Datasets:

---

ftopal

/

huggingface-datasets

like 0

[ "task_categories:text-generation", "task_ids:language-modeling", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:deepset/germanquad", "language:ru", "license:cc-by-4.0", "question-generation", "arxiv:2210.03992", "region:us" ]

[ "2210.03992" ]

[ "ru" ]

[ "### Dataset Summary\n\n\nThis is a subset of QG-Bench, a unified question generation benchmark proposed in\n\"Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference\".\nThis is a modified version of SberQuaD for question generation (QG) task.\nSince the original dataset only contains training/validation set, we manually sample test set from training set, which\nhas no overlap in terms of the paragraph with the training set.", "### Supported Tasks and Leaderboards\n\n\n* 'question-generation': The dataset is assumed to be used to train a model for question generation.\nSuccess on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail).", "### Languages\n\n\nRussian (ru)\n\n\nDataset Structure\n-----------------\n\n\nAn example of 'train' looks as follows.\n\n\nThe data fields are the same among all splits.\n\n\n* 'question': a 'string' feature.\n* 'paragraph': a 'string' feature.\n* 'answer': a 'string' feature.\n* 'sentence': a 'string' feature.\n* 'paragraph\\_answer': a 'string' feature, which is same as the paragraph but the answer is highlighted by a special token ''.\n* 'paragraph\\_sentence': a 'string' feature, which is same as the paragraph but a sentence containing the answer is highlighted by a special token ''.\n* 'sentence\\_answer': a 'string' feature, which is same as the sentence but the answer is highlighted by a special token ''.\n\n\nEach of 'paragraph\\_answer', 'paragraph\\_sentence', and 'sentence\\_answer' feature is assumed to be used to train a question generation model,\nbut with different information. The 'paragraph\\_answer' and 'sentence\\_answer' features are for answer-aware question generation and\n'paragraph\\_sentence' feature is for sentence-aware question generation.\n\n\nData Splits\n-----------" ]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-deepset/germanquad #language-Russian #license-cc-by-4.0 #question-generation #arxiv-2210.03992 #region-us \n", "### Dataset Summary\n\n\nThis is a subset of QG-Bench, a unified question generation benchmark proposed in\n\"Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference\".\nThis is a modified version of SberQuaD for question generation (QG) task.\nSince the original dataset only contains training/validation set, we manually sample test set from training set, which\nhas no overlap in terms of the paragraph with the training set.", "### Supported Tasks and Leaderboards\n\n\n* 'question-generation': The dataset is assumed to be used to train a model for question generation.\nSuccess on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail).", "### Languages\n\n\nRussian (ru)\n\n\nDataset Structure\n-----------------\n\n\nAn example of 'train' looks as follows.\n\n\nThe data fields are the same among all splits.\n\n\n* 'question': a 'string' feature.\n* 'paragraph': a 'string' feature.\n* 'answer': a 'string' feature.\n* 'sentence': a 'string' feature.\n* 'paragraph\\_answer': a 'string' feature, which is same as the paragraph but the answer is highlighted by a special token ''.\n* 'paragraph\\_sentence': a 'string' feature, which is same as the paragraph but a sentence containing the answer is highlighted by a special token ''.\n* 'sentence\\_answer': a 'string' feature, which is same as the sentence but the answer is highlighted by a special token ''.\n\n\nEach of 'paragraph\\_answer', 'paragraph\\_sentence', and 'sentence\\_answer' feature is assumed to be used to train a question generation model,\nbut with different information. The 'paragraph\\_answer' and 'sentence\\_answer' features are for answer-aware question generation and\n'paragraph\\_sentence' feature is for sentence-aware question generation.\n\n\nData Splits\n-----------" ]

[ "task_categories:text-generation", "task_ids:language-modeling", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:squad_es", "language:it", "license:cc-by-4.0", "question-generation", "arxiv:2210.03992", "region:us" ]

[ "2210.03992" ]

[ "it" ]

[ "### Dataset Summary\n\n\nThis is a subset of QG-Bench, a unified question generation benchmark proposed in\n\"Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference\".\nThis is a modified version of SQuAD-it for question generation (QG) task.\nSince the original dataset only contains training/validation set, we manually sample test set from training set, which\nhas no overlap in terms of the paragraph with the training set.", "### Supported Tasks and Leaderboards\n\n\n* 'question-generation': The dataset is assumed to be used to train a model for question generation.\nSuccess on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail).", "### Languages\n\n\nItalian (it)\n\n\nDataset Structure\n-----------------\n\n\nAn example of 'train' looks as follows.\n\n\nThe data fields are the same among all splits.\n\n\n* 'question': a 'string' feature.\n* 'paragraph': a 'string' feature.\n* 'answer': a 'string' feature.\n* 'sentence': a 'string' feature.\n* 'paragraph\\_answer': a 'string' feature, which is same as the paragraph but the answer is highlighted by a special token ''.\n* 'paragraph\\_sentence': a 'string' feature, which is same as the paragraph but a sentence containing the answer is highlighted by a special token ''.\n* 'sentence\\_answer': a 'string' feature, which is same as the sentence but the answer is highlighted by a special token ''.\n\n\nEach of 'paragraph\\_answer', 'paragraph\\_sentence', and 'sentence\\_answer' feature is assumed to be used to train a question generation model,\nbut with different information. The 'paragraph\\_answer' and 'sentence\\_answer' features are for answer-aware question generation and\n'paragraph\\_sentence' feature is for sentence-aware question generation.\n\n\nData Splits\n-----------" ]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-squad_es #language-Italian #license-cc-by-4.0 #question-generation #arxiv-2210.03992 #region-us \n", "### Dataset Summary\n\n\nThis is a subset of QG-Bench, a unified question generation benchmark proposed in\n\"Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference\".\nThis is a modified version of SQuAD-it for question generation (QG) task.\nSince the original dataset only contains training/validation set, we manually sample test set from training set, which\nhas no overlap in terms of the paragraph with the training set.", "### Supported Tasks and Leaderboards\n\n\n* 'question-generation': The dataset is assumed to be used to train a model for question generation.\nSuccess on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail).", "### Languages\n\n\nItalian (it)\n\n\nDataset Structure\n-----------------\n\n\nAn example of 'train' looks as follows.\n\n\nThe data fields are the same among all splits.\n\n\n* 'question': a 'string' feature.\n* 'paragraph': a 'string' feature.\n* 'answer': a 'string' feature.\n* 'sentence': a 'string' feature.\n* 'paragraph\\_answer': a 'string' feature, which is same as the paragraph but the answer is highlighted by a special token ''.\n* 'paragraph\\_sentence': a 'string' feature, which is same as the paragraph but a sentence containing the answer is highlighted by a special token ''.\n* 'sentence\\_answer': a 'string' feature, which is same as the sentence but the answer is highlighted by a special token ''.\n\n\nEach of 'paragraph\\_answer', 'paragraph\\_sentence', and 'sentence\\_answer' feature is assumed to be used to train a question generation model,\nbut with different information. The 'paragraph\\_answer' and 'sentence\\_answer' features are for answer-aware question generation and\n'paragraph\\_sentence' feature is for sentence-aware question generation.\n\n\nData Splits\n-----------" ]

[ "task_categories:text-generation", "task_ids:language-modeling", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:deepset/germanquad", "language:de", "license:cc-by-4.0", "question-generation", "arxiv:2210.03992", "region:us" ]

[ "2210.03992" ]

[ "de" ]

[ "### Dataset Summary\n\n\nThis is a subset of QG-Bench, a unified question generation benchmark proposed in\n\"Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference\".\nThis is a modified version of GermanQuAD for question generation (QG) task.\nSince the original dataset only contains training/validation set, we manually sample test set from training set, which\nhas no overlap in terms of the paragraph with the training set.", "### Supported Tasks and Leaderboards\n\n\n* 'question-generation': The dataset is assumed to be used to train a model for question generation.\nSuccess on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail).", "### Languages\n\n\nSpanish (es)\n\n\nDataset Structure\n-----------------\n\n\nAn example of 'train' looks as follows.\n\n\nData Fields\n-----------\n\n\nThe data fields are the same among all splits.\n\n\n* 'question': a 'string' feature.\n* 'paragraph': a 'string' feature.\n* 'answer': a 'string' feature.\n* 'sentence': a 'string' feature.\n* 'paragraph\\_answer': a 'string' feature, which is same as the paragraph but the answer is highlighted by a special token ''.\n* 'paragraph\\_sentence': a 'string' feature, which is same as the paragraph but a sentence containing the answer is highlighted by a special token ''.\n* 'sentence\\_answer': a 'string' feature, which is same as the sentence but the answer is highlighted by a special token ''.\n\n\nEach of 'paragraph\\_answer', 'paragraph\\_sentence', and 'sentence\\_answer' feature is assumed to be used to train a question generation model,\nbut with different information. The 'paragraph\\_answer' and 'sentence\\_answer' features are for answer-aware question generation and\n'paragraph\\_sentence' feature is for sentence-aware question generation.", "### Data Splits" ]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-deepset/germanquad #language-German #license-cc-by-4.0 #question-generation #arxiv-2210.03992 #region-us \n", "### Dataset Summary\n\n\nThis is a subset of QG-Bench, a unified question generation benchmark proposed in\n\"Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference\".\nThis is a modified version of GermanQuAD for question generation (QG) task.\nSince the original dataset only contains training/validation set, we manually sample test set from training set, which\nhas no overlap in terms of the paragraph with the training set.", "### Supported Tasks and Leaderboards\n\n\n* 'question-generation': The dataset is assumed to be used to train a model for question generation.\nSuccess on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail).", "### Languages\n\n\nSpanish (es)\n\n\nDataset Structure\n-----------------\n\n\nAn example of 'train' looks as follows.\n\n\nData Fields\n-----------\n\n\nThe data fields are the same among all splits.\n\n\n* 'question': a 'string' feature.\n* 'paragraph': a 'string' feature.\n* 'answer': a 'string' feature.\n* 'sentence': a 'string' feature.\n* 'paragraph\\_answer': a 'string' feature, which is same as the paragraph but the answer is highlighted by a special token ''.\n* 'paragraph\\_sentence': a 'string' feature, which is same as the paragraph but a sentence containing the answer is highlighted by a special token ''.\n* 'sentence\\_answer': a 'string' feature, which is same as the sentence but the answer is highlighted by a special token ''.\n\n\nEach of 'paragraph\\_answer', 'paragraph\\_sentence', and 'sentence\\_answer' feature is assumed to be used to train a question generation model,\nbut with different information. The 'paragraph\\_answer' and 'sentence\\_answer' features are for answer-aware question generation and\n'paragraph\\_sentence' feature is for sentence-aware question generation.", "### Data Splits" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for cdna_test_dset", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: \n- Point of Contact: myemail@URL", "### Dataset Summary\n\nThis dataset is very nice", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances\n\nthis is how the data could look \n{\n 'sequence':'ACTGGTTC',\n}", "### Data Fields", "### Data Splits\n\nno splits yet", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information" ]

[ "TAGS\n#region-us \n", "# Dataset Card for cdna_test_dset", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: \n- Point of Contact: myemail@URL", "### Dataset Summary\n\nThis dataset is very nice", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances\n\nthis is how the data could look \n{\n 'sequence':'ACTGGTTC',\n}", "### Data Fields", "### Data Splits\n\nno splits yet", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information" ]

[ "language:en", "language modeling", "region:us" ]

[]

[ "en" ]

[ "## Dataset overview\nThis dataset contains all lyrics from songs produced by The Beatles, 180 in total. There a two splits available in the dictionary:\n\n- dataset_cleaned: contains all lyrics including Intro, Outro, Chorus tagging. \n- dataset_full: contains only lyrics without any tagging\n\nEach split contains the title, album, the lyrics for the song, the length of the lyrics field (tokens) and a number." ]

[ "TAGS\n#language-English #language modeling #region-us \n", "## Dataset overview\nThis dataset contains all lyrics from songs produced by The Beatles, 180 in total. There a two splits available in the dictionary:\n\n- dataset_cleaned: contains all lyrics including Intro, Outro, Chorus tagging. \n- dataset_full: contains only lyrics without any tagging\n\nEach split contains the title, album, the lyrics for the song, the length of the lyrics field (tokens) and a number." ]

[ "region:us" ]

[]

[]

[ "# BEA 19 Shared task.\n\nBEA 19 Shared task dataset already preprocessed to have the original and corrupted sentence.\n\nI merged the def and train datasets into one and applied all the annotated edits.\n\nSource: URL" ]

[ "TAGS\n#region-us \n", "# BEA 19 Shared task.\n\nBEA 19 Shared task dataset already preprocessed to have the original and corrupted sentence.\n\nI merged the def and train datasets into one and applied all the annotated edits.\n\nSource: URL" ]

[ "language:en", "license:mit", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#language-English #license-mit #region-us \n" ]

[ "language:si", "license:mit", "region:us" ]

[]

[ "si" ]

[]

[ "TAGS\n#language-Sinhala #license-mit #region-us \n" ]

[ "language:si", "license:mit", "region:us" ]

[]

[ "si" ]

[]

[ "TAGS\n#language-Sinhala #license-mit #region-us \n" ]

[ "language:si", "license:mit", "region:us" ]

[]

[ "si" ]

[]

[ "TAGS\n#language-Sinhala #license-mit #region-us \n" ]

[ "language:ta", "language:si", "license:mit", "region:us" ]

[]

[ "ta", "si" ]

[]

[ "TAGS\n#language-Tamil #language-Sinhala #license-mit #region-us \n" ]

[ "language:si", "license:mit", "region:us" ]

[]

[ "si" ]

[]

[ "TAGS\n#language-Sinhala #license-mit #region-us \n" ]

[ "region:us" ]

[]

[]

[]

[ "TAGS\n#region-us \n" ]

[ "license:creativeml-openrail-m", "arxiv:2203.11096", "region:us" ]

[ "2203.11096" ]

[]

[ "# GamePhysics Dataset\n\n![Website](URL\n![arXiv](URL\n![Hugging Face Spaces](URL\n\nThe GamePhysics dataset is a collection of gameplay bug videos sourced from the GamePhysics subreddit.", "## Sample videos\n<video src=\"URL controls=\"controls\" muted=\"muted\" playsinline=\"playsinline\" width=480></video>\n<video src=\"URL controls=\"controls\" muted=\"muted\" playsinline=\"playsinline\" width=480></video>\n<video src=\"URL controls=\"controls\" muted=\"muted\" playsinline=\"playsinline\" width=480></video>\n<video src=\"URL controls=\"controls\" muted=\"muted\" playsinline=\"playsinline\" width=480></video>" ]

[ "TAGS\n#license-creativeml-openrail-m #arxiv-2203.11096 #region-us \n", "# GamePhysics Dataset\n\n![Website](URL\n![arXiv](URL\n![Hugging Face Spaces](URL\n\nThe GamePhysics dataset is a collection of gameplay bug videos sourced from the GamePhysics subreddit.", "## Sample videos\n<video src=\"URL controls=\"controls\" muted=\"muted\" playsinline=\"playsinline\" width=480></video>\n<video src=\"URL controls=\"controls\" muted=\"muted\" playsinline=\"playsinline\" width=480></video>\n<video src=\"URL controls=\"controls\" muted=\"muted\" playsinline=\"playsinline\" width=480></video>\n<video src=\"URL controls=\"controls\" muted=\"muted\" playsinline=\"playsinline\" width=480></video>" ]

[ "structured-data", "tabular-data", "classification", "region:us" ]

[]

[]

[]

[ "TAGS\n#structured-data #tabular-data #classification #region-us \n" ]

[ "task_categories:conversational", "task_ids:dialogue-generation", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:ru", "region:us" ]

[]

[ "ru" ]

[ "# Dataset Card for 2ch_b_dialogues", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: URL\n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nRussian language dialogues mined from URL", "### Supported Tasks and Leaderboards", "### Languages\n\nRussian", "## Dataset Structure", "### Data Instances\n\n{\n \"dialogue\": [\"Glad to hear!\", \"Fine, thank you!\", \"Hi, how are you?\"]\n}", "### Data Fields\n\n- dialogue: list of posts ordered last-to-first", "### Data Splits", "## Dataset Creation", "### Curation Rationale\n\nFun", "### Source Data", "#### Initial Data Collection and Normalization\n\nIn a thread graph only vertices with single parent were selected. Then non-overlapping threads of dialogues were build.", "#### Who are the source language producers?\n\nURL users", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nMorally questionable data", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nblacks_samorez", "### Licensing Information" ]

[ "TAGS\n#task_categories-conversational #task_ids-dialogue-generation #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Russian #region-us \n", "# Dataset Card for 2ch_b_dialogues", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: URL\n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nRussian language dialogues mined from URL", "### Supported Tasks and Leaderboards", "### Languages\n\nRussian", "## Dataset Structure", "### Data Instances\n\n{\n \"dialogue\": [\"Glad to hear!\", \"Fine, thank you!\", \"Hi, how are you?\"]\n}", "### Data Fields\n\n- dialogue: list of posts ordered last-to-first", "### Data Splits", "## Dataset Creation", "### Curation Rationale\n\nFun", "### Source Data", "#### Initial Data Collection and Normalization\n\nIn a thread graph only vertices with single parent were selected. Then non-overlapping threads of dialogues were build.", "#### Who are the source language producers?\n\nURL users", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nMorally questionable data", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nblacks_samorez", "### Licensing Information" ]

[ "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" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "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" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "task_categories:text-classification", "annotations_creators:expert-generated", "language_creators:other", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:apache-2.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nTSATC: Twitter Sentiment Analysis Training Corpus\nThe original Twitter Sentiment Analysis Dataset contains 1,578,627 classified tweets, each row is marked as 1 for positive sentiment and 0 for negative sentiment. It can be downloaded from URL\nThe dataset is based on data from the following two sources:\n\n\nUniversity of Michigan Sentiment Analysis competition on Kaggle\nTwitter Sentiment Corpus by Niek Sanders\n\n\nThis dataset has been transformed, selecting in a random way a subset of them, applying a cleaning process, and dividing them between the test and train subsets, keeping a balance between the number of positive and negative tweets within each of these subsets. These two files can be founded on URL\n\n\nFinally, the train subset has been divided in two smallest datasets, train (80%) and validation (20%). The final dataset has been created with these two new subdatasets plus the previous test dataset.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nThe text in the dataset is in English.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nBelow are two examples from the dataset:", "### Data Fields\n\n\nIn the final dataset, all files are in the JSON format with f columns:\n\n\n\nEach feeling has two possible values: '0' indicates the sentence has a negative sentiment, while '1' indicates a positive feeling.", "### Data Splits\n\n\nThe number of examples and the proportion sentiments are shown below:\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nExisting paraphrase identification datasets lack sentence pairs that have high lexical overlap without being paraphrases. Models trained on such data fail to distinguish pairs like *flights from New York to Florida* and *flights from Florida to New York*.", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?\n\n\nMentioned above.", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Contributions\n\n\nThanks to myself @carblacac for adding this transformed dataset from the original one." ]

[ "TAGS\n#task_categories-text-classification #annotations_creators-expert-generated #language_creators-other #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-apache-2.0 #region-us \n", "### Dataset Summary\n\n\nTSATC: Twitter Sentiment Analysis Training Corpus\nThe original Twitter Sentiment Analysis Dataset contains 1,578,627 classified tweets, each row is marked as 1 for positive sentiment and 0 for negative sentiment. It can be downloaded from URL\nThe dataset is based on data from the following two sources:\n\n\nUniversity of Michigan Sentiment Analysis competition on Kaggle\nTwitter Sentiment Corpus by Niek Sanders\n\n\nThis dataset has been transformed, selecting in a random way a subset of them, applying a cleaning process, and dividing them between the test and train subsets, keeping a balance between the number of positive and negative tweets within each of these subsets. These two files can be founded on URL\n\n\nFinally, the train subset has been divided in two smallest datasets, train (80%) and validation (20%). The final dataset has been created with these two new subdatasets plus the previous test dataset.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nThe text in the dataset is in English.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nBelow are two examples from the dataset:", "### Data Fields\n\n\nIn the final dataset, all files are in the JSON format with f columns:\n\n\n\nEach feeling has two possible values: '0' indicates the sentence has a negative sentiment, while '1' indicates a positive feeling.", "### Data Splits\n\n\nThe number of examples and the proportion sentiments are shown below:\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nExisting paraphrase identification datasets lack sentence pairs that have high lexical overlap without being paraphrases. Models trained on such data fail to distinguish pairs like *flights from New York to Florida* and *flights from Florida to New York*.", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?\n\n\nMentioned above.", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Contributions\n\n\nThanks to myself @carblacac for adding this transformed dataset from the original one." ]

[ "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" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "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" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "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" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "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" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "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" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "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" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "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" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "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" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "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" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "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" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "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" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "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" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "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" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "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" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "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" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "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" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "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" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "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" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "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" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "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" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "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" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "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" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "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" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "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" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "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" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "arxiv:1904.0429", "region:us" ]

[ "1904.0429" ]

[]

[ "# MidcurveNN\nPaired images of Profiles and their respective Midcurves\n\n---\nlicense: apache-2.0\n---", "# Dataset for Midcurve Computation\n\n![](URL", "## Description\nDataset: set of images, can be considered as pairs, profiles and their corresponding midcurves, with naming convention as\n\t- Profile: \"I_Profile_mirrored_0.png\" has corresponding\n\t- Midcurve: \"I_Midcurve_mirrored_0.png\"\n\t- Format is: shape name_Profile/Midcurve_transformation_parameter.png\n\nUsage: Encoder Decoder like Semantic Segmentation or Pix2Pix on images to learn dimension reduction", "## Usage\nURL", "## References\n- Vixra paper MidcurveNN: Encoder-Decoder Neural Network for Computing Midcurve of a Thin Polygon, URL e-Print archive, viXra:1904.0429 URL \n- ODSC proposal URL\n- CAD Conference 2021, Barcelona, pages 223-225 URL" ]

[ "TAGS\n#arxiv-1904.0429 #region-us \n", "# MidcurveNN\nPaired images of Profiles and their respective Midcurves\n\n---\nlicense: apache-2.0\n---", "# Dataset for Midcurve Computation\n\n![](URL", "## Description\nDataset: set of images, can be considered as pairs, profiles and their corresponding midcurves, with naming convention as\n\t- Profile: \"I_Profile_mirrored_0.png\" has corresponding\n\t- Midcurve: \"I_Midcurve_mirrored_0.png\"\n\t- Format is: shape name_Profile/Midcurve_transformation_parameter.png\n\nUsage: Encoder Decoder like Semantic Segmentation or Pix2Pix on images to learn dimension reduction", "## Usage\nURL", "## References\n- Vixra paper MidcurveNN: Encoder-Decoder Neural Network for Computing Midcurve of a Thin Polygon, URL e-Print archive, viXra:1904.0429 URL \n- ODSC proposal URL\n- CAD Conference 2021, Barcelona, pages 223-225 URL" ]

[ "license:afl-3.0", "region:us" ]

[]

[]

[]

[ "TAGS\n#license-afl-3.0 #region-us \n" ]

[ "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" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "task_categories:multiple-choice", "task_categories:question-answering", "task_categories:zero-shot-classification", "task_categories:text2text-generation", "task_categories:table-question-answering", "task_categories:text-generation", "task_categories:text-classification", "task_categories:tabular-classification", "task_ids:multiple-choice-qa", "task_ids:extractive-qa", "task_ids:open-domain-qa", "task_ids:closed-domain-qa", "task_ids:closed-book-qa", "task_ids:open-book-qa", "task_ids:language-modeling", "task_ids:multi-class-classification", "task_ids:natural-language-inference", "task_ids:topic-classification", "task_ids:multi-label-classification", "task_ids:tabular-multi-class-classification", "task_ids:tabular-multi-label-classification", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "language:en", "license:apache-2.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for Fewshot Table Dataset", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: Paper-Title\n- Leaderboard: \n- Point of Contact: junshern@URL, perez@URL", "### Dataset Summary\n\nThe Fewshot Table dataset consists of tables that naturally occur on the web, that are formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. The dataset consists of approximately 413K tables that are extracted from the WDC Web Table Corpora 2015, which is released under the Apache-2.0 license. The WDC Web Table Corpora \"contains vast amounts of HTML tables. [...] The Web Data Commons project extracts relational Web tables from the Common Crawl, the largest and most up-to-date Web corpus that is currently available to the public.\"", "### Supported Tasks and Leaderboards\n\nSince the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide i.e. we have 1000's tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e. 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g. multiple-choice, question-answering, table-question-answering, text-classification, etc.\n\nThe intended use of this dataset is to improve few-shot performance by finetuning/pretraining onour dataset.", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances\n\nEach table, i.e. task is represented as a json-lines file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from. \n\nThere are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.", "### Data Fields\n\n'task': task identifier\n\n'input': column elements of a specific row in table. \n\n'options': for multiple choice classification, it provides the options to choose from.\n\n'output': target column element of same row as input.\n\n'pageTitle': the title of the page containing the table. \n\n'outputColName': ?? (potentially remove this from data)\n\n'url': url to the website containing the table\n\n'wdcFile': ? (potentially remove this from data)", "### Data Splits", "## Dataset Creation", "### Curation Rationale\n\nHow do we convert tables to few-shot tasks?\nUnlike unstructured text, structured data in the form of tables lends itself easily to the few-shot task format. Given a table where each row is an instance of a similar class and the columns describe the attributes of each instance, we can turn each row into a task example to predict one attribute given the others. When the table has more than one row, we instantly have multiple examples of this task by using each row as a single example, and thus each table becomes a few-shot dataset for a particular task. \n\nThe few-shot setting in this setting is significant: Tables often do not come with clear instructions for each field, so tasks may be underspecified if prompted in a zero-shot manner, but the intended task becomes clearer when examples are provided. This makes a good two-way match: The few-shot format is a perfect setup for table learning, and tables provide a natural dataset for few-shot training.", "### Source Data", "#### Initial Data Collection and Normalization\n\nWe downloaded the WDC Web Table Corpora 2015 dataset and focus on relational tables. In the following, we describe the steps we executed to filter the WDC Web Table Corpora and create our task dataset. Given a set of relation tables, we apply defined preprocessing steps to ensure all the tables can be handled consistently. Each table can then spawn one or more tasks using a simple predict-one-column approach. Finally, all tasks produced in this manner undergo simple rule-based checks, i.e. any candidates that do not meet some defined minimum requirements for a well-formed task are rejected. Following this approach, we start with 50 million tables in the initial corpus and produce a longlist of 400K tasks. \n\n1. We select only relational tables. \n2. We make sure all tables are vertical (horizontal tables are simply transposed) and remove duplicate rows. \n3. To create task we use what in the literature is referred to as verbalizers. For example, a table with 3 columns may be cast as three different tasks: predict column A given B and C, predict column B given A and C, and predict column C given A and B. \n4. Rule-based-checks to reject tables: \na) We reject 25M tables that have fewer than 6 rows (so we can do at least k=5-shot learning)\nb) We reject tables with > 20% non-English text as measured by SpaCy\nc) Given 2 Million passing tables we consider each table column as a potential output column, and concatenate all other columns to form the input (which produces 5.6 M candidate tasks)\n5. Rule-based-checks to reject tasks\na) We reject a task if it has less than 6 rows. Note that tasks may have fewer rows than their origin tables since we remove rows where the output column is empty. \nb) We reject tasks if any input maps to multiple outputs. \nc) We reject tasks if it has fewer than 2 output classes. \nd) We reject a task if the output column alone has >20% non-English text. \ne) We reject a task if the classes are heavily imbalanced.\n\n6. Lastly we apply domain-level filtering. Initial iterations of our dataset found a significant imbalance in terms of the website of origin for our generated tasks. In particular, we found that the mos-frequent domain in the WDC corpus, URL, was emphasized by our export criteria such that this website alone represented 41% of our total tasks. Since we want our dataset to represent the diversity of all the tables available on the web, we apply a hard fix for this imbalance by limiting the number of tasks per domain. Starting from the initial corpus of 50M tables from 323160 web domains, our resulting longlist of tasks comprises more than X for a total of 413350 tasks.", "#### Who are the source language producers?\n\nThe dataset is extracted from WDC Web Table Corpora.", "### Annotations", "#### Annotation process\n\nNo annotation Process", "#### Who are the annotators?\n\n-", "### Personal and Sensitive Information\n\nThe data was extracted from WDC Web Table Corpora, which in turn extracted tables from the Common Crawl. We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g. data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe purpose of this dataset is to help develop models that are better at few-shot learning and have higher few-shot performance by fine-tuning few-shot tasks extracted from tables.\n\nWhile tables have a similar structure to few-shot tasks and we do see an improved performance on few-shot tasks in our paper, we want to make clear that finetuning on tables also has its risks. First of all, since the tables are extracted from the web, they may contain user identities or otherwise sensitive information which a model might reveal at inference, or which could influence the learning process of a model in a negative way. Second, since tables are very diverse in nature, the model also trains on low-quality data or data with an unusual structure. While it is interesting that training on such data improves few-shot performance on downstream tasks, this could also imply that the model learns concepts that are very dissimilar to human concepts that would be useful for a certain downstream task. In other words, it is possible that the model learns weird things that are helpful on the evaluated downstream tasks, but might lead to bad out-of-distribution behavior.", "### Discussion of Biases\n\nSince our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content for toxic content. \nThis implies that a model trained on our dataset will reinforce harmful biases and toxic text that exist in our dataset.", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\nMention all authors", "### Licensing Information\nApache 2.0" ]

[ "TAGS\n#task_categories-multiple-choice #task_categories-question-answering #task_categories-zero-shot-classification #task_categories-text2text-generation #task_categories-table-question-answering #task_categories-text-generation #task_categories-text-classification #task_categories-tabular-classification #task_ids-multiple-choice-qa #task_ids-extractive-qa #task_ids-open-domain-qa #task_ids-closed-domain-qa #task_ids-closed-book-qa #task_ids-open-book-qa #task_ids-language-modeling #task_ids-multi-class-classification #task_ids-natural-language-inference #task_ids-topic-classification #task_ids-multi-label-classification #task_ids-tabular-multi-class-classification #task_ids-tabular-multi-label-classification #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #language-English #license-apache-2.0 #region-us \n", "# Dataset Card for Fewshot Table Dataset", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: Paper-Title\n- Leaderboard: \n- Point of Contact: junshern@URL, perez@URL", "### Dataset Summary\n\nThe Fewshot Table dataset consists of tables that naturally occur on the web, that are formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. The dataset consists of approximately 413K tables that are extracted from the WDC Web Table Corpora 2015, which is released under the Apache-2.0 license. The WDC Web Table Corpora \"contains vast amounts of HTML tables. [...] The Web Data Commons project extracts relational Web tables from the Common Crawl, the largest and most up-to-date Web corpus that is currently available to the public.\"", "### Supported Tasks and Leaderboards\n\nSince the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide i.e. we have 1000's tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e. 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g. multiple-choice, question-answering, table-question-answering, text-classification, etc.\n\nThe intended use of this dataset is to improve few-shot performance by finetuning/pretraining onour dataset.", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances\n\nEach table, i.e. task is represented as a json-lines file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from. \n\nThere are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.", "### Data Fields\n\n'task': task identifier\n\n'input': column elements of a specific row in table. \n\n'options': for multiple choice classification, it provides the options to choose from.\n\n'output': target column element of same row as input.\n\n'pageTitle': the title of the page containing the table. \n\n'outputColName': ?? (potentially remove this from data)\n\n'url': url to the website containing the table\n\n'wdcFile': ? (potentially remove this from data)", "### Data Splits", "## Dataset Creation", "### Curation Rationale\n\nHow do we convert tables to few-shot tasks?\nUnlike unstructured text, structured data in the form of tables lends itself easily to the few-shot task format. Given a table where each row is an instance of a similar class and the columns describe the attributes of each instance, we can turn each row into a task example to predict one attribute given the others. When the table has more than one row, we instantly have multiple examples of this task by using each row as a single example, and thus each table becomes a few-shot dataset for a particular task. \n\nThe few-shot setting in this setting is significant: Tables often do not come with clear instructions for each field, so tasks may be underspecified if prompted in a zero-shot manner, but the intended task becomes clearer when examples are provided. This makes a good two-way match: The few-shot format is a perfect setup for table learning, and tables provide a natural dataset for few-shot training.", "### Source Data", "#### Initial Data Collection and Normalization\n\nWe downloaded the WDC Web Table Corpora 2015 dataset and focus on relational tables. In the following, we describe the steps we executed to filter the WDC Web Table Corpora and create our task dataset. Given a set of relation tables, we apply defined preprocessing steps to ensure all the tables can be handled consistently. Each table can then spawn one or more tasks using a simple predict-one-column approach. Finally, all tasks produced in this manner undergo simple rule-based checks, i.e. any candidates that do not meet some defined minimum requirements for a well-formed task are rejected. Following this approach, we start with 50 million tables in the initial corpus and produce a longlist of 400K tasks. \n\n1. We select only relational tables. \n2. We make sure all tables are vertical (horizontal tables are simply transposed) and remove duplicate rows. \n3. To create task we use what in the literature is referred to as verbalizers. For example, a table with 3 columns may be cast as three different tasks: predict column A given B and C, predict column B given A and C, and predict column C given A and B. \n4. Rule-based-checks to reject tables: \na) We reject 25M tables that have fewer than 6 rows (so we can do at least k=5-shot learning)\nb) We reject tables with > 20% non-English text as measured by SpaCy\nc) Given 2 Million passing tables we consider each table column as a potential output column, and concatenate all other columns to form the input (which produces 5.6 M candidate tasks)\n5. Rule-based-checks to reject tasks\na) We reject a task if it has less than 6 rows. Note that tasks may have fewer rows than their origin tables since we remove rows where the output column is empty. \nb) We reject tasks if any input maps to multiple outputs. \nc) We reject tasks if it has fewer than 2 output classes. \nd) We reject a task if the output column alone has >20% non-English text. \ne) We reject a task if the classes are heavily imbalanced.\n\n6. Lastly we apply domain-level filtering. Initial iterations of our dataset found a significant imbalance in terms of the website of origin for our generated tasks. In particular, we found that the mos-frequent domain in the WDC corpus, URL, was emphasized by our export criteria such that this website alone represented 41% of our total tasks. Since we want our dataset to represent the diversity of all the tables available on the web, we apply a hard fix for this imbalance by limiting the number of tasks per domain. Starting from the initial corpus of 50M tables from 323160 web domains, our resulting longlist of tasks comprises more than X for a total of 413350 tasks.", "#### Who are the source language producers?\n\nThe dataset is extracted from WDC Web Table Corpora.", "### Annotations", "#### Annotation process\n\nNo annotation Process", "#### Who are the annotators?\n\n-", "### Personal and Sensitive Information\n\nThe data was extracted from WDC Web Table Corpora, which in turn extracted tables from the Common Crawl. We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g. data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe purpose of this dataset is to help develop models that are better at few-shot learning and have higher few-shot performance by fine-tuning few-shot tasks extracted from tables.\n\nWhile tables have a similar structure to few-shot tasks and we do see an improved performance on few-shot tasks in our paper, we want to make clear that finetuning on tables also has its risks. First of all, since the tables are extracted from the web, they may contain user identities or otherwise sensitive information which a model might reveal at inference, or which could influence the learning process of a model in a negative way. Second, since tables are very diverse in nature, the model also trains on low-quality data or data with an unusual structure. While it is interesting that training on such data improves few-shot performance on downstream tasks, this could also imply that the model learns concepts that are very dissimilar to human concepts that would be useful for a certain downstream task. In other words, it is possible that the model learns weird things that are helpful on the evaluated downstream tasks, but might lead to bad out-of-distribution behavior.", "### Discussion of Biases\n\nSince our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content for toxic content. \nThis implies that a model trained on our dataset will reinforce harmful biases and toxic text that exist in our dataset.", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\nMention all authors", "### Licensing Information\nApache 2.0" ]

[ "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" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "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" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "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" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "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" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe 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.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* '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....\"}'\n* '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?\"}'\n* '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\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "task_categories:text-classification", "task_ids:multi-label-classification", "task_ids:topic-classification", "annotations_creators:found", "language_creators:found", "language_creators:machine-generated", "multilinguality:multilingual", "size_categories:10K<n<100K", "source_datasets:extended|multi_eurlex", "language:en", "language:de", "language:fr", "language:el", "language:sk", "license:cc-by-sa-4.0", "region:us" ]

[]

[ "en", "de", "fr", "el", "sk" ]

[ "### Dataset Summary\n\n\nDocuments\n\n\nMultiEURLEX of Chalkidis et al. (2021) comprises 65k EU laws in 23 official EU languages. Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU. Each EUROVOC label ID is associated with a *label descriptor*, e.g., [60, agri-foodstuffs], [6006, plant product], [1115, fruit]. The descriptors are also available in the 23 languages. Chalkidis et al. (2019) published a monolingual (English) version of this dataset, called EUR-LEX, comprising 57k EU laws with the originally assigned gold labels.\n\n\nIn this new version, dubbed \"Non-Parallel MultiEURLEX (incl. Translations)\", MultiEURLEX comprises non-parallel documents across 5 languages (English, German, French, Greek, and Slovak), i.e., 11,000 different documents per language, including also translations from English to the rest of the 4 available languages.", "### Supported Tasks and Leaderboards\n\n\nMultiEURLEX can be used for legal topic classification, a multi-label classification task where legal documents need to be assigned concepts (in our case, from EUROVOC) reflecting their topics. Unlike EUR-LEX, however, MultiEURLEX supports labels from three different granularities (EUROVOC levels). More importantly, apart from monolingual (*one-to-one*) experiments, it can be used to study cross-lingual transfer scenarios, including *one-to-many* (systems trained in one language and used in other languages with no training data), and *many-to-one* or *many-to-many* (systems jointly trained in multiple languages and used in one or more other languages).\n\n\nThe dataset is not yet part of an established benchmark.", "### Languages\n\n\nThe EU has 24 official languages. When new members join the EU, the set of official languages usually expands, except the languages are already included. MultiEURLEX covers 23 languages from seven language families (Germanic, Romance, Slavic, Uralic, Baltic, Semitic, Hellenic). EU laws are published in all official languages, except Irish, for resource-related reasons (Read more at URL This wide coverage makes MultiEURLEX a valuable testbed for cross-lingual transfer. All languages use the Latin script, except for Bulgarian (Cyrillic script) and Greek. Several other languages are also spoken in EU countries. The EU is home to over 60 additional indigenous regional or minority languages, e.g., Basque, Catalan, Frisian, Saami, and Yiddish, among others, spoken by approx. 40 million people, but these additional languages are not considered official (in terms of EU), and EU laws are not translated to them.\n\n\nThis version of MultiEURLEX covers 5 EU languages (English, German, French, Greek, and Slovak). It also includes machine-translated versions of the documents using the EasyNMT framework (URL utilizing the many-to-many M2M\\_100\\_418M model of Fan et al. (2020) for el-to-en and el-to-de pairs and the OPUS-MT (Tiedemann et al., 2020) models for the rest.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nMultilingual use of the dataset\n\n\nWhen the dataset is used in a multilingual setting selecting the the 'all\\_languages' flag:\n\n\nMonolingual use of the dataset\n\n\nWhen the dataset is used in a monolingual setting selecting the ISO language code for one of the 5 supported languages, or supported translation pairs in the form src2trg, where src and trg are ISO language codes, e.g., en2fr for English translated to French. For example:", "### Data Fields\n\n\nMultilingual use of the dataset\n\n\nThe following data fields are provided for documents ('train', 'dev', 'test'):\n\n\n'celex\\_id': (str) The official ID of the document. The CELEX number is the unique identifier for all publications in both Eur-Lex and CELLAR. \n\n'text': (dict[str]) A dictionary with the 23 languages as keys and the full content of each document as values. \n\n'labels': (List[int]) The relevant EUROVOC concepts (labels).\n\n\nMonolingual use of the dataset\n\n\nThe following data fields are provided for documents ('train', 'dev', 'test'):\n\n\n'celex\\_id': (str) The official ID of the document. The CELEX number is the unique identifier for all publications in both Eur-Lex and CELLAR. \n\n'text': (str) The full content of each document across languages. \n\n'labels': (List[int]) The relevant EUROVOC concepts (labels).\n\n\nIf you want to use the descriptors of the EUROVOC concepts, similar to Chalkidis et al. (2020), please download the relevant JSON file here.\nThen you may load it and use it:", "### Data Splits\n\n\n\n\n\n\n[1] Native and Total EU speakers percentage (%) \n\n[2] Training / Development / Test Splits\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThe original dataset was curated by Chalkidis et al. (2021). \n\nThe new version of the dataset was curated by Xenouleas et al. (2022). \n\nThe documents have been annotated by the Publications Office of EU (URL", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe original data are available at the EUR-LEX portal (URL) in unprocessed formats (HTML, XML, RDF). The documents were downloaded from the EUR-LEX portal in HTML. The relevant EUROVOC concepts were downloaded from the SPARQL endpoint of the Publications Office of EU (URL\nChalkidis et al. (2021) stripped HTML mark-up to provide the documents in plain text format and inferred the labels for EUROVOC levels 1--3, by backtracking the EUROVOC hierarchy branches, from the originally assigned labels to their ancestors in levels 1--3, respectively.", "#### Who are the source language producers?", "### Annotations", "#### Annotation process\n\n\nAll the documents of the dataset have been annotated by the Publications Office of EU (URL with multiple concepts from EUROVOC (URL EUROVOC has eight levels of concepts. Each document is assigned one or more concepts (labels). If a document is assigned a concept, the ancestors and descendants of that concept are typically not assigned to the same document. The documents were originally annotated with concepts from levels 3 to 8.\nChalkidis et al. (2021)augmented the annotation with three alternative sets of labels per document, replacing each assigned concept by its ancestor from level 1, 2, or 3, respectively.\nThus, Chalkidis et al. (2021) provide four sets of gold labels per document, one for each of the first three levels of the hierarchy, plus the original sparse label assignment.Levels 4 to 8 cannot be used independently, as many documents have gold concepts from the third level; thus many documents will be mislabeled, if we discard level 3.", "#### Who are the annotators?\n\n\nPublications Office of EU (URL", "### Personal and Sensitive Information\n\n\nThe dataset contains publicly available EU laws that do not include personal or sensitive information with the exception of trivial information presented by consent, e.g., the names of the current presidents of the European Parliament and European Council, and other administration bodies.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nXenouleas et al. (2021)", "### Licensing Information\n\n\nWe provide MultiEURLEX with the same licensing as the original EU data (CC-BY-4.0):\n\n\n© European Union, 1998-2021\n\n\nThe Commission’s document reuse policy is based on Decision 2011/833/EU. Unless otherwise specified, you can re-use the legal documents published in EUR-Lex for commercial or non-commercial purposes.\n\n\nThe copyright for the editorial content of this website, the summaries of EU legislation and the consolidated texts, which is owned by the EU, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.\n\n\nSource: URL \n\nRead more: URL\n\n\n*Stratos Xenouleas, Alexia Tsoukara, Giannis Panagiotakis Ilias Chalkidis, and Ion Androutsopoulos.*\n*Realistic Zero-Shot Cross-Lingual Transfer in Legal Topic Classification.*\n*Proceedings of 12th Hellenic Conference on Artificial Intelligence (SETN 2022). Corfu, Greece. 2022*", "### Contributions\n\n\nThanks to @iliaschalkidis for adding this dataset." ]

[ "TAGS\n#task_categories-text-classification #task_ids-multi-label-classification #task_ids-topic-classification #annotations_creators-found #language_creators-found #language_creators-machine-generated #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-extended|multi_eurlex #language-English #language-German #language-French #language-Modern Greek (1453-) #language-Slovak #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nDocuments\n\n\nMultiEURLEX of Chalkidis et al. (2021) comprises 65k EU laws in 23 official EU languages. Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU. Each EUROVOC label ID is associated with a *label descriptor*, e.g., [60, agri-foodstuffs], [6006, plant product], [1115, fruit]. The descriptors are also available in the 23 languages. Chalkidis et al. (2019) published a monolingual (English) version of this dataset, called EUR-LEX, comprising 57k EU laws with the originally assigned gold labels.\n\n\nIn this new version, dubbed \"Non-Parallel MultiEURLEX (incl. Translations)\", MultiEURLEX comprises non-parallel documents across 5 languages (English, German, French, Greek, and Slovak), i.e., 11,000 different documents per language, including also translations from English to the rest of the 4 available languages.", "### Supported Tasks and Leaderboards\n\n\nMultiEURLEX can be used for legal topic classification, a multi-label classification task where legal documents need to be assigned concepts (in our case, from EUROVOC) reflecting their topics. Unlike EUR-LEX, however, MultiEURLEX supports labels from three different granularities (EUROVOC levels). More importantly, apart from monolingual (*one-to-one*) experiments, it can be used to study cross-lingual transfer scenarios, including *one-to-many* (systems trained in one language and used in other languages with no training data), and *many-to-one* or *many-to-many* (systems jointly trained in multiple languages and used in one or more other languages).\n\n\nThe dataset is not yet part of an established benchmark.", "### Languages\n\n\nThe EU has 24 official languages. When new members join the EU, the set of official languages usually expands, except the languages are already included. MultiEURLEX covers 23 languages from seven language families (Germanic, Romance, Slavic, Uralic, Baltic, Semitic, Hellenic). EU laws are published in all official languages, except Irish, for resource-related reasons (Read more at URL This wide coverage makes MultiEURLEX a valuable testbed for cross-lingual transfer. All languages use the Latin script, except for Bulgarian (Cyrillic script) and Greek. Several other languages are also spoken in EU countries. The EU is home to over 60 additional indigenous regional or minority languages, e.g., Basque, Catalan, Frisian, Saami, and Yiddish, among others, spoken by approx. 40 million people, but these additional languages are not considered official (in terms of EU), and EU laws are not translated to them.\n\n\nThis version of MultiEURLEX covers 5 EU languages (English, German, French, Greek, and Slovak). It also includes machine-translated versions of the documents using the EasyNMT framework (URL utilizing the many-to-many M2M\\_100\\_418M model of Fan et al. (2020) for el-to-en and el-to-de pairs and the OPUS-MT (Tiedemann et al., 2020) models for the rest.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nMultilingual use of the dataset\n\n\nWhen the dataset is used in a multilingual setting selecting the the 'all\\_languages' flag:\n\n\nMonolingual use of the dataset\n\n\nWhen the dataset is used in a monolingual setting selecting the ISO language code for one of the 5 supported languages, or supported translation pairs in the form src2trg, where src and trg are ISO language codes, e.g., en2fr for English translated to French. For example:", "### Data Fields\n\n\nMultilingual use of the dataset\n\n\nThe following data fields are provided for documents ('train', 'dev', 'test'):\n\n\n'celex\\_id': (str) The official ID of the document. The CELEX number is the unique identifier for all publications in both Eur-Lex and CELLAR. \n\n'text': (dict[str]) A dictionary with the 23 languages as keys and the full content of each document as values. \n\n'labels': (List[int]) The relevant EUROVOC concepts (labels).\n\n\nMonolingual use of the dataset\n\n\nThe following data fields are provided for documents ('train', 'dev', 'test'):\n\n\n'celex\\_id': (str) The official ID of the document. The CELEX number is the unique identifier for all publications in both Eur-Lex and CELLAR. \n\n'text': (str) The full content of each document across languages. \n\n'labels': (List[int]) The relevant EUROVOC concepts (labels).\n\n\nIf you want to use the descriptors of the EUROVOC concepts, similar to Chalkidis et al. (2020), please download the relevant JSON file here.\nThen you may load it and use it:", "### Data Splits\n\n\n\n\n\n\n[1] Native and Total EU speakers percentage (%) \n\n[2] Training / Development / Test Splits\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThe original dataset was curated by Chalkidis et al. (2021). \n\nThe new version of the dataset was curated by Xenouleas et al. (2022). \n\nThe documents have been annotated by the Publications Office of EU (URL", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe original data are available at the EUR-LEX portal (URL) in unprocessed formats (HTML, XML, RDF). The documents were downloaded from the EUR-LEX portal in HTML. The relevant EUROVOC concepts were downloaded from the SPARQL endpoint of the Publications Office of EU (URL\nChalkidis et al. (2021) stripped HTML mark-up to provide the documents in plain text format and inferred the labels for EUROVOC levels 1--3, by backtracking the EUROVOC hierarchy branches, from the originally assigned labels to their ancestors in levels 1--3, respectively.", "#### Who are the source language producers?", "### Annotations", "#### Annotation process\n\n\nAll the documents of the dataset have been annotated by the Publications Office of EU (URL with multiple concepts from EUROVOC (URL EUROVOC has eight levels of concepts. Each document is assigned one or more concepts (labels). If a document is assigned a concept, the ancestors and descendants of that concept are typically not assigned to the same document. The documents were originally annotated with concepts from levels 3 to 8.\nChalkidis et al. (2021)augmented the annotation with three alternative sets of labels per document, replacing each assigned concept by its ancestor from level 1, 2, or 3, respectively.\nThus, Chalkidis et al. (2021) provide four sets of gold labels per document, one for each of the first three levels of the hierarchy, plus the original sparse label assignment.Levels 4 to 8 cannot be used independently, as many documents have gold concepts from the third level; thus many documents will be mislabeled, if we discard level 3.", "#### Who are the annotators?\n\n\nPublications Office of EU (URL", "### Personal and Sensitive Information\n\n\nThe dataset contains publicly available EU laws that do not include personal or sensitive information with the exception of trivial information presented by consent, e.g., the names of the current presidents of the European Parliament and European Council, and other administration bodies.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nXenouleas et al. (2021)", "### Licensing Information\n\n\nWe provide MultiEURLEX with the same licensing as the original EU data (CC-BY-4.0):\n\n\n© European Union, 1998-2021\n\n\nThe Commission’s document reuse policy is based on Decision 2011/833/EU. Unless otherwise specified, you can re-use the legal documents published in EUR-Lex for commercial or non-commercial purposes.\n\n\nThe copyright for the editorial content of this website, the summaries of EU legislation and the consolidated texts, which is owned by the EU, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.\n\n\nSource: URL \n\nRead more: URL\n\n\n*Stratos Xenouleas, Alexia Tsoukara, Giannis Panagiotakis Ilias Chalkidis, and Ion Androutsopoulos.*\n*Realistic Zero-Shot Cross-Lingual Transfer in Legal Topic Classification.*\n*Proceedings of 12th Hellenic Conference on Artificial Intelligence (SETN 2022). Corfu, Greece. 2022*", "### Contributions\n\n\nThanks to @iliaschalkidis for adding this dataset." ]

[ "annotations_creators:expert-generated", "language_creators:Digital Umuganda", "multilinguality:multilingual", "size_categories:40K<n<50K", "language:en", "language:rw", "license:cc-by-4.0", "region:us" ]

[]

[ "en", "rw" ]

[ "# Kinyarwanda English Parallel Datasets for Machine translation\n\nA 48,000 Kinyarwanda English Parallel datasets for machine translation, made by curating and translating normal Kinyarwanda sentences into English" ]

[ "TAGS\n#annotations_creators-expert-generated #language_creators-Digital Umuganda #multilinguality-multilingual #size_categories-40K<n<50K #language-English #language-Kinyarwanda #license-cc-by-4.0 #region-us \n", "# Kinyarwanda English Parallel Datasets for Machine translation\n\nA 48,000 Kinyarwanda English Parallel datasets for machine translation, made by curating and translating normal Kinyarwanda sentences into English" ]

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for MIT_movies_fixed", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Galileo Homepage: Galileo ML Data Intelligence Platform\n- Repository: \n- Dataset Blog: Improving Your ML Datasets With Galileo, Part 2\n- Leaderboard: \n- Point of Contact: \n- MIT movies Homepage: newsgroups homepage", "### Dataset Summary\n\nThis dataset is a version of the MIT movies", "### Curation Rationale\n\nThis dataset was created to showcase the power of Galileo as a Data Intelligence Platform. Through Galileo, we identify critical error patterns within the original MIT movies dataset - annotation errors, ill-formed samples etc. Moreover, we observe that these errors permeate throughout the test dataset. As a result of our analysis, we fix 4% of the dataset by re-annotating the samples, and provide the dataset for NER research. To learn more about the process of fixing this dataset, please refer to our Blog.", "## Dataset Structure", "### Data Instances\nEvery sample is blank line separated, every row is tab separated, and contains the word and its corresponding NER tag. This dataset uses the BIOES tagging schema. \n\nAn example from the dataset looks as follows:", "### Data Splits\n\nThe data is split into a training and test split. The training data has ~9700 samples and the test data has ~2700 samples.", "### Data Classes\nThe dataset contains the following 12 classes: ACTOR, YEAR, TITLE, GENRE, DIRECTOR, SONG, PLOT, REVIEW, CHARACTER, RATING, RATINGS_AVERAGE, TRAILER. Some of the classes have high semantic overlap (e.g. RATING/RATINGS_AVERAGE and ACTOR/DIRECTOR)." ]

[ "TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-unknown #region-us \n", "# Dataset Card for MIT_movies_fixed", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Galileo Homepage: Galileo ML Data Intelligence Platform\n- Repository: \n- Dataset Blog: Improving Your ML Datasets With Galileo, Part 2\n- Leaderboard: \n- Point of Contact: \n- MIT movies Homepage: newsgroups homepage", "### Dataset Summary\n\nThis dataset is a version of the MIT movies", "### Curation Rationale\n\nThis dataset was created to showcase the power of Galileo as a Data Intelligence Platform. Through Galileo, we identify critical error patterns within the original MIT movies dataset - annotation errors, ill-formed samples etc. Moreover, we observe that these errors permeate throughout the test dataset. As a result of our analysis, we fix 4% of the dataset by re-annotating the samples, and provide the dataset for NER research. To learn more about the process of fixing this dataset, please refer to our Blog.", "## Dataset Structure", "### Data Instances\nEvery sample is blank line separated, every row is tab separated, and contains the word and its corresponding NER tag. This dataset uses the BIOES tagging schema. \n\nAn example from the dataset looks as follows:", "### Data Splits\n\nThe data is split into a training and test split. The training data has ~9700 samples and the test data has ~2700 samples.", "### Data Classes\nThe dataset contains the following 12 classes: ACTOR, YEAR, TITLE, GENRE, DIRECTOR, SONG, PLOT, REVIEW, CHARACTER, RATING, RATINGS_AVERAGE, TRAILER. Some of the classes have high semantic overlap (e.g. RATING/RATINGS_AVERAGE and ACTOR/DIRECTOR)." ]

[ "region:us" ]

[]

[]

[]

[ "TAGS\n#region-us \n" ]

[ "region:us" ]

[]

[]

[]

[ "TAGS\n#region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for [test]", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:" ]

[ "TAGS\n#region-us \n", "# Dataset Card for [test]", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:" ]

[ "license:wtfpl", "region:us" ]

[]

[]

[]

[ "TAGS\n#license-wtfpl #region-us \n" ]

[ "task_categories:text-classification", "task_categories:text-generation", "task_ids:text-scoring", "task_ids:dialogue-modeling", "annotations_creators:crowdsourced", "language_creators:expert-generated", "language_creators:other", "multilinguality:monolingual", "size_categories:100K<n<1M", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-nc-4.0", "arxiv:2004.09544", "region:us" ]

[ "2004.09544" ]

[ "en" ]

[ "### Dataset Summary\n\n\nThis is the repo for the paper \"Grounding Conversations with Improvised Dialogues\" (ACL2020).\nThe *Selected Pairs of Learnable ImprovisatioN* (SPOLIN) corpus is a collection of more than 68,000 \"Yes, and\" type dialogue pairs extracted from the Spontaneanation podcast by Paul F. Tompkins, the Cornell Movie-Dialogs Corpus, and the SubTle corpus. For more information, refer to our paper or our project page.", "### Available SPOLIN Versions:\n\n\nThe core dataset that was used for the experiments in the paper only includes *yes-ands* and non-*yes-ands* from Spontaneanation and most of what is provided in those extracted from the Cornell Movie-Dialogs Corpus. After the submitting the paper, we continued our iterative data augmentation process, repeating another iteration with the Cornell Movie-Dialogs Corpus and extracting from the SubTle corpus. This expanded version is also included in this repository here. This latest version of SPOLIN was used to train the model used in our demo.\n\n\nIn the 'data' folder, we provide two versions of the SPOLIN training set:\n\n\n1. Version used for experiments in the ACL paper: 'data/URL'\n2. Expanded version: 'data/URL'", "### Relevant Links:\n\n\n* Project page: URL\n* Github repo: URL\n* SpolinBot Demo: URL\n* ACL2020 Paper: URL\n\n\nDataset Structure\n-----------------\n\n\nFields\n\n\n* 'id': unique identifier\n* 'prompt': first utterance in utterance pair\n* 'response': second utterance in utterance pair\n* 'label': yesand = 1, non-yesand = 0\n* 'source': the source for the sample\n* 'split': whether the sample belongs to the training set or the validation set\n\n\nDataset Statistics\n------------------", "##### 'URL':", "##### 'URL':", "##### 'URL':\n\n\n\n\\*Artificially collected by mix & matching positive Spontaneanation samples to balance dataset for training classifier\n\n\nOther Information\n-----------------", "### ACL Presentation\n\n\nVideo recording\n\n\nIf you use our data for your work, please cite our ACL2020 paper:", "### Licensing Information\n\n\nThis work is licensed under a [Creative Commons Attribution-NonCommercial 4.0 International License](URL).\n\n\n[](URL)" ]

[ "TAGS\n#task_categories-text-classification #task_categories-text-generation #task_ids-text-scoring #task_ids-dialogue-modeling #annotations_creators-crowdsourced #language_creators-expert-generated #language_creators-other #multilinguality-monolingual #size_categories-100K<n<1M #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-nc-4.0 #arxiv-2004.09544 #region-us \n", "### Dataset Summary\n\n\nThis is the repo for the paper \"Grounding Conversations with Improvised Dialogues\" (ACL2020).\nThe *Selected Pairs of Learnable ImprovisatioN* (SPOLIN) corpus is a collection of more than 68,000 \"Yes, and\" type dialogue pairs extracted from the Spontaneanation podcast by Paul F. Tompkins, the Cornell Movie-Dialogs Corpus, and the SubTle corpus. For more information, refer to our paper or our project page.", "### Available SPOLIN Versions:\n\n\nThe core dataset that was used for the experiments in the paper only includes *yes-ands* and non-*yes-ands* from Spontaneanation and most of what is provided in those extracted from the Cornell Movie-Dialogs Corpus. After the submitting the paper, we continued our iterative data augmentation process, repeating another iteration with the Cornell Movie-Dialogs Corpus and extracting from the SubTle corpus. This expanded version is also included in this repository here. This latest version of SPOLIN was used to train the model used in our demo.\n\n\nIn the 'data' folder, we provide two versions of the SPOLIN training set:\n\n\n1. Version used for experiments in the ACL paper: 'data/URL'\n2. Expanded version: 'data/URL'", "### Relevant Links:\n\n\n* Project page: URL\n* Github repo: URL\n* SpolinBot Demo: URL\n* ACL2020 Paper: URL\n\n\nDataset Structure\n-----------------\n\n\nFields\n\n\n* 'id': unique identifier\n* 'prompt': first utterance in utterance pair\n* 'response': second utterance in utterance pair\n* 'label': yesand = 1, non-yesand = 0\n* 'source': the source for the sample\n* 'split': whether the sample belongs to the training set or the validation set\n\n\nDataset Statistics\n------------------", "##### 'URL':", "##### 'URL':", "##### 'URL':\n\n\n\n\\*Artificially collected by mix & matching positive Spontaneanation samples to balance dataset for training classifier\n\n\nOther Information\n-----------------", "### ACL Presentation\n\n\nVideo recording\n\n\nIf you use our data for your work, please cite our ACL2020 paper:", "### Licensing Information\n\n\nThis work is licensed under a [Creative Commons Attribution-NonCommercial 4.0 International License](URL).\n\n\n[](URL)" ]

[ "task_categories:tabular-regression", "annotations_creators:expert", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:extended", "language:en", "license:cc-by-4.0", "arxiv:2206.10540", "doi:10.57967/hf/0763", "region:us" ]

[ "2206.10540" ]

[ "en" ]

[ "# Dataset Card for SRSD-Feynman (Easy set)", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository: URL\n- Paper: Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery\n- Point of Contact: Yoshitaka Ushiku", "### Dataset Summary\n\nOur SRSD (Feynman) datasets are designed to discuss the performance of Symbolic Regression for Scientific Discovery.\nWe carefully reviewed the properties of each formula and its variables in the Feynman Symbolic Regression Database to design reasonably realistic sampling range of values so that our SRSD datasets can be used for evaluating the potential of SRSD such as whether or not an SR method con (re)discover physical laws from such datasets.\n\nThis is the *Easy set* of our SRSD-Feynman datasets, which consists of the following 30 different physics formulas:\n\n![Click here to open a PDF file](URL\n\n\nMore details of these datasets are provided in the paper and its supplementary material.", "### Supported Tasks and Leaderboards\n\nSymbolic Regression", "## Dataset Structure", "### Data Instances\n\nTabular data + Ground-truth equation per equation\n\nTabular data: (num_samples, num_variables+1), where the last (rightmost) column indicate output of the target function for given variables.\nNote that the number of variables ('num_variables') varies from equation to equation.\n \nGround-truth equation: *pickled* symbolic representation (equation with symbols in sympy) of the target function.", "### Data Fields\n\nFor each dataset, we have \n1. train split (txt file, whitespace as a delimiter)\n2. val split (txt file, whitespace as a delimiter)\n3. test split (txt file, whitespace as a delimiter)\n4. true equation (pickle file for sympy object)", "### Data Splits\n\n- train: 8,000 samples per equation\n- val: 1,000 samples per equation\n- test: 1,000 samples per equation", "## Dataset Creation", "### Curation Rationale\n\nWe chose target equations based on the Feynman Symbolic Regression Database.", "### Annotations", "#### Annotation process\n\nWe significantly revised the sampling range for each variable from the annotations in the Feynman Symbolic Regression Database.\nFirst, we checked the properties of each variable and treat physical constants (e.g., light speed, gravitational constant) as constants.\nNext, variable ranges were defined to correspond to each typical physics experiment to confirm the physical phenomenon for each equation.\nIn cases where a specific experiment is difficult to be assumed, ranges were set within which the corresponding physical phenomenon can be seen.\nGenerally, the ranges are set to be sampled on log scales within their orders as 10^2 in order to take both large and small changes in value as the order changes.\nVariables such as angles, for which a linear distribution is expected are set to be sampled uniformly.\nIn addition, variables that take a specific sign were set to be sampled within that range.", "#### Who are the annotators?\n\nThe main annotators are\n- Naoya Chiba (@nchiba)\n- Ryo Igarashi (@rigarash)", "### Personal and Sensitive Information\n\nN/A", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nWe annotated this dataset, assuming typical physical experiments. The dataset will engage research on symbolic regression for scientific discovery (SRSD) and help researchers discuss the potential of symbolic regression methods towards data-driven scientific discovery.", "### Discussion of Biases\n\nOur choices of target equations are based on the Feynman Symbolic Regression Database, which are focused on a field of Physics.", "### Other Known Limitations\n\nSome variables used in our datasets indicate some numbers (counts), which should be treated as integer.\nDue to the capacity of 32-bit integer, however, we treated some of such variables as float e.g., number of molecules (10^{23} - 10^{25})", "## Additional Information", "### Dataset Curators\n\nThe main curators are\n- Naoya Chiba (@nchiba)\n- Ryo Igarashi (@rigarash)", "### Licensing Information\n\nCreative Commons Attribution 4.0\n\n\n\n[Preprint]", "### Contributions\n\nAuthors:\n- Yoshitomo Matsubara (@yoshitomo-matsubara)\n- Naoya Chiba (@nchiba)\n- Ryo Igarashi (@rigarash)\n- Yoshitaka Ushiku (@yushiku)" ]

[ "TAGS\n#task_categories-tabular-regression #annotations_creators-expert #language_creators-expert-generated #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended #language-English #license-cc-by-4.0 #arxiv-2206.10540 #doi-10.57967/hf/0763 #region-us \n", "# Dataset Card for SRSD-Feynman (Easy set)", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository: URL\n- Paper: Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery\n- Point of Contact: Yoshitaka Ushiku", "### Dataset Summary\n\nOur SRSD (Feynman) datasets are designed to discuss the performance of Symbolic Regression for Scientific Discovery.\nWe carefully reviewed the properties of each formula and its variables in the Feynman Symbolic Regression Database to design reasonably realistic sampling range of values so that our SRSD datasets can be used for evaluating the potential of SRSD such as whether or not an SR method con (re)discover physical laws from such datasets.\n\nThis is the *Easy set* of our SRSD-Feynman datasets, which consists of the following 30 different physics formulas:\n\n![Click here to open a PDF file](URL\n\n\nMore details of these datasets are provided in the paper and its supplementary material.", "### Supported Tasks and Leaderboards\n\nSymbolic Regression", "## Dataset Structure", "### Data Instances\n\nTabular data + Ground-truth equation per equation\n\nTabular data: (num_samples, num_variables+1), where the last (rightmost) column indicate output of the target function for given variables.\nNote that the number of variables ('num_variables') varies from equation to equation.\n \nGround-truth equation: *pickled* symbolic representation (equation with symbols in sympy) of the target function.", "### Data Fields\n\nFor each dataset, we have \n1. train split (txt file, whitespace as a delimiter)\n2. val split (txt file, whitespace as a delimiter)\n3. test split (txt file, whitespace as a delimiter)\n4. true equation (pickle file for sympy object)", "### Data Splits\n\n- train: 8,000 samples per equation\n- val: 1,000 samples per equation\n- test: 1,000 samples per equation", "## Dataset Creation", "### Curation Rationale\n\nWe chose target equations based on the Feynman Symbolic Regression Database.", "### Annotations", "#### Annotation process\n\nWe significantly revised the sampling range for each variable from the annotations in the Feynman Symbolic Regression Database.\nFirst, we checked the properties of each variable and treat physical constants (e.g., light speed, gravitational constant) as constants.\nNext, variable ranges were defined to correspond to each typical physics experiment to confirm the physical phenomenon for each equation.\nIn cases where a specific experiment is difficult to be assumed, ranges were set within which the corresponding physical phenomenon can be seen.\nGenerally, the ranges are set to be sampled on log scales within their orders as 10^2 in order to take both large and small changes in value as the order changes.\nVariables such as angles, for which a linear distribution is expected are set to be sampled uniformly.\nIn addition, variables that take a specific sign were set to be sampled within that range.", "#### Who are the annotators?\n\nThe main annotators are\n- Naoya Chiba (@nchiba)\n- Ryo Igarashi (@rigarash)", "### Personal and Sensitive Information\n\nN/A", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nWe annotated this dataset, assuming typical physical experiments. The dataset will engage research on symbolic regression for scientific discovery (SRSD) and help researchers discuss the potential of symbolic regression methods towards data-driven scientific discovery.", "### Discussion of Biases\n\nOur choices of target equations are based on the Feynman Symbolic Regression Database, which are focused on a field of Physics.", "### Other Known Limitations\n\nSome variables used in our datasets indicate some numbers (counts), which should be treated as integer.\nDue to the capacity of 32-bit integer, however, we treated some of such variables as float e.g., number of molecules (10^{23} - 10^{25})", "## Additional Information", "### Dataset Curators\n\nThe main curators are\n- Naoya Chiba (@nchiba)\n- Ryo Igarashi (@rigarash)", "### Licensing Information\n\nCreative Commons Attribution 4.0\n\n\n\n[Preprint]", "### Contributions\n\nAuthors:\n- Yoshitomo Matsubara (@yoshitomo-matsubara)\n- Naoya Chiba (@nchiba)\n- Ryo Igarashi (@rigarash)\n- Yoshitaka Ushiku (@yushiku)" ]

[ "task_categories:tabular-regression", "annotations_creators:expert", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:extended", "language:en", "license:cc-by-4.0", "arxiv:2206.10540", "doi:10.57967/hf/0762", "region:us" ]

[ "2206.10540" ]

[ "en" ]

[ "# Dataset Card for SRSD-Feynman (Medium set)", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository: URL\n- Paper: Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery\n- Point of Contact: Yoshitaka Ushiku", "### Dataset Summary\n\nOur SRSD (Feynman) datasets are designed to discuss the performance of Symbolic Regression for Scientific Discovery.\nWe carefully reviewed the properties of each formula and its variables in the Feynman Symbolic Regression Database to design reasonably realistic sampling range of values so that our SRSD datasets can be used for evaluating the potential of SRSD such as whether or not an SR method con (re)discover physical laws from such datasets.\n\nThis is the *Medium set* of our SRSD-Feynman datasets, which consists of the following 40 different physics formulas:\n\n![Click here to open a PDF file](URL\n\n\nMore details of these datasets are provided in the paper and its supplementary material.", "### Supported Tasks and Leaderboards\n\nSymbolic Regression", "## Dataset Structure", "### Data Instances\n\nTabular data + Ground-truth equation per equation\n\nTabular data: (num_samples, num_variables+1), where the last (rightmost) column indicate output of the target function for given variables.\nNote that the number of variables ('num_variables') varies from equation to equation.\n \nGround-truth equation: *pickled* symbolic representation (equation with symbols in sympy) of the target function.", "### Data Fields\n\nFor each dataset, we have \n1. train split (txt file, whitespace as a delimiter)\n2. val split (txt file, whitespace as a delimiter)\n3. test split (txt file, whitespace as a delimiter)\n4. true equation (pickle file for sympy object)", "### Data Splits\n\n- train: 8,000 samples per equation\n- val: 1,000 samples per equation\n- test: 1,000 samples per equation", "## Dataset Creation", "### Curation Rationale\n\nWe chose target equations based on the Feynman Symbolic Regression Database.", "### Annotations", "#### Annotation process\n\nWe significantly revised the sampling range for each variable from the annotations in the Feynman Symbolic Regression Database.\nFirst, we checked the properties of each variable and treat physical constants (e.g., light speed, gravitational constant) as constants.\nNext, variable ranges were defined to correspond to each typical physics experiment to confirm the physical phenomenon for each equation.\nIn cases where a specific experiment is difficult to be assumed, ranges were set within which the corresponding physical phenomenon can be seen.\nGenerally, the ranges are set to be sampled on log scales within their orders as 10^2 in order to take both large and small changes in value as the order changes.\nVariables such as angles, for which a linear distribution is expected are set to be sampled uniformly.\nIn addition, variables that take a specific sign were set to be sampled within that range.", "#### Who are the annotators?\n\nThe main annotators are\n- Naoya Chiba (@nchiba)\n- Ryo Igarashi (@rigarash)", "### Personal and Sensitive Information\n\nN/A", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nWe annotated this dataset, assuming typical physical experiments. The dataset will engage research on symbolic regression for scientific discovery (SRSD) and help researchers discuss the potential of symbolic regression methods towards data-driven scientific discovery.", "### Discussion of Biases\n\nOur choices of target equations are based on the Feynman Symbolic Regression Database, which are focused on a field of Physics.", "### Other Known Limitations\n\nSome variables used in our datasets indicate some numbers (counts), which should be treated as integer.\nDue to the capacity of 32-bit integer, however, we treated some of such variables as float e.g., number of molecules (10^{23} - 10^{25})", "## Additional Information", "### Dataset Curators\n\nThe main curators are\n- Naoya Chiba (@nchiba)\n- Ryo Igarashi (@rigarash)", "### Licensing Information\n\nCreative Commons Attribution 4.0\n\n\n\n[Preprint]", "### Contributions\n\nAuthors:\n- Yoshitomo Matsubara (@yoshitomo-matsubara)\n- Naoya Chiba (@nchiba)\n- Ryo Igarashi (@rigarash)\n- Yoshitaka Ushiku (@yushiku)" ]

[ "TAGS\n#task_categories-tabular-regression #annotations_creators-expert #language_creators-expert-generated #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended #language-English #license-cc-by-4.0 #arxiv-2206.10540 #doi-10.57967/hf/0762 #region-us \n", "# Dataset Card for SRSD-Feynman (Medium set)", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository: URL\n- Paper: Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery\n- Point of Contact: Yoshitaka Ushiku", "### Dataset Summary\n\nOur SRSD (Feynman) datasets are designed to discuss the performance of Symbolic Regression for Scientific Discovery.\nWe carefully reviewed the properties of each formula and its variables in the Feynman Symbolic Regression Database to design reasonably realistic sampling range of values so that our SRSD datasets can be used for evaluating the potential of SRSD such as whether or not an SR method con (re)discover physical laws from such datasets.\n\nThis is the *Medium set* of our SRSD-Feynman datasets, which consists of the following 40 different physics formulas:\n\n![Click here to open a PDF file](URL\n\n\nMore details of these datasets are provided in the paper and its supplementary material.", "### Supported Tasks and Leaderboards\n\nSymbolic Regression", "## Dataset Structure", "### Data Instances\n\nTabular data + Ground-truth equation per equation\n\nTabular data: (num_samples, num_variables+1), where the last (rightmost) column indicate output of the target function for given variables.\nNote that the number of variables ('num_variables') varies from equation to equation.\n \nGround-truth equation: *pickled* symbolic representation (equation with symbols in sympy) of the target function.", "### Data Fields\n\nFor each dataset, we have \n1. train split (txt file, whitespace as a delimiter)\n2. val split (txt file, whitespace as a delimiter)\n3. test split (txt file, whitespace as a delimiter)\n4. true equation (pickle file for sympy object)", "### Data Splits\n\n- train: 8,000 samples per equation\n- val: 1,000 samples per equation\n- test: 1,000 samples per equation", "## Dataset Creation", "### Curation Rationale\n\nWe chose target equations based on the Feynman Symbolic Regression Database.", "### Annotations", "#### Annotation process\n\nWe significantly revised the sampling range for each variable from the annotations in the Feynman Symbolic Regression Database.\nFirst, we checked the properties of each variable and treat physical constants (e.g., light speed, gravitational constant) as constants.\nNext, variable ranges were defined to correspond to each typical physics experiment to confirm the physical phenomenon for each equation.\nIn cases where a specific experiment is difficult to be assumed, ranges were set within which the corresponding physical phenomenon can be seen.\nGenerally, the ranges are set to be sampled on log scales within their orders as 10^2 in order to take both large and small changes in value as the order changes.\nVariables such as angles, for which a linear distribution is expected are set to be sampled uniformly.\nIn addition, variables that take a specific sign were set to be sampled within that range.", "#### Who are the annotators?\n\nThe main annotators are\n- Naoya Chiba (@nchiba)\n- Ryo Igarashi (@rigarash)", "### Personal and Sensitive Information\n\nN/A", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nWe annotated this dataset, assuming typical physical experiments. The dataset will engage research on symbolic regression for scientific discovery (SRSD) and help researchers discuss the potential of symbolic regression methods towards data-driven scientific discovery.", "### Discussion of Biases\n\nOur choices of target equations are based on the Feynman Symbolic Regression Database, which are focused on a field of Physics.", "### Other Known Limitations\n\nSome variables used in our datasets indicate some numbers (counts), which should be treated as integer.\nDue to the capacity of 32-bit integer, however, we treated some of such variables as float e.g., number of molecules (10^{23} - 10^{25})", "## Additional Information", "### Dataset Curators\n\nThe main curators are\n- Naoya Chiba (@nchiba)\n- Ryo Igarashi (@rigarash)", "### Licensing Information\n\nCreative Commons Attribution 4.0\n\n\n\n[Preprint]", "### Contributions\n\nAuthors:\n- Yoshitomo Matsubara (@yoshitomo-matsubara)\n- Naoya Chiba (@nchiba)\n- Ryo Igarashi (@rigarash)\n- Yoshitaka Ushiku (@yushiku)" ]

[ "task_categories:tabular-regression", "annotations_creators:expert", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:extended", "language:en", "license:cc-by-4.0", "arxiv:2206.10540", "doi:10.57967/hf/0761", "region:us" ]

[ "2206.10540" ]

[ "en" ]

[ "# Dataset Card for SRSD-Feynman (Hard set)", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository: URL\n- Paper: Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery\n- Point of Contact: Yoshitaka Ushiku", "### Dataset Summary\n\nOur SRSD (Feynman) datasets are designed to discuss the performance of Symbolic Regression for Scientific Discovery.\nWe carefully reviewed the properties of each formula and its variables in the Feynman Symbolic Regression Database to design reasonably realistic sampling range of values so that our SRSD datasets can be used for evaluating the potential of SRSD such as whether or not an SR method con (re)discover physical laws from such datasets.\n\nThis is the *Hard set* of our SRSD-Feynman datasets, which consists of the following 50 different physics formulas:\n\n![Click here to open a PDF file](URL\n\n\nMore details of these datasets are provided in the paper and its supplementary material.", "### Supported Tasks and Leaderboards\n\nSymbolic Regression", "## Dataset Structure", "### Data Instances\n\nTabular data + Ground-truth equation per equation\n\nTabular data: (num_samples, num_variables+1), where the last (rightmost) column indicate output of the target function for given variables.\nNote that the number of variables ('num_variables') varies from equation to equation.\n \nGround-truth equation: *pickled* symbolic representation (equation with symbols in sympy) of the target function.", "### Data Fields\n\nFor each dataset, we have \n1. train split (txt file, whitespace as a delimiter)\n2. val split (txt file, whitespace as a delimiter)\n3. test split (txt file, whitespace as a delimiter)\n4. true equation (pickle file for sympy object)", "### Data Splits\n\n- train: 8,000 samples per equation\n- val: 1,000 samples per equation\n- test: 1,000 samples per equation", "## Dataset Creation", "### Curation Rationale\n\nWe chose target equations based on the Feynman Symbolic Regression Database.", "### Annotations", "#### Annotation process\n\nWe significantly revised the sampling range for each variable from the annotations in the Feynman Symbolic Regression Database.\nFirst, we checked the properties of each variable and treat physical constants (e.g., light speed, gravitational constant) as constants.\nNext, variable ranges were defined to correspond to each typical physics experiment to confirm the physical phenomenon for each equation.\nIn cases where a specific experiment is difficult to be assumed, ranges were set within which the corresponding physical phenomenon can be seen.\nGenerally, the ranges are set to be sampled on log scales within their orders as 10^2 in order to take both large and small changes in value as the order changes.\nVariables such as angles, for which a linear distribution is expected are set to be sampled uniformly.\nIn addition, variables that take a specific sign were set to be sampled within that range.", "#### Who are the annotators?\n\nThe main annotators are\n- Naoya Chiba (@nchiba)\n- Ryo Igarashi (@rigarash)", "### Personal and Sensitive Information\n\nN/A", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nWe annotated this dataset, assuming typical physical experiments. The dataset will engage research on symbolic regression for scientific discovery (SRSD) and help researchers discuss the potential of symbolic regression methods towards data-driven scientific discovery.", "### Discussion of Biases\n\nOur choices of target equations are based on the Feynman Symbolic Regression Database, which are focused on a field of Physics.", "### Other Known Limitations\n\nSome variables used in our datasets indicate some numbers (counts), which should be treated as integer.\nDue to the capacity of 32-bit integer, however, we treated some of such variables as float e.g., number of molecules (10^{23} - 10^{25})", "## Additional Information", "### Dataset Curators\n\nThe main curators are\n- Naoya Chiba (@nchiba)\n- Ryo Igarashi (@rigarash)", "### Licensing Information\n\nCreative Commons Attribution 4.0\n\n\n\n[Preprint]", "### Contributions\n\nAuthors:\n- Yoshitomo Matsubara (@yoshitomo-matsubara)\n- Naoya Chiba (@nchiba)\n- Ryo Igarashi (@rigarash)\n- Yoshitaka Ushiku (@yushiku)" ]

[ "TAGS\n#task_categories-tabular-regression #annotations_creators-expert #language_creators-expert-generated #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended #language-English #license-cc-by-4.0 #arxiv-2206.10540 #doi-10.57967/hf/0761 #region-us \n", "# Dataset Card for SRSD-Feynman (Hard set)", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository: URL\n- Paper: Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery\n- Point of Contact: Yoshitaka Ushiku", "### Dataset Summary\n\nOur SRSD (Feynman) datasets are designed to discuss the performance of Symbolic Regression for Scientific Discovery.\nWe carefully reviewed the properties of each formula and its variables in the Feynman Symbolic Regression Database to design reasonably realistic sampling range of values so that our SRSD datasets can be used for evaluating the potential of SRSD such as whether or not an SR method con (re)discover physical laws from such datasets.\n\nThis is the *Hard set* of our SRSD-Feynman datasets, which consists of the following 50 different physics formulas:\n\n![Click here to open a PDF file](URL\n\n\nMore details of these datasets are provided in the paper and its supplementary material.", "### Supported Tasks and Leaderboards\n\nSymbolic Regression", "## Dataset Structure", "### Data Instances\n\nTabular data + Ground-truth equation per equation\n\nTabular data: (num_samples, num_variables+1), where the last (rightmost) column indicate output of the target function for given variables.\nNote that the number of variables ('num_variables') varies from equation to equation.\n \nGround-truth equation: *pickled* symbolic representation (equation with symbols in sympy) of the target function.", "### Data Fields\n\nFor each dataset, we have \n1. train split (txt file, whitespace as a delimiter)\n2. val split (txt file, whitespace as a delimiter)\n3. test split (txt file, whitespace as a delimiter)\n4. true equation (pickle file for sympy object)", "### Data Splits\n\n- train: 8,000 samples per equation\n- val: 1,000 samples per equation\n- test: 1,000 samples per equation", "## Dataset Creation", "### Curation Rationale\n\nWe chose target equations based on the Feynman Symbolic Regression Database.", "### Annotations", "#### Annotation process\n\nWe significantly revised the sampling range for each variable from the annotations in the Feynman Symbolic Regression Database.\nFirst, we checked the properties of each variable and treat physical constants (e.g., light speed, gravitational constant) as constants.\nNext, variable ranges were defined to correspond to each typical physics experiment to confirm the physical phenomenon for each equation.\nIn cases where a specific experiment is difficult to be assumed, ranges were set within which the corresponding physical phenomenon can be seen.\nGenerally, the ranges are set to be sampled on log scales within their orders as 10^2 in order to take both large and small changes in value as the order changes.\nVariables such as angles, for which a linear distribution is expected are set to be sampled uniformly.\nIn addition, variables that take a specific sign were set to be sampled within that range.", "#### Who are the annotators?\n\nThe main annotators are\n- Naoya Chiba (@nchiba)\n- Ryo Igarashi (@rigarash)", "### Personal and Sensitive Information\n\nN/A", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nWe annotated this dataset, assuming typical physical experiments. The dataset will engage research on symbolic regression for scientific discovery (SRSD) and help researchers discuss the potential of symbolic regression methods towards data-driven scientific discovery.", "### Discussion of Biases\n\nOur choices of target equations are based on the Feynman Symbolic Regression Database, which are focused on a field of Physics.", "### Other Known Limitations\n\nSome variables used in our datasets indicate some numbers (counts), which should be treated as integer.\nDue to the capacity of 32-bit integer, however, we treated some of such variables as float e.g., number of molecules (10^{23} - 10^{25})", "## Additional Information", "### Dataset Curators\n\nThe main curators are\n- Naoya Chiba (@nchiba)\n- Ryo Igarashi (@rigarash)", "### Licensing Information\n\nCreative Commons Attribution 4.0\n\n\n\n[Preprint]", "### Contributions\n\nAuthors:\n- Yoshitomo Matsubara (@yoshitomo-matsubara)\n- Naoya Chiba (@nchiba)\n- Ryo Igarashi (@rigarash)\n- Yoshitaka Ushiku (@yushiku)" ]

[ "task_categories:image-to-text", "task_categories:image-classification", "task_categories:visual-question-answering", "task_categories:sentence-similarity", "language:en", "image captioning", "language grounding", "visual semantic", "semantic similarity", "arxiv:2301.08784", "arxiv:1408.5882", "region:us" ]

[ "2301.08784", "1408.5882" ]

[ "en" ]

[ "#### Update: OCT-2023 ### \nAdd v2 with recent SoTA model swinV2 classifier for both soft/*hard-label* visual_caption_cosine_score_v2 with _person_ label (0.2, 0.3 and 0.4)", "# Introduction\n\nModern image captaining relies heavily on extracting knowledge, from images such as objects,\nto capture the concept of static story in the image. In this paper, we propose a textual visual context dataset \nfor captioning, where the publicly available dataset COCO caption (Lin et al., 2014) has been extended with information \nabout the scene (such as objects in the image). Since this information has textual form, it can be used to leverage any NLP task,\nsuch as text similarity or semantic relation methods, into captioning systems, either as an end-to-end training strategy or a post-processing based approach. \n\nPlease refer to project page and Github for more information. ![arXiv](URL ![Website URL](URL\n\nFor quick start please have a look this demo and pre-trained model with th 0.2, 0.3, 0.4", "# Overview\n\n We enrich COCO-Caption with textual Visual Context information. We use ResNet152, CLIP, \n and Faster R-CNN to extract object information for each image. We use three filter approaches \n to ensure the quality of the dataset (1) Threshold: to filter out predictions where the object classifier \n is not confident enough, and (2) semantic alignment with semantic similarity to remove duplicated objects. \n (3) semantic relatedness score as soft-label: to guarantee the visual context and caption have a strong \n relation. In particular, we use Sentence-RoBERTa-sts via cosine similarity to give a soft score, and then \n we use a threshold to annotate the final label (if th ≥ 0.2, 0.3, 0.4 then 1,0). Finally, to take advantage \n of the visual overlap between caption and visual context, and to extract global information, we use BERT followed by a shallow 1D-CNN (Kim, 2014)\n to estimate the visual relatedness score.", "### Download \n\n0. Dowload Raw data with ID and Visual context -> original dataset with related ID caption train2014\n1. Downlod Data with cosine score-> soft cosine lable with th 0.2, 0.3, 0.4 and 0.5 and hardlabel [0,1]\n2. Dowload Overlaping visual with caption-> Overlap visual context and the human annotated caption \n3. Download Dataset (tsv file) 0.0-> raw data with hard lable without cosine similairty and with threshold cosine sim degree of the relation beteween the visual and caption = 0.2, 0.3, 0.4\n4. Download Dataset GenderBias-> man/woman replaced with person class label\n\n\n\nFor future work, we plan to extract the visual context from the caption (without using a visual classifier) and estimate the visual relatedness score by\nemploying unsupervised learning (i.e. contrastive learning). (work in progress)\n \n 1. Download CC -> Caption dataset from Conceptinal Caption (CC) 2M (2255927 captions)\n 2. Download CC+wiki -> CC+1M-wiki 3M (3255928) \n 3. Download CC+wiki+COCO -> CC+wiki+COCO-Caption 3.5M (366984)\n 4. Download COCO-caption+wiki -> COCO-caption +wiki 1.4M (1413915)\n 5. Download COCO-caption+wiki+CC+8Mwiki -> COCO-caption+wiki+CC+8Mwiki 11M (11541667) \n\nThe details of this repo are described in the following paper. If you find this repo useful, please kindly cite it:" ]

[ "TAGS\n#task_categories-image-to-text #task_categories-image-classification #task_categories-visual-question-answering #task_categories-sentence-similarity #language-English #image captioning #language grounding #visual semantic #semantic similarity #arxiv-2301.08784 #arxiv-1408.5882 #region-us \n", "#### Update: OCT-2023 ### \nAdd v2 with recent SoTA model swinV2 classifier for both soft/*hard-label* visual_caption_cosine_score_v2 with _person_ label (0.2, 0.3 and 0.4)", "# Introduction\n\nModern image captaining relies heavily on extracting knowledge, from images such as objects,\nto capture the concept of static story in the image. In this paper, we propose a textual visual context dataset \nfor captioning, where the publicly available dataset COCO caption (Lin et al., 2014) has been extended with information \nabout the scene (such as objects in the image). Since this information has textual form, it can be used to leverage any NLP task,\nsuch as text similarity or semantic relation methods, into captioning systems, either as an end-to-end training strategy or a post-processing based approach. \n\nPlease refer to project page and Github for more information. ![arXiv](URL ![Website URL](URL\n\nFor quick start please have a look this demo and pre-trained model with th 0.2, 0.3, 0.4", "# Overview\n\n We enrich COCO-Caption with textual Visual Context information. We use ResNet152, CLIP, \n and Faster R-CNN to extract object information for each image. We use three filter approaches \n to ensure the quality of the dataset (1) Threshold: to filter out predictions where the object classifier \n is not confident enough, and (2) semantic alignment with semantic similarity to remove duplicated objects. \n (3) semantic relatedness score as soft-label: to guarantee the visual context and caption have a strong \n relation. In particular, we use Sentence-RoBERTa-sts via cosine similarity to give a soft score, and then \n we use a threshold to annotate the final label (if th ≥ 0.2, 0.3, 0.4 then 1,0). Finally, to take advantage \n of the visual overlap between caption and visual context, and to extract global information, we use BERT followed by a shallow 1D-CNN (Kim, 2014)\n to estimate the visual relatedness score.", "### Download \n\n0. Dowload Raw data with ID and Visual context -> original dataset with related ID caption train2014\n1. Downlod Data with cosine score-> soft cosine lable with th 0.2, 0.3, 0.4 and 0.5 and hardlabel [0,1]\n2. Dowload Overlaping visual with caption-> Overlap visual context and the human annotated caption \n3. Download Dataset (tsv file) 0.0-> raw data with hard lable without cosine similairty and with threshold cosine sim degree of the relation beteween the visual and caption = 0.2, 0.3, 0.4\n4. Download Dataset GenderBias-> man/woman replaced with person class label\n\n\n\nFor future work, we plan to extract the visual context from the caption (without using a visual classifier) and estimate the visual relatedness score by\nemploying unsupervised learning (i.e. contrastive learning). (work in progress)\n \n 1. Download CC -> Caption dataset from Conceptinal Caption (CC) 2M (2255927 captions)\n 2. Download CC+wiki -> CC+1M-wiki 3M (3255928) \n 3. Download CC+wiki+COCO -> CC+wiki+COCO-Caption 3.5M (366984)\n 4. Download COCO-caption+wiki -> COCO-caption +wiki 1.4M (1413915)\n 5. Download COCO-caption+wiki+CC+8Mwiki -> COCO-caption+wiki+CC+8Mwiki 11M (11541667) \n\nThe details of this repo are described in the following paper. If you find this repo useful, please kindly cite it:" ]

[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:multilingual", "size_categories:unknown", "source_datasets:extended|wikipedia", "language:en", "language:ar", "language:bn", "language:fi", "language:id", "language:ja", "language:sw", "language:ko", "language:ru", "language:te", "language:th", "license:apache-2.0", "arxiv:2003.11080", "region:us" ]

[ "2003.11080" ]

[ "en", "ar", "bn", "fi", "id", "ja", "sw", "ko", "ru", "te", "th" ]

[ "### Dataset Summary\n\n\nTyDi QA is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs.\nThe languages of TyDi QA are diverse with regard to their typology -- the set of linguistic features that each language\nexpresses -- such that we expect models performing well on this set to generalize across a large number of the languages\nin the world. It contains language phenomena that would not be found in English-only corpora. To provide a realistic\ninformation-seeking task and avoid priming effects, questions are written by people who want to know the answer, but\ndon’t know the answer yet, (unlike SQuAD and its descendents) and the data is collected directly in each language without\nthe use of translation (unlike MLQA and XQuAD).\n\n\nWe also include \"translate-train\" and \"translate-test\" splits for each non-English languages from XTREME (Hu et al., 2020). These splits are the automatic translations from English to each target language used in the XTREME paper [URL The \"translate-train\" split purposefully ignores the non-English TyDiQA-GoldP training data to simulate the transfer learning scenario where original-language data is not available and system builders must rely on labeled English data plus existing machine translation systems.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nDataset Structure\n-----------------", "### Data Instances", "#### primary\\_task\n\n\n* Size of downloaded dataset files: 1863.37 MB\n* Size of the generated dataset: 5757.59 MB\n* Total amount of disk used: 7620.96 MB\n\n\nAn example of 'validation' looks as follows.", "#### secondary\\_task\n\n\n* Size of downloaded dataset files: 1863.37 MB\n* Size of the generated dataset: 55.34 MB\n* Total amount of disk used: 1918.71 MB\n\n\nAn example of 'validation' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### primary\\_task\n\n\n* 'passage\\_answer\\_candidates': a dictionary feature containing:\n\t+ 'plaintext\\_start\\_byte': a 'int32' feature.\n\t+ 'plaintext\\_end\\_byte': a 'int32' feature.\n* 'question\\_text': a 'string' feature.\n* 'document\\_title': a 'string' feature.\n* 'language': a 'string' feature.\n* 'annotations': a dictionary feature containing:\n\t+ 'passage\\_answer\\_candidate\\_index': a 'int32' feature.\n\t+ 'minimal\\_answers\\_start\\_byte': a 'int32' feature.\n\t+ 'minimal\\_answers\\_end\\_byte': a 'int32' feature.\n\t+ 'yes\\_no\\_answer': a 'string' feature.\n* 'document\\_plaintext': a 'string' feature.\n* 'document\\_url': a 'string' feature.", "#### secondary\\_task\n\n\n* 'id': a 'string' feature.\n* 'title': a 'string' feature.\n* 'context': a 'string' feature.\n* 'question': a 'string' feature.\n* 'answers': a dictionary feature containing:\n\t+ 'text': a 'string' feature.\n\t+ 'answer\\_start': a 'int32' feature.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information" ]

[ "TAGS\n#task_categories-question-answering #task_ids-extractive-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-multilingual #size_categories-unknown #source_datasets-extended|wikipedia #language-English #language-Arabic #language-Bengali #language-Finnish #language-Indonesian #language-Japanese #language-Swahili (macrolanguage) #language-Korean #language-Russian #language-Telugu #language-Thai #license-apache-2.0 #arxiv-2003.11080 #region-us \n", "### Dataset Summary\n\n\nTyDi QA is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs.\nThe languages of TyDi QA are diverse with regard to their typology -- the set of linguistic features that each language\nexpresses -- such that we expect models performing well on this set to generalize across a large number of the languages\nin the world. It contains language phenomena that would not be found in English-only corpora. To provide a realistic\ninformation-seeking task and avoid priming effects, questions are written by people who want to know the answer, but\ndon’t know the answer yet, (unlike SQuAD and its descendents) and the data is collected directly in each language without\nthe use of translation (unlike MLQA and XQuAD).\n\n\nWe also include \"translate-train\" and \"translate-test\" splits for each non-English languages from XTREME (Hu et al., 2020). These splits are the automatic translations from English to each target language used in the XTREME paper [URL The \"translate-train\" split purposefully ignores the non-English TyDiQA-GoldP training data to simulate the transfer learning scenario where original-language data is not available and system builders must rely on labeled English data plus existing machine translation systems.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nDataset Structure\n-----------------", "### Data Instances", "#### primary\\_task\n\n\n* Size of downloaded dataset files: 1863.37 MB\n* Size of the generated dataset: 5757.59 MB\n* Total amount of disk used: 7620.96 MB\n\n\nAn example of 'validation' looks as follows.", "#### secondary\\_task\n\n\n* Size of downloaded dataset files: 1863.37 MB\n* Size of the generated dataset: 55.34 MB\n* Total amount of disk used: 1918.71 MB\n\n\nAn example of 'validation' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### primary\\_task\n\n\n* 'passage\\_answer\\_candidates': a dictionary feature containing:\n\t+ 'plaintext\\_start\\_byte': a 'int32' feature.\n\t+ 'plaintext\\_end\\_byte': a 'int32' feature.\n* 'question\\_text': a 'string' feature.\n* 'document\\_title': a 'string' feature.\n* 'language': a 'string' feature.\n* 'annotations': a dictionary feature containing:\n\t+ 'passage\\_answer\\_candidate\\_index': a 'int32' feature.\n\t+ 'minimal\\_answers\\_start\\_byte': a 'int32' feature.\n\t+ 'minimal\\_answers\\_end\\_byte': a 'int32' feature.\n\t+ 'yes\\_no\\_answer': a 'string' feature.\n* 'document\\_plaintext': a 'string' feature.\n* 'document\\_url': a 'string' feature.", "#### secondary\\_task\n\n\n* 'id': a 'string' feature.\n* 'title': a 'string' feature.\n* 'context': a 'string' feature.\n* 'question': a 'string' feature.\n* 'answers': a dictionary feature containing:\n\t+ 'text': a 'string' feature.\n\t+ 'answer\\_start': a 'int32' feature.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information" ]

[ "task_categories:sentence-similarity", "task_categories:text-classification", "task_categories:text-generation", "size_categories:1M<n<10M", "language:ja", "region:us" ]

[]

[ "ja" ]

[ "# Dataset \n\n5M (5121625) clean Japanese full sentence with the context. This dataset can be used to learn unsupervised semantic similarity, etc." ]

[ "TAGS\n#task_categories-sentence-similarity #task_categories-text-classification #task_categories-text-generation #size_categories-1M<n<10M #language-Japanese #region-us \n", "# Dataset \n\n5M (5121625) clean Japanese full sentence with the context. This dataset can be used to learn unsupervised semantic similarity, etc." ]

[ "region:us" ]

[]

[]

[ "# SWAHILI-NER-DATASET\n\nSwahili NER dataset is a Named Entity Recognition (NER) dataset generated from <URL using back-translation techniques.\n\nIn case you're interested to explore more about the script used to generate this dataset, please have a look into Augumented Swahili Data.\n\nThis data has been cleaned using a couple of techniques and is ready for training a Spacy NER model without any modifications, with this data we were able to train a swahili-spacy-ner.", "# EXPLORING DATA\n\nHere is an example of how the dataset has been structured;", "## CONTRIBUTION\n\nThis dataset is open source under therefore you're warmly welcome to contribute,.", "## ISSUES\n\nIn case you're having any issues, please raise one so we can quickly fix it.", "## CREDITS\n\nAll the credits to;\n1. Kalebu\n2. Anthony Mipawa\n3. akshayb7\n \n</samp>" ]

[ "TAGS\n#region-us \n", "# SWAHILI-NER-DATASET\n\nSwahili NER dataset is a Named Entity Recognition (NER) dataset generated from <URL using back-translation techniques.\n\nIn case you're interested to explore more about the script used to generate this dataset, please have a look into Augumented Swahili Data.\n\nThis data has been cleaned using a couple of techniques and is ready for training a Spacy NER model without any modifications, with this data we were able to train a swahili-spacy-ner.", "# EXPLORING DATA\n\nHere is an example of how the dataset has been structured;", "## CONTRIBUTION\n\nThis dataset is open source under therefore you're warmly welcome to contribute,.", "## ISSUES\n\nIn case you're having any issues, please raise one so we can quickly fix it.", "## CREDITS\n\nAll the credits to;\n1. Kalebu\n2. Anthony Mipawa\n3. akshayb7\n \n</samp>" ]

[ "region:us" ]

[]

[]

[ "# Chat Dataset\n\nDerived from Hitomi Team's Convo Dataset on Github, the Chat Dataset is a vast dataset with diverse data used for training models to assist in conversation analysis and generation.", "## Getting Started", "### Prerequisites\n\n- Python\n- Git LFS", "## DISCLAIMER\n\nIn order to efficiently process the data, this repository contains language that may be offensive! View at your own risk!", "## License\n\nThis project is licensed under GNU Public License version 2.0. See LICENSE for details." ]

[ "TAGS\n#region-us \n", "# Chat Dataset\n\nDerived from Hitomi Team's Convo Dataset on Github, the Chat Dataset is a vast dataset with diverse data used for training models to assist in conversation analysis and generation.", "## Getting Started", "### Prerequisites\n\n- Python\n- Git LFS", "## DISCLAIMER\n\nIn order to efficiently process the data, this repository contains language that may be offensive! View at your own risk!", "## License\n\nThis project is licensed under GNU Public License version 2.0. See LICENSE for details." ]

[ "task_categories:multiple-choice", "task_categories:text-generation", "task_categories:question-answering", "task_ids:multiple-choice-qa", "task_ids:language-modeling", "task_ids:open-domain-qa", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:en", "license:apache-2.0", "arxiv:2109.07958", "region:us" ]

[ "2109.07958" ]

[ "en" ]

[ "### Dataset Summary\n\n\nTruthfulQA is a benchmark to measure whether a language model is truthful in generating answers to questions. The benchmark comprises 817 questions that span 38 categories, including health, law, finance and politics. Questions are crafted so that some humans would answer falsely due to a false belief or misconception. To perform well, models must avoid generating false answers learned from imitating human texts.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nThe text in the dataset is in English. The associated BCP-47 code is 'en'.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nNote: Both 'generation' and 'multiple\\_choice' configurations have the same questions.", "#### generation\n\n\nAn example of 'generation' looks as follows:", "#### multiple\\_choice\n\n\nAn example of 'multiple\\_choice' looks as follows:", "### Data Fields", "#### generation\n\n\n* 'type': A 'string' denoting whether the question was produced by an adversarial procedure or not ('\"Adversarial\"' or '\"Non-Adversarial\"').\n* 'category': The category ('string') of the question. E.g. '\"Law\"', '\"Health\"', etc.\n* 'question': The question 'string' designed to cause imitative falsehoods (false answers).\n* 'best\\_answer': The best correct and truthful answer 'string'.\n* 'correct\\_answers': A list of correct (truthful) answer 'string's.\n* 'incorrect\\_answers': A list of incorrect (false) answer 'string's.\n* 'source': The source 'string' where the 'question' contents were found.", "#### multiple\\_choice\n\n\n* 'question': The question string designed to cause imitative falsehoods (false answers).\n* 'mc1\\_targets': A dictionary containing the fields:\n\t+ 'choices': 4-5 answer-choice strings.\n\t+ 'labels': A list of 'int32' labels to the 'question' where '0' is wrong and '1' is correct. There is a single correct label '1' in this list.\n* 'mc2\\_targets': A dictionary containing the fields:\n\t+ 'choices': 4 or more answer-choice strings.\n\t+ 'labels': A list of 'int32' labels to the 'question' where '0' is wrong and '1' is correct. There can be multiple correct labels ('1') in this list.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nFrom the paper:\n\n\n\n> \n> The questions in TruthfulQA were designed to be “adversarial” in the sense of testing for a weakness in the truthfulness of language models (rather than testing models on a useful task).\n> \n> \n>", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nFrom the paper:\n\n\n\n> \n> We constructed the questions using the following adversarial procedure, with GPT-3-175B (QA prompt) as the target model: 1. We wrote questions that some humans would answer falsely. We tested them on the target model and filtered out most (but not all) questions that the model answered correctly. We produced 437 questions this way, which we call the “filtered” questions. 2. Using this experience of testing on the target model, we wrote 380 additional questions that we expected some humans and models to answer falsely. Since we did not test on the target model, these are called the “unfiltered” questions.\n> \n> \n>", "#### Who are the source language producers?\n\n\nThe authors of the paper; Stephanie Lin, Jacob Hilton, and Owain Evans.", "### Annotations", "#### Annotation process", "#### Who are the annotators?\n\n\nThe authors of the paper; Stephanie Lin, Jacob Hilton, and Owain Evans.", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nThis dataset is licensed under the Apache License, Version 2.0.", "### Contributions\n\n\nThanks to @jon-tow for adding this dataset." ]

[ "TAGS\n#task_categories-multiple-choice #task_categories-text-generation #task_categories-question-answering #task_ids-multiple-choice-qa #task_ids-language-modeling #task_ids-open-domain-qa #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-apache-2.0 #arxiv-2109.07958 #region-us \n", "### Dataset Summary\n\n\nTruthfulQA is a benchmark to measure whether a language model is truthful in generating answers to questions. The benchmark comprises 817 questions that span 38 categories, including health, law, finance and politics. Questions are crafted so that some humans would answer falsely due to a false belief or misconception. To perform well, models must avoid generating false answers learned from imitating human texts.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nThe text in the dataset is in English. The associated BCP-47 code is 'en'.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nNote: Both 'generation' and 'multiple\\_choice' configurations have the same questions.", "#### generation\n\n\nAn example of 'generation' looks as follows:", "#### multiple\\_choice\n\n\nAn example of 'multiple\\_choice' looks as follows:", "### Data Fields", "#### generation\n\n\n* 'type': A 'string' denoting whether the question was produced by an adversarial procedure or not ('\"Adversarial\"' or '\"Non-Adversarial\"').\n* 'category': The category ('string') of the question. E.g. '\"Law\"', '\"Health\"', etc.\n* 'question': The question 'string' designed to cause imitative falsehoods (false answers).\n* 'best\\_answer': The best correct and truthful answer 'string'.\n* 'correct\\_answers': A list of correct (truthful) answer 'string's.\n* 'incorrect\\_answers': A list of incorrect (false) answer 'string's.\n* 'source': The source 'string' where the 'question' contents were found.", "#### multiple\\_choice\n\n\n* 'question': The question string designed to cause imitative falsehoods (false answers).\n* 'mc1\\_targets': A dictionary containing the fields:\n\t+ 'choices': 4-5 answer-choice strings.\n\t+ 'labels': A list of 'int32' labels to the 'question' where '0' is wrong and '1' is correct. There is a single correct label '1' in this list.\n* 'mc2\\_targets': A dictionary containing the fields:\n\t+ 'choices': 4 or more answer-choice strings.\n\t+ 'labels': A list of 'int32' labels to the 'question' where '0' is wrong and '1' is correct. There can be multiple correct labels ('1') in this list.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nFrom the paper:\n\n\n\n> \n> The questions in TruthfulQA were designed to be “adversarial” in the sense of testing for a weakness in the truthfulness of language models (rather than testing models on a useful task).\n> \n> \n>", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nFrom the paper:\n\n\n\n> \n> We constructed the questions using the following adversarial procedure, with GPT-3-175B (QA prompt) as the target model: 1. We wrote questions that some humans would answer falsely. We tested them on the target model and filtered out most (but not all) questions that the model answered correctly. We produced 437 questions this way, which we call the “filtered” questions. 2. Using this experience of testing on the target model, we wrote 380 additional questions that we expected some humans and models to answer falsely. Since we did not test on the target model, these are called the “unfiltered” questions.\n> \n> \n>", "#### Who are the source language producers?\n\n\nThe authors of the paper; Stephanie Lin, Jacob Hilton, and Owain Evans.", "### Annotations", "#### Annotation process", "#### Who are the annotators?\n\n\nThe authors of the paper; Stephanie Lin, Jacob Hilton, and Owain Evans.", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nThis dataset is licensed under the Apache License, Version 2.0.", "### Contributions\n\n\nThanks to @jon-tow for adding this dataset." ]

[ "license:mit", "region:us" ]

[]

[]

[]

[ "TAGS\n#license-mit #region-us \n" ]

[ "task_categories:multiple-choice", "task_categories:question-answering", "task_categories:text-classification", "task_categories:text-generation", "task_categories:zero-shot-classification", "task_categories:other", "task_ids:multiple-choice-qa", "task_ids:extractive-qa", "task_ids:open-domain-qa", "task_ids:closed-domain-qa", "task_ids:fact-checking", "task_ids:acceptability-classification", "task_ids:intent-classification", "task_ids:multi-class-classification", "task_ids:multi-label-classification", "task_ids:text-scoring", "task_ids:hate-speech-detection", "task_ids:language-modeling", "annotations_creators:crowdsourced", "annotations_creators:expert-generated", "annotations_creators:machine-generated", "language_creators:crowdsourced", "language_creators:expert-generated", "language_creators:machine-generated", "language_creators:other", "multilinguality:multilingual", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:en", "license:apache-2.0", "arxiv:2206.04615", "region:us" ]

[ "2206.04615" ]

[ "en" ]

[ "### Dataset Summary\n\n\nThe Beyond the Imitation Game Benchmark (BIG-bench) is a collaborative benchmark intended to probe large language models and extrapolate their future capabilities. Tasks included in BIG-bench are summarized by keyword here, and by task name here. A paper introducing the benchmark, including evaluation results on large language models, is currently in preparation.", "### Supported Tasks and Leaderboards\n\n\nBIG-Bench consists of both json and programmatic tasks.\nThis implementation in HuggingFace datasets implements\n\n\n* 24 BIG-bench Lite tasks\n* 167 BIG-bench json tasks (includes BIG-bench Lite)\n\n\nTo study the remaining programmatic tasks, please see the BIG-bench GitHub repo", "### Languages\n\n\nAlthough predominantly English, BIG-bench contains tasks in over 1000 written languages, as well as some synthetic and programming languages.\nSee BIG-bench organized by keywords. Relevant keywords include 'multilingual', 'non-english', 'low-resource-language', 'translation'.\n\n\nFor tasks specifically targeting low-resource languages, see the table below:\n\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nEach dataset contains 5 features. For example an instance from the 'emoji\\_movie' task is:\n\n\nFor tasks that do not have multiple choice targets, the lists are empty.", "### Data Fields\n\n\nEvery example has the following fields\n\n\n* 'idx': an 'int' feature\n* 'inputs': a 'string' feature\n* 'targets': a sequence of 'string' feature\n* 'multiple\\_choice\\_targets': a sequence of 'string' features\n* 'multiple\\_choice\\_scores': a sequence of 'int' features", "### Data Splits\n\n\nEach task has a 'default', 'train' and 'validation' split.\nThe split 'default' uses all the samples for each task (and it's the same as 'all' used in the 'bigbench.bbseqio' implementation.)\nFor standard evaluation on BIG-bench, we recommend using the 'default' split, and the 'train' and 'validation' split is to be used if one wants to train a model on BIG-bench.\n\n\nDataset Creation\n----------------\n\n\nBIG-bench tasks were collaboratively submitted through GitHub pull requests.\n\n\nEach task went through a review and meta-review process with criteria outlined in the BIG-bench repository documentation.\nEach task was required to describe the data source and curation methods on the task README page.", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------\n\n\nBIG-bench contains a wide range of tasks, some of which are sensitive and should be used with care.\n\n\nSome tasks are specifically designed to test biases and failures common to large language models, and so may elicit inappropriate or harmful responses.\nFor a more thorough discussion see the BIG-bench paper.\n\n\nTo view tasks designed to probe pro-social behavior, including alignment, social, racial, gender, religious or political bias; toxicity; inclusion; and other issues please see tasks under the pro-social behavior keywords on the BIG-bench repository.", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------\n\n\nFor a more thorough discussion of all aspects of BIG-bench including dataset creation and evaluations see the BIG-bench repository URL and paper []", "### Dataset Curators", "### Licensing Information\n\n\nApache License 2.0", "### Contributions\n\n\nFor a full list of contributors to the BIG-bench dataset, see the paper.\n\n\nThanks to @andersjohanandreassen and @ethansdyer for adding this dataset to HuggingFace." ]

[ "TAGS\n#task_categories-multiple-choice #task_categories-question-answering #task_categories-text-classification #task_categories-text-generation #task_categories-zero-shot-classification #task_categories-other #task_ids-multiple-choice-qa #task_ids-extractive-qa #task_ids-open-domain-qa #task_ids-closed-domain-qa #task_ids-fact-checking #task_ids-acceptability-classification #task_ids-intent-classification #task_ids-multi-class-classification #task_ids-multi-label-classification #task_ids-text-scoring #task_ids-hate-speech-detection #task_ids-language-modeling #annotations_creators-crowdsourced #annotations_creators-expert-generated #annotations_creators-machine-generated #language_creators-crowdsourced #language_creators-expert-generated #language_creators-machine-generated #language_creators-other #multilinguality-multilingual #multilinguality-monolingual #size_categories-unknown #source_datasets-original #language-English #license-apache-2.0 #arxiv-2206.04615 #region-us \n", "### Dataset Summary\n\n\nThe Beyond the Imitation Game Benchmark (BIG-bench) is a collaborative benchmark intended to probe large language models and extrapolate their future capabilities. Tasks included in BIG-bench are summarized by keyword here, and by task name here. A paper introducing the benchmark, including evaluation results on large language models, is currently in preparation.", "### Supported Tasks and Leaderboards\n\n\nBIG-Bench consists of both json and programmatic tasks.\nThis implementation in HuggingFace datasets implements\n\n\n* 24 BIG-bench Lite tasks\n* 167 BIG-bench json tasks (includes BIG-bench Lite)\n\n\nTo study the remaining programmatic tasks, please see the BIG-bench GitHub repo", "### Languages\n\n\nAlthough predominantly English, BIG-bench contains tasks in over 1000 written languages, as well as some synthetic and programming languages.\nSee BIG-bench organized by keywords. Relevant keywords include 'multilingual', 'non-english', 'low-resource-language', 'translation'.\n\n\nFor tasks specifically targeting low-resource languages, see the table below:\n\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nEach dataset contains 5 features. For example an instance from the 'emoji\\_movie' task is:\n\n\nFor tasks that do not have multiple choice targets, the lists are empty.", "### Data Fields\n\n\nEvery example has the following fields\n\n\n* 'idx': an 'int' feature\n* 'inputs': a 'string' feature\n* 'targets': a sequence of 'string' feature\n* 'multiple\\_choice\\_targets': a sequence of 'string' features\n* 'multiple\\_choice\\_scores': a sequence of 'int' features", "### Data Splits\n\n\nEach task has a 'default', 'train' and 'validation' split.\nThe split 'default' uses all the samples for each task (and it's the same as 'all' used in the 'bigbench.bbseqio' implementation.)\nFor standard evaluation on BIG-bench, we recommend using the 'default' split, and the 'train' and 'validation' split is to be used if one wants to train a model on BIG-bench.\n\n\nDataset Creation\n----------------\n\n\nBIG-bench tasks were collaboratively submitted through GitHub pull requests.\n\n\nEach task went through a review and meta-review process with criteria outlined in the BIG-bench repository documentation.\nEach task was required to describe the data source and curation methods on the task README page.", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------\n\n\nBIG-bench contains a wide range of tasks, some of which are sensitive and should be used with care.\n\n\nSome tasks are specifically designed to test biases and failures common to large language models, and so may elicit inappropriate or harmful responses.\nFor a more thorough discussion see the BIG-bench paper.\n\n\nTo view tasks designed to probe pro-social behavior, including alignment, social, racial, gender, religious or political bias; toxicity; inclusion; and other issues please see tasks under the pro-social behavior keywords on the BIG-bench repository.", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------\n\n\nFor a more thorough discussion of all aspects of BIG-bench including dataset creation and evaluations see the BIG-bench repository URL and paper []", "### Dataset Curators", "### Licensing Information\n\n\nApache License 2.0", "### Contributions\n\n\nFor a full list of contributors to the BIG-bench dataset, see the paper.\n\n\nThanks to @andersjohanandreassen and @ethansdyer for adding this dataset to HuggingFace." ]

[ "license:mit", "region:us" ]

[]

[]

[]

[ "TAGS\n#license-mit #region-us \n" ]

[ "license:mit", "region:us" ]

[]

[]

[]

[ "TAGS\n#license-mit #region-us \n" ]

[ "task_categories:image-classification", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:odbl", "region:us" ]

[]

[ "en" ]

[ "### Introduction\n\n\n* The dataset features 15 different classes of Human Activities.\n* The dataset contains about 12k+ labelled images including the validation images.\n* Each image has only one human activity category and are saved in separate folders of the labelled classes", "### PROBLEM STATEMENT\n\n\n* Human Action Recognition (HAR) aims to understand human behavior and assign a label to each action. It has a wide range of applications, and therefore has been attracting increasing attention in the field of computer vision. Human actions can be represented using various data modalities, such as RGB, skeleton, depth, infrared, point cloud, event stream, audio, acceleration, radar, and WiFi signal, which encode different sources of useful yet distinct information and have various advantages depending on the application scenarios.\n* Consequently, lots of existing works have attempted to investigate different types of approaches for HAR using various modalities.\n* Your Task is to build an Image Classification Model using CNN that classifies to which class of activity a human is performing.", "### About Files\n\n\n* Train - contains all the images that are to be used for training your model. In this folder you will find 15 folders namely - 'calling', ’clapping’, ’cycling’, ’dancing’, ‘drinking’, ‘eating’, ‘fighting’, ‘hugging’, ‘laughing’, ‘listeningtomusic’, ‘running’, ‘sitting’, ‘sleeping’, texting’, ‘using\\_laptop’ which contain the images of the respective human activities.\n* Test - contains 5400 images of Human Activities. For these images you are required to make predictions as the respective class names -'calling', ’clapping’, ’cycling’, ’dancing’, ‘drinking’, ‘eating’, ‘fighting’, ‘hugging’, ‘laughing’, ‘listeningtomusic’, ‘running’, ‘sitting’, ‘sleeping’, texting’, ‘using\\_laptop’.\n* Testing\\_set.csv - this is the order of the predictions for each image that is to be submitted on the platform. Make sure the predictions you download are with their image’s filename in the same order as given in this file.\n* sample\\_submission: This is a csv file that contains the sample submission for the data sprint.", "### Data Fields\n\n\nThe data instances have the following fields:\n\n\n* '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]'.\n* 'labels': an 'int' classification label. All 'test' data is labeled 0.", "### Class Label Mappings:", "### Data Splits", "### Data Size\n\n\n* download: 311.96 MiB\n* generated: 312.59 MiB\n* total: 624.55 MiB" ]

[ "TAGS\n#task_categories-image-classification #size_categories-10K<n<100K #source_datasets-original #language-English #license-odbl #region-us \n", "### Introduction\n\n\n* The dataset features 15 different classes of Human Activities.\n* The dataset contains about 12k+ labelled images including the validation images.\n* Each image has only one human activity category and are saved in separate folders of the labelled classes", "### PROBLEM STATEMENT\n\n\n* Human Action Recognition (HAR) aims to understand human behavior and assign a label to each action. It has a wide range of applications, and therefore has been attracting increasing attention in the field of computer vision. Human actions can be represented using various data modalities, such as RGB, skeleton, depth, infrared, point cloud, event stream, audio, acceleration, radar, and WiFi signal, which encode different sources of useful yet distinct information and have various advantages depending on the application scenarios.\n* Consequently, lots of existing works have attempted to investigate different types of approaches for HAR using various modalities.\n* Your Task is to build an Image Classification Model using CNN that classifies to which class of activity a human is performing.", "### About Files\n\n\n* Train - contains all the images that are to be used for training your model. In this folder you will find 15 folders namely - 'calling', ’clapping’, ’cycling’, ’dancing’, ‘drinking’, ‘eating’, ‘fighting’, ‘hugging’, ‘laughing’, ‘listeningtomusic’, ‘running’, ‘sitting’, ‘sleeping’, texting’, ‘using\\_laptop’ which contain the images of the respective human activities.\n* Test - contains 5400 images of Human Activities. For these images you are required to make predictions as the respective class names -'calling', ’clapping’, ’cycling’, ’dancing’, ‘drinking’, ‘eating’, ‘fighting’, ‘hugging’, ‘laughing’, ‘listeningtomusic’, ‘running’, ‘sitting’, ‘sleeping’, texting’, ‘using\\_laptop’.\n* Testing\\_set.csv - this is the order of the predictions for each image that is to be submitted on the platform. Make sure the predictions you download are with their image’s filename in the same order as given in this file.\n* sample\\_submission: This is a csv file that contains the sample submission for the data sprint.", "### Data Fields\n\n\nThe data instances have the following fields:\n\n\n* '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]'.\n* 'labels': an 'int' classification label. All 'test' data is labeled 0.", "### Class Label Mappings:", "### Data Splits", "### Data Size\n\n\n* download: 311.96 MiB\n* generated: 312.59 MiB\n* total: 624.55 MiB" ]

[ "task_categories:text-classification", "language:en", "region:us" ]

[]

[ "en" ]

[ "### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]

[ "TAGS\n#task_categories-text-classification #language-English #region-us \n", "### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]

[ "task_categories:image-classification", "task_ids:multi-class-image-classification", "annotations_creators:machine-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10M<n<100M", "source_datasets:original", "language:en", "license:cc-by-4.0", "arxiv:1704.03477", "region:us" ]

[ "1704.03477" ]

[ "en" ]

[ "### Dataset Summary\n\n\nThe Quick Draw Dataset is a collection of 50 million drawings across 345 categories, contributed by players of the game Quick, Draw!. The drawings were captured as timestamped vectors, tagged with metadata including what the player was asked to draw and in which country the player was located.", "### Supported Tasks and Leaderboards\n\n\n* 'image-classification': The goal of this task is to classify a given sketch into one of 345 classes.\nThe (closed) leaderboard for this task is available here.", "### Languages\n\n\nEnglish.\n\n\nDataset Structure\n-----------------", "### Data Instances", "#### 'raw'\n\n\nA data point comprises a drawing and its metadata.", "#### 'preprocessed\\_simplified\\_drawings'\n\n\nThe simplified version of the dataset generated from the 'raw' data with the simplified vectors, removed timing information, and the data positioned and scaled into a 256x256 region.\nThe simplification process was:\n1.Align the drawing to the top-left corner, to have minimum values of 0.\n2.Uniformly scale the drawing, to have a maximum value of 255.\n3.Resample all strokes with a 1 pixel spacing.\n4.Simplify all strokes using the Ramer-Douglas-Peucker algorithm with an epsilon value of 2.0.", "#### 'preprocessed\\_bitmaps' (default configuration)\n\n\nThis configuration contains the 28x28 grayscale bitmap images that were generated from the simplified data, but are aligned to the center of the drawing's bounding box rather than the top-left corner. The code that was used for generation is available here.", "#### 'sketch\\_rnn' and 'sketch\\_rnn\\_full'\n\n\nThe 'sketch\\_rnn\\_full' configuration stores the data in the format suitable for inputs into a recurrent neural network and was used for for training the Sketch-RNN model. Unlike 'sketch\\_rnn' where the samples have been randomly selected from each category, the 'sketch\\_rnn\\_full' configuration contains the full data for each category.", "### Data Fields", "#### 'raw'\n\n\n* 'key\\_id': A unique identifier across all drawings.\n* 'word': Category the player was prompted to draw.\n* 'recognized': Whether the word was recognized by the game.\n* 'timestamp': When the drawing was created.\n* 'countrycode': A two letter country code (ISO 3166-1 alpha-2) of where the player was located.\n* 'drawing': A dictionary where 'x' and 'y' are the pixel coordinates, and 't' is the time in milliseconds since the first point. 'x' and 'y' are real-valued while 't' is an integer. 'x', 'y' and 't' match in lenght and are represented as lists of lists where each sublist corresponds to a single stroke. The raw drawings can have vastly different bounding boxes and number of points due to the different devices used for display and input.", "#### 'preprocessed\\_simplified\\_drawings'\n\n\n* 'key\\_id': A unique identifier across all drawings.\n* 'word': Category the player was prompted to draw.\n* 'recognized': Whether the word was recognized by the game.\n* 'timestamp': When the drawing was created.\n* 'countrycode': A two letter country code (ISO 3166-1 alpha-2) of where the player was located.\n* 'drawing': A simplified drawing represented as a dictionary where 'x' and 'y' are the pixel coordinates. The simplification processed is described in the 'Data Instances' section.", "#### 'preprocessed\\_bitmaps' (default configuration)\n\n\n* 'image': A 'PIL.Image.Image' object containing the 28x28 grayscale bitmap. 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]'.\n* 'label': Category the player was prompted to draw.\n\n\n\n\n Click here to see the full class labels mapping:", "#### 'sketch\\_rnn' and 'sketch\\_rnn\\_full'\n\n\n* 'word': Category the player was prompted to draw.\n* 'drawing': An array of strokes. Strokes are represented as 3-tuples consisting of x-offset, y-offset, and a binary variable which is 1 if the pen is lifted between this position and the next, and 0 otherwise.\n\n\n\n\n Click here to see the code for visualizing drawings in Jupyter Notebook or Google Colab:\n \n\n\n> \n> Note: Sketch-RNN takes for input strokes represented as 5-tuples with drawings padded to a common maximum length and prefixed by the special start token '[0, 0, 1, 0, 0]'. The 5-tuple representation consists of x-offset, y-offset, and p\\_1, p\\_2, p\\_3, a binary one-hot vector of 3 possible pen states: pen down, pen up, end of sketch. More precisely, the first two elements are the offset distance in the x and y directions of the pen from the previous point. The last 3 elements represents a binary one-hot vector of 3 possible states. The first pen state, p1, indicates that the pen is currently touching the paper, and that a line will be drawn connecting the next point with the current point. The second pen state, p2, indicates that the pen will be lifted from the paper after the current point, and that no line will be drawn next. The final pen state, p3, indicates that the drawing has ended, and subsequent points, including the current point, will not be rendered.\n> \n> \n> \n> \n> Click here to see the code for converting drawings to Sketch-RNN input format:\n> \n> \n>", "### Data Splits\n\n\nIn the configurations 'raw', 'preprocessed\\_simplified\\_drawings' and 'preprocessed\\_bitamps' (default configuration), all the data is contained in the training set, which has 50426266 examples.\n\n\n'sketch\\_rnn' and 'sketch\\_rnn\\_full' have the data split into training, validation and test split. In the 'sketch\\_rnn' configuration, 75K samples (70K Training, 2.5K Validation, 2.5K Test) have been randomly selected from each category. Therefore, the training set contains 24150000 examples, the validation set 862500 examples and the test set 862500 examples. The 'sketch\\_rnn\\_full' configuration has the full (training) data for each category, which leads to the training set having 43988874 examples, the validation set 862500 and the test set 862500 examples.\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nFrom the GitHub repository:\n\n\n\n> \n> The Quick Draw Dataset is a collection of 50 million drawings across 345 categories, contributed by players of the game Quick, Draw!. The drawings were captured as timestamped vectors, tagged with metadata including what the player was asked to draw and in which country the player was located. You can browse the recognized drawings on URL\n> \n> \n> We're sharing them here for developers, researchers, and artists to explore, study, and learn from\n> \n> \n>", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThis dataset contains vector drawings obtained from Quick, Draw!, an online game where the players are asked to draw objects belonging to a particular object class in less than 20 seconds.", "#### Who are the source language producers?\n\n\nThe participants in the Quick, Draw! game.", "### Annotations", "#### Annotation process\n\n\nThe annotations are machine-generated and match the category the player was prompted to draw.", "#### Who are the annotators?\n\n\nThe annotations are machine-generated.", "### Personal and Sensitive Information\n\n\nSome sketches are known to be problematic (see URL and URL\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nJonas Jongejan, Henry Rowley, Takashi Kawashima, Jongmin Kim and Nick Fox-Gieg.", "### Licensing Information\n\n\nThe data is made available by Google, Inc. under the Creative Commons Attribution 4.0 International license.", "### Contributions\n\n\nThanks to @mariosasko for adding this dataset." ]

[ "TAGS\n#task_categories-image-classification #task_ids-multi-class-image-classification #annotations_creators-machine-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10M<n<100M #source_datasets-original #language-English #license-cc-by-4.0 #arxiv-1704.03477 #region-us \n", "### Dataset Summary\n\n\nThe Quick Draw Dataset is a collection of 50 million drawings across 345 categories, contributed by players of the game Quick, Draw!. The drawings were captured as timestamped vectors, tagged with metadata including what the player was asked to draw and in which country the player was located.", "### Supported Tasks and Leaderboards\n\n\n* 'image-classification': The goal of this task is to classify a given sketch into one of 345 classes.\nThe (closed) leaderboard for this task is available here.", "### Languages\n\n\nEnglish.\n\n\nDataset Structure\n-----------------", "### Data Instances", "#### 'raw'\n\n\nA data point comprises a drawing and its metadata.", "#### 'preprocessed\\_simplified\\_drawings'\n\n\nThe simplified version of the dataset generated from the 'raw' data with the simplified vectors, removed timing information, and the data positioned and scaled into a 256x256 region.\nThe simplification process was:\n1.Align the drawing to the top-left corner, to have minimum values of 0.\n2.Uniformly scale the drawing, to have a maximum value of 255.\n3.Resample all strokes with a 1 pixel spacing.\n4.Simplify all strokes using the Ramer-Douglas-Peucker algorithm with an epsilon value of 2.0.", "#### 'preprocessed\\_bitmaps' (default configuration)\n\n\nThis configuration contains the 28x28 grayscale bitmap images that were generated from the simplified data, but are aligned to the center of the drawing's bounding box rather than the top-left corner. The code that was used for generation is available here.", "#### 'sketch\\_rnn' and 'sketch\\_rnn\\_full'\n\n\nThe 'sketch\\_rnn\\_full' configuration stores the data in the format suitable for inputs into a recurrent neural network and was used for for training the Sketch-RNN model. Unlike 'sketch\\_rnn' where the samples have been randomly selected from each category, the 'sketch\\_rnn\\_full' configuration contains the full data for each category.", "### Data Fields", "#### 'raw'\n\n\n* 'key\\_id': A unique identifier across all drawings.\n* 'word': Category the player was prompted to draw.\n* 'recognized': Whether the word was recognized by the game.\n* 'timestamp': When the drawing was created.\n* 'countrycode': A two letter country code (ISO 3166-1 alpha-2) of where the player was located.\n* 'drawing': A dictionary where 'x' and 'y' are the pixel coordinates, and 't' is the time in milliseconds since the first point. 'x' and 'y' are real-valued while 't' is an integer. 'x', 'y' and 't' match in lenght and are represented as lists of lists where each sublist corresponds to a single stroke. The raw drawings can have vastly different bounding boxes and number of points due to the different devices used for display and input.", "#### 'preprocessed\\_simplified\\_drawings'\n\n\n* 'key\\_id': A unique identifier across all drawings.\n* 'word': Category the player was prompted to draw.\n* 'recognized': Whether the word was recognized by the game.\n* 'timestamp': When the drawing was created.\n* 'countrycode': A two letter country code (ISO 3166-1 alpha-2) of where the player was located.\n* 'drawing': A simplified drawing represented as a dictionary where 'x' and 'y' are the pixel coordinates. The simplification processed is described in the 'Data Instances' section.", "#### 'preprocessed\\_bitmaps' (default configuration)\n\n\n* 'image': A 'PIL.Image.Image' object containing the 28x28 grayscale bitmap. 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]'.\n* 'label': Category the player was prompted to draw.\n\n\n\n\n Click here to see the full class labels mapping:", "#### 'sketch\\_rnn' and 'sketch\\_rnn\\_full'\n\n\n* 'word': Category the player was prompted to draw.\n* 'drawing': An array of strokes. Strokes are represented as 3-tuples consisting of x-offset, y-offset, and a binary variable which is 1 if the pen is lifted between this position and the next, and 0 otherwise.\n\n\n\n\n Click here to see the code for visualizing drawings in Jupyter Notebook or Google Colab:\n \n\n\n> \n> Note: Sketch-RNN takes for input strokes represented as 5-tuples with drawings padded to a common maximum length and prefixed by the special start token '[0, 0, 1, 0, 0]'. The 5-tuple representation consists of x-offset, y-offset, and p\\_1, p\\_2, p\\_3, a binary one-hot vector of 3 possible pen states: pen down, pen up, end of sketch. More precisely, the first two elements are the offset distance in the x and y directions of the pen from the previous point. The last 3 elements represents a binary one-hot vector of 3 possible states. The first pen state, p1, indicates that the pen is currently touching the paper, and that a line will be drawn connecting the next point with the current point. The second pen state, p2, indicates that the pen will be lifted from the paper after the current point, and that no line will be drawn next. The final pen state, p3, indicates that the drawing has ended, and subsequent points, including the current point, will not be rendered.\n> \n> \n> \n> \n> Click here to see the code for converting drawings to Sketch-RNN input format:\n> \n> \n>", "### Data Splits\n\n\nIn the configurations 'raw', 'preprocessed\\_simplified\\_drawings' and 'preprocessed\\_bitamps' (default configuration), all the data is contained in the training set, which has 50426266 examples.\n\n\n'sketch\\_rnn' and 'sketch\\_rnn\\_full' have the data split into training, validation and test split. In the 'sketch\\_rnn' configuration, 75K samples (70K Training, 2.5K Validation, 2.5K Test) have been randomly selected from each category. Therefore, the training set contains 24150000 examples, the validation set 862500 examples and the test set 862500 examples. The 'sketch\\_rnn\\_full' configuration has the full (training) data for each category, which leads to the training set having 43988874 examples, the validation set 862500 and the test set 862500 examples.\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nFrom the GitHub repository:\n\n\n\n> \n> The Quick Draw Dataset is a collection of 50 million drawings across 345 categories, contributed by players of the game Quick, Draw!. The drawings were captured as timestamped vectors, tagged with metadata including what the player was asked to draw and in which country the player was located. You can browse the recognized drawings on URL\n> \n> \n> We're sharing them here for developers, researchers, and artists to explore, study, and learn from\n> \n> \n>", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThis dataset contains vector drawings obtained from Quick, Draw!, an online game where the players are asked to draw objects belonging to a particular object class in less than 20 seconds.", "#### Who are the source language producers?\n\n\nThe participants in the Quick, Draw! game.", "### Annotations", "#### Annotation process\n\n\nThe annotations are machine-generated and match the category the player was prompted to draw.", "#### Who are the annotators?\n\n\nThe annotations are machine-generated.", "### Personal and Sensitive Information\n\n\nSome sketches are known to be problematic (see URL and URL\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nJonas Jongejan, Henry Rowley, Takashi Kawashima, Jongmin Kim and Nick Fox-Gieg.", "### Licensing Information\n\n\nThe data is made available by Google, Inc. under the Creative Commons Attribution 4.0 International license.", "### Contributions\n\n\nThanks to @mariosasko for adding this dataset." ]

[ "task_categories:tabular-classification", "task_categories:tabular-regression", "language:en", "license:apache-2.0", "scikit-learn", "region:us" ]

[]

[ "en" ]

[ "## Auto Miles per Gallon (MPG) Dataset\n\nFollowing description was taken from UCI machine learning repository.\nSource: This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University. The dataset was used in the 1983 American Statistical Association Exposition.", "## Data Set Information:\n\nThis dataset is a slightly modified version of the dataset provided in the StatLib library. In line with the use by Ross Quinlan (1993) in predicting the attribute \"mpg\", 8 of the original instances were removed because they had unknown values for the \"mpg\" attribute. The original dataset is available in the file \"URL-original\".\n\n\"The data concerns city-cycle fuel consumption in miles per gallon, to be predicted in terms of 3 multivalued discrete and 5 continuous attributes.\" (Quinlan, 1993)", "## Attribute Information:\n\n- mpg: continuous\n- cylinders: multi-valued discrete\n- displacement: continuous\n- horsepower: continuous\n- weight: continuous\n- acceleration: continuous\n- model year: multi-valued discrete\n- origin: multi-valued discrete\n- car name: string (unique for each instance)" ]

[ "TAGS\n#task_categories-tabular-classification #task_categories-tabular-regression #language-English #license-apache-2.0 #scikit-learn #region-us \n", "## Auto Miles per Gallon (MPG) Dataset\n\nFollowing description was taken from UCI machine learning repository.\nSource: This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University. The dataset was used in the 1983 American Statistical Association Exposition.", "## Data Set Information:\n\nThis dataset is a slightly modified version of the dataset provided in the StatLib library. In line with the use by Ross Quinlan (1993) in predicting the attribute \"mpg\", 8 of the original instances were removed because they had unknown values for the \"mpg\" attribute. The original dataset is available in the file \"URL-original\".\n\n\"The data concerns city-cycle fuel consumption in miles per gallon, to be predicted in terms of 3 multivalued discrete and 5 continuous attributes.\" (Quinlan, 1993)", "## Attribute Information:\n\n- mpg: continuous\n- cylinders: multi-valued discrete\n- displacement: continuous\n- horsepower: continuous\n- weight: continuous\n- acceleration: continuous\n- model year: multi-valued discrete\n- origin: multi-valued discrete\n- car name: string (unique for each instance)" ]

[ "task_categories:text-classification", "language:en", "region:us" ]

[]

[ "en" ]

[ "### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]

[ "TAGS\n#task_categories-text-classification #language-English #region-us \n", "### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]

[ "task_categories:automatic-speech-recognition", "task_categories:text-to-speech", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:10K<n<100K", "source_datasets:original", "language:ajz", "language:bam", "language:bi", "language:bis", "language:bjn", "language:bm", "language:boz", "language:bze", "language:bzi", "language:cak", "language:ceb", "language:chd", "language:chp", "language:clo", "language:csw", "language:en", "language:eng", "language:es", "language:fli", "language:fr", "language:fra", "language:gu", "language:guj", "language:hbb", "language:hi", "language:hin", "language:id", "language:ind", "language:jmx", "language:jra", "language:kan", "language:kbq", "language:kek", "language:kjb", "language:kmu", "language:kn", "language:kqr", "language:kwu", "language:loh", "language:mai", "language:mal", "language:mam", "language:mar", "language:ml", "language:mle", "language:mr", "language:my", "language:mya", "language:myk", "language:nas", "language:nsk", "language:nsn", "language:oj", "language:oji", "language:omw", "language:por", "language:pt", "language:quc", "language:sdk", "language:snk", "language:spa", "language:stk", "language:ta", "language:taj", "language:tam", "language:tbj", "language:tdc", "language:tgl", "language:tl", "language:tpi", "language:tuz", "language:tzj", "license:cc-by-nc-4.0", "license:cc-by-sa-4.0", "license:cc-by-nc-nd-4.0", "license:cc-by-nc-sa-4.0", "region:us" ]

[]

[ "ajz", "bam", "bi", "bis", "bjn", "bm", "boz", "bze", "bzi", "cak", "ceb", "chd", "chp", "clo", "csw", "en", "eng", "es", "fli", "fr", "fra", "gu", "guj", "hbb", "hi", "hin", "id", "ind", "jmx", "jra", "kan", "kbq", "kek", "kjb", "kmu", "kn", "kqr", "kwu", "loh", "mai", "mal", "mam", "mar", "ml", "mle", "mr", "my", "mya", "myk", "nas", "nsk", "nsn", "oj", "oji", "omw", "por", "pt", "quc", "sdk", "snk", "spa", "stk", "ta", "taj", "tam", "tbj", "tdc", "tgl", "tl", "tpi", "tuz", "tzj" ]

[ "### Data Instances\n\n\nThe examples look like this for Hindi:\n\n\nThis would produce an output:\n\n\nWhereas if you wish to gather all the text for a language you may use this:", "### Data Fields\n\n\nThe metadata fields are below. In terms of licenses, all stories included in the current release are released under a Creative Commons license (even if the individual story metadata fields are missing).\n\n\n* file: the local path to the audio file\n* audio: a dictionary with a path, array, and sampling\\_rate as is standard for Hugging Face audio\n* text: the transcribed text\n* book: title of the book, e.g. \"बो मेस्सी और शैम्पू\".\n* instance: unique ID for each book/translation assigned by Bloom Library. For example the Hindi version of 'बो मेस्सी और शैम्पू' is 'eba60f56-eade-4d78-a66f-f52870f6bfdd'\n* license: specific license used, e.g. \"cc-by-sa\" for \"Creative Commons, by attribution, share-alike\".\n* credits: attribution of contributors as described in the book metadata, including authors, editors, etc. if available\n* original\\_lang\\_tag: the language tag originally assigned in Bloom Library. This may include information on script type, etc.", "### Data Splits\n\n\nAll languages include a train, validation, and test split. However, for language having a small number of stories, certain of these splits maybe empty. In such cases, we recommend using any data for testing only or for zero-shot experiments.\n\n\nChangelog\n---------\n\n\n* 26 September 2022 Page initiated" ]

[ "TAGS\n#task_categories-automatic-speech-recognition #task_categories-text-to-speech #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #language-Amri Karbi #language-Bambara #language-Bislama #language-Bislama #language-Banjar #language-Bambara #language-Tiéyaxo Bozo #language-Jenaama Bozo #language-Bisu #language-Kaqchikel #language-Cebuano #language-Highland Oaxaca Chontal #language-Chipewyan #language-Lowland Oaxaca Chontal #language-Swampy Cree #language-English #language-English #language-Spanish #language-Fali #language-French #language-French #language-Gujarati #language-Gujarati #language-Huba #language-Hindi #language-Hindi #language-Indonesian #language-Indonesian #language-Western Juxtlahuaca Mixtec #language-Jarai #language-Kannada #language-Kamano #language-Kekchí #language-Q'anjob'al #language-Kanite #language-Kannada #language-Kimaragang #language-Kwakum #language-Laarim #language-Maithili #language-Malayalam #language-Mam #language-Marathi #language-Malayalam #language-Manambu #language-Marathi #language-Burmese #language-Burmese #language-Mamara Senoufo #language-Naasioi #language-Naskapi #language-Nehan #language-Ojibwa #language-Ojibwa #language-South Tairora #language-Portuguese #language-Portuguese #language-K'iche' #language-Sos Kundi #language-Soninke #language-Spanish #language-Arammba #language-Tamil #language-Eastern Tamang #language-Tamil #language-Tiang #language-Emberá-Tadó #language-Tagalog #language-Tagalog #language-Tok Pisin #language-Turka #language-Tz'utujil #license-cc-by-nc-4.0 #license-cc-by-sa-4.0 #license-cc-by-nc-nd-4.0 #license-cc-by-nc-sa-4.0 #region-us \n", "### Data Instances\n\n\nThe examples look like this for Hindi:\n\n\nThis would produce an output:\n\n\nWhereas if you wish to gather all the text for a language you may use this:", "### Data Fields\n\n\nThe metadata fields are below. In terms of licenses, all stories included in the current release are released under a Creative Commons license (even if the individual story metadata fields are missing).\n\n\n* file: the local path to the audio file\n* audio: a dictionary with a path, array, and sampling\\_rate as is standard for Hugging Face audio\n* text: the transcribed text\n* book: title of the book, e.g. \"बो मेस्सी और शैम्पू\".\n* instance: unique ID for each book/translation assigned by Bloom Library. For example the Hindi version of 'बो मेस्सी और शैम्पू' is 'eba60f56-eade-4d78-a66f-f52870f6bfdd'\n* license: specific license used, e.g. \"cc-by-sa\" for \"Creative Commons, by attribution, share-alike\".\n* credits: attribution of contributors as described in the book metadata, including authors, editors, etc. if available\n* original\\_lang\\_tag: the language tag originally assigned in Bloom Library. This may include information on script type, etc.", "### Data Splits\n\n\nAll languages include a train, validation, and test split. However, for language having a small number of stories, certain of these splits maybe empty. In such cases, we recommend using any data for testing only or for zero-shot experiments.\n\n\nChangelog\n---------\n\n\n* 26 September 2022 Page initiated" ]

[ "license:apache-2.0", "region:us" ]

[]

[]

[ "# Title 1\n\nhahahoho" ]

[ "TAGS\n#license-apache-2.0 #region-us \n", "# Title 1\n\nhahahoho" ]

[ "task_categories:text-classification", "language:en", "region:us" ]

[]

[ "en" ]

[ "### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]

[ "TAGS\n#task_categories-text-classification #language-English #region-us \n", "### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]

[ "task_categories:automatic-speech-recognition", "task_categories:text-to-speech", "task_categories:text-to-audio", "multilinguality:monolingual", "language:en", "license:apache-2.0", "arxiv:2106.06909", "region:us" ]

[ "2106.06909" ]

[ "en" ]

[ "### Example Usage\n\n\nThe training split has several configurations of various size:\nXS, S, M, L, XL. See the Section on \"Data Splits\" for more information. To download the XS configuration:\n\n\nIt is possible to download only the development or test data:", "### Supported Tasks and Leaderboards\n\n\n* 'automatic-speech-recognition': The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active leaderboard which can be found at URL and ranks models based on their WER.\n* 'text-to-speech', 'text-to-audio': The dataset can also be used to train a model for Text-To-Speech (TTS).", "### Languages\n\n\nGigaspeech contains audio and transcription data in English.\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields\n\n\n* segment\\_id (string) - string id of the segment.\n* speaker (string) - string id of the speaker (can be \"N/A\").\n* text (string) - transcription of the segment.\n* begin\\_time (float) - start time of the segment in an original full audio.\n* end\\_time (float32) - end time of the segment in an original full audio.\n* audio (Audio feature) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate.\nIn non-streaming mode (default), the path point to the locally extracted audio. In streaming mode, the path is the relative path of an audio.\nsegment inside its archive (as files are not downloaded and extracted locally).\n* audio\\_id (string) - string idea of the original full audio.\n* title (string) - title of the original full audio.\n* url (string) - url of the original full audio.\n* source (ClassLabel) - id of the audio source. Sources are audiobook (0), podcast (1), and YouYube (2).\n* category (ClassLabel) - id of the audio category, categories are listed below.\n* original\\_full\\_path (string) - the relative path to the original full audio sample in the original data directory.\n\n\nCategories are assigned from the following labels:\n\"People and Blogs\", \"Business\", \"Nonprofits and Activism\", \"Crime\", \"History\", \"Pets and Animals\",\n\"News and Politics\", \"Travel and Events\", \"Kids and Family\", \"Leisure\", \"N/A\", \"Comedy\", \"News and Politics\",\n\"Sports\", \"Arts\", \"Science and Technology\", \"Autos and Vehicles\", \"Science and Technology\", \"People and Blogs\",\n\"Music\", \"Society and Culture\", \"Education\", \"Howto and Style\", \"Film and Animation\", \"Gaming\", \"Entertainment\",\n\"Travel and Events\", \"Health and Fitness\", \"audiobook\".", "### Data Splits\n\n\nThe dataset has three splits: train, evaluation (dev) and test. The train split has five configurations of various sizes:\nXS, S, M, L, XL. Larger subsets are supersets of smaller subsets, e.g., the L subset contains all the data from the M subset.", "#### Transcribed Training Subsets Size", "#### Transcribed Evaluation Subsets\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?\n\n\nDevelopment and test subsets are annotated by professional human annotators.", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nSpeechColab does not own the copyright of the audio files. For researchers and educators who wish to use the audio files for\nnon-commercial research and/or educational purposes, we can provide access through our site under certain conditions and terms.\n\n\nIn general, when training a machine learning model on a given dataset, the license of the model is independent to that of the\ndataset. That is to say, speech recognition models trained on the GigaSpeech dataset may be eligible for commercial license,\nprovided they abide to the 'Fair Use' terms of the underlying data and do not violate any explicit copyright restrictions.\nThis is likely to be true in most use-cases. However, it is your responsiblity to verify the appropriate model license for\nyour specific use-case by confirming that the dataset usage abides by the Fair Use terms. SpeechColab is not responsible\nfor the license of any machine learning model trained on the GigaSpeech dataset.\n\n\nPlease cite this paper if you find this work useful:", "### Contributions\n\n\nThanks to @polinaeterna and @sanchit-gandhi\nfor adding this dataset.\n\n\nTerms of Access\n---------------\n\n\nThe \"Researcher\" has requested permission to use the GigaSpeech database (the \"Database\")\nat Tsinghua University. In exchange for such permission, Researcher hereby agrees to the\nfollowing terms and conditions:\n\n\n1. Researcher shall use the Database only for non-commercial research and educational purposes.\n2. The SpeechColab team and Tsinghua University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.\n3. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the SpeechColab team and Tsinghua University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted audio files that he or she may create from the Database.\n4. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions.\n5. The SpeechColab team and Tsinghua University reserve the right to terminate Researcher's access to the Database at any time.\n6. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer." ]

[ "TAGS\n#task_categories-automatic-speech-recognition #task_categories-text-to-speech #task_categories-text-to-audio #multilinguality-monolingual #language-English #license-apache-2.0 #arxiv-2106.06909 #region-us \n", "### Example Usage\n\n\nThe training split has several configurations of various size:\nXS, S, M, L, XL. See the Section on \"Data Splits\" for more information. To download the XS configuration:\n\n\nIt is possible to download only the development or test data:", "### Supported Tasks and Leaderboards\n\n\n* 'automatic-speech-recognition': The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active leaderboard which can be found at URL and ranks models based on their WER.\n* 'text-to-speech', 'text-to-audio': The dataset can also be used to train a model for Text-To-Speech (TTS).", "### Languages\n\n\nGigaspeech contains audio and transcription data in English.\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields\n\n\n* segment\\_id (string) - string id of the segment.\n* speaker (string) - string id of the speaker (can be \"N/A\").\n* text (string) - transcription of the segment.\n* begin\\_time (float) - start time of the segment in an original full audio.\n* end\\_time (float32) - end time of the segment in an original full audio.\n* audio (Audio feature) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate.\nIn non-streaming mode (default), the path point to the locally extracted audio. In streaming mode, the path is the relative path of an audio.\nsegment inside its archive (as files are not downloaded and extracted locally).\n* audio\\_id (string) - string idea of the original full audio.\n* title (string) - title of the original full audio.\n* url (string) - url of the original full audio.\n* source (ClassLabel) - id of the audio source. Sources are audiobook (0), podcast (1), and YouYube (2).\n* category (ClassLabel) - id of the audio category, categories are listed below.\n* original\\_full\\_path (string) - the relative path to the original full audio sample in the original data directory.\n\n\nCategories are assigned from the following labels:\n\"People and Blogs\", \"Business\", \"Nonprofits and Activism\", \"Crime\", \"History\", \"Pets and Animals\",\n\"News and Politics\", \"Travel and Events\", \"Kids and Family\", \"Leisure\", \"N/A\", \"Comedy\", \"News and Politics\",\n\"Sports\", \"Arts\", \"Science and Technology\", \"Autos and Vehicles\", \"Science and Technology\", \"People and Blogs\",\n\"Music\", \"Society and Culture\", \"Education\", \"Howto and Style\", \"Film and Animation\", \"Gaming\", \"Entertainment\",\n\"Travel and Events\", \"Health and Fitness\", \"audiobook\".", "### Data Splits\n\n\nThe dataset has three splits: train, evaluation (dev) and test. The train split has five configurations of various sizes:\nXS, S, M, L, XL. Larger subsets are supersets of smaller subsets, e.g., the L subset contains all the data from the M subset.", "#### Transcribed Training Subsets Size", "#### Transcribed Evaluation Subsets\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?\n\n\nDevelopment and test subsets are annotated by professional human annotators.", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nSpeechColab does not own the copyright of the audio files. For researchers and educators who wish to use the audio files for\nnon-commercial research and/or educational purposes, we can provide access through our site under certain conditions and terms.\n\n\nIn general, when training a machine learning model on a given dataset, the license of the model is independent to that of the\ndataset. That is to say, speech recognition models trained on the GigaSpeech dataset may be eligible for commercial license,\nprovided they abide to the 'Fair Use' terms of the underlying data and do not violate any explicit copyright restrictions.\nThis is likely to be true in most use-cases. However, it is your responsiblity to verify the appropriate model license for\nyour specific use-case by confirming that the dataset usage abides by the Fair Use terms. SpeechColab is not responsible\nfor the license of any machine learning model trained on the GigaSpeech dataset.\n\n\nPlease cite this paper if you find this work useful:", "### Contributions\n\n\nThanks to @polinaeterna and @sanchit-gandhi\nfor adding this dataset.\n\n\nTerms of Access\n---------------\n\n\nThe \"Researcher\" has requested permission to use the GigaSpeech database (the \"Database\")\nat Tsinghua University. In exchange for such permission, Researcher hereby agrees to the\nfollowing terms and conditions:\n\n\n1. Researcher shall use the Database only for non-commercial research and educational purposes.\n2. The SpeechColab team and Tsinghua University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.\n3. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the SpeechColab team and Tsinghua University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted audio files that he or she may create from the Database.\n4. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions.\n5. The SpeechColab team and Tsinghua University reserve the right to terminate Researcher's access to the Database at any time.\n6. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer." ]

[ "task_categories:summarization", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:cc-by-sa-4.0", "query-based-summarization", "arxiv:2111.06474", "region:us" ]

[ "2111.06474" ]

[ "en" ]

[ "# Dataset Card for answersumm", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: URL\n- Paper: AnswerSumm: A Manually-Curated Dataset and Pipeline for Answer Summarization\n- Point of Contact: Alex Fabbri", "### Dataset Summary\n\nThe AnswerSumm dataset is an English-language dataset of questions and answers collected from a StackExchange data dump. The dataset was created to support the task of query-focused answer summarization with an emphasis on multi-perspective answers. \nThe dataset consists of over 4200 such question-answer threads annotated by professional linguists and includes over 8700 summaries. We decompose the task into several annotation stages, including sentence selection, sentence clustering, cluster summarization, and overall summarization. For each thread, the annotator writes two summaries, one in which the annotator is asked to mark sentences that are included in the final summary and instructed to more closely use the words in these sentences rather than abstract. We have multiple annotators for a subset of the examples in the test set.", "### Languages\n\nThe text in the dataset is in English.", "## Dataset Structure", "### Data Instances\n\nA data point comprises a question with a 'title' field containing the overview of the question and a 'question' that elaborates on the title. The answers are sentence tokenized and contain relevance labels, labels for inclusion in the final summary, and cluster labels. We include cluster summaries, overall summaries, and additional metadata. \n\nAn example from the AnswerSumm test set looks as follows:", "### Data Fields\n\n- question: contains metadata about the question and forum\n - question: the body of the question post\n - title: the title of the question post\n - question_tags: user-provided question tags\n - link: link to the original question\n - author: link to the author's user page (as requested by StackExchange's attribution policy)\n\n- answers: list of sentence-tokenized answers\n - answer_details: dictionary consisting of link to answer author's user page (author) and community-assigned score (score)\n - sents: sentences that compose the answer\n - text: the sentence text\n - label: a list (to generalize to multi-annotator scenarios) of whether the sentence is labeled as relevant or not for answering the question. \n - label_summ: a list of whether the sentence was used to write the first annotator-created summary (that is the first summary in 'summaries')\n - cluster_id: a list of lists (potentially multiple annotators and a sentence can be in potentially multiple clusters) of the clusters a sentence belongs to. -1 implies no cluster. This label can be used to aggregate sentences into clusters across answers. \n\n- summaries: list of list of summaries. Each annotator wrote two summaries. The first in the list is the summary in which the instructor was told to mark sentences relevant for inclusion in the summary and then closely use the words of these sentences, while for the second summary the annotator was asked to paraphrase and condense the cluster summaries but was not asked to reduce abstraction.\n\n- annotator_id: a list of the ids of the annotator(s) who completed all tasks related to that thread.\n\n- mismatch_info: a dict of any issues in processing the excel files on which annotations were completed. \n - rel_sent_not_in_cluster: list of booleans indicating whether there are sentences that are labeled as relevant but were not included in a cluster. \n - cluster_sents_not_matched: list of sentences that were found in a cluster but which our processing script didn't automatically match to sentences in the source answers. If cluster summarization is of interest to the user you may want to process these examples separately using clusters_orig.", "### Data Splits\n\nThe data is split into training, validation, and test sets using stratified sampling on the source forums. There are 2783, 500, and 1000 train/validation/test threads, respectively.", "## Dataset Creation", "### Curation Rationale\n\nAnswerSumm was built to provide a testbed for query-focused summarization of multi-perspective answers. The data collection was designed to tackle multiple subtasks including sentence selection, clustering, cluster summarization, and overall summarization.", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe data was obtained by filtering examples based on a whitelist of forums from StackExchange which we believed would be able to be summarized by a lay person. We describe. We asked annotators to remove examples which required technical knowledge or additional context beyond what was present in the answers.", "#### Who are the source language producers?\n\nThe language producers are the users of the StackExchange forums sampled.", "### Annotations", "#### Annotation process\n\nPlease see our paper for additional annotation details. We began with a pre-pilot of 50 examples, followed by a pilot of 500 and a final annotation of 5000 examples. This release contains the results of the final data collection. We will release the instructions used in data collection.", "#### Who are the annotators?\n\nThe annotators are professional linguists who were obtained through an internal contractor.", "### Personal and Sensitive Information\n\nWe did not anonymize the data. We followed the specifications from StackExchange here to include author information.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe purpose of this dataset is to help develop systems that automatically summarize multi-perspective answers. A system that succeeds at this task would be able to summarize many perspectives present in an answer and not limit itself to a single perspective.", "### Discussion of Biases\n\nWhile StackExchange allows for the exchange of information and ideas, hate and harassment may exist on this site. While our annotators did not flag examples in this process, we encourage users of the dataset to reach out with concerns. \nWe also note that this dataset is limited in its monolingual coverage.", "## Additional Information", "### Dataset Curators\n\nThe dataset was collected by Alex Fabbri, Xiaojian Wu, Srini Iyer, Haoran Li, and Mona Diab during work done at Facebook.", "### Licensing Information\n\nThe data is released under cc-by-sa 4.0 following the original StackExchange release." ]

[ "TAGS\n#task_categories-summarization #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc-by-sa-4.0 #query-based-summarization #arxiv-2111.06474 #region-us \n", "# Dataset Card for answersumm", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: URL\n- Paper: AnswerSumm: A Manually-Curated Dataset and Pipeline for Answer Summarization\n- Point of Contact: Alex Fabbri", "### Dataset Summary\n\nThe AnswerSumm dataset is an English-language dataset of questions and answers collected from a StackExchange data dump. The dataset was created to support the task of query-focused answer summarization with an emphasis on multi-perspective answers. \nThe dataset consists of over 4200 such question-answer threads annotated by professional linguists and includes over 8700 summaries. We decompose the task into several annotation stages, including sentence selection, sentence clustering, cluster summarization, and overall summarization. For each thread, the annotator writes two summaries, one in which the annotator is asked to mark sentences that are included in the final summary and instructed to more closely use the words in these sentences rather than abstract. We have multiple annotators for a subset of the examples in the test set.", "### Languages\n\nThe text in the dataset is in English.", "## Dataset Structure", "### Data Instances\n\nA data point comprises a question with a 'title' field containing the overview of the question and a 'question' that elaborates on the title. The answers are sentence tokenized and contain relevance labels, labels for inclusion in the final summary, and cluster labels. We include cluster summaries, overall summaries, and additional metadata. \n\nAn example from the AnswerSumm test set looks as follows:", "### Data Fields\n\n- question: contains metadata about the question and forum\n - question: the body of the question post\n - title: the title of the question post\n - question_tags: user-provided question tags\n - link: link to the original question\n - author: link to the author's user page (as requested by StackExchange's attribution policy)\n\n- answers: list of sentence-tokenized answers\n - answer_details: dictionary consisting of link to answer author's user page (author) and community-assigned score (score)\n - sents: sentences that compose the answer\n - text: the sentence text\n - label: a list (to generalize to multi-annotator scenarios) of whether the sentence is labeled as relevant or not for answering the question. \n - label_summ: a list of whether the sentence was used to write the first annotator-created summary (that is the first summary in 'summaries')\n - cluster_id: a list of lists (potentially multiple annotators and a sentence can be in potentially multiple clusters) of the clusters a sentence belongs to. -1 implies no cluster. This label can be used to aggregate sentences into clusters across answers. \n\n- summaries: list of list of summaries. Each annotator wrote two summaries. The first in the list is the summary in which the instructor was told to mark sentences relevant for inclusion in the summary and then closely use the words of these sentences, while for the second summary the annotator was asked to paraphrase and condense the cluster summaries but was not asked to reduce abstraction.\n\n- annotator_id: a list of the ids of the annotator(s) who completed all tasks related to that thread.\n\n- mismatch_info: a dict of any issues in processing the excel files on which annotations were completed. \n - rel_sent_not_in_cluster: list of booleans indicating whether there are sentences that are labeled as relevant but were not included in a cluster. \n - cluster_sents_not_matched: list of sentences that were found in a cluster but which our processing script didn't automatically match to sentences in the source answers. If cluster summarization is of interest to the user you may want to process these examples separately using clusters_orig.", "### Data Splits\n\nThe data is split into training, validation, and test sets using stratified sampling on the source forums. There are 2783, 500, and 1000 train/validation/test threads, respectively.", "## Dataset Creation", "### Curation Rationale\n\nAnswerSumm was built to provide a testbed for query-focused summarization of multi-perspective answers. The data collection was designed to tackle multiple subtasks including sentence selection, clustering, cluster summarization, and overall summarization.", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe data was obtained by filtering examples based on a whitelist of forums from StackExchange which we believed would be able to be summarized by a lay person. We describe. We asked annotators to remove examples which required technical knowledge or additional context beyond what was present in the answers.", "#### Who are the source language producers?\n\nThe language producers are the users of the StackExchange forums sampled.", "### Annotations", "#### Annotation process\n\nPlease see our paper for additional annotation details. We began with a pre-pilot of 50 examples, followed by a pilot of 500 and a final annotation of 5000 examples. This release contains the results of the final data collection. We will release the instructions used in data collection.", "#### Who are the annotators?\n\nThe annotators are professional linguists who were obtained through an internal contractor.", "### Personal and Sensitive Information\n\nWe did not anonymize the data. We followed the specifications from StackExchange here to include author information.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe purpose of this dataset is to help develop systems that automatically summarize multi-perspective answers. A system that succeeds at this task would be able to summarize many perspectives present in an answer and not limit itself to a single perspective.", "### Discussion of Biases\n\nWhile StackExchange allows for the exchange of information and ideas, hate and harassment may exist on this site. While our annotators did not flag examples in this process, we encourage users of the dataset to reach out with concerns. \nWe also note that this dataset is limited in its monolingual coverage.", "## Additional Information", "### Dataset Curators\n\nThe dataset was collected by Alex Fabbri, Xiaojian Wu, Srini Iyer, Haoran Li, and Mona Diab during work done at Facebook.", "### Licensing Information\n\nThe data is released under cc-by-sa 4.0 following the original StackExchange release." ]

[ "license:apache-2.0", "region:us" ]

[]

[]

[]

[ "TAGS\n#license-apache-2.0 #region-us \n" ]

[ "license:cc-by-4.0", "region:us" ]

[]

[]

[]

[ "TAGS\n#license-cc-by-4.0 #region-us \n" ]

[ "license:odc-by", "region:us" ]

[]

[]

[ "# MuP - Multi Perspective Scientific Document Summarization\n\nGenerating summaries of scientific documents is known to be a challenging task. Majority of existing work in summarization assumes only one single best gold summary for each given document. Having only one gold summary negatively impacts our ability to evaluate the quality of summarization systems as writing summaries is a subjective activity. At the same time, annotating multiple gold summaries for scientific documents can be extremely expensive as it requires domain experts to read and understand long scientific documents. This shared task will enable exploring methods for generating multi-perspective summaries. We introduce a novel summarization corpus, leveraging data from scientific peer reviews to capture diverse perspectives from the reader's point of view.\n\nFor more information about the dataset please refer to: URL" ]

[ "TAGS\n#license-odc-by #region-us \n", "# MuP - Multi Perspective Scientific Document Summarization\n\nGenerating summaries of scientific documents is known to be a challenging task. Majority of existing work in summarization assumes only one single best gold summary for each given document. Having only one gold summary negatively impacts our ability to evaluate the quality of summarization systems as writing summaries is a subjective activity. At the same time, annotating multiple gold summaries for scientific documents can be extremely expensive as it requires domain experts to read and understand long scientific documents. This shared task will enable exploring methods for generating multi-perspective summaries. We introduce a novel summarization corpus, leveraging data from scientific peer reviews to capture diverse perspectives from the reader's point of view.\n\nFor more information about the dataset please refer to: URL" ]

[ "task_categories:text-generation", "task_categories:translation", "language_creators:expert-generated", "multilinguality:translation", "multilinguality:multilingual", "size_categories:100K<n<1M", "source_datasets:original", "language:ab", "language:ru", "license:cc0-1.0", "region:us" ]

[]

[ "ab", "ru" ]

[ "## Table of Contents\n- Dataset Description\n - Dataset Summary\n- Considerations for Using the Data\n - Other Known Limitations", "## Dataset Description\n- Point of Contact: Nart Tlisha\n- Size of the generated dataset: 33.5 MB", "### Dataset Summary\n\nThe Abkhaz Russian parallel corpus dataset is a collection of 205,665 sentences/words extracted from different sources; e-books, web scrapping.", "## Dataset Creation", "### Source Data\nHere is a link to the source on github", "## Considerations for Using the Data", "### Other Known Limitations\nThe accuracy of the dataset is around 95% (gramatical, arthographical errors)" ]

[ "TAGS\n#task_categories-text-generation #task_categories-translation #language_creators-expert-generated #multilinguality-translation #multilinguality-multilingual #size_categories-100K<n<1M #source_datasets-original #language-Abkhazian #language-Russian #license-cc0-1.0 #region-us \n", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n- Considerations for Using the Data\n - Other Known Limitations", "## Dataset Description\n- Point of Contact: Nart Tlisha\n- Size of the generated dataset: 33.5 MB", "### Dataset Summary\n\nThe Abkhaz Russian parallel corpus dataset is a collection of 205,665 sentences/words extracted from different sources; e-books, web scrapping.", "## Dataset Creation", "### Source Data\nHere is a link to the source on github", "## Considerations for Using the Data", "### Other Known Limitations\nThe accuracy of the dataset is around 95% (gramatical, arthographical errors)" ]

[ "region:us" ]

[]

[]

[ "# Glue WSC Fixed\n\nThis dataset is a port of the official 'URL' dataset on the Hub. \nAlso, the test split is not labeled; the label column values are always -1." ]

[ "TAGS\n#region-us \n", "# Glue WSC Fixed\n\nThis dataset is a port of the official 'URL' dataset on the Hub. \nAlso, the test split is not labeled; the label column values are always -1." ]

[ "region:us" ]

[]

[]

[ "# Glue WSC\n\nThis dataset is a port of the official 'wsc' dataset on the Hub. \nAlso, the test split is not labeled; the label column values are always -1." ]

[ "TAGS\n#region-us \n", "# Glue WSC\n\nThis dataset is a port of the official 'wsc' dataset on the Hub. \nAlso, the test split is not labeled; the label column values are always -1." ]

[ "region:us" ]

[]

[]

[ "# Customer Reviews\n\nThis dataset is a port of the official 'CR' dataset from this paper.\nThere is no validation split." ]

[ "TAGS\n#region-us \n", "# Customer Reviews\n\nThis dataset is a port of the official 'CR' dataset from this paper.\nThere is no validation split." ]

[ "region:us" ]

[]

[]

[ "# Italian Tweets Test Dataset\nThis is a dataset with 10M italian tweets. It still contains errors. Please do not use.", "## How to Use" ]

[ "TAGS\n#region-us \n", "# Italian Tweets Test Dataset\nThis is a dataset with 10M italian tweets. It still contains errors. Please do not use.", "## How to Use" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for [Dataset Name]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThe Ultimate Arabic News Dataset is a collection of single-label modern Arabic texts that are used in news websites and press articles.\n\nArabic news data was collected by web scraping techniques from many famous news sites such as Al-Arabiya, Al-Youm Al-Sabea (Youm7), the news published on the Google search engine and other various sources.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nlicense: cc-by-4.0", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for [Dataset Name]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThe Ultimate Arabic News Dataset is a collection of single-label modern Arabic texts that are used in news websites and press articles.\n\nArabic news data was collected by web scraping techniques from many famous news sites such as Al-Arabiya, Al-Youm Al-Sabea (Youm7), the news published on the Google search engine and other various sources.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nlicense: cc-by-4.0", "### Contributions" ]

[ "task_categories:image-to-text", "task_categories:feature-extraction", "language_creators:other", "multilinguality:monolingual", "size_categories:1K<n<10K", "language:en", "license:cc-by-sa-3.0", "license:other", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for \"XKCD\"", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n- Dataset Structure\n - Data Instances\n - Data Fields\n- Dataset Creation\n- Considerations for Using the Data\n- Additional Information\n - Licensing Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL URL\n- Repository: Hugging Face repository", "### Dataset Summary\n\nXKCD is an export of all XKCD comics with their transcript and explanation scrapped from \nURL.", "## Dataset Structure", "### Data Instances\n\n- 'id': '1'\n- 'title': 'Barrel - Part 1'\n- 'image_title': 'Barrel - Part 1'\n- 'url': 'URL\n- 'image_url': 'URL\n- 'explained_url': 'URL\n- 'transcript': '[A boy sits in a barrel which is floating in an ocean.] Boy: i wonder where i'll float next?\n[A smaller frame with a zoom out of the boy in the barrel seen from afar. The barrel drifts into the distance. Nothing \nelse can be seen.]'\n- 'explanation': 'The comic shows a young boy floating in a barrel in an ocean that doesn't have a visible end. It \ncomments on the unlikely optimism and perhaps naïveté people sometimes display. The boy is completely lost and seems \nhopelessly alone, without any plan or control of the situation. Yet, rather than afraid or worried, he is instead \nquietly curious: \"I wonder where I'll float next?\" Although not necessarily the situation in this comic, this is a \nbehavior people often exhibit when there is nothing they can do about a problematic situation for a long time; they may \nhave given up hope or developed a cavalier attitude as a coping mechanism. The title text expands on the philosophical \ncontent, with the boy representing the average human being: wandering through life with no real plan, quietly \noptimistic, always opportunistic and clueless as to what the future may hold. The isolation of the boy may also \nrepresent the way in which we often feel lost through life, never knowing quite where we are, believing that there is \nno one to whom to turn. This comic could also reflect on Randall's feelings towards creating xkcd in the first place; \nunsure of what direction the web comic would turn towards, but hopeful that it would eventually become the popular web \ncomic that we know today. This is the first in a six-part series of comics whose parts were randomly published during \nthe first several dozen strips. The series features a character that is not consistent with what would quickly become \nthe xkcd stick figure style. The character is in a barrel. In 1110: Click and Drag there is a reference to this comic \nat 1 North, 48 East . After Randall released the full The Boy and his Barrel story on xkcd, it has been clear that the \noriginal Ferret story should also be included as part of the barrel series. The full series can be found here . They \nare listed below in the order Randall chose for the short story above: '", "### Data Fields\n\n- 'id'\n- 'title'\n- 'url': URL URL\n- 'image_url'\n- 'explained_url': URL URL\n- 'transcript': english text transcript of the comic\n- 'explanation': english explanation of the comic", "## Dataset Creation\n\nThe dataset was scrapped from both URL and URL.\nThe dataset is therefore licensed under the Creative Commons Attribution-ShareAlike 3.0 license for\nthe 'transcript' and 'explanation' fields, while the image itself is licensed under the\nCreative Commons Attribution-NonCommercial 2.5 license.\n\nSee the Copyrights page from \nURL for more explanations.", "### Update\n\nYou can update the dataset by using the 'URL' script.\nFirst install the dependencies:\n\n\n\nThen run the script:", "## Considerations for Using the Data\n\nAs the data was scrapped, it is entirely possible that some fields are missing part of the original data.", "## Additional Information", "### Licensing Information\n\nThe dataset is licensed under the Creative Commons Attribution-ShareAlike 3.0 license for\nthe 'transcript' and 'explanation' fields, while the images are licensed under the\nCreative Commons Attribution-NonCommercial 2.5 license.", "### Contributions\n\nThanks to @OlivierDehaene for adding this dataset." ]

[ "TAGS\n#task_categories-image-to-text #task_categories-feature-extraction #language_creators-other #multilinguality-monolingual #size_categories-1K<n<10K #language-English #license-cc-by-sa-3.0 #license-other #region-us \n", "# Dataset Card for \"XKCD\"", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n- Dataset Structure\n - Data Instances\n - Data Fields\n- Dataset Creation\n- Considerations for Using the Data\n- Additional Information\n - Licensing Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL URL\n- Repository: Hugging Face repository", "### Dataset Summary\n\nXKCD is an export of all XKCD comics with their transcript and explanation scrapped from \nURL.", "## Dataset Structure", "### Data Instances\n\n- 'id': '1'\n- 'title': 'Barrel - Part 1'\n- 'image_title': 'Barrel - Part 1'\n- 'url': 'URL\n- 'image_url': 'URL\n- 'explained_url': 'URL\n- 'transcript': '[A boy sits in a barrel which is floating in an ocean.] Boy: i wonder where i'll float next?\n[A smaller frame with a zoom out of the boy in the barrel seen from afar. The barrel drifts into the distance. Nothing \nelse can be seen.]'\n- 'explanation': 'The comic shows a young boy floating in a barrel in an ocean that doesn't have a visible end. It \ncomments on the unlikely optimism and perhaps naïveté people sometimes display. The boy is completely lost and seems \nhopelessly alone, without any plan or control of the situation. Yet, rather than afraid or worried, he is instead \nquietly curious: \"I wonder where I'll float next?\" Although not necessarily the situation in this comic, this is a \nbehavior people often exhibit when there is nothing they can do about a problematic situation for a long time; they may \nhave given up hope or developed a cavalier attitude as a coping mechanism. The title text expands on the philosophical \ncontent, with the boy representing the average human being: wandering through life with no real plan, quietly \noptimistic, always opportunistic and clueless as to what the future may hold. The isolation of the boy may also \nrepresent the way in which we often feel lost through life, never knowing quite where we are, believing that there is \nno one to whom to turn. This comic could also reflect on Randall's feelings towards creating xkcd in the first place; \nunsure of what direction the web comic would turn towards, but hopeful that it would eventually become the popular web \ncomic that we know today. This is the first in a six-part series of comics whose parts were randomly published during \nthe first several dozen strips. The series features a character that is not consistent with what would quickly become \nthe xkcd stick figure style. The character is in a barrel. In 1110: Click and Drag there is a reference to this comic \nat 1 North, 48 East . After Randall released the full The Boy and his Barrel story on xkcd, it has been clear that the \noriginal Ferret story should also be included as part of the barrel series. The full series can be found here . They \nare listed below in the order Randall chose for the short story above: '", "### Data Fields\n\n- 'id'\n- 'title'\n- 'url': URL URL\n- 'image_url'\n- 'explained_url': URL URL\n- 'transcript': english text transcript of the comic\n- 'explanation': english explanation of the comic", "## Dataset Creation\n\nThe dataset was scrapped from both URL and URL.\nThe dataset is therefore licensed under the Creative Commons Attribution-ShareAlike 3.0 license for\nthe 'transcript' and 'explanation' fields, while the image itself is licensed under the\nCreative Commons Attribution-NonCommercial 2.5 license.\n\nSee the Copyrights page from \nURL for more explanations.", "### Update\n\nYou can update the dataset by using the 'URL' script.\nFirst install the dependencies:\n\n\n\nThen run the script:", "## Considerations for Using the Data\n\nAs the data was scrapped, it is entirely possible that some fields are missing part of the original data.", "## Additional Information", "### Licensing Information\n\nThe dataset is licensed under the Creative Commons Attribution-ShareAlike 3.0 license for\nthe 'transcript' and 'explanation' fields, while the images are licensed under the\nCreative Commons Attribution-NonCommercial 2.5 license.", "### Contributions\n\nThanks to @OlivierDehaene for adding this dataset." ]

[ "license:artistic-2.0", "region:us" ]

[]

[]

[]

[ "TAGS\n#license-artistic-2.0 #region-us \n" ]

[ "license:artistic-2.0", "region:us" ]

[]

[]

[]

[ "TAGS\n#license-artistic-2.0 #region-us \n" ]

[ "license:cc0-1.0", "region:us" ]

[]

[]

[]

[ "TAGS\n#license-cc0-1.0 #region-us \n" ]

[ "license:lgpl-lr", "region:us" ]

[]

[]

[ "# Dataset Card for lefff morpho", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Point of Contact: Benoît Sagot", "### Dataset Summary\n\nThe Lefff, currently in its 3.5 version, is one of the main morphological and syntactic lexicons for French. This Hugging Face dataset provides an easy access to the extensional morphological information in the Lefff, i.e. to the 4-uples (form, lemma, category, morphosyntactic features) and to the amalgams (e.g. _aux_ = _à_ + _les_) it contains. Category and morphosyntactic features are provided both in the original Lefff format and following the UniMorph guidelines.", "### Languages\n\nFrench", "## Dataset Creation\n\nThe main author of the resource is Benoît Sagot (Inria, France).\n\nPlease refer to the main paper and other Lefff-related papers for details.", "## Additional Information", "### Licensing Information\n\nThe dataset, as the whole Lefff, is distributed under the LGPL-LR licence.\n\n\n\nThe main paper regarding the Lefff can be found here. Here is the BibTeX entry for the paper:\n\n\nFor specific parts of speech or other parts of the lexicon, please cite the corresponding papers whenever relevant." ]

[ "TAGS\n#license-lgpl-lr #region-us \n", "# Dataset Card for lefff morpho", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Point of Contact: Benoît Sagot", "### Dataset Summary\n\nThe Lefff, currently in its 3.5 version, is one of the main morphological and syntactic lexicons for French. This Hugging Face dataset provides an easy access to the extensional morphological information in the Lefff, i.e. to the 4-uples (form, lemma, category, morphosyntactic features) and to the amalgams (e.g. _aux_ = _à_ + _les_) it contains. Category and morphosyntactic features are provided both in the original Lefff format and following the UniMorph guidelines.", "### Languages\n\nFrench", "## Dataset Creation\n\nThe main author of the resource is Benoît Sagot (Inria, France).\n\nPlease refer to the main paper and other Lefff-related papers for details.", "## Additional Information", "### Licensing Information\n\nThe dataset, as the whole Lefff, is distributed under the LGPL-LR licence.\n\n\n\nThe main paper regarding the Lefff can be found here. Here is the BibTeX entry for the paper:\n\n\nFor specific parts of speech or other parts of the lexicon, please cite the corresponding papers whenever relevant." ]

[ "annotations_creators:no-annotation", "language_creators:found", "multilinguality:2 languages", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "language:de", "license:mit", "region:us" ]

[]

[ "en", "de" ]

[ "# Dataset Card for syntactic_transformations", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: Coloring the Blank Slate: Pre-training Imparts a Hierarchical Inductive Bias to Sequence-to-sequence Models\n- Leaderboard: \n- Point of Contact: Aaron Mueller", "### Dataset Summary\n\nThis contains the the syntactic transformations datasets used in Coloring the Blank Slate: Pre-training Imparts a Hierarchical Inductive Bias to Sequence-to-sequence Models. It consists of English and German question formation and passivization transformations. This dataset also contains zero-shot cross-lingual transfer training and evaluation data.", "### Supported Tasks and Leaderboards", "### Languages\n\nEnglish and German.", "## Dataset Structure", "### Data Instances\n\nA typical data point consists of a source sequence (\"src\"), a target sequence (\"tgt\"), and a task prefix (\"prefix\"). The prefix indicates whether a given sequence should be kept the same in the target (indicated by the \"decl:\" prefix) or transformed into a question/passive (\"quest:\"/\"passiv:\", respectively). An example follows:\n\n{\"src\": \"the yak has entertained the walruses that have amused the newt.\",\n\"tgt\": \"has the yak entertained the walruses that have amused the newt?\",\n\"prefix\": \"quest: \"\n}", "### Data Fields\n\n- src: the original source sequence.\n- tgt: the transformed target sequence.\n- prefix: indicates which transformation to perform to map from the source to target sequences.", "### Data Splits\n\nThe datasets are split into training, dev, test, and gen (\"generalization\") sets. The training sets are for fine-tuning the model. The dev and test sets are for evaluating model abilities on in-domain transformations. The generalization sets are for evaluating the inductive biases of the model.\n\nNOTE: for the zero-shot cross-lingual transfer datasets, the generalization sets are split into in-domain and out-of-domain syntactic structures. For in-domain transformations, use \"gen_rc_o\" for question formation or \"gen_pp_o\" for passivization. For out-of-domain transformations, use \"gen_rc_s\" for question formation or \"gen_pp_s\" for passivization.", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information" ]

[ "TAGS\n#annotations_creators-no-annotation #language_creators-found #multilinguality-2 languages #size_categories-100K<n<1M #source_datasets-original #language-English #language-German #license-mit #region-us \n", "# Dataset Card for syntactic_transformations", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: Coloring the Blank Slate: Pre-training Imparts a Hierarchical Inductive Bias to Sequence-to-sequence Models\n- Leaderboard: \n- Point of Contact: Aaron Mueller", "### Dataset Summary\n\nThis contains the the syntactic transformations datasets used in Coloring the Blank Slate: Pre-training Imparts a Hierarchical Inductive Bias to Sequence-to-sequence Models. It consists of English and German question formation and passivization transformations. This dataset also contains zero-shot cross-lingual transfer training and evaluation data.", "### Supported Tasks and Leaderboards", "### Languages\n\nEnglish and German.", "## Dataset Structure", "### Data Instances\n\nA typical data point consists of a source sequence (\"src\"), a target sequence (\"tgt\"), and a task prefix (\"prefix\"). The prefix indicates whether a given sequence should be kept the same in the target (indicated by the \"decl:\" prefix) or transformed into a question/passive (\"quest:\"/\"passiv:\", respectively). An example follows:\n\n{\"src\": \"the yak has entertained the walruses that have amused the newt.\",\n\"tgt\": \"has the yak entertained the walruses that have amused the newt?\",\n\"prefix\": \"quest: \"\n}", "### Data Fields\n\n- src: the original source sequence.\n- tgt: the transformed target sequence.\n- prefix: indicates which transformation to perform to map from the source to target sequences.", "### Data Splits\n\nThe datasets are split into training, dev, test, and gen (\"generalization\") sets. The training sets are for fine-tuning the model. The dev and test sets are for evaluating model abilities on in-domain transformations. The generalization sets are for evaluating the inductive biases of the model.\n\nNOTE: for the zero-shot cross-lingual transfer datasets, the generalization sets are split into in-domain and out-of-domain syntactic structures. For in-domain transformations, use \"gen_rc_o\" for question formation or \"gen_pp_o\" for passivization. For out-of-domain transformations, use \"gen_rc_s\" for question formation or \"gen_pp_s\" for passivization.", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information" ]

[ "region:us" ]

[]

[]

[]

[ "TAGS\n#region-us \n" ]

[ "benchmark:raft", "region:us" ]

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[ "# RAFT submissions for raft-test-submission", "## Submitting to the leaderboard\n\nTo make a submission to the leaderboard, there are three main steps:\n\n1. Generate predictions on the unlabeled test set of each task\n2. Validate the predictions are compatible with the evaluation framework\n3. Push the predictions to the Hub!\n\nSee the instructions below for more details.", "### Rules\n\n1. To prevent overfitting to the public leaderboard, we only evaluate one submission per week. You can push predictions to the Hub as many times as you wish, but we will only evaluate the most recent commit in a given week. \n2. Transfer or meta-learning using other datasets, including further pre-training on other corpora, is allowed.\n3. Use of unlabeled test data is allowed, as is it always available in the applied setting. For example, further pre-training using the unlabeled data for a task would be permitted.\n4. Systems may be augmented with information retrieved from the internet, e.g. via automated web searches.", "### Submission file format\n\nFor each task in RAFT, you should create a CSV file called 'URL' with your model's predictions on the unlabeled test set. Each file should have exactly 2 columns:\n\n* ID (int)\n* Label (string)\n\nSee the dummy predictions in the 'data' folder for examples with the expected format. Here is a simple example that creates a majority-class baseline:\n\n\n\nAs you can see in the example, each 'URL' file should be stored in the task's subfolder in 'data' and at the end you should have something like the following:", "### Validate your submission\n\nTo ensure that your submission files are correctly formatted, run the following command from the root of the repository:\n\n\n\nIf everything is correct, you should see the following message:", "### Push your submission to the Hugging Face Hub!\n\nThe final step is to commit your files and push them to the Hub:\n\n\n\nIf there are no errors, you should see the following message:\n\n\n\nwhere the evaluation is run every Sunday and your results will be visible on the leaderboard." ]

[ "TAGS\n#benchmark-raft #region-us \n", "# RAFT submissions for raft-test-submission", "## Submitting to the leaderboard\n\nTo make a submission to the leaderboard, there are three main steps:\n\n1. Generate predictions on the unlabeled test set of each task\n2. Validate the predictions are compatible with the evaluation framework\n3. Push the predictions to the Hub!\n\nSee the instructions below for more details.", "### Rules\n\n1. To prevent overfitting to the public leaderboard, we only evaluate one submission per week. You can push predictions to the Hub as many times as you wish, but we will only evaluate the most recent commit in a given week. \n2. Transfer or meta-learning using other datasets, including further pre-training on other corpora, is allowed.\n3. Use of unlabeled test data is allowed, as is it always available in the applied setting. For example, further pre-training using the unlabeled data for a task would be permitted.\n4. Systems may be augmented with information retrieved from the internet, e.g. via automated web searches.", "### Submission file format\n\nFor each task in RAFT, you should create a CSV file called 'URL' with your model's predictions on the unlabeled test set. Each file should have exactly 2 columns:\n\n* ID (int)\n* Label (string)\n\nSee the dummy predictions in the 'data' folder for examples with the expected format. Here is a simple example that creates a majority-class baseline:\n\n\n\nAs you can see in the example, each 'URL' file should be stored in the task's subfolder in 'data' and at the end you should have something like the following:", "### Validate your submission\n\nTo ensure that your submission files are correctly formatted, run the following command from the root of the repository:\n\n\n\nIf everything is correct, you should see the following message:", "### Push your submission to the Hugging Face Hub!\n\nThe final step is to commit your files and push them to the Hub:\n\n\n\nIf there are no errors, you should see the following message:\n\n\n\nwhere the evaluation is run every Sunday and your results will be visible on the leaderboard." ]