Datasets:

---

ftopal

/

huggingface-datasets

like 0

[ "multilinguality:monolingual", "language:en", "license:cc-by-3.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for JNLPBA", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER\n\n\nNER For Bio-Entities" ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-cc-by-3.0 #region-us \n", "# Dataset Card for JNLPBA", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER\n\n\nNER For Bio-Entities" ]

[ "multilinguality:monolingual", "language:en", "license:cc-by-4.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for LINNAEUS", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,NED\n\n\nLinnaeus is a novel corpus of full-text documents manually annotated for species mentions." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-cc-by-4.0 #region-us \n", "# Dataset Card for LINNAEUS", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,NED\n\n\nLinnaeus is a novel corpus of full-text documents manually annotated for species mentions." ]

[ "multilinguality:monolingual", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for LLL05", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: RE\n\n\nThe LLL05 challenge task is to learn rules to extract protein/gene interactions from biology abstracts from the Medline\nbibliography database. The goal of the challenge is to test the ability of the participating IE systems to identify the\ninteractions and the gene/proteins that interact. The participants will test their IE patterns on a test set with the\naim of extracting the correct agent and target.The challenge focuses on information extraction of gene interactions in\nBacillus subtilis. Extracting gene interaction is the most popular event IE task in biology. Bacillus subtilis (Bs) is\na model bacterium and many papers have been published on direct gene interactions involved in sporulation. The gene\ninteractions are generally mentioned in the abstract and the full text of the paper is not needed. Extracting gene\ninteraction means, extracting the agent (proteins) and the target (genes) of all couples of genic interactions from\nsentences." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-unknown #region-us \n", "# Dataset Card for LLL05", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: RE\n\n\nThe LLL05 challenge task is to learn rules to extract protein/gene interactions from biology abstracts from the Medline\nbibliography database. The goal of the challenge is to test the ability of the participating IE systems to identify the\ninteractions and the gene/proteins that interact. The participants will test their IE patterns on a test set with the\naim of extracting the correct agent and target.The challenge focuses on information extraction of gene interactions in\nBacillus subtilis. Extracting gene interaction is the most popular event IE task in biology. Bacillus subtilis (Bs) is\na model bacterium and many papers have been published on direct gene interactions involved in sporulation. The gene\ninteractions are generally mentioned in the abstract and the full text of the paper is not needed. Extracting gene\ninteraction means, extracting the agent (proteins) and the target (genes) of all couples of genic interactions from\nsentences." ]

[ "multilinguality:monolingual", "language:en", "license:cc0-1.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for MayoSRS", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: STS\n\n\nMayoSRS consists of 101 clinical term pairs whose relatedness was determined by nine medical coders and three physicians from the Mayo Clinic." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-cc0-1.0 #region-us \n", "# Dataset Card for MayoSRS", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: STS\n\n\nMayoSRS consists of 101 clinical term pairs whose relatedness was determined by nine medical coders and three physicians from the Mayo Clinic." ]

[ "multilinguality:multilingual", "language:en", "language:zh", "license:unknown", "region:us" ]

[]

[ "en", "zh" ]

[ "# Dataset Card for MedQA", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: QA\n\n\nIn this work, we present the first free-form multiple-choice OpenQA dataset for solving medical problems, MedQA,\ncollected from the professional medical board exams. It covers three languages: English, simplified Chinese, and\ntraditional Chinese, and contains 12,723, 34,251, and 14,123 questions for the three languages, respectively. Together\nwith the question data, we also collect and release a large-scale corpus from medical textbooks from which the reading\ncomprehension models can obtain necessary knowledge for answering the questions." ]

[ "TAGS\n#multilinguality-multilingual #language-English #language-Chinese #license-unknown #region-us \n", "# Dataset Card for MedQA", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: QA\n\n\nIn this work, we present the first free-form multiple-choice OpenQA dataset for solving medical problems, MedQA,\ncollected from the professional medical board exams. It covers three languages: English, simplified Chinese, and\ntraditional Chinese, and contains 12,723, 34,251, and 14,123 questions for the three languages, respectively. Together\nwith the question data, we also collect and release a large-scale corpus from medical textbooks from which the reading\ncomprehension models can obtain necessary knowledge for answering the questions." ]

[ "multilinguality:monolingual", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for MeDAL", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NED\n\n\nThe Repository for Medical Dataset for Abbreviation Disambiguation for Natural Language Understanding (MeDAL) is\na large medical text dataset curated for abbreviation disambiguation, designed for natural language understanding\npre-training in the medical domain." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-other #region-us \n", "# Dataset Card for MeDAL", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NED\n\n\nThe Repository for Medical Dataset for Abbreviation Disambiguation for Natural Language Understanding (MeDAL) is\na large medical text dataset curated for abbreviation disambiguation, designed for natural language understanding\npre-training in the medical domain." ]

[ "multilinguality:multilingual", "language:en", "language:zh", "license:unknown", "arxiv:2004.03329", "region:us" ]

[ "2004.03329" ]

[ "en", "zh" ]

[ "# Dataset Card for MedDialog", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: TXTCLASS\n\n\n\nThe MedDialog dataset (English) contains conversations (in English) between doctors and patients.It has 0.26 million dialogues. The data is continuously growing and more dialogues will be added. The raw dialogues are from URL and URL.\nAll copyrights of the data belong to URL and URL." ]

[ "TAGS\n#multilinguality-multilingual #language-English #language-Chinese #license-unknown #arxiv-2004.03329 #region-us \n", "# Dataset Card for MedDialog", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: TXTCLASS\n\n\n\nThe MedDialog dataset (English) contains conversations (in English) between doctors and patients.It has 0.26 million dialogues. The data is continuously growing and more dialogues will be added. The raw dialogues are from URL and URL.\nAll copyrights of the data belong to URL and URL." ]

[ "multilinguality:monolingual", "language:es", "license:cc-by-4.0", "region:us" ]

[]

[ "es" ]

[ "# Dataset Card for MEDDOCAN", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: NER\n\n\nMEDDOCAN: Medical Document Anonymization Track\n\nThis dataset is designed for the MEDDOCAN task, sponsored by Plan de Impulso de las Tecnologías del Lenguaje.\n\nIt is a manually classified collection of 1,000 clinical case reports derived from the Spanish Clinical Case Corpus (SPACCC), enriched with PHI expressions.\n\nThe annotation of the entire set of entity mentions was carried out by experts annotatorsand it includes 29 entity types relevant for the annonymiation of medical documents.22 of these annotation types are actually present in the corpus: TERRITORIO, FECHAS, EDAD_SUJETO_ASISTENCIA, NOMBRE_SUJETO_ASISTENCIA, NOMBRE_PERSONAL_SANITARIO, SEXO_SUJETO_ASISTENCIA, CALLE, PAIS, ID_SUJETO_ASISTENCIA, CORREO, ID_TITULACION_PERSONAL_SANITARIO,ID_ASEGURAMIENTO, HOSPITAL, FAMILIARES_SUJETO_ASISTENCIA, INSTITUCION, ID_CONTACTO ASISTENCIAL,NUMERO_TELEFONO, PROFESION, NUMERO_FAX, OTROS_SUJETO_ASISTENCIA, CENTRO_SALUD, ID_EMPLEO_PERSONAL_SANITARIO\n \nFor further information, please visit URL or send an email to encargo-pln-life@URL" ]

[ "TAGS\n#multilinguality-monolingual #language-Spanish #license-cc-by-4.0 #region-us \n", "# Dataset Card for MEDDOCAN", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: NER\n\n\nMEDDOCAN: Medical Document Anonymization Track\n\nThis dataset is designed for the MEDDOCAN task, sponsored by Plan de Impulso de las Tecnologías del Lenguaje.\n\nIt is a manually classified collection of 1,000 clinical case reports derived from the Spanish Clinical Case Corpus (SPACCC), enriched with PHI expressions.\n\nThe annotation of the entire set of entity mentions was carried out by experts annotatorsand it includes 29 entity types relevant for the annonymiation of medical documents.22 of these annotation types are actually present in the corpus: TERRITORIO, FECHAS, EDAD_SUJETO_ASISTENCIA, NOMBRE_SUJETO_ASISTENCIA, NOMBRE_PERSONAL_SANITARIO, SEXO_SUJETO_ASISTENCIA, CALLE, PAIS, ID_SUJETO_ASISTENCIA, CORREO, ID_TITULACION_PERSONAL_SANITARIO,ID_ASEGURAMIENTO, HOSPITAL, FAMILIARES_SUJETO_ASISTENCIA, INSTITUCION, ID_CONTACTO ASISTENCIAL,NUMERO_TELEFONO, PROFESION, NUMERO_FAX, OTROS_SUJETO_ASISTENCIA, CENTRO_SALUD, ID_EMPLEO_PERSONAL_SANITARIO\n \nFor further information, please visit URL or send an email to encargo-pln-life@URL" ]

[ "multilinguality:monolingual", "language:en", "license:cc-by-sa-3.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for MedHop", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: QA\n\n\nWith the same format as WikiHop, this dataset is based on research paper\nabstracts from PubMed, and the queries are about interactions between\npairs of drugs. The correct answer has to be inferred by combining\ninformation from a chain of reactions of drugs and proteins." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-cc-by-sa-3.0 #region-us \n", "# Dataset Card for MedHop", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: QA\n\n\nWith the same format as WikiHop, this dataset is based on research paper\nabstracts from PubMed, and the queries are about interactions between\npairs of drugs. The correct answer has to be inferred by combining\ninformation from a chain of reactions of drugs and proteins." ]

[ "multilinguality:monolingual", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for Medical Data", "## Dataset Description\n\n- Homepage: \n- Pubmed: False\n- Public: False\n- Tasks: TE\n\n\n This dataset is designed to do multiclass classification on medical drugs" ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-unknown #region-us \n", "# Dataset Card for Medical Data", "## Dataset Description\n\n- Homepage: \n- Pubmed: False\n- Public: False\n- Tasks: TE\n\n\n This dataset is designed to do multiclass classification on medical drugs" ]

[ "multilinguality:monolingual", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for MEDIQA NLI", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: False\n- Tasks: TE\n\n\nNatural Language Inference (NLI) is the task of determining whether a given hypothesis can be\ninferred from a given premise. Also known as Recognizing Textual Entailment (RTE), this task has\nenjoyed popularity among researchers for some time. However, almost all datasets for this task\nfocused on open domain data such as as news texts, blogs, and so on. To address this gap, the MedNLI\ndataset was created for language inference in the medical domain. MedNLI is a derived dataset with\ndata sourced from MIMIC-III v1.4. In order to stimulate research for this problem, a shared task on\nMedical Inference and Question Answering (MEDIQA) was organized at the workshop for biomedical\nnatural language processing (BioNLP) 2019. The dataset provided herein is a test set of 405 premise\nhypothesis pairs for the NLI challenge in the MEDIQA shared task. Participants of the shared task\nare expected to use the MedNLI data for development of their models and this dataset was used as an\nunseen dataset for scoring each participant submission." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-other #region-us \n", "# Dataset Card for MEDIQA NLI", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: False\n- Tasks: TE\n\n\nNatural Language Inference (NLI) is the task of determining whether a given hypothesis can be\ninferred from a given premise. Also known as Recognizing Textual Entailment (RTE), this task has\nenjoyed popularity among researchers for some time. However, almost all datasets for this task\nfocused on open domain data such as as news texts, blogs, and so on. To address this gap, the MedNLI\ndataset was created for language inference in the medical domain. MedNLI is a derived dataset with\ndata sourced from MIMIC-III v1.4. In order to stimulate research for this problem, a shared task on\nMedical Inference and Question Answering (MEDIQA) was organized at the workshop for biomedical\nnatural language processing (BioNLP) 2019. The dataset provided herein is a test set of 405 premise\nhypothesis pairs for the NLI challenge in the MEDIQA shared task. Participants of the shared task\nare expected to use the MedNLI data for development of their models and this dataset was used as an\nunseen dataset for scoring each participant submission." ]

[ "multilinguality:monolingual", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for MEDIQA QA", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: QA\n\n\nThe MEDIQA challenge is an ACL-BioNLP 2019 shared task aiming to attract further research efforts in Natural Language Inference (NLI), Recognizing Question Entailment (RQE), and their applications in medical Question Answering (QA).\nMailing List: URL\n\nIn the QA task, participants are tasked to:\n- filter/classify the provided answers (1: correct, 0: incorrect).\n- re-rank the answers." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-unknown #region-us \n", "# Dataset Card for MEDIQA QA", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: QA\n\n\nThe MEDIQA challenge is an ACL-BioNLP 2019 shared task aiming to attract further research efforts in Natural Language Inference (NLI), Recognizing Question Entailment (RQE), and their applications in medical Question Answering (QA).\nMailing List: URL\n\nIn the QA task, participants are tasked to:\n- filter/classify the provided answers (1: correct, 0: incorrect).\n- re-rank the answers." ]

[ "multilinguality:monolingual", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for MEDIQA RQE", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: TXT2CLASS\n\n\nThe MEDIQA challenge is an ACL-BioNLP 2019 shared task aiming to attract further research efforts in Natural Language Inference (NLI), Recognizing Question Entailment (RQE), and their applications in medical Question Answering (QA).\nMailing List: URL\n\nThe objective of the RQE task is to identify entailment between two questions in the context of QA. We use the following definition of question entailment: “a question A entails a question B if every answer to B is also a complete or partial answer to A” [1]\n [1] A. Ben Abacha & D. Demner-Fushman. “Recognizing Question Entailment for Medical Question Answering”. AMIA 2016." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-unknown #region-us \n", "# Dataset Card for MEDIQA RQE", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: TXT2CLASS\n\n\nThe MEDIQA challenge is an ACL-BioNLP 2019 shared task aiming to attract further research efforts in Natural Language Inference (NLI), Recognizing Question Entailment (RQE), and their applications in medical Question Answering (QA).\nMailing List: URL\n\nThe objective of the RQE task is to identify entailment between two questions in the context of QA. We use the following definition of question entailment: “a question A entails a question B if every answer to B is also a complete or partial answer to A” [1]\n [1] A. Ben Abacha & D. Demner-Fushman. “Recognizing Question Entailment for Medical Question Answering”. AMIA 2016." ]

[ "multilinguality:monolingual", "language:en", "license:cc0-1.0", "arxiv:1902.09476", "region:us" ]

[ "1902.09476" ]

[ "en" ]

[ "# Dataset Card for MedMentions", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NED,NER\n\n\nMedMentions is a new manually annotated resource for the recognition of biomedical concepts.\nWhat distinguishes MedMentions from other annotated biomedical corpora is its size (over 4,000\nabstracts and over 350,000 linked mentions), as well as the size of the concept ontology (over\n3 million concepts from UMLS 2017) and its broad coverage of biomedical disciplines.\n\nCorpus: The MedMentions corpus consists of 4,392 papers (Titles and Abstracts) randomly selected\nfrom among papers released on PubMed in 2016, that were in the biomedical field, published in\nthe English language, and had both a Title and an Abstract.\n\nAnnotators: We recruited a team of professional annotators with rich experience in biomedical\ncontent curation to exhaustively annotate all UMLS® (2017AA full version) entity mentions in\nthese papers.\n\nAnnotation quality: We did not collect stringent IAA (Inter-annotator agreement) data. To gain\ninsight on the annotation quality of MedMentions, we randomly selected eight papers from the\nannotated corpus, containing a total of 469 concepts. Two biologists ('Reviewer') who did not\nparticipate in the annotation task then each reviewed four papers. The agreement between\nReviewers and Annotators, an estimate of the Precision of the annotations, was 97.3%." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-cc0-1.0 #arxiv-1902.09476 #region-us \n", "# Dataset Card for MedMentions", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NED,NER\n\n\nMedMentions is a new manually annotated resource for the recognition of biomedical concepts.\nWhat distinguishes MedMentions from other annotated biomedical corpora is its size (over 4,000\nabstracts and over 350,000 linked mentions), as well as the size of the concept ontology (over\n3 million concepts from UMLS 2017) and its broad coverage of biomedical disciplines.\n\nCorpus: The MedMentions corpus consists of 4,392 papers (Titles and Abstracts) randomly selected\nfrom among papers released on PubMed in 2016, that were in the biomedical field, published in\nthe English language, and had both a Title and an Abstract.\n\nAnnotators: We recruited a team of professional annotators with rich experience in biomedical\ncontent curation to exhaustively annotate all UMLS® (2017AA full version) entity mentions in\nthese papers.\n\nAnnotation quality: We did not collect stringent IAA (Inter-annotator agreement) data. To gain\ninsight on the annotation quality of MedMentions, we randomly selected eight papers from the\nannotated corpus, containing a total of 469 concepts. Two biologists ('Reviewer') who did not\nparticipate in the annotation task then each reviewed four papers. The agreement between\nReviewers and Annotators, an estimate of the Precision of the annotations, was 97.3%." ]

[ "multilinguality:monolingual", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for MeQSum", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: SUM\n\n\nDataset for medical question summarization introduced in the ACL 2019 paper \"On the Summarization of Consumer Health\nQuestions\". Question understanding is one of the main challenges in question answering. In real world applications,\nusers often submit natural language questions that are longer than needed and include peripheral information that\nincreases the complexity of the question, leading to substantially more false positives in answer retrieval. In this\npaper, we study neural abstractive models for medical question summarization. We introduce the MeQSum corpus of 1,000\nsummarized consumer health questions." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-unknown #region-us \n", "# Dataset Card for MeQSum", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: SUM\n\n\nDataset for medical question summarization introduced in the ACL 2019 paper \"On the Summarization of Consumer Health\nQuestions\". Question understanding is one of the main challenges in question answering. In real world applications,\nusers often submit natural language questions that are longer than needed and include peripheral information that\nincreases the complexity of the question, leading to substantially more false positives in answer retrieval. In this\npaper, we study neural abstractive models for medical question summarization. We introduce the MeQSum corpus of 1,000\nsummarized consumer health questions." ]

[ "multilinguality:monolingual", "language:en", "license:cc0-1.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for MiniMayoSRS", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: STS\n\n\nMiniMayoSRS is a subset of the MayoSRS and consists of 30 term pairs on which a higher inter-annotator agreement was\nachieved. The average correlation between physicians is 0.68. The average correlation between medical coders is 0.78." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-cc0-1.0 #region-us \n", "# Dataset Card for MiniMayoSRS", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: STS\n\n\nMiniMayoSRS is a subset of the MayoSRS and consists of 30 term pairs on which a higher inter-annotator agreement was\nachieved. The average correlation between physicians is 0.68. The average correlation between medical coders is 0.78." ]

[ "multilinguality:monolingual", "language:en", "license:cc-by-nc-3.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for miRNA", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,NED\n\n\nThe corpus consists of 301 Medline citations. The documents were screened for\nmentions of miRNA in the abstract text. Gene, disease and miRNA entities were manually\nannotated. The corpus comprises of two separate files, a train and a test set, coming\nfrom 201 and 100 documents respectively." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-cc-by-nc-3.0 #region-us \n", "# Dataset Card for miRNA", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,NED\n\n\nThe corpus consists of 301 Medline citations. The documents were screened for\nmentions of miRNA in the abstract text. Gene, disease and miRNA entities were manually\nannotated. The corpus comprises of two separate files, a train and a test set, coming\nfrom 201 and 100 documents respectively." ]

[ "multilinguality:monolingual", "language:en", "license:cc-by-nc-sa-3.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for MLEE", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: EE,NER,RE,COREF\n\n\nMLEE is an event extraction corpus consisting of manually annotated abstracts of papers\non angiogenesis. It contains annotations for entities, relations, events and coreferences\nThe annotations span molecular, cellular, tissue, and organ-level processes." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-cc-by-nc-sa-3.0 #region-us \n", "# Dataset Card for MLEE", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: EE,NER,RE,COREF\n\n\nMLEE is an event extraction corpus consisting of manually annotated abstracts of papers\non angiogenesis. It contains annotations for entities, relations, events and coreferences\nThe annotations span molecular, cellular, tissue, and organ-level processes." ]

[ "multilinguality:monolingual", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for MQP", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: STS\n\n\nMedical Question Pairs dataset by McCreery et al (2020) contains pairs of medical questions and paraphrased versions of \nthe question prepared by medical professional. Paraphrased versions were labelled as similar (syntactically dissimilar \nbut contextually similar ) or dissimilar (syntactically may look similar but contextually dissimilar). Labels 1: similar, 0: dissimilar" ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-unknown #region-us \n", "# Dataset Card for MQP", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: STS\n\n\nMedical Question Pairs dataset by McCreery et al (2020) contains pairs of medical questions and paraphrased versions of \nthe question prepared by medical professional. Paraphrased versions were labelled as similar (syntactically dissimilar \nbut contextually similar ) or dissimilar (syntactically may look similar but contextually dissimilar). Labels 1: similar, 0: dissimilar" ]

[ "multilinguality:monolingual", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for MSH WSD", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: False\n- Tasks: NED\n\n\nEvaluation of Word Sense Disambiguation methods (WSD) in the biomedical domain is difficult because the available\nresources are either too small or too focused on specific types of entities (e.g. diseases or genes). We have\ndeveloped a method that can be used to automatically develop a WSD test collection using the Unified Medical Language\nSystem (UMLS) Metathesaurus and the manual MeSH indexing of MEDLINE. The resulting dataset is called MSH WSD and\nconsists of 106 ambiguous abbreviations, 88 ambiguous terms and 9 which are a combination of both, for a total of 203\nambiguous words. Each instance containing the ambiguous word was assigned a CUI from the 2009AB version of the UMLS.\nFor each ambiguous term/abbreviation, the data set contains a maximum of 100 instances per sense obtained from\nMEDLINE; totaling 37,888 ambiguity cases in 37,090 MEDLINE citations." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-other #region-us \n", "# Dataset Card for MSH WSD", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: False\n- Tasks: NED\n\n\nEvaluation of Word Sense Disambiguation methods (WSD) in the biomedical domain is difficult because the available\nresources are either too small or too focused on specific types of entities (e.g. diseases or genes). We have\ndeveloped a method that can be used to automatically develop a WSD test collection using the Unified Medical Language\nSystem (UMLS) Metathesaurus and the manual MeSH indexing of MEDLINE. The resulting dataset is called MSH WSD and\nconsists of 106 ambiguous abbreviations, 88 ambiguous terms and 9 which are a combination of both, for a total of 203\nambiguous words. Each instance containing the ambiguous word was assigned a CUI from the 2009AB version of the UMLS.\nFor each ambiguous term/abbreviation, the data set contains a maximum of 100 instances per sense obtained from\nMEDLINE; totaling 37,888 ambiguity cases in 37,090 MEDLINE citations." ]

[ "multilinguality:multilingual", "language:en", "language:de", "license:unknown", "region:us" ]

[]

[ "en", "de" ]

[ "# Dataset Card for MuchMore", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: TRANSL,NER,NED,RE\n\n\nThe corpus used in the MuchMore project is a parallel corpus of English-German scientific\nmedical abstracts obtained from the Springer Link web site. The corpus consists\napproximately of 1 million tokens for each language. Abstracts are from 41 medical\njournals, each of which constitutes a relatively homogeneous medical sub-domain (e.g.\nNeurology, Radiology, etc.). The corpus of downloaded HTML documents is normalized in\nvarious ways, in order to produce a clean, plain text version, consisting of a title, abstract\nand keywords. Additionally, the corpus was aligned on the sentence level.\n\nAutomatic (!) annotation includes: Part-of-Speech; Morphology (inflection and\ndecomposition); Chunks; Semantic Classes (UMLS: Unified Medical Language System,\nMeSH: Medical Subject Headings, EuroWordNet); Semantic Relations from UMLS." ]

[ "TAGS\n#multilinguality-multilingual #language-English #language-German #license-unknown #region-us \n", "# Dataset Card for MuchMore", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: TRANSL,NER,NED,RE\n\n\nThe corpus used in the MuchMore project is a parallel corpus of English-German scientific\nmedical abstracts obtained from the Springer Link web site. The corpus consists\napproximately of 1 million tokens for each language. Abstracts are from 41 medical\njournals, each of which constitutes a relatively homogeneous medical sub-domain (e.g.\nNeurology, Radiology, etc.). The corpus of downloaded HTML documents is normalized in\nvarious ways, in order to produce a clean, plain text version, consisting of a title, abstract\nand keywords. Additionally, the corpus was aligned on the sentence level.\n\nAutomatic (!) annotation includes: Part-of-Speech; Morphology (inflection and\ndecomposition); Chunks; Semantic Classes (UMLS: Unified Medical Language System,\nMeSH: Medical Subject Headings, EuroWordNet); Semantic Relations from UMLS." ]

[ "multilinguality:monolingual", "language:en", "license:mit", "arxiv:2010.14235", "region:us" ]

[ "2010.14235" ]

[ "en" ]

[ "# Dataset Card for Multi-XScience", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: PARA,SUM\n\n\nMulti-document summarization is a challenging task for which there exists little large-scale datasets. \nWe propose Multi-XScience, a large-scale multi-document summarization dataset created from scientific articles. \nMulti-XScience introduces a challenging multi-document summarization task: writing the related-work section \nof a paper based on its abstract and the articles it references. Our work is inspired by extreme summarization, \na dataset construction protocol that favours abstractive modeling approaches. Descriptive statistics and \nempirical results---using several state-of-the-art models trained on the Multi-XScience dataset---reveal t\nhat Multi-XScience is well suited for abstractive models." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-mit #arxiv-2010.14235 #region-us \n", "# Dataset Card for Multi-XScience", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: PARA,SUM\n\n\nMulti-document summarization is a challenging task for which there exists little large-scale datasets. \nWe propose Multi-XScience, a large-scale multi-document summarization dataset created from scientific articles. \nMulti-XScience introduces a challenging multi-document summarization task: writing the related-work section \nof a paper based on its abstract and the articles it references. Our work is inspired by extreme summarization, \na dataset construction protocol that favours abstractive modeling approaches. Descriptive statistics and \nempirical results---using several state-of-the-art models trained on the Multi-XScience dataset---reveal t\nhat Multi-XScience is well suited for abstractive models." ]

[ "multilinguality:monolingual", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for n2c2 2006 De-identification", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: False\n- Tasks: NER\n\n\nThe data for the de-identification challenge came from Partners Healthcare and\nincluded solely medical discharge summaries. We prepared the data for the\nchallengeby annotating and by replacing all authentic PHI with realistic\nsurrogates.\n\nGiven the above definitions, we marked the authentic PHI in the records in two stages.\nIn the first stage, we used an automatic system.31 In the second stage, we validated\nthe output of the automatic system manually. Three annotators, including undergraduate\nand graduate students and a professor, serially made three manual passes over each record.\nThey marked and discussed the PHI tags they disagreed on and finalized these tags\nafter discussion.\n\nThe original dataset does not have spans for each entity. The spans are\ncomputed in this loader and the final text that correspond with the\ntags is preserved in the source format" ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-other #region-us \n", "# Dataset Card for n2c2 2006 De-identification", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: False\n- Tasks: NER\n\n\nThe data for the de-identification challenge came from Partners Healthcare and\nincluded solely medical discharge summaries. We prepared the data for the\nchallengeby annotating and by replacing all authentic PHI with realistic\nsurrogates.\n\nGiven the above definitions, we marked the authentic PHI in the records in two stages.\nIn the first stage, we used an automatic system.31 In the second stage, we validated\nthe output of the automatic system manually. Three annotators, including undergraduate\nand graduate students and a professor, serially made three manual passes over each record.\nThey marked and discussed the PHI tags they disagreed on and finalized these tags\nafter discussion.\n\nThe original dataset does not have spans for each entity. The spans are\ncomputed in this loader and the final text that correspond with the\ntags is preserved in the source format" ]

[ "multilinguality:monolingual", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for n2c2 2006 Smoking Status", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: False\n- Tasks: TXTCLASS\n\n\nThe data for the n2c2 2006 smoking challenge consisted of discharge summaries\nfrom Partners HealthCare, which were then de-identified, tokenized, broken into\nsentences, converted into XML format, and separated into training and test sets.\n\nTwo pulmonologists annotated each record with the smoking status of patients based\nstrictly on the explicitly stated smoking-related facts in the records. These\nannotations constitute the textual judgments of the annotators. The annotators\nwere asked to classify patient records into five possible smoking status categories:\na past smoker, a current smoker, a smoker, a non-smoker and an unknown. A total of\n502 de-identified medical discharge records were used for the smoking challenge." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-other #region-us \n", "# Dataset Card for n2c2 2006 Smoking Status", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: False\n- Tasks: TXTCLASS\n\n\nThe data for the n2c2 2006 smoking challenge consisted of discharge summaries\nfrom Partners HealthCare, which were then de-identified, tokenized, broken into\nsentences, converted into XML format, and separated into training and test sets.\n\nTwo pulmonologists annotated each record with the smoking status of patients based\nstrictly on the explicitly stated smoking-related facts in the records. These\nannotations constitute the textual judgments of the annotators. The annotators\nwere asked to classify patient records into five possible smoking status categories:\na past smoker, a current smoker, a smoker, a non-smoker and an unknown. A total of\n502 de-identified medical discharge records were used for the smoking challenge." ]

[ "multilinguality:monolingual", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for n2c2 2008 Obesity", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: False\n- Tasks: TXTCLASS\n\n\nThe data for the n2c2 2008 obesity challenge consisted of discharge summaries from\nthe Partners HealthCare Research Patient Data Repository. These data were chosen \nfrom the discharge summaries of patients who were overweight or diabetic and had \nbeen hospitalized for obesity or diabetes sometime since 12/1/04. De-identification\nwas performed semi-automatically. All private health information was replaced with\nsynthetic identifiers.\n\nThe data for the challenge were annotated by two obesity experts from the \nMassachusetts General Hospital Weight Center. The experts were given a textual task, \nwhich asked them to classify each disease (see list of diseases above) as Present, \nAbsent, Questionable, or Unmentioned based on explicitly documented information in \nthe discharge summaries, e.g., the statement “the patient is obese”. The experts were \nalso given an intuitive task, which asked them to classify each disease as Present, \nAbsent, or Questionable by applying their intuition and judgment to information in \nthe discharge summaries." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-other #region-us \n", "# Dataset Card for n2c2 2008 Obesity", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: False\n- Tasks: TXTCLASS\n\n\nThe data for the n2c2 2008 obesity challenge consisted of discharge summaries from\nthe Partners HealthCare Research Patient Data Repository. These data were chosen \nfrom the discharge summaries of patients who were overweight or diabetic and had \nbeen hospitalized for obesity or diabetes sometime since 12/1/04. De-identification\nwas performed semi-automatically. All private health information was replaced with\nsynthetic identifiers.\n\nThe data for the challenge were annotated by two obesity experts from the \nMassachusetts General Hospital Weight Center. The experts were given a textual task, \nwhich asked them to classify each disease (see list of diseases above) as Present, \nAbsent, Questionable, or Unmentioned based on explicitly documented information in \nthe discharge summaries, e.g., the statement “the patient is obese”. The experts were \nalso given an intuitive task, which asked them to classify each disease as Present, \nAbsent, or Questionable by applying their intuition and judgment to information in \nthe discharge summaries." ]

[ "multilinguality:monolingual", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for n2c2 2009 Medications", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: False\n- Tasks: NER\n\n\nThe Third i2b2 Workshop on Natural Language Processing Challenges for Clinical Records\nfocused on the identification of medications, their dosages, modes (routes) of administration,\nfrequencies, durations, and reasons for administration in discharge summaries.\nThe third i2b2 challenge—that is, the medication challenge—extends information\nextraction to relation extraction; it requires extraction of medications and\nmedication-related information followed by determination of which medication\nbelongs to which medication-related details.\n\nThe medication challenge was designed as an information extraction task.\nThe goal, for each discharge summary, was to extract the following information\non medications experienced by the patient:\n1. Medications (m): including names, brand names, generics, and collective names of prescription substances,\nover the counter medications, and other biological substances for which the patient is the experiencer.\n2. Dosages (do): indicating the amount of a medication used in each administration.\n3. Modes (mo): indicating the route for administering the medication.\n4. Frequencies (f): indicating how often each dose of the medication should be taken.\n5. Durations (du): indicating how long the medication is to be administered.\n6. Reasons (r): stating the medical reason for which the medication is given.\n7. Certainty (c): stating whether the event occurs. Certainty can be expressed by uncertainty words,\ne.g., “suggested”, or via modals, e.g., “should” indicates suggestion.\n8. Event (e): stating on whether the medication is started, stopped, or continued.\n9. Temporal (t): stating whether the medication was administered in the past,\nis being administered currently, or will be administered in the future, to the extent\nthat this information is expressed in the tense of the verbs and auxiliary verbs used to express events.\n10. List/narrative (ln): indicating whether the medication information appears in a\nlist structure or in narrative running text in the discharge summary.\n\nThe medication challenge asked that systems extract the text corresponding to each of the fields\nfor each of the mentions of the medications that were experienced by the patients.\n\nThe values for the set of fields related to a medication mention, if presented within a\ntwo-line window of the mention, were linked in order to create what we defined as an ‘entry’.\nIf the value of a field for a mention were not specified within a two-line window,\nthen the value ‘nm’ for ‘not mentioned’ was entered and the offsets were left unspecified.\n\nSince the dataset annotations were crowd-sourced, it contains various violations that are handled\nthroughout the data loader via means of exception catching or conditional statements. e.g.\nannotation: anticoagulation, while in text all words are to be separated by space which\nmeans words at end of sentence will always contain '.' and hence won't be an exact match\ni.e. 'anticoagulation' != 'anticoagulation.' from doc_id: 818404" ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-other #region-us \n", "# Dataset Card for n2c2 2009 Medications", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: False\n- Tasks: NER\n\n\nThe Third i2b2 Workshop on Natural Language Processing Challenges for Clinical Records\nfocused on the identification of medications, their dosages, modes (routes) of administration,\nfrequencies, durations, and reasons for administration in discharge summaries.\nThe third i2b2 challenge—that is, the medication challenge—extends information\nextraction to relation extraction; it requires extraction of medications and\nmedication-related information followed by determination of which medication\nbelongs to which medication-related details.\n\nThe medication challenge was designed as an information extraction task.\nThe goal, for each discharge summary, was to extract the following information\non medications experienced by the patient:\n1. Medications (m): including names, brand names, generics, and collective names of prescription substances,\nover the counter medications, and other biological substances for which the patient is the experiencer.\n2. Dosages (do): indicating the amount of a medication used in each administration.\n3. Modes (mo): indicating the route for administering the medication.\n4. Frequencies (f): indicating how often each dose of the medication should be taken.\n5. Durations (du): indicating how long the medication is to be administered.\n6. Reasons (r): stating the medical reason for which the medication is given.\n7. Certainty (c): stating whether the event occurs. Certainty can be expressed by uncertainty words,\ne.g., “suggested”, or via modals, e.g., “should” indicates suggestion.\n8. Event (e): stating on whether the medication is started, stopped, or continued.\n9. Temporal (t): stating whether the medication was administered in the past,\nis being administered currently, or will be administered in the future, to the extent\nthat this information is expressed in the tense of the verbs and auxiliary verbs used to express events.\n10. List/narrative (ln): indicating whether the medication information appears in a\nlist structure or in narrative running text in the discharge summary.\n\nThe medication challenge asked that systems extract the text corresponding to each of the fields\nfor each of the mentions of the medications that were experienced by the patients.\n\nThe values for the set of fields related to a medication mention, if presented within a\ntwo-line window of the mention, were linked in order to create what we defined as an ‘entry’.\nIf the value of a field for a mention were not specified within a two-line window,\nthen the value ‘nm’ for ‘not mentioned’ was entered and the offsets were left unspecified.\n\nSince the dataset annotations were crowd-sourced, it contains various violations that are handled\nthroughout the data loader via means of exception catching or conditional statements. e.g.\nannotation: anticoagulation, while in text all words are to be separated by space which\nmeans words at end of sentence will always contain '.' and hence won't be an exact match\ni.e. 'anticoagulation' != 'anticoagulation.' from doc_id: 818404" ]

[ "multilinguality:monolingual", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for n2c2 2010 Concepts, Assertions, and Relations", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: False\n- Tasks: NER,RE\n\n\nThe i2b2/VA corpus contained de-identified discharge summaries from Beth Israel\nDeaconess Medical Center, Partners Healthcare, and University of Pittsburgh Medical\nCenter (UPMC). In addition, UPMC contributed de-identified progress notes to the\ni2b2/VA corpus. This dataset contains the records from Beth Israel and Partners.\n\nThe 2010 i2b2/VA Workshop on Natural Language Processing Challenges for Clinical Records comprises three tasks:\n1) a concept extraction task focused on the extraction of medical concepts from patient reports;\n2) an assertion classification task focused on assigning assertion types for medical problem concepts;\n3) a relation classification task focused on assigning relation types that hold between medical problems,\ntests, and treatments.\n\ni2b2 and the VA provided an annotated reference standard corpus for the three tasks.\nUsing this reference standard, 22 systems were developed for concept extraction,\n21 for assertion classification, and 16 for relation classification." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-other #region-us \n", "# Dataset Card for n2c2 2010 Concepts, Assertions, and Relations", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: False\n- Tasks: NER,RE\n\n\nThe i2b2/VA corpus contained de-identified discharge summaries from Beth Israel\nDeaconess Medical Center, Partners Healthcare, and University of Pittsburgh Medical\nCenter (UPMC). In addition, UPMC contributed de-identified progress notes to the\ni2b2/VA corpus. This dataset contains the records from Beth Israel and Partners.\n\nThe 2010 i2b2/VA Workshop on Natural Language Processing Challenges for Clinical Records comprises three tasks:\n1) a concept extraction task focused on the extraction of medical concepts from patient reports;\n2) an assertion classification task focused on assigning assertion types for medical problem concepts;\n3) a relation classification task focused on assigning relation types that hold between medical problems,\ntests, and treatments.\n\ni2b2 and the VA provided an annotated reference standard corpus for the three tasks.\nUsing this reference standard, 22 systems were developed for concept extraction,\n21 for assertion classification, and 16 for relation classification." ]

[ "multilinguality:monolingual", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for n2c2 2011 Coreference", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: False\n- Tasks: COREF\n\n\nThe i2b2/VA corpus contained de-identified discharge summaries from Beth Israel\nDeaconess Medical Center, Partners Healthcare, and University of Pittsburgh Medical\nCenter (UPMC). In addition, UPMC contributed de-identified progress notes to the\ni2b2/VA corpus. This dataset contains the records from Beth Israel and Partners.\n\nThe i2b2/VA corpus contained five concept categories: problem, person, pronoun,\ntest, and treatment. Each record in the i2b2/VA corpus was annotated by two\nindependent annotators for coreference pairs. Then the pairs were post-processed\nin order to create coreference chains. These chains were presented to an adjudicator,\nwho resolved the disagreements between the original annotations, and added or deleted\nannotations as necessary. The outputs of the adjudicators were then re-adjudicated, with\nparticular attention being paid to duplicates and enforcing consistency in the annotations." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-other #region-us \n", "# Dataset Card for n2c2 2011 Coreference", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: False\n- Tasks: COREF\n\n\nThe i2b2/VA corpus contained de-identified discharge summaries from Beth Israel\nDeaconess Medical Center, Partners Healthcare, and University of Pittsburgh Medical\nCenter (UPMC). In addition, UPMC contributed de-identified progress notes to the\ni2b2/VA corpus. This dataset contains the records from Beth Israel and Partners.\n\nThe i2b2/VA corpus contained five concept categories: problem, person, pronoun,\ntest, and treatment. Each record in the i2b2/VA corpus was annotated by two\nindependent annotators for coreference pairs. Then the pairs were post-processed\nin order to create coreference chains. These chains were presented to an adjudicator,\nwho resolved the disagreements between the original annotations, and added or deleted\nannotations as necessary. The outputs of the adjudicators were then re-adjudicated, with\nparticular attention being paid to duplicates and enforcing consistency in the annotations." ]

[ "multilinguality:monolingual", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for n2c2 2014 De-identification", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: False\n- Tasks: NER\n\n\nThe 2014 i2b2/UTHealth Natural Language Processing (NLP) shared task featured two tracks.\nThe first of these was the de-identification track focused on identifying protected health\ninformation (PHI) in longitudinal clinical narratives.\n\nTRACK 1: NER PHI\n\nHIPAA requires that patient medical records have all identifying information removed in order to\nprotect patient privacy. There are 18 categories of Protected Health Information (PHI) identifiers of the\npatient or of relatives, employers, or household members of the patient that must be removed in order\nfor a file to be considered de-identified.\nIn order to de-identify the records, each file has PHI marked up. All PHI has an\nXML tag indicating its category and type, where applicable. For the purposes of this task,\nthe 18 HIPAA categories have been grouped into 6 main categories and 25 sub categories" ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-other #region-us \n", "# Dataset Card for n2c2 2014 De-identification", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: False\n- Tasks: NER\n\n\nThe 2014 i2b2/UTHealth Natural Language Processing (NLP) shared task featured two tracks.\nThe first of these was the de-identification track focused on identifying protected health\ninformation (PHI) in longitudinal clinical narratives.\n\nTRACK 1: NER PHI\n\nHIPAA requires that patient medical records have all identifying information removed in order to\nprotect patient privacy. There are 18 categories of Protected Health Information (PHI) identifiers of the\npatient or of relatives, employers, or household members of the patient that must be removed in order\nfor a file to be considered de-identified.\nIn order to de-identify the records, each file has PHI marked up. All PHI has an\nXML tag indicating its category and type, where applicable. For the purposes of this task,\nthe 18 HIPAA categories have been grouped into 6 main categories and 25 sub categories" ]

[ "multilinguality:monolingual", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for n2c2 2018 Selection Criteria", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: False\n- Tasks: TXTCLASS\n\n\nTrack 1 of the 2018 National NLP Clinical Challenges shared tasks focused\non identifying which patients in a corpus of longitudinal medical records\nmeet and do not meet identified selection criteria.\n\nThis shared task aimed to determine whether NLP systems could be trained to identify if patients met or did not meet\na set of selection criteria taken from real clinical trials. The selected criteria required measurement detection (\n“Any HbA1c value between 6.5 and 9.5%”), inference (“Use of aspirin to prevent myocardial infarction”),\ntemporal reasoning (“Diagnosis of ketoacidosis in the past year”), and expert judgment to assess (“Major\ndiabetes-related complication”). For the corpus, we used the dataset of American English, longitudinal clinical\nnarratives from the 2014 i2b2/UTHealth shared task 4.\n\nThe final selected 13 selection criteria are as follows:\n1. DRUG-ABUSE: Drug abuse, current or past\n2. ALCOHOL-ABUSE: Current alcohol use over weekly recommended limits\n3. ENGLISH: Patient must speak English\n4. MAKES-DECISIONS: Patient must make their own medical decisions\n5. ABDOMINAL: History of intra-abdominal surgery, small or large intestine\nresection, or small bowel obstruction.\n6. MAJOR-DIABETES: Major diabetes-related complication. For the purposes of\nthis annotation, we define “major complication” (as opposed to “minor complication”)\nas any of the following that are a result of (or strongly correlated with) uncontrolled diabetes:\n a. Amputation\n b. Kidney damage\n c. Skin conditions\n d. Retinopathy\n e. nephropathy\n f. neuropathy\n7. ADVANCED-CAD: Advanced cardiovascular disease (CAD).\nFor the purposes of this annotation, we define “advanced” as having 2 or more of the following:\n a. Taking 2 or more medications to treat CAD\n b. History of myocardial infarction (MI)\n c. Currently experiencing angina\n d. Ischemia, past or present\n8. MI-6MOS: MI in the past 6 months\n9. KETO-1YR: Diagnosis of ketoacidosis in the past year\n10. DIETSUPP-2MOS: Taken a dietary supplement (excluding vitamin D) in the past 2 months\n11. ASP-FOR-MI: Use of aspirin to prevent MI\n12. HBA1C: Any hemoglobin A1c (HbA1c) value between 6.5% and 9.5%\n13. CREATININE: Serum creatinine > upper limit of normal\n\nThe training consists of 202 patient records with document-level annotations, 10 records\nwith textual spans indicating annotator’s evidence for their annotations while test set contains 86.\n\nNote:\n* The inter-annotator average agreement is 84.9%\n* Whereabouts of 10 records with textual spans indicating annotator’s evidence are unknown.\nHowever, author did a simple script based validation to check if any of the tags contained any text\nin any of the training set and they do not, which confirms that atleast train and test do not\n have any evidence tagged alongside corresponding tags." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-other #region-us \n", "# Dataset Card for n2c2 2018 Selection Criteria", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: False\n- Tasks: TXTCLASS\n\n\nTrack 1 of the 2018 National NLP Clinical Challenges shared tasks focused\non identifying which patients in a corpus of longitudinal medical records\nmeet and do not meet identified selection criteria.\n\nThis shared task aimed to determine whether NLP systems could be trained to identify if patients met or did not meet\na set of selection criteria taken from real clinical trials. The selected criteria required measurement detection (\n“Any HbA1c value between 6.5 and 9.5%”), inference (“Use of aspirin to prevent myocardial infarction”),\ntemporal reasoning (“Diagnosis of ketoacidosis in the past year”), and expert judgment to assess (“Major\ndiabetes-related complication”). For the corpus, we used the dataset of American English, longitudinal clinical\nnarratives from the 2014 i2b2/UTHealth shared task 4.\n\nThe final selected 13 selection criteria are as follows:\n1. DRUG-ABUSE: Drug abuse, current or past\n2. ALCOHOL-ABUSE: Current alcohol use over weekly recommended limits\n3. ENGLISH: Patient must speak English\n4. MAKES-DECISIONS: Patient must make their own medical decisions\n5. ABDOMINAL: History of intra-abdominal surgery, small or large intestine\nresection, or small bowel obstruction.\n6. MAJOR-DIABETES: Major diabetes-related complication. For the purposes of\nthis annotation, we define “major complication” (as opposed to “minor complication”)\nas any of the following that are a result of (or strongly correlated with) uncontrolled diabetes:\n a. Amputation\n b. Kidney damage\n c. Skin conditions\n d. Retinopathy\n e. nephropathy\n f. neuropathy\n7. ADVANCED-CAD: Advanced cardiovascular disease (CAD).\nFor the purposes of this annotation, we define “advanced” as having 2 or more of the following:\n a. Taking 2 or more medications to treat CAD\n b. History of myocardial infarction (MI)\n c. Currently experiencing angina\n d. Ischemia, past or present\n8. MI-6MOS: MI in the past 6 months\n9. KETO-1YR: Diagnosis of ketoacidosis in the past year\n10. DIETSUPP-2MOS: Taken a dietary supplement (excluding vitamin D) in the past 2 months\n11. ASP-FOR-MI: Use of aspirin to prevent MI\n12. HBA1C: Any hemoglobin A1c (HbA1c) value between 6.5% and 9.5%\n13. CREATININE: Serum creatinine > upper limit of normal\n\nThe training consists of 202 patient records with document-level annotations, 10 records\nwith textual spans indicating annotator’s evidence for their annotations while test set contains 86.\n\nNote:\n* The inter-annotator average agreement is 84.9%\n* Whereabouts of 10 records with textual spans indicating annotator’s evidence are unknown.\nHowever, author did a simple script based validation to check if any of the tags contained any text\nin any of the training set and they do not, which confirms that atleast train and test do not\n have any evidence tagged alongside corresponding tags." ]

[ "multilinguality:monolingual", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for n2c2 2018 ADE", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: False\n- Tasks: NER,RE\n\n\nThe National NLP Clinical Challenges (n2c2), organized in 2018, continued the\nlegacy of i2b2 (Informatics for Biology and the Bedside), adding 2 new tracks and 2\nnew sets of data to the shared tasks organized since 2006. Track 2 of 2018\nn2c2 shared tasks focused on the extraction of medications, with their signature\ninformation, and adverse drug events (ADEs) from clinical narratives.\nThis track built on our previous medication challenge, but added a special focus on ADEs.\n\nADEs are injuries resulting from a medical intervention related to a drugs and\ncan include allergic reactions, drug interactions, overdoses, and medication errors.\nCollectively, ADEs are estimated to account for 30% of all hospital adverse\nevents; however, ADEs are preventable. Identifying potential drug interactions,\noverdoses, allergies, and errors at the point of care and alerting the caregivers of\npotential ADEs can improve health delivery, reduce the risk of ADEs, and improve health\noutcomes.\n\nA step in this direction requires processing narratives of clinical records\nthat often elaborate on the medications given to a patient, as well as the known\nallergies, reactions, and adverse events of the patient. Extraction of this information\nfrom narratives complements the structured medication information that can be\nobtained from prescriptions, allowing a more thorough assessment of potential ADEs\nbefore they happen.\n\nThe 2018 n2c2 shared task Track 2, hereon referred to as the ADE track,\ntackled these natural language processing tasks in 3 different steps,\nwhich we refer to as tasks:\n1. Concept Extraction: identification of concepts related to medications,\ntheir signature information, and ADEs\n2. Relation Classification: linking the previously mentioned concepts to\ntheir medication by identifying relations on gold standard concepts\n3. End-to-End: building end-to-end systems that process raw narrative text\nto discover concepts and find relations of those concepts to their medications\n\nShared tasks provide a venue for head-to-head comparison of systems developed\nfor the same task and on the same data, allowing researchers to identify the state\nof the art in a particular task, learn from it, and build on it." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-other #region-us \n", "# Dataset Card for n2c2 2018 ADE", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: False\n- Tasks: NER,RE\n\n\nThe National NLP Clinical Challenges (n2c2), organized in 2018, continued the\nlegacy of i2b2 (Informatics for Biology and the Bedside), adding 2 new tracks and 2\nnew sets of data to the shared tasks organized since 2006. Track 2 of 2018\nn2c2 shared tasks focused on the extraction of medications, with their signature\ninformation, and adverse drug events (ADEs) from clinical narratives.\nThis track built on our previous medication challenge, but added a special focus on ADEs.\n\nADEs are injuries resulting from a medical intervention related to a drugs and\ncan include allergic reactions, drug interactions, overdoses, and medication errors.\nCollectively, ADEs are estimated to account for 30% of all hospital adverse\nevents; however, ADEs are preventable. Identifying potential drug interactions,\noverdoses, allergies, and errors at the point of care and alerting the caregivers of\npotential ADEs can improve health delivery, reduce the risk of ADEs, and improve health\noutcomes.\n\nA step in this direction requires processing narratives of clinical records\nthat often elaborate on the medications given to a patient, as well as the known\nallergies, reactions, and adverse events of the patient. Extraction of this information\nfrom narratives complements the structured medication information that can be\nobtained from prescriptions, allowing a more thorough assessment of potential ADEs\nbefore they happen.\n\nThe 2018 n2c2 shared task Track 2, hereon referred to as the ADE track,\ntackled these natural language processing tasks in 3 different steps,\nwhich we refer to as tasks:\n1. Concept Extraction: identification of concepts related to medications,\ntheir signature information, and ADEs\n2. Relation Classification: linking the previously mentioned concepts to\ntheir medication by identifying relations on gold standard concepts\n3. End-to-End: building end-to-end systems that process raw narrative text\nto discover concepts and find relations of those concepts to their medications\n\nShared tasks provide a venue for head-to-head comparison of systems developed\nfor the same task and on the same data, allowing researchers to identify the state\nof the art in a particular task, learn from it, and build on it." ]

[ "multilinguality:monolingual", "language:en", "license:cc0-1.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for NCBI Disease", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,NED\n\n\nThe NCBI disease corpus is fully annotated at the mention and concept level to serve as a research\nresource for the biomedical natural language processing community." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-cc0-1.0 #region-us \n", "# Dataset Card for NCBI Disease", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,NED\n\n\nThe NCBI disease corpus is fully annotated at the mention and concept level to serve as a research\nresource for the biomedical natural language processing community." ]

[ "multilinguality:monolingual", "language:en", "license:cc0-1.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for NLM-Gene", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,NED\n\n\nNLM-Gene consists of 550 PubMed articles, from 156 journals, and contains more than 15 thousand unique gene names, corresponding to more than five thousand gene identifiers (NCBI Gene taxonomy). This corpus contains gene annotation data from 28 organisms. The annotated articles contain on average 29 gene names, and 10 gene identifiers per article. These characteristics demonstrate that this article set is an important benchmark dataset to test the accuracy of gene recognition algorithms both on multi-species and ambiguous data. The NLM-Gene corpus will be invaluable for advancing text-mining techniques for gene identification tasks in biomedical text." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-cc0-1.0 #region-us \n", "# Dataset Card for NLM-Gene", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,NED\n\n\nNLM-Gene consists of 550 PubMed articles, from 156 journals, and contains more than 15 thousand unique gene names, corresponding to more than five thousand gene identifiers (NCBI Gene taxonomy). This corpus contains gene annotation data from 28 organisms. The annotated articles contain on average 29 gene names, and 10 gene identifiers per article. These characteristics demonstrate that this article set is an important benchmark dataset to test the accuracy of gene recognition algorithms both on multi-species and ambiguous data. The NLM-Gene corpus will be invaluable for advancing text-mining techniques for gene identification tasks in biomedical text." ]

[ "multilinguality:monolingual", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for NLM WSD", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: False\n- Tasks: NED\n\n\nIn order to support research investigating the automatic resolution of word sense ambiguity using natural language\nprocessing techniques, we have constructed this test collection of medical text in which the ambiguities were resolved\nby hand. Evaluators were asked to examine instances of an ambiguous word and determine the sense intended by selecting\nthe Metathesaurus concept (if any) that best represents the meaning of that sense. The test collection consists of 50\nhighly frequent ambiguous UMLS concepts from 1998 MEDLINE. Each of the 50 ambiguous cases has 100 ambiguous instances\nrandomly selected from the 1998 MEDLINE citations. For a total of 5,000 instances. We had a total of 11 evaluators of\nwhich 8 completed 100% of the 5,000 instances, 1 completed 56%, 1 completed 44%, and the final evaluator completed 12%\nof the instances. Evaluations were only used when the evaluators completed all 100 instances for a given ambiguity." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-other #region-us \n", "# Dataset Card for NLM WSD", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: False\n- Tasks: NED\n\n\nIn order to support research investigating the automatic resolution of word sense ambiguity using natural language\nprocessing techniques, we have constructed this test collection of medical text in which the ambiguities were resolved\nby hand. Evaluators were asked to examine instances of an ambiguous word and determine the sense intended by selecting\nthe Metathesaurus concept (if any) that best represents the meaning of that sense. The test collection consists of 50\nhighly frequent ambiguous UMLS concepts from 1998 MEDLINE. Each of the 50 ambiguous cases has 100 ambiguous instances\nrandomly selected from the 1998 MEDLINE citations. For a total of 5,000 instances. We had a total of 11 evaluators of\nwhich 8 completed 100% of the 5,000 instances, 1 completed 56%, 1 completed 44%, and the final evaluator completed 12%\nof the instances. Evaluations were only used when the evaluators completed all 100 instances for a given ambiguity." ]

[ "multilinguality:monolingual", "language:en", "license:cc0-1.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for NLM-Chem", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,NED,TXTCLASS\n\n\nNLM-Chem corpus consists of 150 full-text articles from the PubMed Central Open Access dataset,\ncomprising 67 different chemical journals, aiming to cover a general distribution of usage of chemical\nnames in the biomedical literature.\nArticles were selected so that human annotation was most valuable (meaning that they were rich in bio-entities,\nand current state-of-the-art named entity recognition systems disagreed on bio-entity recognition." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-cc0-1.0 #region-us \n", "# Dataset Card for NLM-Chem", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,NED,TXTCLASS\n\n\nNLM-Chem corpus consists of 150 full-text articles from the PubMed Central Open Access dataset,\ncomprising 67 different chemical journals, aiming to cover a general distribution of usage of chemical\nnames in the biomedical literature.\nArticles were selected so that human annotation was most valuable (meaning that they were rich in bio-entities,\nand current state-of-the-art named entity recognition systems disagreed on bio-entity recognition." ]

[ "multilinguality:multilingual", "language:en", "language:zh", "language:ja", "license:cc-by-4.0", "region:us" ]

[]

[ "en", "zh", "ja" ]

[ "# Dataset Card for NTCIR-13 MedWeb", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: False\n- Tasks: TRANSL,TXTCLASS\n\n\nNTCIR-13 MedWeb (Medical Natural Language Processing for Web Document) task requires\nto perform a multi-label classification that labels for eight diseases/symptoms must\nbe assigned to each tweet. Given pseudo-tweets, the output are Positive:p or Negative:n\nlabels for eight diseases/symptoms. The achievements of this task can almost be\ndirectly applied to a fundamental engine for actual applications.\n\nThis task provides pseudo-Twitter messages in a cross-language and multi-label corpus,\ncovering three languages (Japanese, English, and Chinese), and annotated with eight\nlabels such as influenza, diarrhea/stomachache, hay fever, cough/sore throat, headache,\nfever, runny nose, and cold.\n\nFor more information, see:\nURL\n\nAs this dataset also provides a parallel corpus of pseudo-tweets for english,\njapanese and chinese it can also be used to train translation models between\nthese three languages." ]

[ "TAGS\n#multilinguality-multilingual #language-English #language-Chinese #language-Japanese #license-cc-by-4.0 #region-us \n", "# Dataset Card for NTCIR-13 MedWeb", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: False\n- Tasks: TRANSL,TXTCLASS\n\n\nNTCIR-13 MedWeb (Medical Natural Language Processing for Web Document) task requires\nto perform a multi-label classification that labels for eight diseases/symptoms must\nbe assigned to each tweet. Given pseudo-tweets, the output are Positive:p or Negative:n\nlabels for eight diseases/symptoms. The achievements of this task can almost be\ndirectly applied to a fundamental engine for actual applications.\n\nThis task provides pseudo-Twitter messages in a cross-language and multi-label corpus,\ncovering three languages (Japanese, English, and Chinese), and annotated with eight\nlabels such as influenza, diarrhea/stomachache, hay fever, cough/sore throat, headache,\nfever, runny nose, and cold.\n\nFor more information, see:\nURL\n\nAs this dataset also provides a parallel corpus of pseudo-tweets for english,\njapanese and chinese it can also be used to train translation models between\nthese three languages." ]

[ "multilinguality:monolingual", "language:en", "license:cc-by-3.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for OSIRIS", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,NED\n\n\nThe OSIRIS corpus is a set of MEDLINE abstracts manually annotated\nwith human variation mentions. The corpus is distributed under the terms\nof the Creative Commons Attribution License\nCreative Commons Attribution 3.0 Unported License,\nwhich permits unrestricted use, distribution, and reproduction in any medium,\nprovided the original work is properly cited (Furlong et al, BMC Bioinformatics 2008, 9:84)." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-cc-by-3.0 #region-us \n", "# Dataset Card for OSIRIS", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,NED\n\n\nThe OSIRIS corpus is a set of MEDLINE abstracts manually annotated\nwith human variation mentions. The corpus is distributed under the terms\nof the Creative Commons Attribution License\nCreative Commons Attribution 3.0 Unported License,\nwhich permits unrestricted use, distribution, and reproduction in any medium,\nprovided the original work is properly cited (Furlong et al, BMC Bioinformatics 2008, 9:84)." ]

[ "multilinguality:multilingual", "language:en", "language:zh", "license:cc-by-4.0", "region:us" ]

[]

[ "en", "zh" ]

[ "# Dataset Card for ParaMed", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: TRANSL\n\n\nNEJM is a Chinese-English parallel corpus crawled from the New England Journal of Medicine website. \nEnglish articles are distributed through URL and Chinese articles are distributed through \nURL The corpus contains all article pairs (around 2000 pairs) since 2011." ]

[ "TAGS\n#multilinguality-multilingual #language-English #language-Chinese #license-cc-by-4.0 #region-us \n", "# Dataset Card for ParaMed", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: TRANSL\n\n\nNEJM is a Chinese-English parallel corpus crawled from the New England Journal of Medicine website. \nEnglish articles are distributed through URL and Chinese articles are distributed through \nURL The corpus contains all article pairs (around 2000 pairs) since 2011." ]

[ "multilinguality:monolingual", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for PCR", "## Dataset Description\n\n- Homepage: http://210.107.182.73/URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,EE\n\n\n\nA corpus for plant / herb and chemical entities and for the relationships between them. The corpus contains 2218 plant and chemical entities and 600 plant-chemical relationships which are drawn from 1109 sentences in 245 PubMed abstracts." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-unknown #region-us \n", "# Dataset Card for PCR", "## Dataset Description\n\n- Homepage: http://210.107.182.73/URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,EE\n\n\n\nA corpus for plant / herb and chemical entities and for the relationships between them. The corpus contains 2218 plant and chemical entities and 600 plant-chemical relationships which are drawn from 1109 sentences in 245 PubMed abstracts." ]

[ "multilinguality:monolingual", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for PDR", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,EE,COREF\n\n\n\nThe corpus of plant-disease relation consists of plants and diseases and their relation to PubMed abstract.\nThe corpus consists of about 2400 plant and disease entities and 300 annotated relations from 179 abstracts." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-unknown #region-us \n", "# Dataset Card for PDR", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,EE,COREF\n\n\n\nThe corpus of plant-disease relation consists of plants and diseases and their relation to PubMed abstract.\nThe corpus consists of about 2400 plant and disease entities and 300 annotated relations from 179 abstracts." ]

[ "multilinguality:monolingual", "language:es", "license:cc-by-4.0", "region:us" ]

[]

[ "es" ]

[ "# Dataset Card for PharmaCoNER", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: NER,TXTCLASS", "### Subtrack 1\n\nPharmaCoNER: Pharmacological Substances, Compounds and Proteins Named Entity Recognition track\n\nThis dataset is designed for the PharmaCoNER task, sponsored by Plan de Impulso de las Tecnologías del Lenguaje.\n\nIt is a manually classified collection of clinical case studies derived from the Spanish Clinical Case Corpus (SPACCC), an open access electronic library that gathers Spanish medical publications from SciELO (Scientific Electronic Library Online).\n\nThe annotation of the entire set of entity mentions was carried out by medicinal chemistry experts and it includes the following 4 entity types: NORMALIZABLES, NO_NORMALIZABLES, PROTEINAS and UNCLEAR.\n\nThe PharmaCoNER corpus contains a total of 396,988 words and 1,000 clinical cases that have been randomly sampled into 3 subsets. The training set contains 500 clinical cases, while the development and test sets contain 250 clinical cases each.\n\nFor further information, please visit URL or send an email to encargo-pln-life@URL\n\n\nSUBTRACK 1: NER offset and entity type classification\n\nThe first subtrack consists in the classical entity-based or instanced-based evaluation that requires that system outputs match exactly the beginning and end locations of each entity tag, as well as match the entity annotation type of the gold standard annotations.", "### Subtrack 2\n\nPharmaCoNER: Pharmacological Substances, Compounds and Proteins Named Entity Recognition track\n\nThis dataset is designed for the PharmaCoNER task, sponsored by Plan de Impulso de las Tecnologías del Lenguaje.\n\nIt is a manually classified collection of clinical case studies derived from the Spanish Clinical Case Corpus (SPACCC), an open access electronic library that gathers Spanish medical publications from SciELO (Scientific Electronic Library Online).\n\nThe annotation of the entire set of entity mentions was carried out by medicinal chemistry experts and it includes the following 4 entity types: NORMALIZABLES, NO_NORMALIZABLES, PROTEINAS and UNCLEAR.\n\nThe PharmaCoNER corpus contains a total of 396,988 words and 1,000 clinical cases that have been randomly sampled into 3 subsets. The training set contains 500 clinical cases, while the development and test sets contain 250 clinical cases each.\n\nFor further information, please visit URL or send an email to encargo-pln-life@URL\n\n\nSUBTRACK 2: CONCEPT INDEXING\n\nIn the second subtask, a list of unique SNOMED concept identifiers have to be generated for each document. The predictions are compared to the manually annotated concept ids corresponding to chemical compounds and pharmacological substances.", "### Full Task\n\nPharmaCoNER: Pharmacological Substances, Compounds and Proteins Named Entity Recognition track\n\nThis dataset is designed for the PharmaCoNER task, sponsored by Plan de Impulso de las Tecnologías del Lenguaje.\n\nIt is a manually classified collection of clinical case studies derived from the Spanish Clinical Case Corpus (SPACCC), an open access electronic library that gathers Spanish medical publications from SciELO (Scientific Electronic Library Online).\n\nThe annotation of the entire set of entity mentions was carried out by medicinal chemistry experts and it includes the following 4 entity types: NORMALIZABLES, NO_NORMALIZABLES, PROTEINAS and UNCLEAR.\n\nThe PharmaCoNER corpus contains a total of 396,988 words and 1,000 clinical cases that have been randomly sampled into 3 subsets. The training set contains 500 clinical cases, while the development and test sets contain 250 clinical cases each.\n\nFor further information, please visit URL or send an email to encargo-pln-life@URL\n\n\nSUBTRACK 1: NER offset and entity type classification\n\nThe first subtrack consists in the classical entity-based or instanced-based evaluation that requires that system outputs match exactly the beginning and end locations of each entity tag, as well as match the entity annotation type of the gold standard annotations.\n\n\nSUBTRACK 2: CONCEPT INDEXING\n\nIn the second subtask, a list of unique SNOMED concept identifiers have to be generated for each document. The predictions are compared to the manually annotated concept ids corresponding to chemical compounds and pharmacological substances." ]

[ "TAGS\n#multilinguality-monolingual #language-Spanish #license-cc-by-4.0 #region-us \n", "# Dataset Card for PharmaCoNER", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: NER,TXTCLASS", "### Subtrack 1\n\nPharmaCoNER: Pharmacological Substances, Compounds and Proteins Named Entity Recognition track\n\nThis dataset is designed for the PharmaCoNER task, sponsored by Plan de Impulso de las Tecnologías del Lenguaje.\n\nIt is a manually classified collection of clinical case studies derived from the Spanish Clinical Case Corpus (SPACCC), an open access electronic library that gathers Spanish medical publications from SciELO (Scientific Electronic Library Online).\n\nThe annotation of the entire set of entity mentions was carried out by medicinal chemistry experts and it includes the following 4 entity types: NORMALIZABLES, NO_NORMALIZABLES, PROTEINAS and UNCLEAR.\n\nThe PharmaCoNER corpus contains a total of 396,988 words and 1,000 clinical cases that have been randomly sampled into 3 subsets. The training set contains 500 clinical cases, while the development and test sets contain 250 clinical cases each.\n\nFor further information, please visit URL or send an email to encargo-pln-life@URL\n\n\nSUBTRACK 1: NER offset and entity type classification\n\nThe first subtrack consists in the classical entity-based or instanced-based evaluation that requires that system outputs match exactly the beginning and end locations of each entity tag, as well as match the entity annotation type of the gold standard annotations.", "### Subtrack 2\n\nPharmaCoNER: Pharmacological Substances, Compounds and Proteins Named Entity Recognition track\n\nThis dataset is designed for the PharmaCoNER task, sponsored by Plan de Impulso de las Tecnologías del Lenguaje.\n\nIt is a manually classified collection of clinical case studies derived from the Spanish Clinical Case Corpus (SPACCC), an open access electronic library that gathers Spanish medical publications from SciELO (Scientific Electronic Library Online).\n\nThe annotation of the entire set of entity mentions was carried out by medicinal chemistry experts and it includes the following 4 entity types: NORMALIZABLES, NO_NORMALIZABLES, PROTEINAS and UNCLEAR.\n\nThe PharmaCoNER corpus contains a total of 396,988 words and 1,000 clinical cases that have been randomly sampled into 3 subsets. The training set contains 500 clinical cases, while the development and test sets contain 250 clinical cases each.\n\nFor further information, please visit URL or send an email to encargo-pln-life@URL\n\n\nSUBTRACK 2: CONCEPT INDEXING\n\nIn the second subtask, a list of unique SNOMED concept identifiers have to be generated for each document. The predictions are compared to the manually annotated concept ids corresponding to chemical compounds and pharmacological substances.", "### Full Task\n\nPharmaCoNER: Pharmacological Substances, Compounds and Proteins Named Entity Recognition track\n\nThis dataset is designed for the PharmaCoNER task, sponsored by Plan de Impulso de las Tecnologías del Lenguaje.\n\nIt is a manually classified collection of clinical case studies derived from the Spanish Clinical Case Corpus (SPACCC), an open access electronic library that gathers Spanish medical publications from SciELO (Scientific Electronic Library Online).\n\nThe annotation of the entire set of entity mentions was carried out by medicinal chemistry experts and it includes the following 4 entity types: NORMALIZABLES, NO_NORMALIZABLES, PROTEINAS and UNCLEAR.\n\nThe PharmaCoNER corpus contains a total of 396,988 words and 1,000 clinical cases that have been randomly sampled into 3 subsets. The training set contains 500 clinical cases, while the development and test sets contain 250 clinical cases each.\n\nFor further information, please visit URL or send an email to encargo-pln-life@URL\n\n\nSUBTRACK 1: NER offset and entity type classification\n\nThe first subtrack consists in the classical entity-based or instanced-based evaluation that requires that system outputs match exactly the beginning and end locations of each entity tag, as well as match the entity annotation type of the gold standard annotations.\n\n\nSUBTRACK 2: CONCEPT INDEXING\n\nIn the second subtask, a list of unique SNOMED concept identifiers have to be generated for each document. The predictions are compared to the manually annotated concept ids corresponding to chemical compounds and pharmacological substances." ]

[ "multilinguality:monolingual", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for PICO Annotation", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER\n\n\nThis dataset contains annotations for Participants, Interventions, and Outcomes (referred to as PICO task).\nFor 423 sentences, annotations collected by 3 medical experts are available.\nTo get the final annotations, we perform the majority voting." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-unknown #region-us \n", "# Dataset Card for PICO Annotation", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER\n\n\nThis dataset contains annotations for Participants, Interventions, and Outcomes (referred to as PICO task).\nFor 423 sentences, annotations collected by 3 medical experts are available.\nTo get the final annotations, we perform the majority voting." ]

[ "multilinguality:monolingual", "language:en", "license:cc-by-nc-sa-4.0", "arxiv:2202.13876", "region:us" ]

[ "2202.13876" ]

[ "en" ]

[ "# Dataset Card for PMC-Patients", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: STS\n\n\nThis dataset is used for calculating the similarity between two patient descriptions." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-cc-by-nc-sa-4.0 #arxiv-2202.13876 #region-us \n", "# Dataset Card for PMC-Patients", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: STS\n\n\nThis dataset is used for calculating the similarity between two patient descriptions." ]

[ "multilinguality:monolingual", "language:en", "license:cc-by-4.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for ProGene", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER\n\n\nThe Protein/Gene corpus was developed at the JULIE Lab Jena under supervision of Prof. Udo Hahn.\nThe executing scientist was Dr. Joachim Wermter.\nThe main annotator was Dr. Rico Pusch who is an expert in biology.\nThe corpus was developed in the context of the StemNet project (URL" ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-cc-by-4.0 #region-us \n", "# Dataset Card for ProGene", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER\n\n\nThe Protein/Gene corpus was developed at the JULIE Lab Jena under supervision of Prof. Udo Hahn.\nThe executing scientist was Dr. Joachim Wermter.\nThe main annotator was Dr. Rico Pusch who is an expert in biology.\nThe corpus was developed in the context of the StemNet project (URL" ]

[ "multilinguality:monolingual", "language:en", "license:cc-by-4.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for PsyTAR", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: False\n- Tasks: NER,TXTCLASS\n\n\nThe \"Psychiatric Treatment Adverse Reactions\" (PsyTAR) dataset contains 891 drugs\nreviews posted by patients on \"URL\", about the effectiveness and adverse\ndrug events associated with Zoloft, Lexapro, Cymbalta, and Effexor XR.\n\nThis dataset can be used for (multi-label) sentence classification of Adverse Drug\nReaction (ADR), Withdrawal Symptoms (WDs), Sign/Symptoms/Illness (SSIs), Drug\nIndications (DIs), Drug Effectiveness (EF), Drug Infectiveness (INF) and Others, as well\nas for recognition of 5 different types of named entity (in the categories ADRs, WDs,\nSSIs and DIs)" ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-cc-by-4.0 #region-us \n", "# Dataset Card for PsyTAR", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: False\n- Tasks: NER,TXTCLASS\n\n\nThe \"Psychiatric Treatment Adverse Reactions\" (PsyTAR) dataset contains 891 drugs\nreviews posted by patients on \"URL\", about the effectiveness and adverse\ndrug events associated with Zoloft, Lexapro, Cymbalta, and Effexor XR.\n\nThis dataset can be used for (multi-label) sentence classification of Adverse Drug\nReaction (ADR), Withdrawal Symptoms (WDs), Sign/Symptoms/Illness (SSIs), Drug\nIndications (DIs), Drug Effectiveness (EF), Drug Infectiveness (INF) and Others, as well\nas for recognition of 5 different types of named entity (in the categories ADRs, WDs,\nSSIs and DIs)" ]

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

[]

[ "en" ]

[ "# Dataset Card for PUBHEALTH", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: TXTCLASS\n\n\nA dataset of 11,832 claims for fact- checking, which are related a range of health topics\nincluding biomedical subjects (e.g., infectious diseases, stem cell research), government healthcare policy\n(e.g., abortion, mental health, women’s health), and other public health-related stories" ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-mit #region-us \n", "# Dataset Card for PUBHEALTH", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: TXTCLASS\n\n\nA dataset of 11,832 claims for fact- checking, which are related a range of health topics\nincluding biomedical subjects (e.g., infectious diseases, stem cell research), government healthcare policy\n(e.g., abortion, mental health, women’s health), and other public health-related stories" ]

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

[]

[ "en" ]

[ "# Dataset Card for PubMedQA", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: QA\n\n\nPubMedQA is a novel biomedical question answering (QA) dataset collected from PubMed abstracts.\nThe task of PubMedQA is to answer research biomedical questions with yes/no/maybe using the corresponding abstracts.\nPubMedQA has 1k expert-annotated (PQA-L), 61.2k unlabeled (PQA-U) and 211.3k artificially generated QA instances (PQA-A).\n\nEach PubMedQA instance is composed of:\n (1) a question which is either an existing research article title or derived from one,\n (2) a context which is the corresponding PubMed abstract without its conclusion,\n (3) a long answer, which is the conclusion of the abstract and, presumably, answers the research question, and\n (4) a yes/no/maybe answer which summarizes the conclusion.\n\nPubMedQA is the first QA dataset where reasoning over biomedical research texts,\nespecially their quantitative contents, is required to answer the questions.\n\nPubMedQA datasets comprise of 3 different subsets:\n (1) PubMedQA Labeled (PQA-L): A labeled PubMedQA subset comprises of 1k manually annotated yes/no/maybe QA data collected from PubMed articles.\n (2) PubMedQA Artificial (PQA-A): An artificially labelled PubMedQA subset comprises of 211.3k PubMed articles with automatically generated questions from the statement titles and yes/no answer labels generated using a simple heuristic.\n (3) PubMedQA Unlabeled (PQA-U): An unlabeled PubMedQA subset comprises of 61.2k context-question pairs data collected from PubMed articles." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-mit #region-us \n", "# Dataset Card for PubMedQA", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: QA\n\n\nPubMedQA is a novel biomedical question answering (QA) dataset collected from PubMed abstracts.\nThe task of PubMedQA is to answer research biomedical questions with yes/no/maybe using the corresponding abstracts.\nPubMedQA has 1k expert-annotated (PQA-L), 61.2k unlabeled (PQA-U) and 211.3k artificially generated QA instances (PQA-A).\n\nEach PubMedQA instance is composed of:\n (1) a question which is either an existing research article title or derived from one,\n (2) a context which is the corresponding PubMed abstract without its conclusion,\n (3) a long answer, which is the conclusion of the abstract and, presumably, answers the research question, and\n (4) a yes/no/maybe answer which summarizes the conclusion.\n\nPubMedQA is the first QA dataset where reasoning over biomedical research texts,\nespecially their quantitative contents, is required to answer the questions.\n\nPubMedQA datasets comprise of 3 different subsets:\n (1) PubMedQA Labeled (PQA-L): A labeled PubMedQA subset comprises of 1k manually annotated yes/no/maybe QA data collected from PubMed articles.\n (2) PubMedQA Artificial (PQA-A): An artificially labelled PubMedQA subset comprises of 211.3k PubMed articles with automatically generated questions from the statement titles and yes/no answer labels generated using a simple heuristic.\n (3) PubMedQA Unlabeled (PQA-U): An unlabeled PubMedQA subset comprises of 61.2k context-question pairs data collected from PubMed articles." ]

[ "multilinguality:monolingual", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for PubTator Central", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,NED\n\n\nPubTator Central (PTC, URL is a web service for\nexploring and retrieving bioconcept annotations in full text biomedical articles. PTC provides\nautomated annotations from state-of-the-art text mining systems for genes/proteins, genetic\nvariants, diseases, chemicals, species and cell lines, all available for immediate download. PTC\nannotates PubMed (30 million abstracts), the PMC Open Access Subset and the Author Manuscript\nCollection (3 million full text articles). Updated entity identification methods and a\ndisambiguation module based on cutting-edge deep learning techniques provide increased accuracy." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-other #region-us \n", "# Dataset Card for PubTator Central", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,NED\n\n\nPubTator Central (PTC, URL is a web service for\nexploring and retrieving bioconcept annotations in full text biomedical articles. PTC provides\nautomated annotations from state-of-the-art text mining systems for genes/proteins, genetic\nvariants, diseases, chemicals, species and cell lines, all available for immediate download. PTC\nannotates PubMed (30 million abstracts), the PMC Open Access Subset and the Author Manuscript\nCollection (3 million full text articles). Updated entity identification methods and a\ndisambiguation module based on cutting-edge deep learning techniques provide increased accuracy." ]

[ "multilinguality:monolingual", "language:fr", "license:other", "region:us" ]

[]

[ "fr" ]

[ "# Dataset Card for QUAERO", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,NED\n\n\nThe QUAERO French Medical Corpus has been initially developed as a resource for named entity recognition and normalization [1]. It was then improved with the purpose of creating a gold standard set of normalized entities for French biomedical text, that was used in the CLEF eHealth evaluation lab [2][3].\n\nA selection of MEDLINE titles and EMEA documents were manually annotated. The annotation process was guided by concepts in the Unified Medical Language System (UMLS):\n\n1. Ten types of clinical entities, as defined by the following UMLS Semantic Groups (Bodenreider and McCray 2003) were annotated: Anatomy, Chemical and Drugs, Devices, Disorders, Geographic Areas, Living Beings, Objects, Phenomena, Physiology, Procedures.\n\n2. The annotations were made in a comprehensive fashion, so that nested entities were marked, and entities could be mapped to more than one UMLS concept. In particular: (a) If a mention can refer to more than one Semantic Group, all the relevant Semantic Groups should be annotated. For instance, the mention “récidive” (recurrence) in the phrase “prévention des récidives” (recurrence prevention) should be annotated with the category “DISORDER” (CUI C2825055) and the category “PHENOMENON” (CUI C0034897); (b) If a mention can refer to more than one UMLS concept within the same Semantic Group, all the relevant concepts should be annotated. For instance, the mention “maniaques” (obsessive) in the phrase “patients maniaques” (obsessive patients) should be annotated with CUIs C0564408 and C0338831 (category “DISORDER”); (c) Entities which span overlaps with that of another entity should still be annotated. For instance, in the phrase “infarctus du myocarde” (myocardial infarction), the mention “myocarde” (myocardium) should be annotated with category “ANATOMY” (CUI C0027061) and the mention “infarctus du myocarde” should be annotated with category “DISORDER” (CUI C0027051)\n\nThe QUAERO French Medical Corpus BioC release comprises a subset of the QUAERO French Medical corpus, as follows:\n\nTraining data (BRAT version used in CLEF eHealth 2015 task 1b as training data): \n- MEDLINE_train_bioc file: 833 MEDLINE titles, annotated with normalized entities in the BioC format \n- EMEA_train_bioc file: 3 EMEA documents, segmented into 11 sub-documents, annotated with normalized entities in the BioC format \n\nDevelopment data (BRAT version used in CLEF eHealth 2015 task 1b as test data and in CLEF eHealth 2016 task 2 as development data): \n- MEDLINE_dev_bioc file: 832 MEDLINE titles, annotated with normalized entities in the BioC format\n- EMEA_dev_bioc file: 3 EMEA documents, segmented into 12 sub-documents, annotated with normalized entities in the BioC format \n\nTest data (BRAT version used in CLEF eHealth 2016 task 2 as test data): \n- MEDLINE_test_bioc folder: 833 MEDLINE titles, annotated with normalized entities in the BioC format \n- EMEA folder_test_bioc: 4 EMEA documents, segmented into 15 sub-documents, annotated with normalized entities in the BioC format \n\n\n\nThis release of the QUAERO French medical corpus, BioC version, comes in the BioC format, through automatic conversion from the original BRAT format obtained with the Brat2BioC tool URL developped by Jimeno Yepes et al.\n\nAntonio Jimeno Yepes, Mariana Neves, Karin Verspoor \nBrat2BioC: conversion tool between brat and BioC\nBioCreative IV track 1 - BioC: The BioCreative Interoperability Initiative, 2013\n\n\nPlease note that the original version of the QUAERO corpus distributed in the CLEF eHealth challenge 2015 and 2016 came in the BRAT stand alone format. It was distributed with the CLEF eHealth evaluation tool. This original distribution of the QUAERO French Medical corpus is available separately from URL \n\nAll questions regarding the task or data should be addressed to URL@URL" ]

[ "TAGS\n#multilinguality-monolingual #language-French #license-other #region-us \n", "# Dataset Card for QUAERO", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,NED\n\n\nThe QUAERO French Medical Corpus has been initially developed as a resource for named entity recognition and normalization [1]. It was then improved with the purpose of creating a gold standard set of normalized entities for French biomedical text, that was used in the CLEF eHealth evaluation lab [2][3].\n\nA selection of MEDLINE titles and EMEA documents were manually annotated. The annotation process was guided by concepts in the Unified Medical Language System (UMLS):\n\n1. Ten types of clinical entities, as defined by the following UMLS Semantic Groups (Bodenreider and McCray 2003) were annotated: Anatomy, Chemical and Drugs, Devices, Disorders, Geographic Areas, Living Beings, Objects, Phenomena, Physiology, Procedures.\n\n2. The annotations were made in a comprehensive fashion, so that nested entities were marked, and entities could be mapped to more than one UMLS concept. In particular: (a) If a mention can refer to more than one Semantic Group, all the relevant Semantic Groups should be annotated. For instance, the mention “récidive” (recurrence) in the phrase “prévention des récidives” (recurrence prevention) should be annotated with the category “DISORDER” (CUI C2825055) and the category “PHENOMENON” (CUI C0034897); (b) If a mention can refer to more than one UMLS concept within the same Semantic Group, all the relevant concepts should be annotated. For instance, the mention “maniaques” (obsessive) in the phrase “patients maniaques” (obsessive patients) should be annotated with CUIs C0564408 and C0338831 (category “DISORDER”); (c) Entities which span overlaps with that of another entity should still be annotated. For instance, in the phrase “infarctus du myocarde” (myocardial infarction), the mention “myocarde” (myocardium) should be annotated with category “ANATOMY” (CUI C0027061) and the mention “infarctus du myocarde” should be annotated with category “DISORDER” (CUI C0027051)\n\nThe QUAERO French Medical Corpus BioC release comprises a subset of the QUAERO French Medical corpus, as follows:\n\nTraining data (BRAT version used in CLEF eHealth 2015 task 1b as training data): \n- MEDLINE_train_bioc file: 833 MEDLINE titles, annotated with normalized entities in the BioC format \n- EMEA_train_bioc file: 3 EMEA documents, segmented into 11 sub-documents, annotated with normalized entities in the BioC format \n\nDevelopment data (BRAT version used in CLEF eHealth 2015 task 1b as test data and in CLEF eHealth 2016 task 2 as development data): \n- MEDLINE_dev_bioc file: 832 MEDLINE titles, annotated with normalized entities in the BioC format\n- EMEA_dev_bioc file: 3 EMEA documents, segmented into 12 sub-documents, annotated with normalized entities in the BioC format \n\nTest data (BRAT version used in CLEF eHealth 2016 task 2 as test data): \n- MEDLINE_test_bioc folder: 833 MEDLINE titles, annotated with normalized entities in the BioC format \n- EMEA folder_test_bioc: 4 EMEA documents, segmented into 15 sub-documents, annotated with normalized entities in the BioC format \n\n\n\nThis release of the QUAERO French medical corpus, BioC version, comes in the BioC format, through automatic conversion from the original BRAT format obtained with the Brat2BioC tool URL developped by Jimeno Yepes et al.\n\nAntonio Jimeno Yepes, Mariana Neves, Karin Verspoor \nBrat2BioC: conversion tool between brat and BioC\nBioCreative IV track 1 - BioC: The BioCreative Interoperability Initiative, 2013\n\n\nPlease note that the original version of the QUAERO corpus distributed in the CLEF eHealth challenge 2015 and 2016 came in the BRAT stand alone format. It was distributed with the CLEF eHealth evaluation tool. This original distribution of the QUAERO French Medical corpus is available separately from URL \n\nAll questions regarding the task or data should be addressed to URL@URL" ]

[ "multilinguality:monolingual", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for SCAI Chemical", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER\n\n\nSCAI Chemical is a corpus of MEDLINE abstracts that has been annotated\nto give an overview of the different chemical name classes\nfound in MEDLINE text." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-unknown #region-us \n", "# Dataset Card for SCAI Chemical", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER\n\n\nSCAI Chemical is a corpus of MEDLINE abstracts that has been annotated\nto give an overview of the different chemical name classes\nfound in MEDLINE text." ]

[ "multilinguality:monolingual", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for SCAI Disease", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER\n\n\nSCAI Disease is a dataset annotated in 2010 with mentions of diseases and\nadverse effects. It is a corpus containing 400 randomly selected MEDLINE\nabstracts generated using ‘Disease OR Adverse effect’ as a PubMed query. This\nevaluation corpus was annotated by two individuals who hold a Master’s degree\nin life sciences." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-unknown #region-us \n", "# Dataset Card for SCAI Disease", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER\n\n\nSCAI Disease is a dataset annotated in 2010 with mentions of diseases and\nadverse effects. It is a corpus containing 400 randomly selected MEDLINE\nabstracts generated using ‘Disease OR Adverse effect’ as a PubMed query. This\nevaluation corpus was annotated by two individuals who hold a Master’s degree\nin life sciences." ]

[ "multilinguality:monolingual", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for SciCite", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: TXTCLASS\n\n\nSciCite is a dataset of 11K manually annotated citation intents based on\ncitation context in the computer science and biomedical domains." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-unknown #region-us \n", "# Dataset Card for SciCite", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: TXTCLASS\n\n\nSciCite is a dataset of 11K manually annotated citation intents based on\ncitation context in the computer science and biomedical domains." ]

[ "multilinguality:multilingual", "language:en", "language:es", "language:pt", "license:cc-by-4.0", "region:us" ]

[]

[ "en", "es", "pt" ]

[ "# Dataset Card for SciELO", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: TRANSL\n\n\nA parallel corpus of full-text scientific articles collected from Scielo database in the following languages: English, Portuguese and Spanish. The corpus is sentence aligned for all language pairs, as well as trilingual aligned for a small subset of sentences. Alignment was carried out using the Hunalign algorithm." ]

[ "TAGS\n#multilinguality-multilingual #language-English #language-Spanish #language-Portuguese #license-cc-by-4.0 #region-us \n", "# Dataset Card for SciELO", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: TRANSL\n\n\nA parallel corpus of full-text scientific articles collected from Scielo database in the following languages: English, Portuguese and Spanish. The corpus is sentence aligned for all language pairs, as well as trilingual aligned for a small subset of sentences. Alignment was carried out using the Hunalign algorithm." ]

[ "multilinguality:monolingual", "language:en", "license:cc-by-nc-2.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for SciFact", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: TXT2CLASS", "### Scifact Corpus Source\n\n SciFact is a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts, and annotated with labels and rationales.\n This config has abstracts and document ids.", "### Scifact Claims Source\n\n {_DESCRIPTION_BASE} This config connects the claims to the evidence and doc ids.", "### Scifact Rationale Bigbio Pairs\n\n {_DESCRIPTION_BASE} This task is the following: given a claim and a text span composed of one or more sentences from an abstract, predict a label from (\"rationale\", \"not_rationale\") indicating if the span is evidence (can be supporting or refuting) for the claim. This roughly corresponds to the second task outlined in Section 5 of the paper.\"", "### Scifact Labelprediction Bigbio Pairs\n\n {_DESCRIPTION_BASE} This task is the following: given a claim and a text span composed of one or more sentences from an abstract, predict a label from (\"SUPPORT\", \"NOINFO\", \"CONTRADICT\") indicating if the span supports, provides no info, or contradicts the claim. This roughly corresponds to the thrid task outlined in Section 5 of the paper." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-cc-by-nc-2.0 #region-us \n", "# Dataset Card for SciFact", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: TXT2CLASS", "### Scifact Corpus Source\n\n SciFact is a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts, and annotated with labels and rationales.\n This config has abstracts and document ids.", "### Scifact Claims Source\n\n {_DESCRIPTION_BASE} This config connects the claims to the evidence and doc ids.", "### Scifact Rationale Bigbio Pairs\n\n {_DESCRIPTION_BASE} This task is the following: given a claim and a text span composed of one or more sentences from an abstract, predict a label from (\"rationale\", \"not_rationale\") indicating if the span is evidence (can be supporting or refuting) for the claim. This roughly corresponds to the second task outlined in Section 5 of the paper.\"", "### Scifact Labelprediction Bigbio Pairs\n\n {_DESCRIPTION_BASE} This task is the following: given a claim and a text span composed of one or more sentences from an abstract, predict a label from (\"SUPPORT\", \"NOINFO\", \"CONTRADICT\") indicating if the span supports, provides no info, or contradicts the claim. This roughly corresponds to the thrid task outlined in Section 5 of the paper." ]

[ "multilinguality:monolingual", "language:en", "license:cc-by-nc-3.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for SciQ", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: QA\n\n\nThe SciQ dataset contains 13,679 crowdsourced science exam questions about Physics, Chemistry and Biology, among others. The questions are in multiple-choice format with 4 answer options each. For most questions, an additional paragraph with supporting evidence for the correct answer is provided." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-cc-by-nc-3.0 #region-us \n", "# Dataset Card for SciQ", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: QA\n\n\nThe SciQ dataset contains 13,679 crowdsourced science exam questions about Physics, Chemistry and Biology, among others. The questions are in multiple-choice format with 4 answer options each. For most questions, an additional paragraph with supporting evidence for the correct answer is provided." ]

[ "multilinguality:monolingual", "language:en", "license:apache-2.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for SETH Corpus", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,RE\n\n\nSNP named entity recognition corpus consisting of 630 PubMed citations." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-apache-2.0 #region-us \n", "# Dataset Card for SETH Corpus", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,RE\n\n\nSNP named entity recognition corpus consisting of 630 PubMed citations." ]

[ "multilinguality:monolingual", "language:en", "license:cc0-1.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for SPL ADR", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: NER,NED,RE\n\n\nThe United States Food and Drug Administration (FDA) partnered with the National Library\nof Medicine to create a pilot dataset containing standardised information about known\nadverse reactions for 200 FDA-approved drugs. The Structured Product Labels (SPLs),\nthe documents FDA uses to exchange information about drugs and other products, were\nmanually annotated for adverse reactions at the mention level to facilitate development\nand evaluation of text mining tools for extraction of ADRs from all SPLs. The ADRs were\nthen normalised to the Unified Medical Language System (UMLS) and to the Medical\nDictionary for Regulatory Activities (MedDRA)." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-cc0-1.0 #region-us \n", "# Dataset Card for SPL ADR", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: NER,NED,RE\n\n\nThe United States Food and Drug Administration (FDA) partnered with the National Library\nof Medicine to create a pilot dataset containing standardised information about known\nadverse reactions for 200 FDA-approved drugs. The Structured Product Labels (SPLs),\nthe documents FDA uses to exchange information about drugs and other products, were\nmanually annotated for adverse reactions at the mention level to facilitate development\nand evaluation of text mining tools for extraction of ADRs from all SPLs. The ADRs were\nthen normalised to the Unified Medical Language System (UMLS) and to the Medical\nDictionary for Regulatory Activities (MedDRA)." ]

[ "multilinguality:monolingual", "language:sv", "license:cc-by-sa-4.0", "region:us" ]

[]

[ "sv" ]

[ "# Dataset Card for Swedish Medical NER", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: NER\n\n\nswedish_medical_ner is Named Entity Recognition dataset on medical text in Swedish. \nIt consists three subsets which are in turn derived from three different sources \nrespectively: the Swedish Wikipedia (a.k.a. wiki), Läkartidningen (a.k.a. lt), \nand 1177 Vårdguiden (a.k.a. 1177). While the Swedish Wikipedia and Läkartidningen \nsubsets in total contains over 790000 sequences with 60 characters each, \nthe 1177 Vårdguiden subset is manually annotated and contains 927 sentences, \n2740 annotations, out of which 1574 are disorder and findings, 546 are \npharmaceutical drug, and 620 are body structure.\n\nTexts from both Swedish Wikipedia and Läkartidningen were automatically annotated \nusing a list of medical seed terms. Sentences from 1177 Vårdguiden were manuually \nannotated." ]

[ "TAGS\n#multilinguality-monolingual #language-Swedish #license-cc-by-sa-4.0 #region-us \n", "# Dataset Card for Swedish Medical NER", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: NER\n\n\nswedish_medical_ner is Named Entity Recognition dataset on medical text in Swedish. \nIt consists three subsets which are in turn derived from three different sources \nrespectively: the Swedish Wikipedia (a.k.a. wiki), Läkartidningen (a.k.a. lt), \nand 1177 Vårdguiden (a.k.a. 1177). While the Swedish Wikipedia and Läkartidningen \nsubsets in total contains over 790000 sequences with 60 characters each, \nthe 1177 Vårdguiden subset is manually annotated and contains 927 sentences, \n2740 annotations, out of which 1574 are disorder and findings, 546 are \npharmaceutical drug, and 620 are body structure.\n\nTexts from both Swedish Wikipedia and Läkartidningen were automatically annotated \nusing a list of medical seed terms. Sentences from 1177 Vårdguiden were manuually \nannotated." ]

[ "multilinguality:monolingual", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for tmVar v1", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER\n\n\nThis dataset contains 500 PubMed articles manually annotated with mutation mentions of various kinds. It can be used for NER tasks only.\nThe dataset is split into train(334) and test(166) splits" ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-unknown #region-us \n", "# Dataset Card for tmVar v1", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER\n\n\nThis dataset contains 500 PubMed articles manually annotated with mutation mentions of various kinds. It can be used for NER tasks only.\nThe dataset is split into train(334) and test(166) splits" ]

[ "multilinguality:monolingual", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for tmVar v2", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,NED\n\n\nThis dataset contains 158 PubMed articles manually annotated with mutation mentions of various kinds and dbsnp normalizations for each of them.\nIt can be used for NER tasks and NED tasks, This dataset has a single split" ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-unknown #region-us \n", "# Dataset Card for tmVar v2", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,NED\n\n\nThis dataset contains 158 PubMed articles manually annotated with mutation mentions of various kinds and dbsnp normalizations for each of them.\nIt can be used for NER tasks and NED tasks, This dataset has a single split" ]

[ "multilinguality:monolingual", "language:en", "license:unknown", "arxiv:2204.03637", "region:us" ]

[ "2204.03637" ]

[ "en" ]

[ "# Dataset Card for tmVar v3", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,NED\n\n\nThis dataset contains 500 PubMed articles manually annotated with mutation mentions of various kinds and dbsnp normalizations for each of them. In addition, it contains variant normalization options such as allele-specific identifiers from the ClinGen Allele Registry It can be used for NER tasks and NED tasks, This dataset does NOT have splits." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-unknown #arxiv-2204.03637 #region-us \n", "# Dataset Card for tmVar v3", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,NED\n\n\nThis dataset contains 500 PubMed articles manually annotated with mutation mentions of various kinds and dbsnp normalizations for each of them. In addition, it contains variant normalization options such as allele-specific identifiers from the ClinGen Allele Registry It can be used for NER tasks and NED tasks, This dataset does NOT have splits." ]

[ "multilinguality:monolingual", "language:en", "license:cc-by-4.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for TwADR-L", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: NER,NED\n\n\n\nThe TwADR-L dataset contains medical concepts written on social media (Twitter) mapped to how they are formally written in medical ontologies (SIDER 4)." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-cc-by-4.0 #region-us \n", "# Dataset Card for TwADR-L", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: NER,NED\n\n\n\nThe TwADR-L dataset contains medical concepts written on social media (Twitter) mapped to how they are formally written in medical ontologies (SIDER 4)." ]

[ "multilinguality:monolingual", "language:en", "license:cc0-1.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for UMNSRS", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: STS\n\n\nUMNSRS, developed by Pakhomov, et al., consists of 725 clinical term pairs whose semantic similarity and relatedness.\nThe similarity and relatedness of each term pair was annotated based on a continuous scale by having the resident touch\na bar on a touch sensitive computer screen to indicate the degree of similarity or relatedness.\nThe following subsets are available:\n- similarity: A set of 566 UMLS concept pairs manually rated for semantic similarity (e.g. whale-dolphin) using a\n continuous response scale.\n- relatedness: A set of 588 UMLS concept pairs manually rated for semantic relatedness (e.g. needle-thread) using a\n continuous response scale.\n- similarity_mod: Modification of the UMNSRS-Similarity dataset to exclude control samples and those pairs that did not\n match text in clinical, biomedical and general English corpora. Exact modifications are detailed in the paper (Corpus\n Domain Effects on Distributional Semantic Modeling of Medical Terms. Serguei V.S. Pakhomov, Greg Finley, Reed McEwan,\n Yan Wang, and Genevieve B. Melton. Bioinformatics. 2016; 32(23):3635-3644). The resulting dataset contains 449 pairs.\n- relatedness_mod: Modification of the UMNSRS-Relatedness dataset to exclude control samples and those pairs that did\n not match text in clinical, biomedical and general English corpora. Exact modifications are detailed in the paper\n (Corpus Domain Effects on Distributional Semantic Modeling of Medical Terms. Serguei V.S. Pakhomov, Greg Finley,\n Reed McEwan, Yan Wang, and Genevieve B. Melton. Bioinformatics. 2016; 32(23):3635-3644).\n The resulting dataset contains 458 pairs." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-cc0-1.0 #region-us \n", "# Dataset Card for UMNSRS", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: STS\n\n\nUMNSRS, developed by Pakhomov, et al., consists of 725 clinical term pairs whose semantic similarity and relatedness.\nThe similarity and relatedness of each term pair was annotated based on a continuous scale by having the resident touch\na bar on a touch sensitive computer screen to indicate the degree of similarity or relatedness.\nThe following subsets are available:\n- similarity: A set of 566 UMLS concept pairs manually rated for semantic similarity (e.g. whale-dolphin) using a\n continuous response scale.\n- relatedness: A set of 588 UMLS concept pairs manually rated for semantic relatedness (e.g. needle-thread) using a\n continuous response scale.\n- similarity_mod: Modification of the UMNSRS-Similarity dataset to exclude control samples and those pairs that did not\n match text in clinical, biomedical and general English corpora. Exact modifications are detailed in the paper (Corpus\n Domain Effects on Distributional Semantic Modeling of Medical Terms. Serguei V.S. Pakhomov, Greg Finley, Reed McEwan,\n Yan Wang, and Genevieve B. Melton. Bioinformatics. 2016; 32(23):3635-3644). The resulting dataset contains 449 pairs.\n- relatedness_mod: Modification of the UMNSRS-Relatedness dataset to exclude control samples and those pairs that did\n not match text in clinical, biomedical and general English corpora. Exact modifications are detailed in the paper\n (Corpus Domain Effects on Distributional Semantic Modeling of Medical Terms. Serguei V.S. Pakhomov, Greg Finley,\n Reed McEwan, Yan Wang, and Genevieve B. Melton. Bioinformatics. 2016; 32(23):3635-3644).\n The resulting dataset contains 458 pairs." ]

[ "multilinguality:monolingual", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for Verspoor 2013", "## Dataset Description\n\n- Homepage: NA\n- Pubmed: True\n- Public: True\n- Tasks: NER,RE\n\n\nThis dataset contains annotations for a small corpus of full text journal publications on the subject of inherited colorectal cancer. It is suitable for Named Entity Recognition and Relation Extraction tasks. It uses the Variome Annotation Schema, a schema that aims to capture the core concepts and relations relevant to cataloguing and interpreting human genetic variation and its relationship to disease, as described in the published literature. The schema was inspired by the needs of the database curators of the International Society for Gastrointestinal Hereditary Tumours (InSiGHT) database, but is intended to have application to genetic variation information in a range of diseases." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-unknown #region-us \n", "# Dataset Card for Verspoor 2013", "## Dataset Description\n\n- Homepage: NA\n- Pubmed: True\n- Public: True\n- Tasks: NER,RE\n\n\nThis dataset contains annotations for a small corpus of full text journal publications on the subject of inherited colorectal cancer. It is suitable for Named Entity Recognition and Relation Extraction tasks. It uses the Variome Annotation Schema, a schema that aims to capture the core concepts and relations relevant to cataloguing and interpreting human genetic variation and its relationship to disease, as described in the published literature. The schema was inspired by the needs of the database curators of the International Society for Gastrointestinal Hereditary Tumours (InSiGHT) database, but is intended to have application to genetic variation information in a range of diseases." ]

[ "task_categories:image-to-text", "task_categories:zero-shot-image-classification", "task_categories:feature-extraction", "task_ids:image-captioning", "size_categories:100K<n<1M", "source_datasets:wikimedia/wit_base", "language:da", "license:cc-by-sa-4.0", "region:us" ]

[]

[ "da" ]

[ "### Dataset Summary\n\n\nGoogle presented the Wikipedia Image Text (WIT) dataset in July\n2021, a dataset which contains\nscraped images from Wikipedia along with their descriptions. WikiMedia released\nWIT-Base in September\n2021,\nbeing a modified version of WIT where they have removed the images with empty\n\"reference descriptions\", as well as removing images where a person's face covers more\nthan 10% of the image surface, along with inappropriate images that are candidate for\ndeletion. This dataset is the Danish portion of the WIT-Base dataset, consisting of\nroughly 160,000 images with associated Danish descriptions. We release the dataset\nunder the CC BY-SA 4.0 license, in\naccordance with WIT-Base's identical\nlicense.", "### Supported Tasks and Leaderboards\n\n\nTraining machine learning models for caption generation, zero-shot image classification\nand text-image search are the intended tasks for this dataset. No leaderboard is active\nat this point.", "### Languages\n\n\nThe dataset is available in Danish ('da').\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\n* Size of downloaded dataset files: 7.5 GB\n* Size of the generated dataset: 7.8 GB\n* Total amount of disk used: 15.3 GB\n\n\nAn example from the 'train' split looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'image': an 'Image' feature.\n* 'image\\_url': a 'str' feature.\n* 'embedding': a 'list' feature.\n* 'metadata\\_url': a 'str' feature.\n* 'original\\_height': an 'int' or 'NaN' feature.\n* 'original\\_width': an 'int' or 'NaN' feature.\n* 'mime\\_type': a 'str' or 'None' feature.\n* 'caption\\_attribution\\_description': a 'str' or 'None' feature.\n* 'page\\_url': a 'str' feature.\n* 'attribution\\_passes\\_lang\\_id': a 'bool' or 'None' feature.\n* 'caption\\_alt\\_text\\_description': a 'str' or 'None' feature.\n* 'caption\\_reference\\_description': a 'str' or 'None' feature.\n* 'caption\\_title\\_and\\_reference\\_description': a 'str' or 'None' feature.\n* 'context\\_page\\_description': a 'str' or 'None' feature.\n* 'context\\_section\\_description': a 'str' or 'None' feature.\n* 'hierarchical\\_section\\_title': a 'str' feature.\n* 'is\\_main\\_image': a 'bool' or 'None' feature.\n* 'page\\_changed\\_recently': a 'bool' or 'None' feature.\n* 'page\\_title': a 'str' feature.\n* 'section\\_title': a 'str' or 'None' feature.", "### Data Splits\n\n\nRoughly 2.60% of the WIT-Base dataset comes from the Danish Wikipedia. We have split\nthe resulting 168,740 samples into a training set, validation set and testing set of\nthe following sizes:\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nIt is quite cumbersome to extract the Danish portion of the WIT-Base dataset,\nespecially as the dataset takes up 333 GB of disk space, so the curation of Danish-WIT\nis purely to make it easier to work with the Danish portion of it.", "### Source Data\n\n\nThe original data was collected from WikiMedia's\nWIT-Base dataset, which in turn\ncomes from Google's WIT dataset.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nDan Saattrup Nielsen from the The Alexandra\nInstitute curated this dataset.", "### Licensing Information\n\n\nThe dataset is licensed under the CC BY-SA 4.0\nlicense." ]

[ "TAGS\n#task_categories-image-to-text #task_categories-zero-shot-image-classification #task_categories-feature-extraction #task_ids-image-captioning #size_categories-100K<n<1M #source_datasets-wikimedia/wit_base #language-Danish #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nGoogle presented the Wikipedia Image Text (WIT) dataset in July\n2021, a dataset which contains\nscraped images from Wikipedia along with their descriptions. WikiMedia released\nWIT-Base in September\n2021,\nbeing a modified version of WIT where they have removed the images with empty\n\"reference descriptions\", as well as removing images where a person's face covers more\nthan 10% of the image surface, along with inappropriate images that are candidate for\ndeletion. This dataset is the Danish portion of the WIT-Base dataset, consisting of\nroughly 160,000 images with associated Danish descriptions. We release the dataset\nunder the CC BY-SA 4.0 license, in\naccordance with WIT-Base's identical\nlicense.", "### Supported Tasks and Leaderboards\n\n\nTraining machine learning models for caption generation, zero-shot image classification\nand text-image search are the intended tasks for this dataset. No leaderboard is active\nat this point.", "### Languages\n\n\nThe dataset is available in Danish ('da').\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\n* Size of downloaded dataset files: 7.5 GB\n* Size of the generated dataset: 7.8 GB\n* Total amount of disk used: 15.3 GB\n\n\nAn example from the 'train' split looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'image': an 'Image' feature.\n* 'image\\_url': a 'str' feature.\n* 'embedding': a 'list' feature.\n* 'metadata\\_url': a 'str' feature.\n* 'original\\_height': an 'int' or 'NaN' feature.\n* 'original\\_width': an 'int' or 'NaN' feature.\n* 'mime\\_type': a 'str' or 'None' feature.\n* 'caption\\_attribution\\_description': a 'str' or 'None' feature.\n* 'page\\_url': a 'str' feature.\n* 'attribution\\_passes\\_lang\\_id': a 'bool' or 'None' feature.\n* 'caption\\_alt\\_text\\_description': a 'str' or 'None' feature.\n* 'caption\\_reference\\_description': a 'str' or 'None' feature.\n* 'caption\\_title\\_and\\_reference\\_description': a 'str' or 'None' feature.\n* 'context\\_page\\_description': a 'str' or 'None' feature.\n* 'context\\_section\\_description': a 'str' or 'None' feature.\n* 'hierarchical\\_section\\_title': a 'str' feature.\n* 'is\\_main\\_image': a 'bool' or 'None' feature.\n* 'page\\_changed\\_recently': a 'bool' or 'None' feature.\n* 'page\\_title': a 'str' feature.\n* 'section\\_title': a 'str' or 'None' feature.", "### Data Splits\n\n\nRoughly 2.60% of the WIT-Base dataset comes from the Danish Wikipedia. We have split\nthe resulting 168,740 samples into a training set, validation set and testing set of\nthe following sizes:\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nIt is quite cumbersome to extract the Danish portion of the WIT-Base dataset,\nespecially as the dataset takes up 333 GB of disk space, so the curation of Danish-WIT\nis purely to make it easier to work with the Danish portion of it.", "### Source Data\n\n\nThe original data was collected from WikiMedia's\nWIT-Base dataset, which in turn\ncomes from Google's WIT dataset.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nDan Saattrup Nielsen from the The Alexandra\nInstitute curated this dataset.", "### Licensing Information\n\n\nThe dataset is licensed under the CC BY-SA 4.0\nlicense." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"code-tokenized\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"code-tokenized\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"c4-tokenized-2b\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"c4-tokenized-2b\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:automatic-speech-recognition", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:ko", "region:us" ]

[]

[ "ko" ]

[ "### Dataset Summary\n\n\nThis corpus contains 969 h of general open-domain dialog utterances, spoken by about 2000 native Korean speakers in a clean environment. All data were constructed by recording the dialogue of two people freely conversing on a variety of topics and manually transcribing the utterances. The transcription provides a dual transcription consisting of orthography and pronunciation, and disfluency tags for spontaneity of speech, such as filler words, repeated words, and word fragments. This paper also presents the baseline performance of an end-to-end speech recognition model trained with KsponSpeech. In addition, we investigated the performance of standard end-to-end architectures and the number of sub-word units suitable for Korean. We investigated issues that should be considered in spontaneous speech recognition in Korean. KsponSpeech is publicly available on an open data hub site of the Korea government.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nKorean\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields\n\n\n* audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling\\_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n* text: the transcription of the audio file.\n* id: unique id of the data sample.", "### 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-automatic-speech-recognition #annotations_creators-expert-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Korean #region-us \n", "### Dataset Summary\n\n\nThis corpus contains 969 h of general open-domain dialog utterances, spoken by about 2000 native Korean speakers in a clean environment. All data were constructed by recording the dialogue of two people freely conversing on a variety of topics and manually transcribing the utterances. The transcription provides a dual transcription consisting of orthography and pronunciation, and disfluency tags for spontaneity of speech, such as filler words, repeated words, and word fragments. This paper also presents the baseline performance of an end-to-end speech recognition model trained with KsponSpeech. In addition, we investigated the performance of standard end-to-end architectures and the number of sub-word units suitable for Korean. We investigated issues that should be considered in spontaneous speech recognition in Korean. KsponSpeech is publicly available on an open data hub site of the Korea government.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nKorean\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields\n\n\n* audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling\\_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n* text: the transcription of the audio file.\n* id: unique id of the data sample.", "### 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" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"tokenized-recipe-nlg-gpt2\"\n\nThis a tokenized version of the recipe-nlg database from URL \nThe preprocessing on the original csv was done using the methodology of the original paper (best as I could interpret) along with a similar 0.05 percent train test split. The tokenizer used has some special tokens, but all these parameters are accessible in URL if you want to recreate. This dataset will save you a lot of time getting started if you want to experiment with training GPT2 on the data yourself." ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"tokenized-recipe-nlg-gpt2\"\n\nThis a tokenized version of the recipe-nlg database from URL \nThe preprocessing on the original csv was done using the methodology of the original paper (best as I could interpret) along with a similar 0.05 percent train test split. The tokenizer used has some special tokens, but all these parameters are accessible in URL if you want to recreate. This dataset will save you a lot of time getting started if you want to experiment with training GPT2 on the data yourself." ]

[ "task_categories:automatic-speech-recognition", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:zh", "license:cc-by-nc-nd-4.0", "region:us" ]

[]

[ "zh" ]

[ "# Dataset Card for MMCRSC", "## 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: MAGICDATA Mandarin Chinese Read Speech Corpus\n- Repository:\n- Paper: \n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nMAGICDATA Mandarin Chinese Read Speech Corpus was developed by MAGIC DATA Technology Co., Ltd. and freely published for non-commercial use.\nThe contents and the corresponding descriptions of the corpus include:\n\nThe corpus contains 755 hours of speech data, which is mostly mobile recorded data.\n1080 speakers from different accent areas in China are invited to participate in the recording.\nThe sentence transcription accuracy is higher than 98%.\nRecordings are conducted in a quiet indoor environment.\nThe database is divided into training set, validation set, and testing set in a ratio of 51: 1: 2.\nDetail information such as speech data coding and speaker information is preserved in the metadata file.\nThe domain of recording texts is diversified, including interactive Q&A, music search, SNS messages, home command and control, etc.\nSegmented transcripts are also provided.\nThe corpus aims to support researchers in speech recognition, machine translation, speaker recognition, and other speech-related fields. Therefore, the corpus is totally free for academic use.\nThe corpus is a subset of a much bigger data ( 10566.9 hours Chinese Mandarin Speech Corpus ) set which was recorded in the same environment. Please feel free to contact us via business@URL for more details.", "### Supported Tasks and Leaderboards", "### Languages\n\nzh-CN", "## Dataset Structure", "### Data Instances", "### Data Fields\n\n- file: A path to the downloaded audio file in .wav format.\n- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n- text: the transcription of the audio file.\n- id: unique id of the data sample.\n- speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples.", "### 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\n\n\n\n\nPlease cite the corpus as \"Magic Data Technology Co., Ltd., \"URL 05/2019\"." ]

[ "TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-expert-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Chinese #license-cc-by-nc-nd-4.0 #region-us \n", "# Dataset Card for MMCRSC", "## 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: MAGICDATA Mandarin Chinese Read Speech Corpus\n- Repository:\n- Paper: \n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nMAGICDATA Mandarin Chinese Read Speech Corpus was developed by MAGIC DATA Technology Co., Ltd. and freely published for non-commercial use.\nThe contents and the corresponding descriptions of the corpus include:\n\nThe corpus contains 755 hours of speech data, which is mostly mobile recorded data.\n1080 speakers from different accent areas in China are invited to participate in the recording.\nThe sentence transcription accuracy is higher than 98%.\nRecordings are conducted in a quiet indoor environment.\nThe database is divided into training set, validation set, and testing set in a ratio of 51: 1: 2.\nDetail information such as speech data coding and speaker information is preserved in the metadata file.\nThe domain of recording texts is diversified, including interactive Q&A, music search, SNS messages, home command and control, etc.\nSegmented transcripts are also provided.\nThe corpus aims to support researchers in speech recognition, machine translation, speaker recognition, and other speech-related fields. Therefore, the corpus is totally free for academic use.\nThe corpus is a subset of a much bigger data ( 10566.9 hours Chinese Mandarin Speech Corpus ) set which was recorded in the same environment. Please feel free to contact us via business@URL for more details.", "### Supported Tasks and Leaderboards", "### Languages\n\nzh-CN", "## Dataset Structure", "### Data Instances", "### Data Fields\n\n- file: A path to the downloaded audio file in .wav format.\n- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n- text: the transcription of the audio file.\n- id: unique id of the data sample.\n- speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples.", "### 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\n\n\n\n\nPlease cite the corpus as \"Magic Data Technology Co., Ltd., \"URL 05/2019\"." ]

[ "region:us" ]

[]

[]

[]

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

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

[]

[]

[]

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

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-125m_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-125m_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-350m_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-350m_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-30b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-30b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-6.7b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-6.7b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-66b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-66b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-66b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-66b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-2.7b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-2.7b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-350m_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-350m_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-13b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-13b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-6.7b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-6.7b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-2.7b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-2.7b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-1.3b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-1.3b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-30b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-30b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: facebook/opt-66b\n* Dataset: futin/guess\n* Config: vi\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: facebook/opt-66b\n* Dataset: futin/guess\n* Config: vi\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-13b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-13b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Natural Language Inference\n* Model: MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli\n* Dataset: anli\n* Config: plain_text\n* Split: dev_r1\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @ctkang for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Natural Language Inference\n* Model: MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli\n* Dataset: anli\n* Config: plain_text\n* Split: dev_r1\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @ctkang for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Translation\n* Model: facebook/wmt19-en-de\n* Dataset: wmt19\n* Config: de-en\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @WillHeld for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Translation\n* Model: facebook/wmt19-en-de\n* Dataset: wmt19\n* Config: de-en\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @WillHeld for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-125m_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-125m_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Binary Text Classification\n* Model: autoevaluate/binary-classification\n* Dataset: glue\n* Config: sst2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Binary Text Classification\n* Model: autoevaluate/binary-classification\n* Dataset: glue\n* Config: sst2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-1.3b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-1.3b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-30b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-30b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-13b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-13b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: it5/mt5-base-news-summarization\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mtharrison for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: it5/mt5-base-news-summarization\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mtharrison for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-350m_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-350m_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-6.7b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-6.7b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-2.7b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: inverse-scaling/opt-2.7b_eval\n* Dataset: mathemakitten/winobias_antistereotype_test_v5\n* Config: mathemakitten--winobias_antistereotype_test_v5\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mathemakitten for evaluating this model." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"MiniScans\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"MiniScans\"\n\nMore Information needed" ]