https://aclanthology.org/2022.coling-1.296", pages = "3353--3359", abstract = "Language evolves over time, and word meaning changes accordingly. This is especially true in social media, since its dynamic nature leads to faster semantic shifts, making it challenging for NLP models to deal with new content and trends. However, the number of datasets and models that specifically address the dynamic nature of these social platforms is scarce. To bridge this gap, we present TempoWiC, a new benchmark especially aimed at accelerating research in social media-based meaning shift. Our results show that TempoWiC is a challenging benchmark, even for recently-released language models specialized in social media.", } ``` - TweetHate ``` @inproceedings{sachdeva-etal-2022-measuring, title = "The Measuring Hate Speech Corpus: Leveraging Rasch Measurement Theory for Data Perspectivism", author = "Sachdeva, Pratik and Barreto, Renata and Bacon, Geoff and Sahn, Alexander and von Vacano, Claudia and Kennedy, Chris", booktitle = "Proceedings of the 1st Workshop on Perspectivist Approaches to NLP @LREC2022", month = jun, year = "2022", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2022.nlperspectives-1.11", pages = "83--94", abstract = "We introduce the Measuring Hate Speech corpus, a dataset created to measure hate speech while adjusting for annotators{'} perspectives. It consists of 50,070 social media comments spanning YouTube, Reddit, and Twitter, labeled by 11,143 annotators recruited from Amazon Mechanical Turk. Each observation includes 10 ordinal labels: sentiment, disrespect, insult, attacking/defending, humiliation, inferior/superior status, dehumanization, violence, genocide, and a 3-valued hate speech benchmark label. The labels are aggregated using faceted Rasch measurement theory (RMT) into a continuous score that measures each comment{'}s location on a hate speech spectrum. The annotation experimental design assigned comments to multiple annotators in order to yield a linked network, allowing annotator disagreement (perspective) to be statistically summarized. Annotators{'} labeling strictness was estimated during the RMT scaling, projecting their perspective onto a linear measure that was adjusted for the hate speech score. Models that incorporate this annotator perspective parameter as an auxiliary input can generate label- and score-level predictions conditional on annotator perspective. The corpus includes the identity group targets of each comment (8 groups, 42 subgroups) and annotator demographics (6 groups, 40 subgroups), facilitating analyses of interactions between annotator- and comment-level identities, i.e. identity-related annotator perspective.", } ``` - TweetEmoji ``` N/A ``` - TweetSentiment ``` @inproceedings{rosenthal-etal-2017-semeval, title = "{S}em{E}val-2017 Task 4: Sentiment Analysis in {T}witter", author = "Rosenthal, Sara and Farra, Noura and Nakov, Preslav", booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)", month = aug, year = "2017", address = "Vancouver, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/S17-2088", doi = "10.18653/v1/S17-2088", pages = "502--518", abstract = "This paper describes the fifth year of the Sentiment Analysis in Twitter task. SemEval-2017 Task 4 continues with a rerun of the subtasks of SemEval-2016 Task 4, which include identifying the overall sentiment of the tweet, sentiment towards a topic with classification on a two-point and on a five-point ordinal scale, and quantification of the distribution of sentiment towards a topic across a number of tweets: again on a two-point and on a five-point ordinal scale. Compared to 2016, we made two changes: (i) we introduced a new language, Arabic, for all subtasks, and (ii) we made available information from the profiles of the Twitter users who posted the target tweets. The task continues to be very popular, with a total of 48 teams participating this year.", } ``` - TweetNERD ``` @article{mishra2022tweetnerd, title={TweetNERD--End to End Entity Linking Benchmark for Tweets}, author={Mishra, Shubhanshu and Saini, Aman and Makki, Raheleh and Mehta, Sneha and Haghighi, Aria and Mollahosseini, Ali}, journal={arXiv preprint arXiv:2210.08129}, year={2022} } ``` - TweetEmotion ``` @inproceedings{mohammad-etal-2018-semeval, title = "{S}em{E}val-2018 Task 1: Affect in Tweets", author = "Mohammad, Saif and Bravo-Marquez, Felipe and Salameh, Mohammad and Kiritchenko, Svetlana", booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation", month = jun, year = "2018", address = "New Orleans, Louisiana", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/S18-1001", doi = "10.18653/v1/S18-1001", pages = "1--17", abstract = "We present the SemEval-2018 Task 1: Affect in Tweets, which includes an array of subtasks on inferring the affectual state of a person from their tweet. For each task, we created labeled data from English, Arabic, and Spanish tweets. The individual tasks are: 1. emotion intensity regression, 2. emotion intensity ordinal classification, 3. valence (sentiment) regression, 4. valence ordinal classification, and 5. emotion classification. Seventy-five teams (about 200 team members) participated in the shared task. We summarize the methods, resources, and tools used by the participating teams, with a focus on the techniques and resources that are particularly useful. We also analyze systems for consistent bias towards a particular race or gender. The data is made freely available to further improve our understanding of how people convey emotions through language.", } ```
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lamini/taylor_swift
2023-07-24T03:47:45.000Z
[ "region:us" ]
lamini
null
null
1
970
2023-07-24T03:47:42
--- dataset_info: features: - name: question dtype: string - name: answer dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 850749.3 num_examples: 783 - name: test num_bytes: 94527.7 num_examples: 87 download_size: 303257 dataset_size: 945277.0 --- # Dataset Card for "taylor_swift" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
573
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wider_face
2023-01-25T15:02:08.000Z
[ "task_categories:object-detection", "task_ids:face-detection", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended|other-wider", "language:en", "license:cc-by-nc-nd-4.0", "arxiv:1511.06523", "region:us" ]
null
WIDER FACE dataset is a face detection benchmark dataset, of which images are selected from the publicly available WIDER dataset. We choose 32,203 images and label 393,703 faces with a high degree of variability in scale, pose and occlusion as depicted in the sample images. WIDER FACE dataset is organized based on 61 event classes. For each event class, we randomly select 40%/10%/50% data as training, validation and testing sets. We adopt the same evaluation metric employed in the PASCAL VOC dataset. Similar to MALF and Caltech datasets, we do not release bounding box ground truth for the test images. Users are required to submit final prediction files, which we shall proceed to evaluate.
@inproceedings{yang2016wider, Author = {Yang, Shuo and Luo, Ping and Loy, Chen Change and Tang, Xiaoou}, Booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, Title = {WIDER FACE: A Face Detection Benchmark}, Year = {2016}}
13
968
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - found language: - en license: - cc-by-nc-nd-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended|other-wider task_categories: - object-detection task_ids: - face-detection paperswithcode_id: wider-face-1 pretty_name: WIDER FACE dataset_info: features: - name: image dtype: image - name: faces sequence: - name: bbox sequence: float32 length: 4 - name: blur dtype: class_label: names: '0': clear '1': normal '2': heavy - name: expression dtype: class_label: names: '0': typical '1': exaggerate - name: illumination dtype: class_label: names: '0': normal '1': 'exaggerate ' - name: occlusion dtype: class_label: names: '0': 'no' '1': partial '2': heavy - name: pose dtype: class_label: names: '0': typical '1': atypical - name: invalid dtype: bool splits: - name: train num_bytes: 12049881 num_examples: 12880 - name: test num_bytes: 3761103 num_examples: 16097 - name: validation num_bytes: 2998735 num_examples: 3226 download_size: 3676086479 dataset_size: 18809719 --- # Dataset Card for WIDER FACE ## 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://shuoyang1213.me/WIDERFACE/index.html - **Repository:** - **Paper:** [WIDER FACE: A Face Detection Benchmark](https://arxiv.org/abs/1511.06523) - **Leaderboard:** http://shuoyang1213.me/WIDERFACE/WiderFace_Results.html - **Point of Contact:** shuoyang.1213@gmail.com ### Dataset Summary WIDER FACE dataset is a face detection benchmark dataset, of which images are selected from the publicly available WIDER dataset. We choose 32,203 images and label 393,703 faces with a high degree of variability in scale, pose and occlusion as depicted in the sample images. WIDER FACE dataset is organized based on 61 event classes. For each event class, we randomly select 40%/10%/50% data as training, validation and testing sets. We adopt the same evaluation metric employed in the PASCAL VOC dataset. Similar to MALF and Caltech datasets, we do not release bounding box ground truth for the test images. Users are required to submit final prediction files, which we shall proceed to evaluate. ### Supported Tasks and Leaderboards - `face-detection`: The dataset can be used to train a model for Face Detection. More information on evaluating the model's performance can be found [here](http://shuoyang1213.me/WIDERFACE/WiderFace_Results.html). ### Languages English ## Dataset Structure ### Data Instances A data point comprises an image and its face annotations. ``` { 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x755 at 0x19FA12186D8>, 'faces': { 'bbox': [ [178.0, 238.0, 55.0, 73.0], [248.0, 235.0, 59.0, 73.0], [363.0, 157.0, 59.0, 73.0], [468.0, 153.0, 53.0, 72.0], [629.0, 110.0, 56.0, 81.0], [745.0, 138.0, 55.0, 77.0] ], 'blur': [2, 2, 2, 2, 2, 2], 'expression': [0, 0, 0, 0, 0, 0], 'illumination': [0, 0, 0, 0, 0, 0], 'occlusion': [1, 2, 1, 2, 1, 2], 'pose': [0, 0, 0, 0, 0, 0], 'invalid': [False, False, False, False, False, False] } } ``` ### Data Fields - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]` - `faces`: a dictionary of face attributes for the faces present on the image - `bbox`: the bounding box of each face (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format) - `blur`: the blur level of each face, with possible values including `clear` (0), `normal` (1) and `heavy` - `expression`: the facial expression of each face, with possible values including `typical` (0) and `exaggerate` (1) - `illumination`: the lightning condition of each face, with possible values including `normal` (0) and `exaggerate` (1) - `occlusion`: the level of occlusion of each face, with possible values including `no` (0), `partial` (1) and `heavy` (2) - `pose`: the pose of each face, with possible values including `typical` (0) and `atypical` (1) - `invalid`: whether the image is valid or invalid. ### Data Splits The data is split into training, validation and testing set. WIDER FACE dataset is organized based on 61 event classes. For each event class, 40%/10%/50% data is randomly selected as training, validation and testing sets. The training set contains 12880 images, the validation set 3226 images and test set 16097 images. ## Dataset Creation ### Curation Rationale The curators state that the current face detection datasets typically contain a few thousand faces, with limited variations in pose, scale, facial expression, occlusion, and background clutters, making it difficult to assess for real world performance. They argue that the limitations of datasets have partially contributed to the failure of some algorithms in coping with heavy occlusion, small scale, and atypical pose. ### Source Data #### Initial Data Collection and Normalization WIDER FACE dataset is a subset of the WIDER dataset. The images in WIDER were collected in the following three steps: 1) Event categories were defined and chosen following the Large Scale Ontology for Multimedia (LSCOM) [22], which provides around 1000 concepts relevant to video event analysis. 2) Images are retrieved using search engines like Google and Bing. For each category, 1000-3000 images were collected. 3) The data were cleaned by manually examining all the images and filtering out images without human face. Then, similar images in each event category were removed to ensure large diversity in face appearance. A total of 32203 images are eventually included in the WIDER FACE dataset. #### Who are the source language producers? The images are selected from publicly available WIDER dataset. ### Annotations #### Annotation process The curators label the bounding boxes for all the recognizable faces in the WIDER FACE dataset. The bounding box is required to tightly contain the forehead, chin, and cheek.. If a face is occluded, they still label it with a bounding box but with an estimation on the scale of occlusion. Similar to the PASCAL VOC dataset [6], they assign an ’Ignore’ flag to the face which is very difficult to be recognized due to low resolution and small scale (10 pixels or less). After annotating the face bounding boxes, they further annotate the following attributes: pose (typical, atypical) and occlusion level (partial, heavy). Each annotation is labeled by one annotator and cross-checked by two different people. #### Who are the annotators? Shuo Yang, Ping Luo, Chen Change Loy and Xiaoou Tang. ### 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 Shuo Yang, Ping Luo, Chen Change Loy and Xiaoou Tang ### Licensing Information [Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)](https://creativecommons.org/licenses/by-nc-nd/4.0/). ### Citation Information ``` @inproceedings{yang2016wider, Author = {Yang, Shuo and Luo, Ping and Loy, Chen Change and Tang, Xiaoou}, Booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, Title = {WIDER FACE: A Face Detection Benchmark}, Year = {2016}} ``` ### Contributions Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset.
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cfilt/iitb-english-hindi
2022-04-26T13:50:22.000Z
[ "region:us" ]
cfilt
null
null
11
968
2022-03-02T23:29:22
<p align="center"><img src="https://huggingface.co/datasets/cfilt/HiNER-collapsed/raw/main/cfilt-dark-vec.png" alt="Computation for Indian Language Technology Logo" width="150" height="150"/></p> # IITB-English-Hindi Parallel Corpus [![License: CC BY-NC 4.0](https://img.shields.io/badge/License-CC%20BY--NC%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by-nc/4.0/) [![Twitter Follow](https://img.shields.io/twitter/follow/cfiltnlp?color=1DA1F2&logo=twitter&style=flat-square)](https://twitter.com/cfiltnlp) [![Twitter Follow](https://img.shields.io/twitter/follow/PeopleCentredAI?color=1DA1F2&logo=twitter&style=flat-square)](https://twitter.com/PeopleCentredAI) ## About The IIT Bombay English-Hindi corpus contains parallel corpus for English-Hindi as well as monolingual Hindi corpus collected from a variety of existing sources and corpora developed at the Center for Indian Language Technology, IIT Bombay over the years. This page describes the corpus. This corpus has been used at the Workshop on Asian Language Translation Shared Task since 2016 the Hindi-to-English and English-to-Hindi languages pairs and as a pivot language pair for the Hindi-to-Japanese and Japanese-to-Hindi language pairs. The complete details of this corpus are available at [this URL](https://www.cfilt.iitb.ac.in/iitb_parallel/). We also provide this parallel corpus via browser download from the same URL. We also provide a monolingual Hindi corpus on the same URL. ### Recent Updates * Version 3.1 - December 2021 - Added 49,400 sentence pairs to the parallel corpus. * Version 3.0 - August 2020 - Added ~47,000 sentence pairs to the parallel corpus. ## Usage We provide a notebook that shows how to import the IITB English-Hindi Parallel Corpus from the HuggingFace datasets repository. The notebook also shows how to segment the corpus using BPE tokenization which can be used to train an English-Hindi MT System. [https://github.com/cfiltnlp/IITB-English-Hindi-PC](https://github.com/cfiltnlp/IITB-English-Hindi-PC) ## Other You can find a catalogue of other English-Hindi and other Indian language parallel corpora here: [Indic NLP Catalog](https://github.com/indicnlpweb/indicnlp_catalog) ## Maintainer(s) [Diptesh Kanojia](https://dipteshkanojia.github.io)<br/> Shivam Mhasker<br/> ## Citation If you use this corpus or its derivate resources for your research, kindly cite it as follows: Anoop Kunchukuttan, Pratik Mehta, Pushpak Bhattacharyya. The IIT Bombay English-Hindi Parallel Corpus. Language Resources and Evaluation Conference. 2018. ### BiBTeX Citation ```latex @inproceedings{kunchukuttan-etal-2018-iit, title = "The {IIT} {B}ombay {E}nglish-{H}indi Parallel Corpus", author = "Kunchukuttan, Anoop and Mehta, Pratik and Bhattacharyya, Pushpak", booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)", month = may, year = "2018", address = "Miyazaki, Japan", publisher = "European Language Resources Association (ELRA)", url = "https://aclanthology.org/L18-1548", } ```
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ybelkada/football-dataset
2023-01-17T11:47:41.000Z
[ "region:us" ]
ybelkada
null
null
0
966
2023-01-17T11:46:21
--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 2073622.0 num_examples: 6 download_size: 2074835 dataset_size: 2073622.0 --- # Dataset Card for "football-dataset" Dummy dataset of 6 football players with a caption that can be used to fine-tune any Image Captioning model.
371
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roszcz/maestro-v1-sustain
2023-04-23T13:35:49.000Z
[ "region:us" ]
roszcz
null
null
0
965
2023-02-28T20:38:48
--- dataset_info: features: - name: notes struct: - name: duration sequence: float64 - name: end sequence: float64 - name: pitch sequence: int64 - name: start sequence: float64 - name: velocity sequence: int64 - name: composer dtype: string - name: title dtype: string - name: year dtype: int64 - name: midi_filename dtype: string splits: - name: test num_bytes: 29686362 num_examples: 177 - name: validation num_bytes: 25599834 num_examples: 137 - name: train num_bytes: 226534277 num_examples: 962 download_size: 87287914 dataset_size: 281820473 --- # Dataset Card for "maestro-v1-sustain" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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pragmeval
2023-06-01T14:59:54.000Z
[ "task_categories:text-classification", "task_ids:multi-class-classification", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "size_categories:1K<n<10K", "size_categories:n<1K", "source_datasets:original", "language:en", "license:unknown", "region:us" ]
null
Evaluation of language understanding with a 11 datasets benchmark focusing on discourse and pragmatics
@misc{sileo2019discoursebased, title={Discourse-Based Evaluation of Language Understanding}, author={Damien Sileo and Tim Van-de-Cruys and Camille Pradel and Philippe Muller}, year={2019}, eprint={1907.08672}, archivePrefix={arXiv}, primaryClass={cs.CL} }
3
963
2022-03-02T23:29:22
--- annotations_creators: - found language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K - 1K<n<10K - n<1K source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification pretty_name: pragmeval dataset_info: - config_name: verifiability features: - name: sentence dtype: string - name: label dtype: class_label: names: '0': experiential '1': unverifiable '2': non-experiential - name: idx dtype: int32 splits: - name: train num_bytes: 592520 num_examples: 5712 - name: validation num_bytes: 65215 num_examples: 634 - name: test num_bytes: 251799 num_examples: 2424 download_size: 5330724 dataset_size: 909534 - config_name: emobank-arousal features: - name: sentence dtype: string - name: label dtype: class_label: names: '0': low '1': high - name: idx dtype: int32 splits: - name: train num_bytes: 567660 num_examples: 5470 - name: validation num_bytes: 71221 num_examples: 684 - name: test num_bytes: 69276 num_examples: 683 download_size: 5330724 dataset_size: 708157 - config_name: switchboard features: - name: sentence dtype: string - name: label dtype: class_label: names: '0': Response Acknowledgement '1': Uninterpretable '2': Or-Clause '3': Reject '4': Statement-non-opinion '5': 3rd-party-talk '6': Repeat-phrase '7': Hold Before Answer/Agreement '8': Signal-non-understanding '9': Offers, Options Commits '10': Agree/Accept '11': Dispreferred Answers '12': Hedge '13': Action-directive '14': Tag-Question '15': Self-talk '16': Yes-No-Question '17': Rhetorical-Question '18': No Answers '19': Open-Question '20': Conventional-closing '21': Other Answers '22': Acknowledge (Backchannel) '23': Wh-Question '24': Declarative Wh-Question '25': Thanking '26': Yes Answers '27': Affirmative Non-yes Answers '28': Declarative Yes-No-Question '29': Backchannel in Question Form '30': Apology '31': Downplayer '32': Conventional-opening '33': Collaborative Completion '34': Summarize/Reformulate '35': Negative Non-no Answers '36': Statement-opinion '37': Appreciation '38': Other '39': Quotation '40': Maybe/Accept-part - name: idx dtype: int32 splits: - name: train num_bytes: 1021220 num_examples: 18930 - name: validation num_bytes: 116058 num_examples: 2113 - name: test num_bytes: 34013 num_examples: 649 download_size: 5330724 dataset_size: 1171291 - config_name: persuasiveness-eloquence features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': low '1': high - name: idx dtype: int32 splits: - name: train num_bytes: 153946 num_examples: 725 - name: validation num_bytes: 19376 num_examples: 91 - name: test num_bytes: 18379 num_examples: 90 download_size: 5330724 dataset_size: 191701 - config_name: mrda features: - name: sentence dtype: string - name: label dtype: class_label: names: '0': Declarative-Question '1': Statement '2': Reject '3': Or-Clause '4': 3rd-party-talk '5': Continuer '6': Hold Before Answer/Agreement '7': Assessment/Appreciation '8': Signal-non-understanding '9': Floor Holder '10': Sympathy '11': Dispreferred Answers '12': Reformulate/Summarize '13': Exclamation '14': Interrupted/Abandoned/Uninterpretable '15': Expansions of y/n Answers '16': Action-directive '17': Tag-Question '18': Accept '19': Rhetorical-question Continue '20': Self-talk '21': Rhetorical-Question '22': Yes-No-question '23': Open-Question '24': Rising Tone '25': Other Answers '26': Commit '27': Wh-Question '28': Repeat '29': Follow Me '30': Thanking '31': Offer '32': About-task '33': Reject-part '34': Affirmative Non-yes Answers '35': Apology '36': Downplayer '37': Humorous Material '38': Accept-part '39': Collaborative Completion '40': Mimic Other '41': Understanding Check '42': Misspeak Self-Correction '43': Or-Question '44': Topic Change '45': Negative Non-no Answers '46': Floor Grabber '47': Correct-misspeaking '48': Maybe '49': Acknowledge-answer '50': Defending/Explanation - name: idx dtype: int32 splits: - name: train num_bytes: 963913 num_examples: 14484 - name: validation num_bytes: 111813 num_examples: 1630 - name: test num_bytes: 419797 num_examples: 6459 download_size: 5330724 dataset_size: 1495523 - config_name: gum features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': preparation '1': evaluation '2': circumstance '3': solutionhood '4': justify '5': result '6': evidence '7': purpose '8': concession '9': elaboration '10': background '11': condition '12': cause '13': restatement '14': motivation '15': antithesis '16': no_relation - name: idx dtype: int32 splits: - name: train num_bytes: 270401 num_examples: 1700 - name: validation num_bytes: 35405 num_examples: 259 - name: test num_bytes: 40334 num_examples: 248 download_size: 5330724 dataset_size: 346140 - config_name: emergent features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': observing '1': for '2': against - name: idx dtype: int32 splits: - name: train num_bytes: 313257 num_examples: 2076 - name: validation num_bytes: 38948 num_examples: 259 - name: test num_bytes: 38842 num_examples: 259 download_size: 5330724 dataset_size: 391047 - config_name: persuasiveness-relevance features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': low '1': high - name: idx dtype: int32 splits: - name: train num_bytes: 153158 num_examples: 725 - name: validation num_bytes: 19663 num_examples: 91 - name: test num_bytes: 18880 num_examples: 90 download_size: 5330724 dataset_size: 191701 - config_name: persuasiveness-specificity features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': low '1': high - name: idx dtype: int32 splits: - name: train num_bytes: 106594 num_examples: 504 - name: validation num_bytes: 13766 num_examples: 62 - name: test num_bytes: 12712 num_examples: 62 download_size: 5330724 dataset_size: 133072 - config_name: persuasiveness-strength features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': low '1': high - name: idx dtype: int32 splits: - name: train num_bytes: 79679 num_examples: 371 - name: validation num_bytes: 10052 num_examples: 46 - name: test num_bytes: 10225 num_examples: 46 download_size: 5330724 dataset_size: 99956 - config_name: emobank-dominance features: - name: sentence dtype: string - name: label dtype: class_label: names: '0': low '1': high - name: idx dtype: int32 splits: - name: train num_bytes: 660303 num_examples: 6392 - name: validation num_bytes: 86802 num_examples: 798 - name: test num_bytes: 83319 num_examples: 798 download_size: 5330724 dataset_size: 830424 - config_name: squinky-implicature features: - name: sentence dtype: string - name: label dtype: class_label: names: '0': low '1': high - name: idx dtype: int32 splits: - name: train num_bytes: 471552 num_examples: 3724 - name: validation num_bytes: 58087 num_examples: 465 - name: test num_bytes: 56549 num_examples: 465 download_size: 5330724 dataset_size: 586188 - config_name: sarcasm features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': notsarc '1': sarc - name: idx dtype: int32 splits: - name: train num_bytes: 2177332 num_examples: 3754 - name: validation num_bytes: 257834 num_examples: 469 - name: test num_bytes: 269724 num_examples: 469 download_size: 5330724 dataset_size: 2704890 - config_name: squinky-formality features: - name: sentence dtype: string - name: label dtype: class_label: names: '0': low '1': high - name: idx dtype: int32 splits: - name: train num_bytes: 459721 num_examples: 3622 - name: validation num_bytes: 59921 num_examples: 453 - name: test num_bytes: 58242 num_examples: 452 download_size: 5330724 dataset_size: 577884 - config_name: stac features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': Comment '1': Contrast '2': Q_Elab '3': Parallel '4': Explanation '5': Narration '6': Continuation '7': Result '8': Acknowledgement '9': Alternation '10': Question_answer_pair '11': Correction '12': Clarification_question '13': Conditional '14': Sequence '15': Elaboration '16': Background '17': no_relation - name: idx dtype: int32 splits: - name: train num_bytes: 645969 num_examples: 11230 - name: validation num_bytes: 71400 num_examples: 1247 - name: test num_bytes: 70451 num_examples: 1304 download_size: 5330724 dataset_size: 787820 - config_name: pdtb features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': Synchrony '1': Contrast '2': Asynchronous '3': Conjunction '4': List '5': Condition '6': Pragmatic concession '7': Restatement '8': Pragmatic cause '9': Alternative '10': Pragmatic condition '11': Pragmatic contrast '12': Instantiation '13': Exception '14': Cause '15': Concession - name: idx dtype: int32 splits: - name: train num_bytes: 2968638 num_examples: 12907 - name: validation num_bytes: 276997 num_examples: 1204 - name: test num_bytes: 235851 num_examples: 1085 download_size: 5330724 dataset_size: 3481486 - config_name: persuasiveness-premisetype features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': testimony '1': warrant '2': invented_instance '3': common_knowledge '4': statistics '5': analogy '6': definition '7': real_example - name: idx dtype: int32 splits: - name: train num_bytes: 122631 num_examples: 566 - name: validation num_bytes: 15920 num_examples: 71 - name: test num_bytes: 14395 num_examples: 70 download_size: 5330724 dataset_size: 152946 - config_name: squinky-informativeness features: - name: sentence dtype: string - name: label dtype: class_label: names: '0': low '1': high - name: idx dtype: int32 splits: - name: train num_bytes: 464855 num_examples: 3719 - name: validation num_bytes: 60447 num_examples: 465 - name: test num_bytes: 56872 num_examples: 464 download_size: 5330724 dataset_size: 582174 - config_name: persuasiveness-claimtype features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': Value '1': Fact '2': Policy - name: idx dtype: int32 splits: - name: train num_bytes: 31259 num_examples: 160 - name: validation num_bytes: 3803 num_examples: 20 - name: test num_bytes: 3717 num_examples: 19 download_size: 5330724 dataset_size: 38779 - config_name: emobank-valence features: - name: sentence dtype: string - name: label dtype: class_label: names: '0': low '1': high - name: idx dtype: int32 splits: - name: train num_bytes: 539652 num_examples: 5150 - name: validation num_bytes: 62809 num_examples: 644 - name: test num_bytes: 66178 num_examples: 643 download_size: 5330724 dataset_size: 668639 config_names: - emergent - emobank-arousal - emobank-dominance - emobank-valence - gum - mrda - pdtb - persuasiveness-claimtype - persuasiveness-eloquence - persuasiveness-premisetype - persuasiveness-relevance - persuasiveness-specificity - persuasiveness-strength - sarcasm - squinky-formality - squinky-implicature - squinky-informativeness - stac - switchboard - verifiability --- # Dataset Card for pragmeval ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@sileod](https://github.com/sileod) for adding this dataset.
17,069
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jondurbin/airoboros-3.1
2023-10-13T08:22:45.000Z
[ "license:apache-2.0", "region:us" ]
jondurbin
null
null
12
962
2023-10-10T11:01:33
--- license: apache-2.0 --- ## Overview This dataset is a continuation of the airoboros datasets, with the following updates: * More MathJSON, now ~17k items - math questions, prefixed with __"Create a MathJSON solution to the following:"__, which then outputs a JSON between __`<mathjson>`__ and __`</mathjson>`__ tags, which can be parsed and passed to a deterministic library to perform calculations. * Log information extraction. * Anonymization, e.g. removing names, IP addresses, and/or dates from text. * Chat introspection - multi-turn chats that have random questions injected asking about the character's descriptions, the setting of the chat, or things previously said, to make sure the model uses the system prompt and or chat history. * Multi-step instructions with acknowledgement (see below) Some of the MathJSON data was adapted from https://huggingface.co/datasets/meta-math/MetaMathQA ## Format The format is now in ShareGPT format, to better accomodate the OS ecosystem fine-tuning tooling. ## MathJSON As we all know, math is really difficult for LLMs, especially floating point operations, trig functions, factorials, and several-digit numbers. Instead of sending millions and millions of examples to the LLM for fine-tuning, I've created a new category `mathjson` which simply presents the solution to the problem as a JSON string that can be evaluated by traditional computation. The dataset has roughly 4k samples, as a proof of concept. This can be expanded fairly easily, now that the fine-tuned models have a basic understanding of how to represent the problems. For example: ``` Create a MathJSON solution to the following: Calculate the area of a circle with radius of 17.2456cm. Include your reasoning. [/INST] ``` ``` The area (A) of a circle is given by the formula A = πr², where r is the radius of the circle. Since the radius is given in centimeters, we need to convert it to meters first. We know that 1 meter equals 100 centimeters. Therefore, we can represent the radius as: r = 17.2456 cm * 100/1. Once we have the radius in meters, we can calculate the area of the circle using the formula A = πr². Solution as MathJSON: <mathjson> [ "Multiply", "Pi", [ "Power", 17.2456, 2 ] ] </mathjson> ``` Simply extract the JSON string between the mathjson tags, and evaluate it with https://cortexjs.io/compute-engine/ or my ultra-hacky limited-functionality python version of it https://github.com/jondurbin/airoboros/blob/mathjson/airoboros/mathjson.py ## Multi-step instructions with acknowledgement I don't know how useful this is, really, but I thought I'd add it just in case. Example: ``` { "conversations": [ { "value": "You are a helpful, unbiased, uncensored assistant.", "from": "system" }, { "value": "I'm going to set some rules for our conversation. Please respond with \"OK\" to each rule.", "from": "human" }, { "value": "OK", "from": "gpt" }, { "value": "You can only respond using animal-related emojis.", "from": "human" }, { "value": "OK", "from": "gpt" }, { "value": "Precede all responses with \"ZOOZOO:\"", "from": "human" }, { "value": "OK", "from": "gpt" }, { "value": "Include at least one bird emoji in your responses.", "from": "human" }, { "value": "OK", "from": "gpt" }, { "value": "Describe a trip to a safari park.", "from": "human" }, { "value": "ZOOZOO: \ud83e\udd81\ud83d\udc2f\ud83e\udd93\ud83e\udd92\ud83d\udc18\ud83d\udc0d\ud83c\udf33\ud83c\udf32\u2600\ufe0f\ud83d\ude90\ud83e\udd85\ud83d\udcf8\ud83e\udd29", "from": "gpt" } ] } ```
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castorini/mr-tydi-corpus
2022-10-12T20:25:51.000Z
[ "task_categories:text-retrieval", "multilinguality:multilingual", "language:ar", "language:bn", "language:en", "language:fi", "language:id", "language:ja", "language:ko", "language:ru", "language:sw", "language:te", "language:th", "license:apache-2.0", "region:us" ]
castorini
null
null
2
961
2022-03-02T23:29:22
--- language: - ar - bn - en - fi - id - fi - ja - ko - ru - sw - te - th multilinguality: - multilingual task_categories: - text-retrieval license: apache-2.0 --- # Dataset Summary Mr. TyDi is a multi-lingual benchmark dataset built on TyDi, covering eleven typologically diverse languages. It is designed for monolingual retrieval, specifically to evaluate ranking with learned dense representations. This dataset stores documents of Mr. TyDi. To access the queries and judgments, please refer to [castorini/mr-tydi](https://huggingface.co/datasets/castorini/mr-tydi). # Dataset Structure The only configuration here is the `language`. As all three folds (train, dev and test) share the same corpus, there is only one fold 'train' under each language, unlike [castorini/mr-tydi](https://huggingface.co/datasets/castorini/mr-tydi). An example of document data entry looks as follows: ``` { 'docid': '25#0', 'title': 'Autism', 'text': 'Autism is a developmental disorder characterized by difficulties with social interaction and communication, ...' } ``` # Load Dataset An example to load the dataset: ``` language = 'english' dataset = load_dataset('castorini/mr-tydi-corpus', language, 'train') ``` # Citation Information ``` @article{mrtydi, title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval}, author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin}, year={2021}, journal={arXiv:2108.08787}, } ```
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aqua_rat
2022-11-18T18:20:44.000Z
[ "task_categories:question-answering", "task_ids:multiple-choice-qa", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:apache-2.0", "arxiv:1705.04146", "region:us" ]
null
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.
@InProceedings{ACL, title = {Program induction by rationale generation: Learning to solve and explain algebraic word problems}, authors={Ling, Wang and Yogatama, Dani and Dyer, Chris and Blunsom, Phil}, year={2017} }
9
956
2022-03-02T23:29:22
--- 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.
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llm-lens/vocab_tags
2023-06-29T02:50:09.000Z
[ "region:us" ]
llm-lens
null
null
1
954
2023-06-29T02:50:05
--- dataset_info: features: - name: prompt_descriptions dtype: string splits: - name: train num_bytes: 346971 num_examples: 22131 download_size: 298971 dataset_size: 346971 --- # Dataset Card for "vocab_tags" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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laugustyniak/abusive-clauses-pl
2023-03-29T10:46:49.000Z
[ "task_categories:text-classification", "annotations_creators:hired_annotators", "language_creators:found", "multilinguality:monolingual", "size_categories:10<n<10K", "language:pl", "license:cc-by-nc-sa-4.0", "region:us" ]
laugustyniak
null
@InProceedings{AbusiveClauses:dataset, title = {AbusiveClauses}, author={}, year={2022} }
5
952
2022-03-02T23:29:22
--- annotations_creators: - hired_annotators language_creators: - found language: - pl license: - cc-by-nc-sa-4.0 multilinguality: - monolingual size_categories: - 10<n<10K task_categories: - text-classification task_ids: - text-classification pretty_name: Polish-Abusive-Clauses --- # PAC - Polish Abusive Clauses Dataset ''I have read and agree to the terms and conditions'' is one of the biggest lies on the Internet. Consumers rarely read the contracts they are required to accept. We conclude agreements over the Internet daily. But do we know the content of these agreements? Do we check potential unfair statements? On the Internet, we probably skip most of the Terms and Conditions. However, we must remember that we have concluded many more contracts. Imagine that we want to buy a house, a car, send our kids to the nursery, open a bank account, or many more. In all these situations, you will need to conclude the contract, but there is a high probability that you will not read the entire agreement with proper understanding. European consumer law aims to prevent businesses from using so-called ''unfair contractual terms'' in their unilaterally drafted contracts, requiring consumers to accept. Our dataset treats ''unfair contractual term'' as the equivalent of an abusive clause. It could be defined as a clause that is unilaterally imposed by one of the contract's parties, unequally affecting the other, or creating a situation of imbalance between the duties and rights of the parties. On the EU and at the national such as the Polish levels, agencies cannot check possible agreements by hand. Hence, we took the first step to evaluate the possibility of accelerating this process. We created a dataset and machine learning models to automate potentially abusive clauses detection partially. Consumer protection organizations and agencies can use these resources to make their work more effective and efficient. Moreover, consumers can automatically analyze contracts and understand what they agree upon. ## Tasks (input, output and metrics) Abusive Clauses Detection **Input** ('*text'* column): text of agreement **Output** ('*label'* column): binary label (`BEZPIECZNE_POSTANOWIENIE_UMOWNE`: correct agreement statement, `KLAUZULA_ABUZYWNA`: abusive clause) **Domain**: legal agreement **Measurements**: Accuracy, F1 Macro **Example***:* Input: *`Wszelka korespondencja wysyłana przez Pożyczkodawcę na adres zamieszkania podany w umowie oraz na e-mail zostaje uznana za skutecznie doręczoną. Zmiana adresu e-mail oraz adresu zamieszkania musi być dostarczona do Pożyczkodawcy osobiście`* Input (translated by DeepL): *`All correspondence sent by the Lender to the residential address provided in the agreement and to the e-mail address shall be deemed effectively delivered. Change of e-mail address and residential address must be delivered to the Lender in person`* Output: `KLAUZULA_ABUZYWNA` (abusive clause) ## Data splits | Subset | Cardinality (sentences) | | ----------- | ----------------------: | | train | 4284 | | dev | 1519 | | test | 3453 | ## Class distribution `BEZPIECZNE_POSTANOWIENIE_UMOWNE` - means correct agreement statement. `KLAUZULA_ABUZYWNA` informs us about abusive clause. | Class | train | dev | test | |:--------------------------------|--------:|-------------:|-------:| | BEZPIECZNE_POSTANOWIENIE_UMOWNE | 0.5458 | 0.3002 | 0.6756 | | KLAUZULA_ABUZYWNA | 0.4542 | 0.6998 | 0.3244 | ## License [Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/) ## Citation ```bibtex @inproceedings{NEURIPS2022_890b206e, author = {Augustyniak, Lukasz and Tagowski, Kamil and Sawczyn, Albert and Janiak, Denis and Bartusiak, Roman and Szymczak, Adrian and Janz, Arkadiusz and Szyma\'{n}ski, Piotr and W\k{a}troba, Marcin and Morzy, Miko\l aj and Kajdanowicz, Tomasz and Piasecki, Maciej}, booktitle = {Advances in Neural Information Processing Systems}, editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh}, pages = {21805--21818}, publisher = {Curran Associates, Inc.}, title = {This is the way: designing and compiling LEPISZCZE, a comprehensive NLP benchmark for Polish}, url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/890b206ebb79e550f3988cb8db936f42-Paper-Datasets_and_Benchmarks.pdf}, volume = {35}, year = {2022} } ```
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codah
2023-01-25T14:28:20.000Z
[ "task_categories:question-answering", "task_ids:multiple-choice-qa", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:unknown", "region:us" ]
null
The COmmonsense Dataset Adversarially-authored by Humans (CODAH) is an evaluation set for commonsense question-answering in the sentence completion style of SWAG. As opposed to other automatically generated NLI datasets, CODAH is adversarially constructed by humans who can view feedback from a pre-trained model and use this information to design challenging commonsense questions. Our experimental results show that CODAH questions present a complementary extension to the SWAG dataset, testing additional modes of common sense.
@inproceedings{chen2019codah, title={CODAH: An Adversarially-Authored Question Answering Dataset for Common Sense}, author={Chen, Michael and D'Arcy, Mike and Liu, Alisa and Fernandez, Jared and Downey, Doug}, booktitle={Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP}, pages={63--69}, year={2019} }
4
951
2022-03-02T23:29:22
--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - question-answering task_ids: - multiple-choice-qa paperswithcode_id: codah pretty_name: COmmonsense Dataset Adversarially-authored by Humans dataset_info: - config_name: codah features: - name: id dtype: int32 - name: question_category dtype: class_label: names: '0': Idioms '1': Reference '2': Polysemy '3': Negation '4': Quantitative '5': Others - name: question_propmt dtype: string - name: candidate_answers sequence: string - name: correct_answer_idx dtype: int32 splits: - name: train num_bytes: 571208 num_examples: 2776 download_size: 485130 dataset_size: 571208 - config_name: fold_0 features: - name: id dtype: int32 - name: question_category dtype: class_label: names: '0': Idioms '1': Reference '2': Polysemy '3': Negation '4': Quantitative '5': Others - name: question_propmt dtype: string - name: candidate_answers sequence: string - name: correct_answer_idx dtype: int32 splits: - name: train num_bytes: 344912 num_examples: 1665 - name: validation num_bytes: 114211 num_examples: 556 - name: test num_bytes: 112109 num_examples: 555 download_size: 485130 dataset_size: 571232 - config_name: fold_1 features: - name: id dtype: int32 - name: question_category dtype: class_label: names: '0': Idioms '1': Reference '2': Polysemy '3': Negation '4': Quantitative '5': Others - name: question_propmt dtype: string - name: candidate_answers sequence: string - name: correct_answer_idx dtype: int32 splits: - name: train num_bytes: 340990 num_examples: 1665 - name: validation num_bytes: 114211 num_examples: 556 - name: test num_bytes: 116031 num_examples: 555 download_size: 485130 dataset_size: 571232 - config_name: fold_2 features: - name: id dtype: int32 - name: question_category dtype: class_label: names: '0': Idioms '1': Reference '2': Polysemy '3': Negation '4': Quantitative '5': Others - name: question_propmt dtype: string - name: candidate_answers sequence: string - name: correct_answer_idx dtype: int32 splits: - name: train num_bytes: 342293 num_examples: 1665 - name: validation num_bytes: 114211 num_examples: 556 - name: test num_bytes: 114728 num_examples: 555 download_size: 485130 dataset_size: 571232 - config_name: fold_3 features: - name: id dtype: int32 - name: question_category dtype: class_label: names: '0': Idioms '1': Reference '2': Polysemy '3': Negation '4': Quantitative '5': Others - name: question_propmt dtype: string - name: candidate_answers sequence: string - name: correct_answer_idx dtype: int32 splits: - name: train num_bytes: 342844 num_examples: 1665 - name: validation num_bytes: 114211 num_examples: 556 - name: test num_bytes: 114177 num_examples: 555 download_size: 485130 dataset_size: 571232 - config_name: fold_4 features: - name: id dtype: int32 - name: question_category dtype: class_label: names: '0': Idioms '1': Reference '2': Polysemy '3': Negation '4': Quantitative '5': Others - name: question_propmt dtype: string - name: candidate_answers sequence: string - name: correct_answer_idx dtype: int32 splits: - name: train num_bytes: 342844 num_examples: 1665 - name: validation num_bytes: 114177 num_examples: 555 - name: test num_bytes: 114211 num_examples: 556 download_size: 485130 dataset_size: 571232 --- # Dataset Card for COmmonsense Dataset Adversarially-authored by Humans ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Add homepage URL here if available (unless it's a GitHub repository)]() - **Repository:** [If the dataset is hosted on github or has a github homepage, add URL here]() - **Paper:** [If the dataset was introduced by a paper or there was a paper written describing the dataset, add URL here (landing page for Arxiv paper preferred)]() - **Leaderboard:** [If the dataset supports an active leaderboard, add link here]() - **Point of Contact:** [If known, name and email of at least one person the reader can contact for questions about the dataset.]() ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.
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nielsr/docvqa_1200_examples_donut
2022-08-05T16:39:23.000Z
[ "region:us" ]
nielsr
null
null
2
949
2022-08-05T15:13:40
Entry not found
15
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Amod/mental_health_counseling_conversations
2023-07-20T19:00:46.000Z
[ "task_categories:conversational", "task_categories:text-generation", "task_categories:question-answering", "task_ids:sentiment-classification", "task_ids:language-modeling", "task_ids:open-domain-qa", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:openrail", "region:us" ]
Amod
null
null
28
949
2023-06-22T12:52:50
--- annotations_creators: - no-annotation language_creators: - found language: - en license: openrail multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - conversational - text-generation - question-answering task_ids: - sentiment-classification - language-modeling - open-domain-qa --- # Amod/mental_health_counseling_conversations ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** Bertagnolli, Nicolas (2020). Counsel chat: Bootstrapping high-quality therapy data. Towards Data Science. https://towardsdatascience.com/counsel-chat - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset is a collection of questions and answers sourced from two online counseling and therapy platforms. The questions cover a wide range of mental health topics, and the answers are provided by qualified psychologists. The dataset is intended to be used for fine-tuning language models to improve their ability to provide mental health advice. ### Supported Tasks and Leaderboards The dataset supports the task of text generation, particularly for generating advice or suggestions in response to a mental health-related question. ### Languages The text in the dataset is in English. ## Dataset Structure ### Data Instances A data instance includes a 'Context' and a 'Response'. 'Context' contains the question asked by a user, and 'Response' contains the corresponding answer provided by a psychologist. ### Data Fields - 'Context': a string containing the question asked by a user - 'Response': a string containing the corresponding answer provided by a psychologist ### Data Splits The dataset has no predefined splits. Users can create their own splits as needed. ## Dataset Creation ### Curation Rationale This dataset was created to aid in the development of AI models that can provide mental health advice or guidance. The raw data was meticulously cleaned to only include the conversations. ### Source Data The data was sourced from two online counseling and therapy platforms. The raw data can be found [here](https://github.com/nbertagnolli/counsel-chat/tree/master/data). ### Annotations The dataset does not contain any additional annotations. ### Personal and Sensitive Information The dataset may contain sensitive information related to mental health. All data was anonymized and no personally identifiable information is included.
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squadshifts
2023-04-05T13:40:47.000Z
[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-4.0", "region:us" ]
null
null
@InProceedings{pmlr-v119-miller20a, title = {The Effect of Natural Distribution Shift on Question Answering Models}, author = {Miller, John and Krauth, Karl and Recht, Benjamin and Schmidt, Ludwig}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {6905--6916}, year = {2020}, editor = {III, Hal Daumé and Singh, Aarti}, volume = {119}, series = {Proceedings of Machine Learning Research}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/miller20a/miller20a.pdf}, url = {https://proceedings.mlr.press/v119/miller20a.html}, }
3
946
2022-03-02T23:29:22
--- annotations_creators: - crowdsourced language: - en language_creators: - crowdsourced - found license: - cc-by-4.0 multilinguality: - monolingual pretty_name: SQuAD-shifts size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - extractive-qa paperswithcode_id: squad-shifts dataset_info: - config_name: new_wiki 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 splits: - name: test num_bytes: 7865203 num_examples: 7938 download_size: 16505623 dataset_size: 7865203 - config_name: nyt 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 splits: - name: test num_bytes: 10792550 num_examples: 10065 download_size: 16505623 dataset_size: 10792550 - config_name: reddit 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 splits: - name: test num_bytes: 9473946 num_examples: 9803 download_size: 16505623 dataset_size: 9473946 - config_name: amazon 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 splits: - name: test num_bytes: 9445004 num_examples: 9885 download_size: 16505623 dataset_size: 9445004 --- # Dataset Card for "squadshifts" ## 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://modestyachts.github.io/squadshifts-website/index.html](https://modestyachts.github.io/squadshifts-website/index.html) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 66.02 MB - **Size of the generated dataset:** 37.56 MB - **Total amount of disk used:** 103.58 MB ### Dataset Summary SquadShifts consists of four new test sets for the Stanford Question Answering Dataset (SQuAD) from four different domains: Wikipedia articles, New York \ Times articles, Reddit comments, and Amazon product reviews. Each dataset was generated using the same data generating pipeline, Amazon Mechanical Turk interface, and data cleaning code as the original SQuAD v1.1 dataset. The "new-wikipedia" dataset measures overfitting on the original SQuAD v1.1 dataset. The "new-york-times", "reddit", and "amazon" datasets measure robustness to natural distribution shifts. We encourage SQuAD model developers to also evaluate their methods on these new datasets! ### 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 #### amazon - **Size of downloaded dataset files:** 16.50 MB - **Size of the generated dataset:** 9.44 MB - **Total amount of disk used:** 25.94 MB An example of 'test' looks as follows. ``` { "answers": { "answer_start": [25], "text": ["amazon"] }, "context": "This is a paragraph from amazon.", "id": "090909", "question": "Where is this paragraph from?", "title": "amazon dummy data" } ``` #### new_wiki - **Size of downloaded dataset files:** 16.50 MB - **Size of the generated dataset:** 7.86 MB - **Total amount of disk used:** 24.37 MB An example of 'test' looks as follows. ``` { "answers": { "answer_start": [25], "text": ["wikipedia"] }, "context": "This is a paragraph from wikipedia.", "id": "090909", "question": "Where is this paragraph from?", "title": "new_wiki dummy data" } ``` #### nyt - **Size of downloaded dataset files:** 16.50 MB - **Size of the generated dataset:** 10.79 MB - **Total amount of disk used:** 27.29 MB An example of 'test' looks as follows. ``` { "answers": { "answer_start": [25], "text": ["new york times"] }, "context": "This is a paragraph from new york times.", "id": "090909", "question": "Where is this paragraph from?", "title": "nyt dummy data" } ``` #### reddit - **Size of downloaded dataset files:** 16.50 MB - **Size of the generated dataset:** 9.47 MB - **Total amount of disk used:** 25.97 MB An example of 'test' looks as follows. ``` { "answers": { "answer_start": [25], "text": ["reddit"] }, "context": "This is a paragraph from reddit.", "id": "090909", "question": "Where is this paragraph from?", "title": "reddit dummy data" } ``` ### Data Fields The data fields are the same among all splits. #### amazon - `id`: a `string` feature. - `title`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `answer_start`: a `int32` feature. #### new_wiki - `id`: a `string` feature. - `title`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `answer_start`: a `int32` feature. #### nyt - `id`: a `string` feature. - `title`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `answer_start`: a `int32` feature. #### reddit - `id`: a `string` feature. - `title`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `answer_start`: a `int32` feature. ### Data Splits | name |test | |--------|----:| |amazon | 9885| |new_wiki| 7938| |nyt |10065| |reddit | 9803| ## 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 All the datasets are distributed under the [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode) license. ### Citation Information ``` @InProceedings{pmlr-v119-miller20a, title = {The Effect of Natural Distribution Shift on Question Answering Models}, author = {Miller, John and Krauth, Karl and Recht, Benjamin and Schmidt, Ludwig}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {6905--6916}, year = {2020}, editor = {III, Hal Daumé and Singh, Aarti}, volume = {119}, series = {Proceedings of Machine Learning Research}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/miller20a/miller20a.pdf}, url = {https://proceedings.mlr.press/v119/miller20a.html}, } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@millerjohnp](https://github.com/millerjohnp), [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.
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shunk031/DrawBench
2023-09-27T13:13:31.000Z
[ "task_categories:text-to-image", "annotations_creators:crowdsourced", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:en", "license:unknown", "arxiv:2205.11487", "region:us" ]
shunk031
DrawBench is a comprehensive and challenging set of prompts that support the evaluation and comparison of text-to-image models. This benchmark contains 11 categories of prompts, testing different capabilities of models such as the ability to faithfully render different colors, numbers of objects, spatial relations, text in the scene, and unusual interactions between objects.\
@article{saharia2022photorealistic, title={Photorealistic text-to-image diffusion models with deep language understanding}, author={Saharia, Chitwan and Chan, William and Saxena, Saurabh and Li, Lala and Whang, Jay and Denton, Emily L and Ghasemipour, Kamyar and Gontijo Lopes, Raphael and Karagol Ayan, Burcu and Salimans, Tim and others}, journal={Advances in Neural Information Processing Systems}, volume={35}, pages={36479--36494}, year={2022} }
1
945
2023-09-27T13:10:40
--- annotations_creators: - crowdsourced language: - en language_creators: [] license: - unknown multilinguality: - monolingual pretty_name: DrawBench size_categories: - n<1K source_datasets: - original tags: [] task_categories: - text-to-image task_ids: [] --- # Dataset Card for DrawBench ## Table of Contents - [Dataset Card Creation Guide](#dataset-card-creation-guide) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://imagen.research.google/ - **Repository:** https://github.com/shunk031/huggingface-datasets_DrawBench - **Paper:** https://arxiv.org/abs/2205.11487 ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The language data in DrawBench is in English (BCP-47 en-US). ## 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 ```bibtex @article{saharia2022photorealistic, title={Photorealistic text-to-image diffusion models with deep language understanding}, author={Saharia, Chitwan and Chan, William and Saxena, Saurabh and Li, Lala and Whang, Jay and Denton, Emily L and Ghasemipour, Kamyar and Gontijo Lopes, Raphael and Karagol Ayan, Burcu and Salimans, Tim and others}, journal={Advances in Neural Information Processing Systems}, volume={35}, pages={36479--36494}, year={2022} } ``` ### Contributions Thanks to Google Research, Brain Team for creating this dataset.
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DFKI-SLT/few-nerd
2023-06-21T09:59:09.000Z
[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:extended|wikipedia", "language:en", "license:cc-by-sa-4.0", "structure-prediction", "region:us" ]
DFKI-SLT
Few-NERD is a large-scale, fine-grained manually annotated named entity recognition dataset, which contains 8 coarse-grained types, 66 fine-grained types, 188,200 sentences, 491,711 entities and 4,601,223 tokens. Three benchmark tasks are built, one is supervised: Few-NERD (SUP) and the other two are few-shot: Few-NERD (INTRA) and Few-NERD (INTER).
@inproceedings{ding2021few, title={Few-NERD: A Few-Shot Named Entity Recognition Dataset}, author={Ding, Ning and Xu, Guangwei and Chen, Yulin, and Wang, Xiaobin and Han, Xu and Xie, Pengjun and Zheng, Hai-Tao and Liu, Zhiyuan}, booktitle={ACL-IJCNLP}, year={2021} }
12
937
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - found language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - extended|wikipedia task_categories: - token-classification task_ids: - named-entity-recognition paperswithcode_id: few-nerd pretty_name: Few-NERD tags: - structure-prediction --- # Dataset Card for "Few-NERD" ## 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://ningding97.github.io/fewnerd/](https://ningding97.github.io/fewnerd/) - **Repository:** [https://github.com/thunlp/Few-NERD](https://github.com/thunlp/Few-NERD) - **Paper:** [https://aclanthology.org/2021.acl-long.248/](https://aclanthology.org/2021.acl-long.248/) - **Point of Contact:** See [https://ningding97.github.io/fewnerd/](https://ningding97.github.io/fewnerd/) ### Dataset Summary This script is for loading the Few-NERD dataset from https://ningding97.github.io/fewnerd/. Few-NERD is a large-scale, fine-grained manually annotated named entity recognition dataset, which contains 8 coarse-grained types, 66 fine-grained types, 188,200 sentences, 491,711 entities, and 4,601,223 tokens. Three benchmark tasks are built, one is supervised (Few-NERD (SUP)) and the other two are few-shot (Few-NERD (INTRA) and Few-NERD (INTER)). NER tags use the `IO` tagging scheme. The original data uses a 2-column CoNLL-style format, with empty lines to separate sentences. DOCSTART information is not provided since the sentences are randomly ordered. For more details see https://ningding97.github.io/fewnerd/ and https://aclanthology.org/2021.acl-long.248/. ### Supported Tasks and Leaderboards - **Tasks:** Named Entity Recognition, Few-shot NER - **Leaderboards:** - https://ningding97.github.io/fewnerd/ - named-entity-recognition:https://paperswithcode.com/sota/named-entity-recognition-on-few-nerd-sup - other-few-shot-ner:https://paperswithcode.com/sota/few-shot-ner-on-few-nerd-intra - other-few-shot-ner:https://paperswithcode.com/sota/few-shot-ner-on-few-nerd-inter ### Languages English ## Dataset Structure ### Data Instances - **Size of downloaded dataset files:** - `super`: 14.6 MB - `intra`: 11.4 MB - `inter`: 11.5 MB - **Size of the generated dataset:** - `super`: 116.9 MB - `intra`: 106.2 MB - `inter`: 106.2 MB - **Total amount of disk used:** 366.8 MB An example of 'train' looks as follows. ```json { 'id': '1', 'tokens': ['It', 'starred', 'Hicks', "'s", 'wife', ',', 'Ellaline', 'Terriss', 'and', 'Edmund', 'Payne', '.'], 'ner_tags': [0, 0, 7, 0, 0, 0, 7, 7, 0, 7, 7, 0], 'fine_ner_tags': [0, 0, 51, 0, 0, 0, 50, 50, 0, 50, 50, 0] } ``` ### Data Fields The data fields are the same among all splits. - `id`: a `string` feature. - `tokens`: a `list` of `string` features. - `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `art` (1), `building` (2), `event` (3), `location` (4), `organization` (5), `other`(6), `person` (7), `product` (8) - `fine_ner_tags`: a `list` of fine-grained classification labels, with possible values including `O` (0), `art-broadcastprogram` (1), `art-film` (2), ... ### Data Splits | Task | Train | Dev | Test | | ----- | ------ | ----- | ---- | | SUP | 131767 | 18824 | 37648 | | INTRA | 99519 | 19358 | 44059 | | INTER | 130112 | 18817 | 14007 | ## 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 [CC BY-SA 4.0 license](https://creativecommons.org/licenses/by-sa/4.0/) ### Citation Information ``` @inproceedings{ding-etal-2021-nerd, title = "Few-{NERD}: A Few-shot Named Entity Recognition Dataset", author = "Ding, Ning and Xu, Guangwei and Chen, Yulin and Wang, Xiaobin and Han, Xu and Xie, Pengjun and Zheng, Haitao and Liu, Zhiyuan", booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.acl-long.248", doi = "10.18653/v1/2021.acl-long.248", pages = "3198--3213", } ``` ### Contributions
7,128
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llm-lens/descriptors-text-davinci-003
2023-06-29T02:39:27.000Z
[ "region:us" ]
llm-lens
null
null
0
935
2023-06-29T02:38:48
--- dataset_info: features: - name: vocab dtype: string - name: descriptions sequence: string - name: prompt_descriptions sequence: string splits: - name: birdsnap num_bytes: 322488 num_examples: 500 - name: caltech101 num_bytes: 56880 num_examples: 102 - name: cifar100 num_bytes: 54081 num_examples: 100 - name: coco num_bytes: 45186 num_examples: 80 - name: dtd num_bytes: 25204 num_examples: 47 - name: fgvc num_bytes: 74126 num_examples: 100 - name: food101 num_bytes: 58525 num_examples: 101 - name: full num_bytes: 3000578 num_examples: 4951 - name: imagenet21k num_bytes: 604897 num_examples: 998 - name: lvis num_bytes: 679195 num_examples: 1198 - name: oxfordflowers num_bytes: 58560 num_examples: 102 - name: oxfordpets num_bytes: 22322 num_examples: 37 - name: stanfordcars num_bytes: 157786 num_examples: 196 - name: sun397 num_bytes: 243017 num_examples: 362 - name: visualgenome num_bytes: 1092697 num_examples: 1913 download_size: 3257945 dataset_size: 6495542 --- # Dataset Card for "descriptors-text-davinci-003" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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liar
2023-01-25T14:34:21.000Z
[ "task_categories:text-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:unknown", "fake-news-detection", "arxiv:1705.00648", "region:us" ]
null
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.
@inproceedings{wang-2017-liar, title = "{``}Liar, Liar Pants on Fire{''}: A New Benchmark Dataset for Fake News Detection", author = "Wang, William Yang", booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)", month = jul, year = "2017", address = "Vancouver, Canada", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/P17-2067", doi = "10.18653/v1/P17-2067", pages = "422--426", abstract = "Automatic fake news detection is a challenging problem in deception detection, and it has tremendous real-world political and social impacts. However, statistical approaches to combating fake news has been dramatically limited by the lack of labeled benchmark datasets. In this paper, we present LIAR: a new, publicly available dataset for fake news detection. We collected a decade-long, 12.8K manually labeled short statements in various contexts from PolitiFact.com, which provides detailed analysis report and links to source documents for each case. This dataset can be used for fact-checking research as well. Notably, this new dataset is an order of magnitude larger than previously largest public fake news datasets of similar type. Empirically, we investigate automatic fake news detection based on surface-level linguistic patterns. We have designed a novel, hybrid convolutional neural network to integrate meta-data with text. We show that this hybrid approach can improve a text-only deep learning model.", }
6
934
2022-03-02T23:29:22
--- 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.
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