Datasets:

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ftopal

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huggingface-datasets

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[ "region:us" ]

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

[ "task_categories:token-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:mit", "region:us" ]

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[ "en" ]

[ "# Dataset Card for [Dataset Name]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @github-username for adding this dataset." ]

[ "TAGS\n#task_categories-token-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-mit #region-us \n", "# Dataset Card for [Dataset Name]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @github-username for adding this dataset." ]

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

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[ "# 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." ]

[ "task_categories:fill-mask", "task_categories:text-generation", "task_ids:masked-language-modeling", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "size_categories:100M<n<1B", "language:sl", "license:cc-by-nc-sa-4.0", "gigafida", "gigafida2", "kres", "cckres", "reference corpus", "region:us" ]

[]

[ "sl" ]

[ "# Dataset Card for ccGigafida\n\nThis repository by default loads the publicly available dataset ccGigafida, which contains a small subset of the Gigafida/Gigafida2 corpus. \nThe full datasets are private due to copyright. If you happen to have access to the full datasets, the script will also work with those.\nInstead of \n \nplease use \n \n\nIMPORTANT: The script will process all '.xml' files in the provided directory and its subdirectories - make sure there are no schema or metadata files in there!", "### Dataset Summary\n\nccGigafida is a reference corpus of Slovene texts. It is a publicly available subsample of an even larger reference corpus, Gigafida (and its successor Gigafida 2). \nThe Gigafida corpus is an extensive collection of Slovene text of various genres, from daily newspapers, magazines, all kinds of books (fiction, non-fiction, textbooks), \nweb pages, transcriptions of parliamentary debates and similar.", "### Supported Tasks and Leaderboards\n\nLanguage modeling.", "### Languages\n\nSlovenian.", "## Dataset Structure", "### Data Instances\n\nThe data is loaded at document-level, i.e. one instance is one document.", "### Data Fields\n\n- 'id_doc': the document ID (string); \n- 'doc_title': the document title (string);\n- 'authors': author(s) of the document (list of string): \"neznani novinar\" (sl) = (\"unknown/unspecified journalist\");\n- 'publish_date': publish date (string);\n- 'publisher': publisher, e.g., the name of a news agency (string);\n- 'genres': genre(s) of the document (list of string) - possible genres: '['tisk', 'tisk/knjižno', 'tisk/knjižno/leposlovno', 'tisk/knjižno/strokovno', 'tisk/periodično', 'tisk/periodično/časopis', 'tisk/periodično/revija', 'tisk/drugo', 'internet']';\n- 'doc_tokenized': tokenized document - the top level lists represent paragraphs, the lists in the level deeper represent sentences, and each sentence contains tokens;\n- 'doc_lemmas': lemmatized document - same structure as 'doc_tokenized';\n- 'doc_msds': MSD tags of the document - same structure as 'doc_tokenized' (tagset);\n- 'doc_string': same as 'doc_tokenized' but with properly placed spaces in sentences;\n- 'id_sents': IDs of sentences contained inside paragraphs of the document.", "## Dataset Creation\n\nGigafida consists of texts which were published between 1990 and 2011. The texts come from printed sources and from the web. \nPrinted part contains fiction, non-fiction and textbooks, and periodicals such as daily newspapers and magazines. \nTexts originating from the web were published on news portals, pages of big Slovene companies and more important governmental, \neducational, research, cultural and similar institutions. \n\nFor more information, please check URL", "## Additional Information", "### Dataset Curators\n\nNataša Logar; et al. (please see URL for the full list)", "### Licensing Information\n\nCC BY-NC-SA 4.0.", "### Contributions\n\nThanks to @matejklemen for adding this dataset." ]

[ "TAGS\n#task_categories-fill-mask #task_categories-text-generation #task_ids-masked-language-modeling #task_ids-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #size_categories-100M<n<1B #language-Slovenian #license-cc-by-nc-sa-4.0 #gigafida #gigafida2 #kres #cckres #reference corpus #region-us \n", "# Dataset Card for ccGigafida\n\nThis repository by default loads the publicly available dataset ccGigafida, which contains a small subset of the Gigafida/Gigafida2 corpus. \nThe full datasets are private due to copyright. If you happen to have access to the full datasets, the script will also work with those.\nInstead of \n \nplease use \n \n\nIMPORTANT: The script will process all '.xml' files in the provided directory and its subdirectories - make sure there are no schema or metadata files in there!", "### Dataset Summary\n\nccGigafida is a reference corpus of Slovene texts. It is a publicly available subsample of an even larger reference corpus, Gigafida (and its successor Gigafida 2). \nThe Gigafida corpus is an extensive collection of Slovene text of various genres, from daily newspapers, magazines, all kinds of books (fiction, non-fiction, textbooks), \nweb pages, transcriptions of parliamentary debates and similar.", "### Supported Tasks and Leaderboards\n\nLanguage modeling.", "### Languages\n\nSlovenian.", "## Dataset Structure", "### Data Instances\n\nThe data is loaded at document-level, i.e. one instance is one document.", "### Data Fields\n\n- 'id_doc': the document ID (string); \n- 'doc_title': the document title (string);\n- 'authors': author(s) of the document (list of string): \"neznani novinar\" (sl) = (\"unknown/unspecified journalist\");\n- 'publish_date': publish date (string);\n- 'publisher': publisher, e.g., the name of a news agency (string);\n- 'genres': genre(s) of the document (list of string) - possible genres: '['tisk', 'tisk/knjižno', 'tisk/knjižno/leposlovno', 'tisk/knjižno/strokovno', 'tisk/periodično', 'tisk/periodično/časopis', 'tisk/periodično/revija', 'tisk/drugo', 'internet']';\n- 'doc_tokenized': tokenized document - the top level lists represent paragraphs, the lists in the level deeper represent sentences, and each sentence contains tokens;\n- 'doc_lemmas': lemmatized document - same structure as 'doc_tokenized';\n- 'doc_msds': MSD tags of the document - same structure as 'doc_tokenized' (tagset);\n- 'doc_string': same as 'doc_tokenized' but with properly placed spaces in sentences;\n- 'id_sents': IDs of sentences contained inside paragraphs of the document.", "## Dataset Creation\n\nGigafida consists of texts which were published between 1990 and 2011. The texts come from printed sources and from the web. \nPrinted part contains fiction, non-fiction and textbooks, and periodicals such as daily newspapers and magazines. \nTexts originating from the web were published on news portals, pages of big Slovene companies and more important governmental, \neducational, research, cultural and similar institutions. \n\nFor more information, please check URL", "## Additional Information", "### Dataset Curators\n\nNataša Logar; et al. (please see URL for the full list)", "### Licensing Information\n\nCC BY-NC-SA 4.0.", "### Contributions\n\nThanks to @matejklemen for adding this dataset." ]

[ "region:us" ]

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[ "# Dataset Card for \"multi-label-class-github-issues-text-classification\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"multi-label-class-github-issues-text-classification\"\n\nMore Information needed" ]

[ "task_categories:text-to-image", "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:n<1K", "language:en", "license:afl-3.0", "stable diffusion sound generation text-to-sound text-to-image-to-sound spectrogram", "region:us" ]

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[ "en" ]

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[ "TAGS\n#task_categories-text-to-image #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-n<1K #language-English #license-afl-3.0 #stable diffusion sound generation text-to-sound text-to-image-to-sound spectrogram #region-us \n" ]

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

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[ "# 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: bigscience/bloom-1b1\n* Dataset: mathemakitten/winobias_antistereotype_test\n* Config: mathemakitten--winobias_antistereotype_test\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: bigscience/bloom-1b1\n* Dataset: mathemakitten/winobias_antistereotype_test\n* Config: mathemakitten--winobias_antistereotype_test\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" ]

[]

[]

[]

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

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

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[ "# 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" ]

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[ "# 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" ]

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[ "# 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: autoevaluate/zero-shot-classification\n* Dataset: autoevaluate/zero-shot-classification-sample\n* Config: autoevaluate--zero-shot-classification-sample\n* Split: test\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: Zero-Shot Text Classification\n* Model: autoevaluate/zero-shot-classification\n* Dataset: autoevaluate/zero-shot-classification-sample\n* Config: autoevaluate--zero-shot-classification-sample\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "task_categories:question-answering", "annotations_creators:expert-generated", "language_creators:other", "multilinguality:monolingual", "language:en", "license:apache-2.0", "region:us" ]

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[ "en" ]

[ "### Dataset Summary\n\n\nA contrast set generated from the eval set of SQuAD. Questions and answers were modified\nto help detecting dataset artifacts. This dataset only contains a validation set, which\nshould only be used to evaluate a model.", "### Supported Tasks\n\n\nQuestion Answering (SQuAD).", "### Languages\n\n\nEnglish\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nDataset contains 100 instances", "### Data Fields", "### Data Splits\n\n\nA single 'eval' split is provided\n\n\nDataset Creation\n----------------\n\n\nDataset was created by modifying a sample of 100 examples from SQuAD test split.\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nApache 2.0 license\n\n\nTODO: add citations" ]

[ "TAGS\n#task_categories-question-answering #annotations_creators-expert-generated #language_creators-other #multilinguality-monolingual #language-English #license-apache-2.0 #region-us \n", "### Dataset Summary\n\n\nA contrast set generated from the eval set of SQuAD. Questions and answers were modified\nto help detecting dataset artifacts. This dataset only contains a validation set, which\nshould only be used to evaluate a model.", "### Supported Tasks\n\n\nQuestion Answering (SQuAD).", "### Languages\n\n\nEnglish\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nDataset contains 100 instances", "### Data Fields", "### Data Splits\n\n\nA single 'eval' split is provided\n\n\nDataset Creation\n----------------\n\n\nDataset was created by modifying a sample of 100 examples from SQuAD test split.\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nApache 2.0 license\n\n\nTODO: add citations" ]

[ "region:us" ]

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[ "# Dataset Card for \"olm-test-no-dedup\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"olm-test-no-dedup\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"olm-test-normal-dedup\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"olm-test-normal-dedup\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"olm-test-normal-dedup-sorted\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"olm-test-normal-dedup-sorted\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"olm-test-no-dedup-sorted\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"olm-test-no-dedup-sorted\"\n\nMore Information needed" ]

[ "task_categories:question-answering", "annotations_creators:expert-generated", "language_creators:other", "multilinguality:monolingual", "language:en", "license:apache-2.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nA contrast set to evaluate models trained on SQUAD on out-of-domain data.", "### Supported Tasks\n\n\nEvaluate question-answering", "### Languages\n\n\nEnglish\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nDataset contains 40 instances", "### Data Fields", "### Data Splits\n\n\nA single 'test' split is provided\n\n\nDataset Creation\n----------------\n\n\nDataset was created from Wikipedia articles\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nApache 2.0 license\n\n\nTODO: add citations" ]

[ "TAGS\n#task_categories-question-answering #annotations_creators-expert-generated #language_creators-other #multilinguality-monolingual #language-English #license-apache-2.0 #region-us \n", "### Dataset Summary\n\n\nA contrast set to evaluate models trained on SQUAD on out-of-domain data.", "### Supported Tasks\n\n\nEvaluate question-answering", "### Languages\n\n\nEnglish\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nDataset contains 40 instances", "### Data Fields", "### Data Splits\n\n\nA single 'test' split is provided\n\n\nDataset Creation\n----------------\n\n\nDataset was created from Wikipedia articles\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nApache 2.0 license\n\n\nTODO: add citations" ]

[ "task_categories:text-classification", "task_ids:multi-class-classification", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:original", "language:en", "license:apache-2.0", "stackoverflow", "technical questions", "region:us" ]

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[ "en" ]

[ "# Dataset Card for [Stackoverflow Post Questions]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Languages\n- Dataset Structure\n - Data Splits\n- Dataset Creation\n - Source Data\n - Contributions", "## Dataset Description\n\nCompanies that sell Open-source software tools usually hire an army of Customer representatives to try to answer every question asked about their tool. The first step in this process \nis the prioritization of the question. The classification scale usually consists of 4 values, P0, P1, P2, and P3, with different meanings across every participant in the industry. On \nthe other hand, every software developer in the world has dealt with Stack Overflow (SO); the amount of shared knowledge there is incomparable to any other website. Questions in SO are \nusually annotated and curated by thousands of people, providing metadata about the quality of the question. This dataset aims to provide an accurate prioritization for programming \nquestions.", "### Dataset Summary\n\nThe dataset contains the title and body of stackoverflow questions and a label value(0,1,2,3) that was calculated using thresholds defined by SO badges.", "### Languages\n\nEnglish", "## Dataset Structure\n\ntitle: string,\nbody: string,\nlabel: int", "### Data Splits\n\nThe split is 40/40/20, where classes have been balaned to be around the same size.", "## Dataset Creation\n\nThe data set was extracted and labeled with the following query in BigQuery:", "### Source Data\n\nThe data was extracted from the Big Query public dataset: 'bigquery-public-data.stackoverflow.posts_questions'", "#### Initial Data Collection and Normalization\n\nThe original dataset contained high class imbalance:\n\nlabel\tcount\n0\t977424\n1\t2401534\n2\t3418179\n3\t16222990\nGrand Total\t23020127\n\nThe data was sampled from each class to have around the same amount of records on every class.", "### Contributions\n\nThanks to @pacofvf for adding this dataset." ]

[ "TAGS\n#task_categories-text-classification #task_ids-multi-class-classification #annotations_creators-machine-generated #language_creators-found #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-English #license-apache-2.0 #stackoverflow #technical questions #region-us \n", "# Dataset Card for [Stackoverflow Post Questions]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Languages\n- Dataset Structure\n - Data Splits\n- Dataset Creation\n - Source Data\n - Contributions", "## Dataset Description\n\nCompanies that sell Open-source software tools usually hire an army of Customer representatives to try to answer every question asked about their tool. The first step in this process \nis the prioritization of the question. The classification scale usually consists of 4 values, P0, P1, P2, and P3, with different meanings across every participant in the industry. On \nthe other hand, every software developer in the world has dealt with Stack Overflow (SO); the amount of shared knowledge there is incomparable to any other website. Questions in SO are \nusually annotated and curated by thousands of people, providing metadata about the quality of the question. This dataset aims to provide an accurate prioritization for programming \nquestions.", "### Dataset Summary\n\nThe dataset contains the title and body of stackoverflow questions and a label value(0,1,2,3) that was calculated using thresholds defined by SO badges.", "### Languages\n\nEnglish", "## Dataset Structure\n\ntitle: string,\nbody: string,\nlabel: int", "### Data Splits\n\nThe split is 40/40/20, where classes have been balaned to be around the same size.", "## Dataset Creation\n\nThe data set was extracted and labeled with the following query in BigQuery:", "### Source Data\n\nThe data was extracted from the Big Query public dataset: 'bigquery-public-data.stackoverflow.posts_questions'", "#### Initial Data Collection and Normalization\n\nThe original dataset contained high class imbalance:\n\nlabel\tcount\n0\t977424\n1\t2401534\n2\t3418179\n3\t16222990\nGrand Total\t23020127\n\nThe data was sampled from each class to have around the same amount of records on every class.", "### Contributions\n\nThanks to @pacofvf for adding this dataset." ]

[ "region:us" ]

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

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

[ "region:us" ]

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

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

[ "region:us" ]

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

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

[ "region:us" ]

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

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

[ "region:us" ]

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

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

[ "region:us" ]

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

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

[ "region:us" ]

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[ "# Dataset Card for \"amazon-shoe-reviews\"\n\nMore Information needed" ]

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

[ "task_categories:token-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:mit", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for [Dataset Name]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @github-username for adding this dataset." ]

[ "TAGS\n#task_categories-token-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-mit #region-us \n", "# Dataset Card for [Dataset Name]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @github-username for adding this dataset." ]

[ "task_categories:image-classification", "annotations_creators:expert-generated", "annotations_creators:machine-generated", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:mit", "nailbiting", "image", "preprocesses", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for Nail Biting Classification", "## 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\n- Homepage: URL\n- Repository: URL\n- Point of Contact: alecsharpie@URL", "### Dataset Summary\n\nA binary image dataset for classifying nailbiting. Images are cropped to only show the mouth area.\nShould contain edge cases such as drinking water, talking on the phone, scratching chin etc.. all in \"no biting\" category", "## Dataset Structure", "### Data Instances\n\n - 7147 Images\n - 14879790 bytes total\n - 12332617 bytes download", "### Data Fields\n\n128 x 64 (w x h, pixels)\nBlack and white\n\nLabels\n - '0': biting\n - '1': no_biting", "### Data Splits\n\n- train: 6629 (11965737 bytes)\n- test: 1471 (2914053 bytes)", "## Dataset Creation", "### Curation Rationale\n\nI wanted to create a notification system to help me stop biting my nails. It needed to contain lots of possible no-biting scenarios. eg talking on the phone", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe data was scraped from stock images sites and photos of myself were taken with my webcam. \nMTCNN (URL was then used to crop the images down to only the show the mouth area\nThe images were then converted to a black & white colour scheme.", "### Annotations", "#### Annotation process\n\nDuring the scraping process images were labelled with a description, which I then manually sanity checked. I labelled the ones of me manually.", "#### Who are the annotators?\n\nAlec Sharp", "## Considerations for Using the Data", "### Discussion of Biases & Limitations\n\nTried to make the dataset diverse in terms of age and skin tone. Although, this dataset contains a large number of images of one subject (me) so is biased towards lower quality webcam pictures of a white male with a short beard.", "### Dataset Curators\n\nAlec Sharp", "### Licensing Information\n\nMIT", "### Contributions\n\nThanks to @alecsharpie for adding this dataset." ]

[ "TAGS\n#task_categories-image-classification #annotations_creators-expert-generated #annotations_creators-machine-generated #size_categories-1K<n<10K #source_datasets-original #language-English #license-mit #nailbiting #image #preprocesses #region-us \n", "# Dataset Card for Nail Biting Classification", "## 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\n- Homepage: URL\n- Repository: URL\n- Point of Contact: alecsharpie@URL", "### Dataset Summary\n\nA binary image dataset for classifying nailbiting. Images are cropped to only show the mouth area.\nShould contain edge cases such as drinking water, talking on the phone, scratching chin etc.. all in \"no biting\" category", "## Dataset Structure", "### Data Instances\n\n - 7147 Images\n - 14879790 bytes total\n - 12332617 bytes download", "### Data Fields\n\n128 x 64 (w x h, pixels)\nBlack and white\n\nLabels\n - '0': biting\n - '1': no_biting", "### Data Splits\n\n- train: 6629 (11965737 bytes)\n- test: 1471 (2914053 bytes)", "## Dataset Creation", "### Curation Rationale\n\nI wanted to create a notification system to help me stop biting my nails. It needed to contain lots of possible no-biting scenarios. eg talking on the phone", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe data was scraped from stock images sites and photos of myself were taken with my webcam. \nMTCNN (URL was then used to crop the images down to only the show the mouth area\nThe images were then converted to a black & white colour scheme.", "### Annotations", "#### Annotation process\n\nDuring the scraping process images were labelled with a description, which I then manually sanity checked. I labelled the ones of me manually.", "#### Who are the annotators?\n\nAlec Sharp", "## Considerations for Using the Data", "### Discussion of Biases & Limitations\n\nTried to make the dataset diverse in terms of age and skin tone. Although, this dataset contains a large number of images of one subject (me) so is biased towards lower quality webcam pictures of a white male with a short beard.", "### Dataset Curators\n\nAlec Sharp", "### Licensing Information\n\nMIT", "### Contributions\n\nThanks to @alecsharpie for adding this dataset." ]

[ "task_categories:token-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:mit", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for [Dataset Name]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @github-username for adding this dataset." ]

[ "TAGS\n#task_categories-token-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-mit #region-us \n", "# Dataset Card for [Dataset Name]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @github-username for adding this dataset." ]

[ "source_datasets:extended|go_emotions", "license:openrail", "region:us" ]

[]

[]

[ "# Dataset version 2\n\nWork in progress..." ]

[ "TAGS\n#source_datasets-extended|go_emotions #license-openrail #region-us \n", "# Dataset version 2\n\nWork in progress..." ]

[ "region:us" ]

[]

[]

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

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

[ "annotations_creators:no-annotation", "size_categories:1K<n<10K", "Artificial Intelligence", "Machine Learning", "Computational Geometry", "region:us" ]

[]

[]

[ "# ShapeNet SDF Sample Dataset\n\nThis is a subset of the ShapeNet SDF Dataset provided by the ShapeGan Project.<br>\nOnly Uniform SDF samples are included.", "### Contents\n\nThe dataset contains 8,320 data samples. Each data sample contains 200,000 uniformly distributed points and their corresponding SDF values.<br>\nThe dataset contains three shape classes:\n\n* Airplanes (2156 samples)\n* Chairs (4189 samples)\n* Sofas (1975 samples)" ]

[ "TAGS\n#annotations_creators-no-annotation #size_categories-1K<n<10K #Artificial Intelligence #Machine Learning #Computational Geometry #region-us \n", "# ShapeNet SDF Sample Dataset\n\nThis is a subset of the ShapeNet SDF Dataset provided by the ShapeGan Project.<br>\nOnly Uniform SDF samples are included.", "### Contents\n\nThe dataset contains 8,320 data samples. Each data sample contains 200,000 uniformly distributed points and their corresponding SDF values.<br>\nThe dataset contains three shape classes:\n\n* Airplanes (2156 samples)\n* Chairs (4189 samples)\n* Sofas (1975 samples)" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"laion-hd-subset\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:other", "task_categories:graph-ml", "annotations_creators:machine-generated", "language_creators:machine-generated", "bio", "bio-chem", "molnet", "molecule-net", "biophysics", "arxiv:1703.00564", "region:us" ]

[ "1703.00564" ]

[]

[ "## Table of Contents\n- Dataset Description\n - Dataset Summary\n- Dataset Structure\n - Data Fields\n - Data Splits\n- Additional Information\n - Citation Information\n - Contributions", "# Dataset Description\n- Homepage: URL\n- Repository: URL\n- Paper: URL", "## Dataset Summary\n'tox21_SRp53' is a dataset included in MoleculeNet. The \"Toxicology in the 21st Century\" (Tox21) initiative created a public database measuring toxicity of compounds, which has been used in the 2014 Tox21 Data Challenge. This dataset contains qualitative toxicity measurements for 8k compounds on 12 different targets, including nuclear receptors and stress response pathways.", "# Dataset Structure", "## Data Fields\nEach split contains\n* 'smiles': the SMILES representation of a molecule\n* 'selfies': the SELFIES representation of a molecule\n* 'target': Measured results (Active/Inactive) for bioassays", "## Data Splits\nThe dataset is split into an 80/10/10 train/valid/test split using random split.", "# Additional Information", "## Contributions\nThanks to @SauravMaheshkar and @zanussbaum for adding this dataset" ]

[ "TAGS\n#task_categories-other #task_categories-graph-ml #annotations_creators-machine-generated #language_creators-machine-generated #bio #bio-chem #molnet #molecule-net #biophysics #arxiv-1703.00564 #region-us \n", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n- Dataset Structure\n - Data Fields\n - Data Splits\n- Additional Information\n - Citation Information\n - Contributions", "# Dataset Description\n- Homepage: URL\n- Repository: URL\n- Paper: URL", "## Dataset Summary\n'tox21_SRp53' is a dataset included in MoleculeNet. The \"Toxicology in the 21st Century\" (Tox21) initiative created a public database measuring toxicity of compounds, which has been used in the 2014 Tox21 Data Challenge. This dataset contains qualitative toxicity measurements for 8k compounds on 12 different targets, including nuclear receptors and stress response pathways.", "# Dataset Structure", "## Data Fields\nEach split contains\n* 'smiles': the SMILES representation of a molecule\n* 'selfies': the SELFIES representation of a molecule\n* 'target': Measured results (Active/Inactive) for bioassays", "## Data Splits\nThe dataset is split into an 80/10/10 train/valid/test split using random split.", "# Additional Information", "## Contributions\nThanks to @SauravMaheshkar and @zanussbaum for adding this dataset" ]

[ "language:de", "region:us" ]

[]

[ "de" ]

[ "# Germeval Task 2017: Shared Task on Aspect-based Sentiment in Social Media Customer Feedback\n\nIn the connected, modern world, customer feedback is a valuable source for insights on the quality of products or services. This feedback allows other customers to benefit from the experiences of others and enables businesses to react on requests, complaints or recommendations. However, the more people use a product or service, the more feedback is generated, which results in the major challenge of analyzing huge amounts of feedback in an efficient, but still meaningful way.\n\nThus, we propose a shared task on automatically analyzing customer reviews about “Deutsche Bahn” - the german public train operator with about two billion passengers each year.\n\nExample: \n\n> “RT @XXX: Da hört jemand in der Bahn so laut ‘700 Main Street’ durch seine Kopfhörer, dass ich mithören kann. :( :( :(“\n\nAs shown in the example, insights from reviews can be derived on different granularities. The review contains a general evaluation of the travel (The customer disliked the travel). Furthermore, the review evaluates a dedicated aspect of the train travel (“laut” → customer did not like the noise level).\n\nConsequently, we frame the task as aspect-based sentiment analysis with four sub tasks:", "## Data format", "## Links\n\n- URL\n- URL", "## How to cite" ]

[ "TAGS\n#language-German #region-us \n", "# Germeval Task 2017: Shared Task on Aspect-based Sentiment in Social Media Customer Feedback\n\nIn the connected, modern world, customer feedback is a valuable source for insights on the quality of products or services. This feedback allows other customers to benefit from the experiences of others and enables businesses to react on requests, complaints or recommendations. However, the more people use a product or service, the more feedback is generated, which results in the major challenge of analyzing huge amounts of feedback in an efficient, but still meaningful way.\n\nThus, we propose a shared task on automatically analyzing customer reviews about “Deutsche Bahn” - the german public train operator with about two billion passengers each year.\n\nExample: \n\n> “RT @XXX: Da hört jemand in der Bahn so laut ‘700 Main Street’ durch seine Kopfhörer, dass ich mithören kann. :( :( :(“\n\nAs shown in the example, insights from reviews can be derived on different granularities. The review contains a general evaluation of the travel (The customer disliked the travel). Furthermore, the review evaluates a dedicated aspect of the train travel (“laut” → customer did not like the noise level).\n\nConsequently, we frame the task as aspect-based sentiment analysis with four sub tasks:", "## Data format", "## Links\n\n- URL\n- URL", "## How to cite" ]

[ "task_categories:token-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:mit", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for [Dataset Name]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @github-username for adding this dataset." ]

[ "TAGS\n#task_categories-token-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-mit #region-us \n", "# Dataset Card for [Dataset Name]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @github-username for adding this dataset." ]

[ "task_categories:token-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:mit", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for [Dataset Name]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @github-username for adding this dataset." ]

[ "TAGS\n#task_categories-token-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-mit #region-us \n", "# Dataset Card for [Dataset Name]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @github-username for adding this dataset." ]

[ "annotations_creators:crowdsourced", "language_creators:さくらみこ", "language_creators:hololive production", "multilinguality:monolingual", "language:ja", "license:other", "region:us" ]

[]

[ "ja" ]

[ "# Elite Voice Project\n\nこれはホロライブ所属Vtuberさくらみこ氏の声をデータセット化し音声認識などで活用できるようにする事を目的とした非公式プロジェクトです。\n\n---", "# LICENSEについて", "## データセット内の音声データ\n\nすべてのデータは、hololive productionの二次創作ガイドラインに準拠する形で利用されています。\n\nこれらのデータの著作権はカバー株式会社等が保有しており、リポジトリオーナー、コントリビューターは一切の権利を有しておりません。\n\n---", "# 当プロジェクトへのご協力\n\n当プロジェクトは皆様のご協力を心より歓迎いたします。 以下の方法をご一読いただき、そのうえでプルリクエストをお願い致します。", "## 始める前に\n\nhololive productionの二次創作ガイドラインを必ずお読みください。\n\n---", "## 音声データの追加\n\n基本的には、データセットに追加したい音声データを'audio_raw'ディレクトリ内の所定のディレクトリへ追加していただく形になります。\n\ngit等を使用して音声データを追加する場合にはgit-lfsが必要になります。事前にgit-lfsのインストールをお願い致します。\n\n'audio_raw'ディレクトリ内の構造は以下の通りです。\n\n\n\n- 'youtube', 'twitch', 'twitch'ディレクトリはデータセットに追加するデータの切り出し元のプラットフォーム名です。\n\n- 'train'と'test'ディレクトリについてですが、OpenAI Whisper等の学習を行う際にtrainとtest、2種類のデータが必要になるために存在しています。\n- 'train'と'test'には同じ配信から切り出したデータを入れても良いですが全く同じデータを入れることは辞めてください。正確に学習を行うことができなくなります。\n\n- '<ID>'には音声データを切り出す元になった配信等のIDが入ります。\n - YouTubeであれば'URL'の'X9zw0QF12Kc'がディレクトリ名となります。\n - Twitterであれば'URL'の'1lPKqmyQPOAKb'がディレクトリ名となります。\n - Twitchであれば'URL'の'824387510'がディレクトリ名となります。\n\n- '<ID>'ディレクトリ内には連番でmp3形式の音声ファイルを入れてください。\n - 音声データは30秒以内である必要があります。\n - BGMやSE、ノイズ等が含まれる音声データは避けてください。\n - あまりに短すぎる音声データは避けてください。(既にデータセットにある音声は削除予定です。)\n - 出来る限り30秒に近い音声データを入れていただけると助かります。\n - 文脈のある音声データが望ましいです。\n - 英語の音声は避けてください。\n\n---", "## 書き起こしテキストデータの追加\n\n基本的には、データセットに追加したい音声データの書き起こしテキストデータを'transcript_raw'ディレクトリ内の所定のディレクトリへ追加していただく形になります。\n\n'transcript_raw'ディレクトリ内の構造は以下の通りです。\n\n\n\n- 'youtube', 'twitch', 'twitch'ディレクトリはデータセットに追加するデータの切り出し元のプラットフォーム名です。\n\n- '<ID>'には音声データを切り出す元になった配信等のIDが入ります。\n - YouTubeであれば'URL'の'X9zw0QF12Kc'がディレクトリ名となります。\n - Twitterであれば'URL'の'1lPKqmyQPOAKb'がディレクトリ名となります。\n - Twitchであれば'URL'の'824387510'がディレクトリ名となります。\n\n- '<ID>.csv'について\n - 必ず'audio_raw'に追加した音声データに対応した書き起こしテキストを追加する必要があります。\n - 句読点、!,?等は正確に入れてください。\n - 半角英数字記号を使用してください。(!, ?, 1等)\n - 漢数字は避けてください。\n - csvファイルの1行目は必ず'path,sentence'で始めてください。\n - 書き起こしテキストはWhisper等で一度書き起こしたものを修正して行く方法を推奨致します。", "### CSVファイルの記述例" ]

[ "TAGS\n#annotations_creators-crowdsourced #language_creators-さくらみこ #language_creators-hololive production #multilinguality-monolingual #language-Japanese #license-other #region-us \n", "# Elite Voice Project\n\nこれはホロライブ所属Vtuberさくらみこ氏の声をデータセット化し音声認識などで活用できるようにする事を目的とした非公式プロジェクトです。\n\n---", "# LICENSEについて", "## データセット内の音声データ\n\nすべてのデータは、hololive productionの二次創作ガイドラインに準拠する形で利用されています。\n\nこれらのデータの著作権はカバー株式会社等が保有しており、リポジトリオーナー、コントリビューターは一切の権利を有しておりません。\n\n---", "# 当プロジェクトへのご協力\n\n当プロジェクトは皆様のご協力を心より歓迎いたします。 以下の方法をご一読いただき、そのうえでプルリクエストをお願い致します。", "## 始める前に\n\nhololive productionの二次創作ガイドラインを必ずお読みください。\n\n---", "## 音声データの追加\n\n基本的には、データセットに追加したい音声データを'audio_raw'ディレクトリ内の所定のディレクトリへ追加していただく形になります。\n\ngit等を使用して音声データを追加する場合にはgit-lfsが必要になります。事前にgit-lfsのインストールをお願い致します。\n\n'audio_raw'ディレクトリ内の構造は以下の通りです。\n\n\n\n- 'youtube', 'twitch', 'twitch'ディレクトリはデータセットに追加するデータの切り出し元のプラットフォーム名です。\n\n- 'train'と'test'ディレクトリについてですが、OpenAI Whisper等の学習を行う際にtrainとtest、2種類のデータが必要になるために存在しています。\n- 'train'と'test'には同じ配信から切り出したデータを入れても良いですが全く同じデータを入れることは辞めてください。正確に学習を行うことができなくなります。\n\n- '<ID>'には音声データを切り出す元になった配信等のIDが入ります。\n - YouTubeであれば'URL'の'X9zw0QF12Kc'がディレクトリ名となります。\n - Twitterであれば'URL'の'1lPKqmyQPOAKb'がディレクトリ名となります。\n - Twitchであれば'URL'の'824387510'がディレクトリ名となります。\n\n- '<ID>'ディレクトリ内には連番でmp3形式の音声ファイルを入れてください。\n - 音声データは30秒以内である必要があります。\n - BGMやSE、ノイズ等が含まれる音声データは避けてください。\n - あまりに短すぎる音声データは避けてください。(既にデータセットにある音声は削除予定です。)\n - 出来る限り30秒に近い音声データを入れていただけると助かります。\n - 文脈のある音声データが望ましいです。\n - 英語の音声は避けてください。\n\n---", "## 書き起こしテキストデータの追加\n\n基本的には、データセットに追加したい音声データの書き起こしテキストデータを'transcript_raw'ディレクトリ内の所定のディレクトリへ追加していただく形になります。\n\n'transcript_raw'ディレクトリ内の構造は以下の通りです。\n\n\n\n- 'youtube', 'twitch', 'twitch'ディレクトリはデータセットに追加するデータの切り出し元のプラットフォーム名です。\n\n- '<ID>'には音声データを切り出す元になった配信等のIDが入ります。\n - YouTubeであれば'URL'の'X9zw0QF12Kc'がディレクトリ名となります。\n - Twitterであれば'URL'の'1lPKqmyQPOAKb'がディレクトリ名となります。\n - Twitchであれば'URL'の'824387510'がディレクトリ名となります。\n\n- '<ID>.csv'について\n - 必ず'audio_raw'に追加した音声データに対応した書き起こしテキストを追加する必要があります。\n - 句読点、!,?等は正確に入れてください。\n - 半角英数字記号を使用してください。(!, ?, 1等)\n - 漢数字は避けてください。\n - csvファイルの1行目は必ず'path,sentence'で始めてください。\n - 書き起こしテキストはWhisper等で一度書き起こしたものを修正して行く方法を推奨致します。", "### CSVファイルの記述例" ]

[ "task_categories:text-classification", "task_ids:natural-language-inference", "task_ids:multi-input-text-classification", "multilinguality:monolingual", "language:ja", "license:cc-by-sa-4.0", "natural-language-inference", "nli", "jsnli", "region:us" ]

[]

[ "ja" ]

[ "### Dataset Summary\n\n\n日本語 SNLI(JSNLI) データセット - KUROHASHI-CHU-MURAWAKI LAB より：\n\n\n\n> \n> 本データセットは自然言語推論 (NLI) の標準的ベンチマークである SNLI を日本語に翻訳したものです。\n> \n> \n>", "### Dataset Preprocessing", "### Supported Tasks and Leaderboards", "### Languages\n\n\n注釈はすべて日本語を主要言語としています。\n\n\nDataset Structure\n-----------------\n\n\n\n> \n> データセットは TSV フォーマットで、各行がラベル、前提、仮説の三つ組を表します。前提、仮説は JUMAN++ によって形態素分割されています。以下に例をあげます。\n> \n> \n>", "### Data Instances", "### Data Fields", "### 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\n\n\n\n> \n> SNLI に機械翻訳を適用した後、評価データにクラウドソーシングによる正確なフィルタリング、学習データに計算機による自動フィルタリングを施すことで構築されています。\n> データセットは学習データを全くフィルタリングしていないものと、フィルタリングした中で最も精度が高かったものの 2 種類を公開しています。データサイズは、フィルタリング前の学習データが 548,014 ペア、フィルタリング後の学習データが 533,005 ペア、評価データは 3,916 ペアです。詳細は参考文献を参照してください。\n> \n> \n>", "#### 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----------------------\n\n\n\n> \n> 本データセットに関するご質問は nl-resource あっと URL.i.URL 宛にお願いいたします。\n> \n> \n>", "### Dataset Curators", "### Licensing Information\n\n\n\n> \n> このデータセットのライセンスは、SNLI のライセンスと同じ CC BY-SA 4.0 に従います。SNLI に関しては参考文献を参照してください。\n> \n> \n>", "### Contributions\n\n\nJSNLI データセットを公開してくださった吉越 卓見さま，河原 大輔さま，黒橋 禎夫さまに心から感謝します。" ]

[ "TAGS\n#task_categories-text-classification #task_ids-natural-language-inference #task_ids-multi-input-text-classification #multilinguality-monolingual #language-Japanese #license-cc-by-sa-4.0 #natural-language-inference #nli #jsnli #region-us \n", "### Dataset Summary\n\n\n日本語 SNLI(JSNLI) データセット - KUROHASHI-CHU-MURAWAKI LAB より：\n\n\n\n> \n> 本データセットは自然言語推論 (NLI) の標準的ベンチマークである SNLI を日本語に翻訳したものです。\n> \n> \n>", "### Dataset Preprocessing", "### Supported Tasks and Leaderboards", "### Languages\n\n\n注釈はすべて日本語を主要言語としています。\n\n\nDataset Structure\n-----------------\n\n\n\n> \n> データセットは TSV フォーマットで、各行がラベル、前提、仮説の三つ組を表します。前提、仮説は JUMAN++ によって形態素分割されています。以下に例をあげます。\n> \n> \n>", "### Data Instances", "### Data Fields", "### 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\n\n\n\n> \n> SNLI に機械翻訳を適用した後、評価データにクラウドソーシングによる正確なフィルタリング、学習データに計算機による自動フィルタリングを施すことで構築されています。\n> データセットは学習データを全くフィルタリングしていないものと、フィルタリングした中で最も精度が高かったものの 2 種類を公開しています。データサイズは、フィルタリング前の学習データが 548,014 ペア、フィルタリング後の学習データが 533,005 ペア、評価データは 3,916 ペアです。詳細は参考文献を参照してください。\n> \n> \n>", "#### 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----------------------\n\n\n\n> \n> 本データセットに関するご質問は nl-resource あっと URL.i.URL 宛にお願いいたします。\n> \n> \n>", "### Dataset Curators", "### Licensing Information\n\n\n\n> \n> このデータセットのライセンスは、SNLI のライセンスと同じ CC BY-SA 4.0 に従います。SNLI に関しては参考文献を参照してください。\n> \n> \n>", "### Contributions\n\n\nJSNLI データセットを公開してくださった吉越 卓見さま，河原 大輔さま，黒橋 禎夫さまに心から感謝します。" ]

[ "task_categories:other", "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "license:mit", "bio", "bio-chem", "molnet", "molecule-net", "biophysics", "arxiv:1703.00564", "region:us" ]

[ "1703.00564" ]

[]

[ "# Dataset Card for clintox", "## 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: URL\n- Repository: URL\n- Paper: URL", "### Dataset Summary\n\n'clintox' is a dataset included in MoleculeNet. Qualitative data of drugs approved by the FDA and those that have failed clinical trials for toxicity reasons. This uses the 'CT_TOX' task.\n\nNote, there was one molecule in the training set that could not be converted to SELFIES ('*C(=O)C@HOCCOC)NC(=O)OCCOC')", "## Dataset Structure", "### Data Fields\n\nEach split contains\n\n* 'smiles': the SMILES representation of a molecule\n* 'selfies': the SELFIES representation of a molecule\n* 'target': clinical trial toxicity (or absence of toxicity)", "### Data Splits\n\nThe dataset is split into an 80/10/10 train/valid/test split using scaffold split.", "### Source Data", "#### Initial Data Collection and Normalization\n\nData was originially generated by the Pande Group at Standford", "### Licensing Information\n\nThis dataset was originally released under an MIT license", "### Contributions\n\nThanks to @zanussbaum for adding this dataset." ]

[ "TAGS\n#task_categories-other #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-1K<n<10K #license-mit #bio #bio-chem #molnet #molecule-net #biophysics #arxiv-1703.00564 #region-us \n", "# Dataset Card for clintox", "## 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: URL\n- Repository: URL\n- Paper: URL", "### Dataset Summary\n\n'clintox' is a dataset included in MoleculeNet. Qualitative data of drugs approved by the FDA and those that have failed clinical trials for toxicity reasons. This uses the 'CT_TOX' task.\n\nNote, there was one molecule in the training set that could not be converted to SELFIES ('*C(=O)C@HOCCOC)NC(=O)OCCOC')", "## Dataset Structure", "### Data Fields\n\nEach split contains\n\n* 'smiles': the SMILES representation of a molecule\n* 'selfies': the SELFIES representation of a molecule\n* 'target': clinical trial toxicity (or absence of toxicity)", "### Data Splits\n\nThe dataset is split into an 80/10/10 train/valid/test split using scaffold split.", "### Source Data", "#### Initial Data Collection and Normalization\n\nData was originially generated by the Pande Group at Standford", "### Licensing Information\n\nThis dataset was originally released under an MIT license", "### Contributions\n\nThanks to @zanussbaum for adding this dataset." ]

[ "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: pszemraj/led-large-book-summary-continued-r1\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj 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: pszemraj/led-large-book-summary-continued-r1\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "task_categories:other", "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:n<1K", "license:mit", "bio", "bio-chem", "molnet", "molecule-net", "biophysics", "arxiv:1703.00564", "region:us" ]

[ "1703.00564" ]

[]

[ "# Dataset Card for delaney", "## 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: URL\n- Repository: URL\n- Paper: URL", "### Dataset Summary\n\n'delaney' (aka. 'ESOL') is a dataset included in MoleculeNet. Water solubility data(log solubility in mols per litre) for common organic small molecules.", "## Dataset Structure", "### Data Fields\n\nEach split contains\n\n* 'smiles': the SMILES representation of a molecule\n* 'selfies': the SELFIES representation of a molecule\n* 'target': log solubility in mols per litre", "### Data Splits\n\nThe dataset is split into an 80/10/10 train/valid/test split using scaffold split.", "### Source Data", "#### Initial Data Collection and Normalization\n\nData was originially generated by the Pande Group at Standford", "### Licensing Information\n\nThis dataset was originally released under an MIT license", "### Contributions\n\nThanks to @zanussbaum for adding this dataset." ]

[ "TAGS\n#task_categories-other #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-n<1K #license-mit #bio #bio-chem #molnet #molecule-net #biophysics #arxiv-1703.00564 #region-us \n", "# Dataset Card for delaney", "## 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: URL\n- Repository: URL\n- Paper: URL", "### Dataset Summary\n\n'delaney' (aka. 'ESOL') is a dataset included in MoleculeNet. Water solubility data(log solubility in mols per litre) for common organic small molecules.", "## Dataset Structure", "### Data Fields\n\nEach split contains\n\n* 'smiles': the SMILES representation of a molecule\n* 'selfies': the SELFIES representation of a molecule\n* 'target': log solubility in mols per litre", "### Data Splits\n\nThe dataset is split into an 80/10/10 train/valid/test split using scaffold split.", "### Source Data", "#### Initial Data Collection and Normalization\n\nData was originially generated by the Pande Group at Standford", "### Licensing Information\n\nThis dataset was originally released under an MIT license", "### Contributions\n\nThanks to @zanussbaum for adding this dataset." ]

[ "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: Question Answering\n* Model: Aiyshwariya/bert-finetuned-squad\n* Dataset: squad\n* Config: plain_text\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @bestuh 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: Question Answering\n* Model: Aiyshwariya/bert-finetuned-squad\n* Dataset: squad\n* Config: plain_text\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @bestuh for evaluating this model." ]

[ "task_categories:other", "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:n<1K", "license:mit", "bio", "bio-chem", "molnet", "molecule-net", "biophysics", "arxiv:1703.00564", "arxiv:2209.01712", "region:us" ]

[ "1703.00564", "2209.01712" ]

[]

[ "# Dataset Card for clearance", "## 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: URL\n- Repository: URL\n- Paper: URL", "### Dataset Summary\n\n'clearance' is a dataset included in Chemberta-2 benchmarking.", "## Dataset Structure", "### Data Fields\n\nEach split contains\n\n* 'smiles': the SMILES representation of a molecule\n* 'selfies': the SELFIES representation of a molecule\n* 'target':", "### Data Splits\n\nThe dataset is split into an 80/10/10 train/valid/test split using scaffold split.", "### Source Data", "#### Initial Data Collection and Normalization\n\nData was originially generated by the Pande Group at Standford", "### Licensing Information\n\nThis dataset was originally released under an MIT license", "### Contributions\n\nThanks to @zanussbaum for adding this dataset." ]

[ "TAGS\n#task_categories-other #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-n<1K #license-mit #bio #bio-chem #molnet #molecule-net #biophysics #arxiv-1703.00564 #arxiv-2209.01712 #region-us \n", "# Dataset Card for clearance", "## 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: URL\n- Repository: URL\n- Paper: URL", "### Dataset Summary\n\n'clearance' is a dataset included in Chemberta-2 benchmarking.", "## Dataset Structure", "### Data Fields\n\nEach split contains\n\n* 'smiles': the SMILES representation of a molecule\n* 'selfies': the SELFIES representation of a molecule\n* 'target':", "### Data Splits\n\nThe dataset is split into an 80/10/10 train/valid/test split using scaffold split.", "### Source Data", "#### Initial Data Collection and Normalization\n\nData was originially generated by the Pande Group at Standford", "### Licensing Information\n\nThis dataset was originally released under an MIT license", "### Contributions\n\nThanks to @zanussbaum for adding this dataset." ]

[ "task_categories:other", "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "license:mit", "bio", "bio-chem", "molnet", "molecule-net", "biophysics", "arxiv:1703.00564", "region:us" ]

[ "1703.00564" ]

[]

[ "# Dataset Card for lipo", "## 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: URL\n- Repository: URL\n- Paper: URL", "### Dataset Summary\n\n'lipo' is a dataset included in MoleculeNet. It measures the experimental results of octanol/water distribution coefficient(logD at pH 7.4)", "## Dataset Structure", "### Data Fields\n\nEach split contains\n\n* 'smiles': the SMILES representation of a molecule\n* 'selfies': the SELFIES representation of a molecule\n* 'target': octanol/water distribution coefficient(logD at pH 7.4)", "### Data Splits\n\nThe dataset is split into an 80/10/10 train/valid/test split using scaffold split.", "### Source Data", "#### Initial Data Collection and Normalization\n\nData was originially generated by the Pande Group at Standford", "### Licensing Information\n\nThis dataset was originally released under an MIT license", "### Contributions\n\nThanks to @zanussbaum for adding this dataset." ]

[ "TAGS\n#task_categories-other #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-1K<n<10K #license-mit #bio #bio-chem #molnet #molecule-net #biophysics #arxiv-1703.00564 #region-us \n", "# Dataset Card for lipo", "## 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: URL\n- Repository: URL\n- Paper: URL", "### Dataset Summary\n\n'lipo' is a dataset included in MoleculeNet. It measures the experimental results of octanol/water distribution coefficient(logD at pH 7.4)", "## Dataset Structure", "### Data Fields\n\nEach split contains\n\n* 'smiles': the SMILES representation of a molecule\n* 'selfies': the SELFIES representation of a molecule\n* 'target': octanol/water distribution coefficient(logD at pH 7.4)", "### Data Splits\n\nThe dataset is split into an 80/10/10 train/valid/test split using scaffold split.", "### Source Data", "#### Initial Data Collection and Normalization\n\nData was originially generated by the Pande Group at Standford", "### Licensing Information\n\nThis dataset was originally released under an MIT license", "### Contributions\n\nThanks to @zanussbaum for adding this dataset." ]

[ "task_categories:other", "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "license:mit", "bio", "bio-chem", "molnet", "molecule-net", "biophysics", "arxiv:1703.00564", "region:us" ]

[ "1703.00564" ]

[]

[ "# Dataset Card for bbbp", "## 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: URL\n- Repository: URL\n- Paper: URL", "### Dataset Summary\n\n'bbbp' is a dataset included in MoleculeNet. This dataset has binary labels of blood-brain barrier penetration(permeability).", "## Dataset Structure", "### Data Fields\n\nEach split contains\n\n* 'smiles': the SMILES representation of a molecule\n* 'selfies': the SELFIES representation of a molecule\n* 'target': blood-brain barrier penetration(permeability)", "### Data Splits\n\nThe dataset is split into an 80/10/10 train/valid/test split using scaffold split.", "### Source Data", "#### Initial Data Collection and Normalization\n\nData was originially generated by the Pande Group at Standford", "### Licensing Information\n\nThis dataset was originally released under an MIT license", "### Contributions\n\nThanks to @zanussbaum for adding this dataset." ]

[ "TAGS\n#task_categories-other #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-1K<n<10K #license-mit #bio #bio-chem #molnet #molecule-net #biophysics #arxiv-1703.00564 #region-us \n", "# Dataset Card for bbbp", "## 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: URL\n- Repository: URL\n- Paper: URL", "### Dataset Summary\n\n'bbbp' is a dataset included in MoleculeNet. This dataset has binary labels of blood-brain barrier penetration(permeability).", "## Dataset Structure", "### Data Fields\n\nEach split contains\n\n* 'smiles': the SMILES representation of a molecule\n* 'selfies': the SELFIES representation of a molecule\n* 'target': blood-brain barrier penetration(permeability)", "### Data Splits\n\nThe dataset is split into an 80/10/10 train/valid/test split using scaffold split.", "### Source Data", "#### Initial Data Collection and Normalization\n\nData was originially generated by the Pande Group at Standford", "### Licensing Information\n\nThis dataset was originally released under an MIT license", "### Contributions\n\nThanks to @zanussbaum for adding this dataset." ]

[ "task_categories:text-retrieval", "task_ids:document-retrieval", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10M<n<100M", "source_datasets:original", "language:en", "license:cc-by-nc-sa-4.0", "notice and comment", "regulation", "government", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nOnline comment floods during public consultations have posed unique governance challenges for\nregulatory bodies seeking relevant information on proposed regulations.\nHow should regulatory bodies separate spam and fake comments from genuine submissions by the public,\nespecially when fake comments are designed to imitate ordinary citizens? How can regulatory bodies\nachieve both breadth and depth in their citations to the comment corpus? What is the best way to\nselect comments that represent the average submission and comments that supply highly specialized\ninformation?\n\n\n'fcc-comments' is an annotated version of the comment corpus from the Federal Communications Commission's\n(FCC) 2017 \"Restoring Internet Freedom\" proceeding. The source data were downloaded directly from the FCC's Electronic\nComment Filing System (ECFS) between January and February of 2019 and include raw comment text and metadata on\ncomment submissions. The comment data were processed to be in a consistent format\n(machine-readable pdf or plain text), and annotated with three types of information: whether the comment was cited in the\nagency's final order, the type of commenter (individual, interest group, business group), and whether the comment was associated with an in-person meeting.\n\n\nThe release also includes query-term and document-term matrices to facilitate keyword searches on the comment corpus.\nAn example of how these can be used with the bm25 algorithm can be found\nhere.\n\n\nDataset Structure\n-----------------\n\n\nFCC relational database (URL): The core components of the database include a table for submission metadata,\na table for attachment metadata, a table for filer metadata, and a table that contains comment text if submitted in express format.\nIn addition to these core tables, there are several derived tables specific to the analyses in the paper,\nincluding which submissions and attachments were cited in the final order, which submissions were associated with in-person meetings,\nand which submissions were associated with interest groups. Full documentation of the tables can be found in fcc\\_database.md.\n\n\nAttachments (URL): Attachments to submissions that could be converted to text via OCR and saved in machine-readable pdf format.\nThe filenames are formatted as [submission\\_id]\\_[document\\_id].pdf, where submission\\_id and document\\_id are keys in the relational database.\n\n\nSearch datasets (URL): Objects to facilitate prototyping of search algorithms on the comment corpus. Contains the following elements:", "### Data Fields\n\n\nThe following tables are available in URL:\n\n\n* comments: plain text comments associated with submissions\n\n\ncolumn: comment\\_id, type: character varying(64), description: unique id for plain text comment\ncolumn: comment\\_text, type: text, description: raw text of plain text comment\ncolumn: row\\_id, type: integer, description: row sequence for plain text comments\n\n\n* submissions: metadata for submissions\n\n\ncolumn: submission\\_id, type: character varying(20), description: unique id for submission\ncolumn: submission\\_type, type: character varying(100), description: type of submission (e.g., comment, reply, statement)\ncolumn: express\\_comment, type: numeric, description: 1 if express comment\ncolumn: date\\_received, type: date, description: date submission was received\ncolumn: contact\\_email, type: character varying(255), description: submitter email address\ncolumn: city, type: character varying(255), description: submitter city\ncolumn: address\\_line\\_1, type: character varying(255), description: submitter address line 1\ncolumn: address\\_line\\_2, type: character varying(255), description: submitter address line 2\ncolumn: state, type: character varying(255), description: submitter state\ncolumn: zip\\_code, type: character varying(50), description: submitter zip\ncolumn: comment\\_id, type: character varying(64), description: unique id for plain text comment\n\n\n* filers: names of filers associated with submissions\n\n\ncolumn: submission\\_id, type: character varying(20), description: unique id for submission\ncolumn: filer\\_name, type: character varying(250), description: name of filer associated with submission\n\n\n* documents: attachments associated with submissions\n\n\ncolumn: submission\\_id, type: character varying(20), description: unique id for submission\ncolumn: document\\_name, type: text, description: filename of attachment\ncolumn: download\\_status, type: numeric, description: status of attachment download\ncolumn: document\\_id, type: character varying(64), description: unique id for attachment\ncolumn: file\\_extension, type: character varying(4), description: file extension for attachment\n\n\n* filers\\_cited: citations from final order\n\n\ncolumn: point, type: numeric, description: paragraph number in final order\ncolumn: filer\\_name, type: character varying(250), description: name of cited filer\ncolumn: submission\\_type, type: character varying(12), description: type of submission as indicated in final order\ncolumn: page\\_numbers, type: text[], description: cited page numbers\ncolumn: cite\\_id, type: integer, description: unique id for citation\ncolumn: filer\\_id, type: character varying(250), description: id for cited filer\n\n\n* docs\\_cited: attachments associated with cited submissions\n\n\ncolumn: cite\\_id, type: numeric, description: unique id for citation\ncolumn: submission\\_id, type: character varying(20), description: unique id for submission\ncolumn: document\\_id, type: character varying(64), description: unique id for attachment\n\n\n* near\\_duplicates: lookup table for comment near-duplicates\n\n\ncolumn: target\\_document\\_id, type: unique id for target document, description: \ncolumn: duplicate\\_document\\_id, type: unique id for duplicate of target document, description: \n\n\n* exact\\_duplicates: lookup table for comment exact duplicates\n\n\ncolumn: target\\_document\\_id, type: character varying(100), description: unique id for target document\ncolumn: duplicate\\_document\\_id, type: character varying(100), description: unique id for duplicate of target document\n\n\n* in\\_person\\_exparte: submissions associated with ex parte meeting\n\n\ncolumn: submission\\_id, type: character varying(20), description: unique id for submission\n\n\n* interest\\_groups: submissions associated with interest groups\n\n\ncolumn: submission\\_id, type: character varying(20), description: unique id for submission\ncolumn: business, type: numeric, description: 1 if business group, 0 otherwise\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThe data were curated to perform information retrieval and summarization tasks as documented in URL", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe data for this study come from the FCC's Electronic Comment Filing System (ECFS) system, accessed between January and February of 2019.\nI converted the API responses into a normalized, relational database containing information on 23,951,967 submissions.\n23,938,686 \"express\" submissions contained a single plain text comment submitted directly through the comment form.\n13,821 \"standard\" submissions contained one or more comment documents submitted as attachments in various file formats.\nWhile the FCC permitted any file format for attachments, I only consider documents attached in pdf, plain text, rich text,\nand Microsoft Word file formats, and I drop submitted documents that were simply copies of the FCC’s official documents (e.g., the NPRM itself).\nUsing standard OCR software, I attempted to convert all attachments into plain text and saved them as machine-readable pdfs.", "#### Who are the source language producers?\n\n\nAll submitters of public comments during the public comment period (but see note on fake comments in considerations).", "### Annotations", "#### Annotation process\n\n\n* Citations: I consider citations from the main text of the FCC's final rule. I did not include citations to\nsupporting documents not available through ECFS (e.g., court decisions), nor did I include citations\nto submissions from prior FCC proceedings. The direct citations to filed submissions are included\nin a series of 1,186 footnotes. The FCC’s citation format typically followed a relatively standard\npattern: the name of the filer (e.g., Verizon), a description of the document (e.g., Comment), and\nat times a page number. I extracted citations from the text using regular expressions. Based on a\nrandom sample of paragraphs from the final order, the regular expressions identified 98% of eligible citations,\nwhile successfully excluding all non-citation text. In total, this produced 1,886 unique citations.\nI then identified which of the comments were cited. First, I identified all documents from the cited filer\nthat had enough pages to contain the page number cited (if provided), and, where applicable, whose filename\ncontained the moniker from the FCC’s citation (e.g., \"Reply\"). The majority of citations matched to only one\npossible comment submitted, and I identified the re- maining cited comments through manual review of the citations.\nIn this way, I was able to tag documents associated with all but three citations. When the same cited document was\nsubmitted under multiple separate submissions, I tagged all versions of the document as being cited.\n* Commenter type: Comments are labeled as mass comments if 10 or more duplicate or near-duplicate copies were\nsubmitted by individual commenters. Near-duplicates were defined as comments with non-zero identical information scores.\nTo identify the type of commenter for non-mass comments, I take advantage of the fact that the vast majority of organized\ngroups preferred standard submissions over express submissions. Any non-mass comment submitted as an express comment was\ncoded as coming from an individual. To distinguish between individuals and organizations that used standard submissions,\nI use a first name and surname database from the names dataset Python package to characterize filer names as belonging to\nindividuals or organizations. I also use the domain of the submitter’s email address to re-categorize comments as coming\nfrom organizations if they were submitted on behalf of organizations by an individual. Government officials were identified by\ntheir .gov email addresses. I manually review this procedure for mischaracterizations. After obtaining a list of organization\nnames, I manually code each one as belonging to a business group or a non-business group. Government officials writing in\ntheir official capacity were categorized as a non-business group.\n* In-person meetings: To identify which commenters held in-person meetings with the agency, I collect all comments labeled\nas an ex-parte submission in the EFCS. I manually review these submissions for mention of an in-person meeting. I label\na commenter as having held an in-person meeting if they submitted at least one ex-parte document that mentioned an in-person meeting.", "#### Who are the annotators?\n\n\nAnnotations are a combination of automated and manual review done by the author.", "### Personal and Sensitive Information\n\n\nThis dataset may contain personal and sensitive information, as there were no restrictions on what commenters could submit to\nthe agency. This dataset also contains numerous examples of profanity and spam. These comments represent what the FCC decided was\nappropriate to share publicly on their own website.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Discussion of Biases\n\n\nThis proceeding was famous for the large number of \"fake\" comments (comments impersonating ordinary citizens) submitted to the\nagency (see this report by the NY AG for more information).\nAs such, this comment corpus contains a mix of computer-generated and natural language, and there is currently no way to reliably separate\nmass comments submitted with the approval of the commenter and those submitted on behalf of the commenter without their knowledge.\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nCreativeCommons Attribution-NonCommercial-ShareAlike 4.0 International." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-document-retrieval #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10M<n<100M #source_datasets-original #language-English #license-cc-by-nc-sa-4.0 #notice and comment #regulation #government #region-us \n", "### Dataset Summary\n\n\nOnline comment floods during public consultations have posed unique governance challenges for\nregulatory bodies seeking relevant information on proposed regulations.\nHow should regulatory bodies separate spam and fake comments from genuine submissions by the public,\nespecially when fake comments are designed to imitate ordinary citizens? How can regulatory bodies\nachieve both breadth and depth in their citations to the comment corpus? What is the best way to\nselect comments that represent the average submission and comments that supply highly specialized\ninformation?\n\n\n'fcc-comments' is an annotated version of the comment corpus from the Federal Communications Commission's\n(FCC) 2017 \"Restoring Internet Freedom\" proceeding. The source data were downloaded directly from the FCC's Electronic\nComment Filing System (ECFS) between January and February of 2019 and include raw comment text and metadata on\ncomment submissions. The comment data were processed to be in a consistent format\n(machine-readable pdf or plain text), and annotated with three types of information: whether the comment was cited in the\nagency's final order, the type of commenter (individual, interest group, business group), and whether the comment was associated with an in-person meeting.\n\n\nThe release also includes query-term and document-term matrices to facilitate keyword searches on the comment corpus.\nAn example of how these can be used with the bm25 algorithm can be found\nhere.\n\n\nDataset Structure\n-----------------\n\n\nFCC relational database (URL): The core components of the database include a table for submission metadata,\na table for attachment metadata, a table for filer metadata, and a table that contains comment text if submitted in express format.\nIn addition to these core tables, there are several derived tables specific to the analyses in the paper,\nincluding which submissions and attachments were cited in the final order, which submissions were associated with in-person meetings,\nand which submissions were associated with interest groups. Full documentation of the tables can be found in fcc\\_database.md.\n\n\nAttachments (URL): Attachments to submissions that could be converted to text via OCR and saved in machine-readable pdf format.\nThe filenames are formatted as [submission\\_id]\\_[document\\_id].pdf, where submission\\_id and document\\_id are keys in the relational database.\n\n\nSearch datasets (URL): Objects to facilitate prototyping of search algorithms on the comment corpus. Contains the following elements:", "### Data Fields\n\n\nThe following tables are available in URL:\n\n\n* comments: plain text comments associated with submissions\n\n\ncolumn: comment\\_id, type: character varying(64), description: unique id for plain text comment\ncolumn: comment\\_text, type: text, description: raw text of plain text comment\ncolumn: row\\_id, type: integer, description: row sequence for plain text comments\n\n\n* submissions: metadata for submissions\n\n\ncolumn: submission\\_id, type: character varying(20), description: unique id for submission\ncolumn: submission\\_type, type: character varying(100), description: type of submission (e.g., comment, reply, statement)\ncolumn: express\\_comment, type: numeric, description: 1 if express comment\ncolumn: date\\_received, type: date, description: date submission was received\ncolumn: contact\\_email, type: character varying(255), description: submitter email address\ncolumn: city, type: character varying(255), description: submitter city\ncolumn: address\\_line\\_1, type: character varying(255), description: submitter address line 1\ncolumn: address\\_line\\_2, type: character varying(255), description: submitter address line 2\ncolumn: state, type: character varying(255), description: submitter state\ncolumn: zip\\_code, type: character varying(50), description: submitter zip\ncolumn: comment\\_id, type: character varying(64), description: unique id for plain text comment\n\n\n* filers: names of filers associated with submissions\n\n\ncolumn: submission\\_id, type: character varying(20), description: unique id for submission\ncolumn: filer\\_name, type: character varying(250), description: name of filer associated with submission\n\n\n* documents: attachments associated with submissions\n\n\ncolumn: submission\\_id, type: character varying(20), description: unique id for submission\ncolumn: document\\_name, type: text, description: filename of attachment\ncolumn: download\\_status, type: numeric, description: status of attachment download\ncolumn: document\\_id, type: character varying(64), description: unique id for attachment\ncolumn: file\\_extension, type: character varying(4), description: file extension for attachment\n\n\n* filers\\_cited: citations from final order\n\n\ncolumn: point, type: numeric, description: paragraph number in final order\ncolumn: filer\\_name, type: character varying(250), description: name of cited filer\ncolumn: submission\\_type, type: character varying(12), description: type of submission as indicated in final order\ncolumn: page\\_numbers, type: text[], description: cited page numbers\ncolumn: cite\\_id, type: integer, description: unique id for citation\ncolumn: filer\\_id, type: character varying(250), description: id for cited filer\n\n\n* docs\\_cited: attachments associated with cited submissions\n\n\ncolumn: cite\\_id, type: numeric, description: unique id for citation\ncolumn: submission\\_id, type: character varying(20), description: unique id for submission\ncolumn: document\\_id, type: character varying(64), description: unique id for attachment\n\n\n* near\\_duplicates: lookup table for comment near-duplicates\n\n\ncolumn: target\\_document\\_id, type: unique id for target document, description: \ncolumn: duplicate\\_document\\_id, type: unique id for duplicate of target document, description: \n\n\n* exact\\_duplicates: lookup table for comment exact duplicates\n\n\ncolumn: target\\_document\\_id, type: character varying(100), description: unique id for target document\ncolumn: duplicate\\_document\\_id, type: character varying(100), description: unique id for duplicate of target document\n\n\n* in\\_person\\_exparte: submissions associated with ex parte meeting\n\n\ncolumn: submission\\_id, type: character varying(20), description: unique id for submission\n\n\n* interest\\_groups: submissions associated with interest groups\n\n\ncolumn: submission\\_id, type: character varying(20), description: unique id for submission\ncolumn: business, type: numeric, description: 1 if business group, 0 otherwise\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThe data were curated to perform information retrieval and summarization tasks as documented in URL", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe data for this study come from the FCC's Electronic Comment Filing System (ECFS) system, accessed between January and February of 2019.\nI converted the API responses into a normalized, relational database containing information on 23,951,967 submissions.\n23,938,686 \"express\" submissions contained a single plain text comment submitted directly through the comment form.\n13,821 \"standard\" submissions contained one or more comment documents submitted as attachments in various file formats.\nWhile the FCC permitted any file format for attachments, I only consider documents attached in pdf, plain text, rich text,\nand Microsoft Word file formats, and I drop submitted documents that were simply copies of the FCC’s official documents (e.g., the NPRM itself).\nUsing standard OCR software, I attempted to convert all attachments into plain text and saved them as machine-readable pdfs.", "#### Who are the source language producers?\n\n\nAll submitters of public comments during the public comment period (but see note on fake comments in considerations).", "### Annotations", "#### Annotation process\n\n\n* Citations: I consider citations from the main text of the FCC's final rule. I did not include citations to\nsupporting documents not available through ECFS (e.g., court decisions), nor did I include citations\nto submissions from prior FCC proceedings. The direct citations to filed submissions are included\nin a series of 1,186 footnotes. The FCC’s citation format typically followed a relatively standard\npattern: the name of the filer (e.g., Verizon), a description of the document (e.g., Comment), and\nat times a page number. I extracted citations from the text using regular expressions. Based on a\nrandom sample of paragraphs from the final order, the regular expressions identified 98% of eligible citations,\nwhile successfully excluding all non-citation text. In total, this produced 1,886 unique citations.\nI then identified which of the comments were cited. First, I identified all documents from the cited filer\nthat had enough pages to contain the page number cited (if provided), and, where applicable, whose filename\ncontained the moniker from the FCC’s citation (e.g., \"Reply\"). The majority of citations matched to only one\npossible comment submitted, and I identified the re- maining cited comments through manual review of the citations.\nIn this way, I was able to tag documents associated with all but three citations. When the same cited document was\nsubmitted under multiple separate submissions, I tagged all versions of the document as being cited.\n* Commenter type: Comments are labeled as mass comments if 10 or more duplicate or near-duplicate copies were\nsubmitted by individual commenters. Near-duplicates were defined as comments with non-zero identical information scores.\nTo identify the type of commenter for non-mass comments, I take advantage of the fact that the vast majority of organized\ngroups preferred standard submissions over express submissions. Any non-mass comment submitted as an express comment was\ncoded as coming from an individual. To distinguish between individuals and organizations that used standard submissions,\nI use a first name and surname database from the names dataset Python package to characterize filer names as belonging to\nindividuals or organizations. I also use the domain of the submitter’s email address to re-categorize comments as coming\nfrom organizations if they were submitted on behalf of organizations by an individual. Government officials were identified by\ntheir .gov email addresses. I manually review this procedure for mischaracterizations. After obtaining a list of organization\nnames, I manually code each one as belonging to a business group or a non-business group. Government officials writing in\ntheir official capacity were categorized as a non-business group.\n* In-person meetings: To identify which commenters held in-person meetings with the agency, I collect all comments labeled\nas an ex-parte submission in the EFCS. I manually review these submissions for mention of an in-person meeting. I label\na commenter as having held an in-person meeting if they submitted at least one ex-parte document that mentioned an in-person meeting.", "#### Who are the annotators?\n\n\nAnnotations are a combination of automated and manual review done by the author.", "### Personal and Sensitive Information\n\n\nThis dataset may contain personal and sensitive information, as there were no restrictions on what commenters could submit to\nthe agency. This dataset also contains numerous examples of profanity and spam. These comments represent what the FCC decided was\nappropriate to share publicly on their own website.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Discussion of Biases\n\n\nThis proceeding was famous for the large number of \"fake\" comments (comments impersonating ordinary citizens) submitted to the\nagency (see this report by the NY AG for more information).\nAs such, this comment corpus contains a mix of computer-generated and natural language, and there is currently no way to reliably separate\nmass comments submitted with the approval of the commenter and those submitted on behalf of the commenter without their knowledge.\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nCreativeCommons Attribution-NonCommercial-ShareAlike 4.0 International." ]

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

[]

[]

[ "# Yellow Module embedding", "## Samples\n\n<img alt=\"Samples\" src=\"URL style=\"max-height: 80vh\"/>", "## About\n\nUse this Stable Diffusion embedding to achieve the Project Diva Yellow outfit", "## Usage\n\nTo use this embedding you have to download the file and put it into the \"\\stable-diffusion-webui\\embeddings\" folder \nTo use it in a prompt add __yellow_module-*__ \n\nAdd ( :1.0) around it to modify its weight", "## Additional info\n\nIn order to improve some details you can use the following words: \nPositive prompt: blue eyes, white sport shorts, yellow see-through babydoll, unzipped, yellow legwear \nNegative prompt: white babydoll, skirt, black shorts, yellow shorts", "## Included Files\n- 15000 steps Usage: yellow_module-15000", "## License\n\nThis embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies:\n\n1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content\n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here" ]

[ "TAGS\n#license-creativeml-openrail-m #region-us \n", "# Yellow Module embedding", "## Samples\n\n<img alt=\"Samples\" src=\"URL style=\"max-height: 80vh\"/>", "## About\n\nUse this Stable Diffusion embedding to achieve the Project Diva Yellow outfit", "## Usage\n\nTo use this embedding you have to download the file and put it into the \"\\stable-diffusion-webui\\embeddings\" folder \nTo use it in a prompt add __yellow_module-*__ \n\nAdd ( :1.0) around it to modify its weight", "## Additional info\n\nIn order to improve some details you can use the following words: \nPositive prompt: blue eyes, white sport shorts, yellow see-through babydoll, unzipped, yellow legwear \nNegative prompt: white babydoll, skirt, black shorts, yellow shorts", "## Included Files\n- 15000 steps Usage: yellow_module-15000", "## License\n\nThis embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies:\n\n1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content\n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here" ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

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

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

[ "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: pszemraj/pegasus-x-large-book-summary-C-r2\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj 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: pszemraj/pegasus-x-large-book-summary-C-r2\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "region:us" ]

[]

[]

[ "# Caltech-101 Webdataset (Test set only)\n\nOriginal paper: One-shot learning of object categories\n\nHomepage: URL\n\nBibtex:" ]

[ "TAGS\n#region-us \n", "# Caltech-101 Webdataset (Test set only)\n\nOriginal paper: One-shot learning of object categories\n\nHomepage: URL\n\nBibtex:" ]

[ "region:us" ]

[]

[]

[ "# CIFAR-100 Webdataset (Test set only)\n\nOriginal paper: Learning Multiple Layers of Features from Tiny Images\n\nHomepage: URL\n\nBibtex:" ]

[ "TAGS\n#region-us \n", "# CIFAR-100 Webdataset (Test set only)\n\nOriginal paper: Learning Multiple Layers of Features from Tiny Images\n\nHomepage: URL\n\nBibtex:" ]

[ "arxiv:1612.06890", "region:us" ]

[ "1612.06890" ]

[]

[ "# CLEVR Count All Webdataset (Test set only)\n\nOriginal paper: CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning\n\nHomepage: URL\n\nBibtex:" ]

[ "TAGS\n#arxiv-1612.06890 #region-us \n", "# CLEVR Count All Webdataset (Test set only)\n\nOriginal paper: CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning\n\nHomepage: URL\n\nBibtex:" ]

[ "arxiv:1612.06890", "region:us" ]

[ "1612.06890" ]

[]

[ "# CLEVR Closest Object Distance Webdataset (Test set only)\n\nOriginal paper: CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning\n\nHomepage: URL\n\nBibtex:" ]

[ "TAGS\n#arxiv-1612.06890 #region-us \n", "# CLEVR Closest Object Distance Webdataset (Test set only)\n\nOriginal paper: CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning\n\nHomepage: URL\n\nBibtex:" ]

[ "arxiv:2103.00020", "region:us" ]

[ "2103.00020" ]

[]

[ "# Country-211 (Test set only)\n\nOriginal paper: Learning Transferable Visual Models From Natural Language Supervision\n\nHomepage: URL\n\nDerived from YFCC100M: URL\n\nBibtex:" ]

[ "TAGS\n#arxiv-2103.00020 #region-us \n", "# Country-211 (Test set only)\n\nOriginal paper: Learning Transferable Visual Models From Natural Language Supervision\n\nHomepage: URL\n\nDerived from YFCC100M: URL\n\nBibtex:" ]

[ "region:us" ]

[]

[]

[ "# Describable Textures Dataset (Test set only)\n\nOriginal paper: Describing Textures in the Wild\n\nHomepage: URL\n\nBibtex:" ]

[ "TAGS\n#region-us \n", "# Describable Textures Dataset (Test set only)\n\nOriginal paper: Describing Textures in the Wild\n\nHomepage: URL\n\nBibtex:" ]

[ "arxiv:1910.04867", "region:us" ]

[ "1910.04867" ]

[]

[ "# DMLab Frames (Test set only)\n\nOriginal paper: The Visual Task Adaptation Benchmark\n\nHomepage: URL\n\nBibtex:" ]

[ "TAGS\n#arxiv-1910.04867 #region-us \n", "# DMLab Frames (Test set only)\n\nOriginal paper: The Visual Task Adaptation Benchmark\n\nHomepage: URL\n\nBibtex:" ]

[ "region:us" ]

[]

[]

[ "# dSprites Orientation (Test set only)\n\nOriginal paper: beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework\n\nHomepage: URL\n\nBibtex:" ]

[ "TAGS\n#region-us \n", "# dSprites Orientation (Test set only)\n\nOriginal paper: beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework\n\nHomepage: URL\n\nBibtex:" ]

[ "region:us" ]

[]

[]

[ "# dSprites X Position (Test set only)\n\nOriginal paper: beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework\n\nHomepage: URL\n\nBibtex:" ]

[ "TAGS\n#region-us \n", "# dSprites X Position (Test set only)\n\nOriginal paper: beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework\n\nHomepage: URL\n\nBibtex:" ]

[ "region:us" ]

[]

[]

[ "# dSprites Y Position (Test set only)\n\nOriginal paper: beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework\n\nHomepage: URL\n\nBibtex:" ]

[ "TAGS\n#region-us \n", "# dSprites Y Position (Test set only)\n\nOriginal paper: beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework\n\nHomepage: URL\n\nBibtex:" ]

[ "arxiv:1709.00029", "region:us" ]

[ "1709.00029" ]

[]

[ "# dSprites EuroSAT (Test set only)\n\nOriginal paper: EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification\n\nHomepage: URL\n\nBibtex:" ]

[ "TAGS\n#arxiv-1709.00029 #region-us \n", "# dSprites EuroSAT (Test set only)\n\nOriginal paper: EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification\n\nHomepage: URL\n\nBibtex:" ]

[ "arxiv:1306.5151", "region:us" ]

[ "1306.5151" ]

[]

[ "# FGVC-Aircraft (Test set only)\n\nOriginal paper: Fine-Grained Visual Classification of Aircraft\n\nHomepage: URL\n\nBibtex:" ]

[ "TAGS\n#arxiv-1306.5151 #region-us \n", "# FGVC-Aircraft (Test set only)\n\nOriginal paper: Fine-Grained Visual Classification of Aircraft\n\nHomepage: URL\n\nBibtex:" ]

[ "region:us" ]

[]

[]

[ "# Food-101 (Test set only)\n\nOriginal paper: Food-101 – Mining Discriminative Components with Random Forests\n\nHomepage: URL\n\nBibtex:" ]

[ "TAGS\n#region-us \n", "# Food-101 (Test set only)\n\nOriginal paper: Food-101 – Mining Discriminative Components with Random Forests\n\nHomepage: URL\n\nBibtex:" ]

[ "region:us" ]

[]

[]

[ "# GTSRB (Test set only)\n\nOriginal paper: Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition\n\nHomepage: URL\n\nBibtex:" ]

[ "TAGS\n#region-us \n", "# GTSRB (Test set only)\n\nOriginal paper: Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition\n\nHomepage: URL\n\nBibtex:" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"laion-2b-en-very-unsafe\"\n\nA version of laion5b dataset(en subset) with strictly 'unsafe' images.\nDataset was filtered to retain only examples with 'punsafe' present and > 0.9.\nHowever, due to the way nsfw detector was train, there is a significant amount of false postives.\nThere is, likely, more false positives than real unsafe images." ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"laion-2b-en-very-unsafe\"\n\nA version of laion5b dataset(en subset) with strictly 'unsafe' images.\nDataset was filtered to retain only examples with 'punsafe' present and > 0.9.\nHowever, due to the way nsfw detector was train, there is a significant amount of false postives.\nThere is, likely, more false positives than real unsafe images." ]

[ "arxiv:1905.13549", "region:us" ]

[ "1905.13549" ]

[]

[ "# ImageNet-Sketch (Test set only)\n\nOriginal paper: Learning Robust Global Representations by Penalizing Local Predictive Power\n\nHomepage: URL\n\nBibtex:" ]

[ "TAGS\n#arxiv-1905.13549 #region-us \n", "# ImageNet-Sketch (Test set only)\n\nOriginal paper: Learning Robust Global Representations by Penalizing Local Predictive Power\n\nHomepage: URL\n\nBibtex:" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for [best-selling-video-games]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @arjunpatel7 for adding this dataset." ]

[ "TAGS\n#region-us \n", "# Dataset Card for [best-selling-video-games]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @arjunpatel7 for adding this dataset." ]

[ "arxiv:1902.10811", "region:us" ]

[ "1902.10811" ]

[]

[ "# ImageNetV2 Matched Frequency (Test set only)\n\nOriginal paper: Do ImageNet Classifiers Generalize to ImageNet?\n\nHomepage: URL\n\nBibtex:" ]

[ "TAGS\n#arxiv-1902.10811 #region-us \n", "# ImageNetV2 Matched Frequency (Test set only)\n\nOriginal paper: Do ImageNet Classifiers Generalize to ImageNet?\n\nHomepage: URL\n\nBibtex:" ]

[ "arxiv:1907.07174", "region:us" ]

[ "1907.07174" ]

[]

[ "# ImageNet-A (Test set only)\n\nOriginal paper: Natural Adversarial Examples\n\nHomepage: URL\n\nBibtex:" ]

[ "TAGS\n#arxiv-1907.07174 #region-us \n", "# ImageNet-A (Test set only)\n\nOriginal paper: Natural Adversarial Examples\n\nHomepage: URL\n\nBibtex:" ]

[ "arxiv:1907.07174", "region:us" ]

[ "1907.07174" ]

[]

[ "# ImageNet-O (Test set only)\n\nOriginal paper: Natural Adversarial Examples\n\nHomepage: URL\n\nBibtex:" ]

[ "TAGS\n#arxiv-1907.07174 #region-us \n", "# ImageNet-O (Test set only)\n\nOriginal paper: Natural Adversarial Examples\n\nHomepage: URL\n\nBibtex:" ]

[ "arxiv:2006.16241", "region:us" ]

[ "2006.16241" ]

[]

[ "# ImageNet-R (Test set only)\n\nOriginal paper: The Many Faces of Robustness\n\nHomepage: URL\n\nBibtex:" ]

[ "TAGS\n#arxiv-2006.16241 #region-us \n", "# ImageNet-R (Test set only)\n\nOriginal paper: The Many Faces of Robustness\n\nHomepage: URL\n\nBibtex:" ]

[ "region:us" ]

[]

[]

[ "# KITTI Closest Vehicle Distance (Test set only)\n\nOriginal paper: Vision meets Robotics: The KITTI Dataset\n\nHomepage: URL\n\nBibtex:" ]

[ "TAGS\n#region-us \n", "# KITTI Closest Vehicle Distance (Test set only)\n\nOriginal paper: Vision meets Robotics: The KITTI Dataset\n\nHomepage: URL\n\nBibtex:" ]

[ "region:us" ]

[]

[]

[ "# MNIST (Test set only)\n\nOriginal paper: Gradient-based learning applied to document recognition\n\nHomepage (broken link): URL\n\nBibtex:" ]

[ "TAGS\n#region-us \n", "# MNIST (Test set only)\n\nOriginal paper: Gradient-based learning applied to document recognition\n\nHomepage (broken link): URL\n\nBibtex:" ]

[ "region:us" ]

[]

[]

[ "# Oxford Flowers-102 (Test set only)\n\nOriginal paper: Automated flower classification over a large number of classes\n\nHomepage: URL\n\nBibtex:" ]

[ "TAGS\n#region-us \n", "# Oxford Flowers-102 (Test set only)\n\nOriginal paper: Automated flower classification over a large number of classes\n\nHomepage: URL\n\nBibtex:" ]

[ "region:us" ]

[]

[]

[ "# Oxford-IIIT Pets (Test set only)\n\nOriginal paper: Cats and Dogs\n\nHomepage: URL\n\nBibtex:" ]

[ "TAGS\n#region-us \n", "# Oxford-IIIT Pets (Test set only)\n\nOriginal paper: Cats and Dogs\n\nHomepage: URL\n\nBibtex:" ]

[ "region:us" ]

[]

[]

[ "# Pascal VOC2007 (Test set only)\n\nOriginal paper: The PASCAL Visual Object Classes Challenge: A Retrospective\n\nHomepage: URL\n\nBibtex:" ]

[ "TAGS\n#region-us \n", "# Pascal VOC2007 (Test set only)\n\nOriginal paper: The PASCAL Visual Object Classes Challenge: A Retrospective\n\nHomepage: URL\n\nBibtex:" ]

[ "arxiv:1806.03962", "region:us" ]

[ "1806.03962" ]

[]

[ "# PatchCamelyon (Test set only)\n\nOriginal paper: Rotation Equivariant CNNs for Digital Pathology\n\nHomepage: URL\n\nBibtex:" ]

[ "TAGS\n#arxiv-1806.03962 #region-us \n", "# PatchCamelyon (Test set only)\n\nOriginal paper: Rotation Equivariant CNNs for Digital Pathology\n\nHomepage: URL\n\nBibtex:" ]

[ "task_categories:text-classification", "task_ids:text-scoring", "task_ids:semantic-similarity-scoring", "annotations_creators:automated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K>n", "source_datasets:original", "language:pt", "license:mit", "region:us" ]

[]

[ "pt" ]

[ "# Portuguese Legal Sentences\nCollection of Legal Sentences pairs from the Portuguese Supreme Court of Justice\nThe goal of this dataset was to be used for Semantic Textual Similarity\n\n- Values from 0-1: random sentences across documents\n- Values from 2-4: sentences from the same summary (implying some level of entailment)\n- Values from 4-5: sentences pairs generated through OpenAi' text-davinci-003 (\"Escreve por outras palavras:\\n\\Entrada:\\n\"+originalQuery + \"\\Saída: \\n\")", "### Contributions\n@rufimelo99\n\n\nIf you use this work, please cite:" ]

[ "TAGS\n#task_categories-text-classification #task_ids-text-scoring #task_ids-semantic-similarity-scoring #annotations_creators-automated #language_creators-found #multilinguality-monolingual #size_categories-10K>n #source_datasets-original #language-Portuguese #license-mit #region-us \n", "# Portuguese Legal Sentences\nCollection of Legal Sentences pairs from the Portuguese Supreme Court of Justice\nThe goal of this dataset was to be used for Semantic Textual Similarity\n\n- Values from 0-1: random sentences across documents\n- Values from 2-4: sentences from the same summary (implying some level of entailment)\n- Values from 4-5: sentences pairs generated through OpenAi' text-davinci-003 (\"Escreve por outras palavras:\\n\\Entrada:\\n\"+originalQuery + \"\\Saída: \\n\")", "### Contributions\n@rufimelo99\n\n\nIf you use this work, please cite:" ]

[ "task_categories:feature-extraction", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:multilingual", "size_categories:10M<n<100M", "language:ko", "language:en", "license:cc-by-4.0", "region:us" ]

[]

[ "ko", "en" ]

[ "### Lisence\n\n\nCC-BY-4.0\n\n\nData Structure\n--------------", "### Data Instance", "### Data Size\n\n\ndownload: 1.40 GiB \n\ngenerated: 3.49 GiB \n\ntotal: 4.89 GiB", "### Data Field\n\n\n* 'hash': 'int'\n* 'URL': 'string'\n* 'TEXT': 'string'\n* 'ENG TEXT': 'string', null data are dropped\n* 'WIDTH': 'int', null data are filled with 0\n* 'HEIGHT': 'int', null data are filled with 0\n* 'LICENSE': 'string'\n* 'LANGUAGE': 'string'\n* 'similarity': 'float32', CLIP similarity score, null data are filled with 0.0\n* 'pwatermark': 'float32', Probability of containing a watermark, null data are filled with 0.0\n* 'punsafe': 'float32', Probability of nsfw image, null data are filled with 0.0\n* 'AESTHETIC\\_SCORE': 'float32', null data are filled with 0.0", "### Data Splits", "### polars\n\n\npandas broke my colab session." ]

[ "TAGS\n#task_categories-feature-extraction #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-multilingual #size_categories-10M<n<100M #language-Korean #language-English #license-cc-by-4.0 #region-us \n", "### Lisence\n\n\nCC-BY-4.0\n\n\nData Structure\n--------------", "### Data Instance", "### Data Size\n\n\ndownload: 1.40 GiB \n\ngenerated: 3.49 GiB \n\ntotal: 4.89 GiB", "### Data Field\n\n\n* 'hash': 'int'\n* 'URL': 'string'\n* 'TEXT': 'string'\n* 'ENG TEXT': 'string', null data are dropped\n* 'WIDTH': 'int', null data are filled with 0\n* 'HEIGHT': 'int', null data are filled with 0\n* 'LICENSE': 'string'\n* 'LANGUAGE': 'string'\n* 'similarity': 'float32', CLIP similarity score, null data are filled with 0.0\n* 'pwatermark': 'float32', Probability of containing a watermark, null data are filled with 0.0\n* 'punsafe': 'float32', Probability of nsfw image, null data are filled with 0.0\n* 'AESTHETIC\\_SCORE': 'float32', null data are filled with 0.0", "### Data Splits", "### polars\n\n\npandas broke my colab session." ]

[ "arxiv:2005.04790", "region:us" ]

[ "2005.04790" ]

[]

[ "# The Hateful Memes Challenge README\n\nThe Hateful Memes Challenge is a dataset and benchmark created by Facebook AI to drive and measure progress on multimodal reasoning and understanding. The task focuses on detecting hate speech in multimodal memes.\n\nPlease see the paper for further details:\n\nThe Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes\nD. Kiela, H. Firooz, A. Mohan, V. Goswami, A. Singh, P. Ringshia, D. Testuggine\n\nFor more details, see also the website:\n\nURL", "# Dataset details\n\nThe files for this folder are arranged as follows:\n\nimg/ - the PNG images\nURL - the training set\ndev_seen.jsonl - the \"seen\" development set\ntest_seen.jsonl - the \"seen\" test set\ndev_unseen.jsonl - the \"unseen\" development set\ntest_unseen.jsonl - the \"unseen\" test set\n\nThe \"seen\" dataset was presented in the NeurIPS paper; the “unseen” dev and test set were released as a part of the NeurIPS 2020 competition.\n\nThe .jsonl format contains one JSON-encoded example per line, each of which has the following fields:\n\n‘text’ - the text occurring in the meme\n‘img’ - the path to the image in the img/ directory\n‘label’ - the label for the meme (0=not-hateful, 1=hateful), provided for train and dev\n\nThe metric to use is AUROC. You may also report accuracy in addition, since this is arguably more interpretable. To compute these metrics, we recommend the roc_auc_score and accuracy_score methods in sklearn.metrics, with default settings.", "# Getting started\nTo get started working on this dataset, there's an easy-to-use \"starter kit\" available in MMF: URL", "# Note on Annotator Accuracy\nAs is to be expected with a dataset of this size and nature, some of the examples in the training set have been misclassified. We are not claiming that our dataset labels are completely accurate, or even that all annotators would agree on a particular label. Misclassifications, although possible, should be very rare in the dev and seen test set, however, and we will take extra care with the unseen test set.\n\nAs a reminder, the annotations collected for this dataset were not collected using Facebook annotators and we did not employ Facebook’s hate speech policy. As such, the dataset labels do not in any way reflect Facebook’s official stance on this matter.", "# License\nThe dataset is licensed under the terms in the 'URL' file.", "# Image Attribution\nIf you wish to display example memes in your paper, please provide the following attribution:\n\n*Image is a compilation of assets, including ©Getty Image.*\n\ns\nIf you wish to cite this work, please use the following BiBTeX:", "# Contact\nIf you have any questions or comments on the dataset, please contact hatefulmemeschallenge@URL or one of the authors." ]

[ "TAGS\n#arxiv-2005.04790 #region-us \n", "# The Hateful Memes Challenge README\n\nThe Hateful Memes Challenge is a dataset and benchmark created by Facebook AI to drive and measure progress on multimodal reasoning and understanding. The task focuses on detecting hate speech in multimodal memes.\n\nPlease see the paper for further details:\n\nThe Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes\nD. Kiela, H. Firooz, A. Mohan, V. Goswami, A. Singh, P. Ringshia, D. Testuggine\n\nFor more details, see also the website:\n\nURL", "# Dataset details\n\nThe files for this folder are arranged as follows:\n\nimg/ - the PNG images\nURL - the training set\ndev_seen.jsonl - the \"seen\" development set\ntest_seen.jsonl - the \"seen\" test set\ndev_unseen.jsonl - the \"unseen\" development set\ntest_unseen.jsonl - the \"unseen\" test set\n\nThe \"seen\" dataset was presented in the NeurIPS paper; the “unseen” dev and test set were released as a part of the NeurIPS 2020 competition.\n\nThe .jsonl format contains one JSON-encoded example per line, each of which has the following fields:\n\n‘text’ - the text occurring in the meme\n‘img’ - the path to the image in the img/ directory\n‘label’ - the label for the meme (0=not-hateful, 1=hateful), provided for train and dev\n\nThe metric to use is AUROC. You may also report accuracy in addition, since this is arguably more interpretable. To compute these metrics, we recommend the roc_auc_score and accuracy_score methods in sklearn.metrics, with default settings.", "# Getting started\nTo get started working on this dataset, there's an easy-to-use \"starter kit\" available in MMF: URL", "# Note on Annotator Accuracy\nAs is to be expected with a dataset of this size and nature, some of the examples in the training set have been misclassified. We are not claiming that our dataset labels are completely accurate, or even that all annotators would agree on a particular label. Misclassifications, although possible, should be very rare in the dev and seen test set, however, and we will take extra care with the unseen test set.\n\nAs a reminder, the annotations collected for this dataset were not collected using Facebook annotators and we did not employ Facebook’s hate speech policy. As such, the dataset labels do not in any way reflect Facebook’s official stance on this matter.", "# License\nThe dataset is licensed under the terms in the 'URL' file.", "# Image Attribution\nIf you wish to display example memes in your paper, please provide the following attribution:\n\n*Image is a compilation of assets, including ©Getty Image.*\n\ns\nIf you wish to cite this work, please use the following BiBTeX:", "# Contact\nIf you have any questions or comments on the dataset, please contact hatefulmemeschallenge@URL or one of the authors." ]

[ "task_categories:automatic-speech-recognition", "whisper", "whispering", "medium", "region:us" ]

[]

[]

[ "# Dataset Card for \"whisper-transcripts-mlst\"\n\nMore Information needed" ]

[ "TAGS\n#task_categories-automatic-speech-recognition #whisper #whispering #medium #region-us \n", "# Dataset Card for \"whisper-transcripts-mlst\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# spanish-tweets-small", "## A smaller version of spanish-tweets", "## A corpus of tweets for pretraining embeddings and language models", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Creation\n - Curation Rationale\n - Source Data\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Paper: RoBERTuito: a pre-trained language model for social media text in Spanish\n- Point of Contact: jmperez (at) URL", "### Dataset Summary\n\nA big dataset of (mostly) Spanish tweets for pre-training language models (or other representations).", "### Supported Tasks and Leaderboards\n\nLanguage Modeling", "### Languages\n\nMostly Spanish, but some Portuguese, English, and other languages.", "## Dataset Structure", "### Data Fields\n\n- *tweet_id*: id of the tweet\n- *user_id*: id of the user\n- *text*: text from the tweet", "## Dataset Creation\n\nThe full process of data collection is described in the paper. Here we roughly outline the main points:\n\n- A Spritzer collection uploaded to URL dating from May 2019 was downloaded\n- From this, we only kept tweets with language metadata equal to Spanish, and mark the users who posted these messages.\n- Then, the tweetline from each of these marked users was downloaded.\n\n\nThis corpus consists of ~30M tweets.\n\nPlease note that we did not filter tweets from other languages, so you might find English, Portuguese, Catalan and other languages in the dataset (around 7/8% of the tweets are not in Spanish)" ]

[ "TAGS\n#region-us \n", "# spanish-tweets-small", "## A smaller version of spanish-tweets", "## A corpus of tweets for pretraining embeddings and language models", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Creation\n - Curation Rationale\n - Source Data\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Paper: RoBERTuito: a pre-trained language model for social media text in Spanish\n- Point of Contact: jmperez (at) URL", "### Dataset Summary\n\nA big dataset of (mostly) Spanish tweets for pre-training language models (or other representations).", "### Supported Tasks and Leaderboards\n\nLanguage Modeling", "### Languages\n\nMostly Spanish, but some Portuguese, English, and other languages.", "## Dataset Structure", "### Data Fields\n\n- *tweet_id*: id of the tweet\n- *user_id*: id of the user\n- *text*: text from the tweet", "## Dataset Creation\n\nThe full process of data collection is described in the paper. Here we roughly outline the main points:\n\n- A Spritzer collection uploaded to URL dating from May 2019 was downloaded\n- From this, we only kept tweets with language metadata equal to Spanish, and mark the users who posted these messages.\n- Then, the tweetline from each of these marked users was downloaded.\n\n\nThis corpus consists of ~30M tweets.\n\nPlease note that we did not filter tweets from other languages, so you might find English, Portuguese, Catalan and other languages in the dataset (around 7/8% of the tweets are not in Spanish)" ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

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

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

[ "arxiv:2105.08209", "region:us" ]

[ "2105.08209" ]

[]

[ "# Dataset Card for \"gov_report\"", "## GOV_REPORT\nA dataset \"consisting of about 19.5k U.S. government reports with expert-written ab- stractive summaries.3 GOVREPORT has two impor- tant features: (1) It contains significantly longer documents (9.4k words) and summaries (553 words) than existing datasets, such as PubMed and arXiv (Cohan et al., 2018) (2) Salient content is spread throughout the documents, as opposed\nto cases where summary-worthy words are\nmore heavily concentrated in specific parts of the\ndocument. These properties make GOVREPORT an\nimportant benchmark for producing long document\nsummaries with multiple paragraphs.", "## Links\n\n- Paper\n- GitHub repo\n- GDrive Folder", "## License\nLicensing info TBD. Issue raised in main repo to get info on the license from the original authors." ]

[ "TAGS\n#arxiv-2105.08209 #region-us \n", "# Dataset Card for \"gov_report\"", "## GOV_REPORT\nA dataset \"consisting of about 19.5k U.S. government reports with expert-written ab- stractive summaries.3 GOVREPORT has two impor- tant features: (1) It contains significantly longer documents (9.4k words) and summaries (553 words) than existing datasets, such as PubMed and arXiv (Cohan et al., 2018) (2) Salient content is spread throughout the documents, as opposed\nto cases where summary-worthy words are\nmore heavily concentrated in specific parts of the\ndocument. These properties make GOVREPORT an\nimportant benchmark for producing long document\nsummaries with multiple paragraphs.", "## Links\n\n- Paper\n- GitHub repo\n- GDrive Folder", "## License\nLicensing info TBD. Issue raised in main repo to get info on the license from the original authors." ]

[ "region:us" ]

[]

[]

[ "# Indonesian Dataset Apache Access" ]

[ "TAGS\n#region-us \n", "# Indonesian Dataset Apache Access" ]

[ "task_categories:text-classification", "task_ids:intent-classification", "multilinguality:multilingual", "size_categories:10K<n<100K", "source_datasets:extended|nlu_evaluation_data", "language:en", "language:de", "license:cc-by-4.0", "arxiv:1903.05566", "region:us" ]

[ "1903.05566" ]

[ "en", "de" ]

[ "# NLU Evaluation Data - English and German\nA labeled English and German language multi-domain dataset (21 domains) with 25K user utterances for human-robot interaction.\n\nThis dataset is collected and annotated for evaluating NLU services and platforms.\nThe detailed paper on this dataset can be found at URL:\nBenchmarking Natural Language Understanding Services for building Conversational Agents\n\nThe dataset builds on the annotated data of the xliuhw/NLU-Evaluation-Data\nrepository. We have added an additional column ('answer_de')\nby translating the texts in column 'answer' into German.\nThe translation was made with DeepL.", "## Creator\n\nThis data set was compiled and open sourced by Philip May\nof Deutsche Telekom.", "## Labels\nThe columns 'scenario' and 'intent' can be used for classification tasks.\nHowever, we recommend to use even more fine-grained labels.\nFor this purpose, a new label can be derived by concatenating 'scenario' and 'intent'.\nFor example, this would turn \"alarm\" and \"set\" into \"alarm_set\".", "## Dataset Quirks\nThe original dataset contains some 'NaN' values in the 'answer' column.\nThis means that there are also 'NaN' values in the translations ('answer_de' column).\nThese rows should be filtered.\n\nThe dataset also contains duplicate values.", "## Copyright\nCopyright (c) the authors of xliuhw/NLU-Evaluation-Data\\\nCopyright (c) 2022 Philip May\n\nAll data is released under the\nCreative Commons Attribution 4.0 International License (CC BY 4.0)." ]

[ "TAGS\n#task_categories-text-classification #task_ids-intent-classification #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-extended|nlu_evaluation_data #language-English #language-German #license-cc-by-4.0 #arxiv-1903.05566 #region-us \n", "# NLU Evaluation Data - English and German\nA labeled English and German language multi-domain dataset (21 domains) with 25K user utterances for human-robot interaction.\n\nThis dataset is collected and annotated for evaluating NLU services and platforms.\nThe detailed paper on this dataset can be found at URL:\nBenchmarking Natural Language Understanding Services for building Conversational Agents\n\nThe dataset builds on the annotated data of the xliuhw/NLU-Evaluation-Data\nrepository. We have added an additional column ('answer_de')\nby translating the texts in column 'answer' into German.\nThe translation was made with DeepL.", "## Creator\n\nThis data set was compiled and open sourced by Philip May\nof Deutsche Telekom.", "## Labels\nThe columns 'scenario' and 'intent' can be used for classification tasks.\nHowever, we recommend to use even more fine-grained labels.\nFor this purpose, a new label can be derived by concatenating 'scenario' and 'intent'.\nFor example, this would turn \"alarm\" and \"set\" into \"alarm_set\".", "## Dataset Quirks\nThe original dataset contains some 'NaN' values in the 'answer' column.\nThis means that there are also 'NaN' values in the translations ('answer_de' column).\nThese rows should be filtered.\n\nThe dataset also contains duplicate values.", "## Copyright\nCopyright (c) the authors of xliuhw/NLU-Evaluation-Data\\\nCopyright (c) 2022 Philip May\n\nAll data is released under the\nCreative Commons Attribution 4.0 International License (CC BY 4.0)." ]

[ "region:us" ]

[]

[]

[ "## This is Arabic news data with 9 categories in csv format\n\noriginal data link: URL\n\nData preparation and summary link: URL" ]

[ "TAGS\n#region-us \n", "## This is Arabic news data with 9 categories in csv format\n\noriginal data link: URL\n\nData preparation and summary link: URL" ]

[ "language:ar", "region:us" ]

[]

[ "ar" ]

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

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

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

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# CIFAR-10 Webdataset (Test set only)\n\nOriginal paper: Learning Multiple Layers of Features from Tiny Images\n\nHomepage: URL\n\nBibtex:" ]

[ "TAGS\n#region-us \n", "# CIFAR-10 Webdataset (Test set only)\n\nOriginal paper: Learning Multiple Layers of Features from Tiny Images\n\nHomepage: URL\n\nBibtex:" ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# lex_fridman_podcast", "## Data\n\nThis dataset provides transcribed Lex Fridman Podcast episodes that were shared on Andrej Karpathy's site.\n\nLast update was on 01.12.2022.", "## Goal \n\nThe goal of this dataset is to use it in niph - needle in podcast haystack library to make searching it easier.\n\nThere exist other similar datasets, but they lack timestamp information that makes finding stuff on Karpathy's page easier.", "### Schema", "## Acknowledgements\n\nLONG LIVE ANDREJ KARPATHY!\n\nGLORY TO OpenAI WHISPER!\n\nThank you Lex Fridman for the podcast!" ]

[ "TAGS\n#region-us \n", "# lex_fridman_podcast", "## Data\n\nThis dataset provides transcribed Lex Fridman Podcast episodes that were shared on Andrej Karpathy's site.\n\nLast update was on 01.12.2022.", "## Goal \n\nThe goal of this dataset is to use it in niph - needle in podcast haystack library to make searching it easier.\n\nThere exist other similar datasets, but they lack timestamp information that makes finding stuff on Karpathy's page easier.", "### Schema", "## Acknowledgements\n\nLONG LIVE ANDREJ KARPATHY!\n\nGLORY TO OpenAI WHISPER!\n\nThank you Lex Fridman for the podcast!" ]

[ "task_categories:image-classification", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1K<n<10K", "language:en", "license:unknown", "legal", "signatures", "CEDAR", "region:us" ]

[]

[ "en" ]

[ "# Individuality Of Handwriting (CEDAR)\n\nURL \\\nURL", "## Abstract\n\nMotivated by several rulings in United States courts concerning expert testimony in general, and handwriting testimony in particular, we undertook a study to objectively validate the hypothesis that handwriting is individual. Handwriting samples of 1,500 individuals, representative of the U.S. population with respect to gender, age, ethnic groups, etc., were obtained. Analyzing differences in handwriting was done by using computer algorithms for extracting features from scanned images of handwriting. Attributes characteristic of the handwriting were obtained, e.g., line separation, slant, character shapes, etc. These attributes, which are a subset of attributes used by forensic document examiners (FDEs), were used to quantitatively establish individuality by using machine learning approaches. Using global attributes of handwriting and very few characters in the writing, the ability to determine the writer with a high degree of confidence was established. The work is a step towards providing scientific support for admitting handwriting evidence in court. The mathematical approach and the resulting software also have the promise of aiding the FDE.\n\nSrihari SN, Cha SH, Arora H, Lee S. Individuality of handwriting. J Forensic Sci. 2002 Jul;47(4):856-72. PMID: 12136998." ]

[ "TAGS\n#task_categories-image-classification #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1K<n<10K #language-English #license-unknown #legal #signatures #CEDAR #region-us \n", "# Individuality Of Handwriting (CEDAR)\n\nURL \\\nURL", "## Abstract\n\nMotivated by several rulings in United States courts concerning expert testimony in general, and handwriting testimony in particular, we undertook a study to objectively validate the hypothesis that handwriting is individual. Handwriting samples of 1,500 individuals, representative of the U.S. population with respect to gender, age, ethnic groups, etc., were obtained. Analyzing differences in handwriting was done by using computer algorithms for extracting features from scanned images of handwriting. Attributes characteristic of the handwriting were obtained, e.g., line separation, slant, character shapes, etc. These attributes, which are a subset of attributes used by forensic document examiners (FDEs), were used to quantitatively establish individuality by using machine learning approaches. Using global attributes of handwriting and very few characters in the writing, the ability to determine the writer with a high degree of confidence was established. The work is a step towards providing scientific support for admitting handwriting evidence in court. The mathematical approach and the resulting software also have the promise of aiding the FDE.\n\nSrihari SN, Cha SH, Arora H, Lee S. Individuality of handwriting. J Forensic Sci. 2002 Jul;47(4):856-72. PMID: 12136998." ]

[ "size_categories:1K<n<10K", "license:unknown", "online handwriting", "offline handwriting", "signature", "verification", "region:us" ]

[]

[]

[ "# ICDAR 2011 Signature Verification Competition (SigComp2011)\n\nURL\n\nThe collection contains simultaneously acquired online and offline samples.\nThe collection contains offline and online signature samples. The offline dataset comprises PNG images, scanned at 400 dpi, RGB color. The online dataset comprises ascii files with the format: X, Y, Z (per line). \n\nMarcus Liwicki, Michael Blumenstein, Elisa van den Heuvel, Charles E.H. Berger, Reinoud D. Stoel, Bryan Found, Xiaohong Chen, Muhammad Imran Malik. \"SigComp11: Signature Verification Competition for On- and Offline Skilled Forgeries\", Proc. 11th Int. Conference on Document Analysis and Recognition, 2011" ]

[ "TAGS\n#size_categories-1K<n<10K #license-unknown #online handwriting #offline handwriting #signature #verification #region-us \n", "# ICDAR 2011 Signature Verification Competition (SigComp2011)\n\nURL\n\nThe collection contains simultaneously acquired online and offline samples.\nThe collection contains offline and online signature samples. The offline dataset comprises PNG images, scanned at 400 dpi, RGB color. The online dataset comprises ascii files with the format: X, Y, Z (per line). \n\nMarcus Liwicki, Michael Blumenstein, Elisa van den Heuvel, Charles E.H. Berger, Reinoud D. Stoel, Bryan Found, Xiaohong Chen, Muhammad Imran Malik. \"SigComp11: Signature Verification Competition for On- and Offline Skilled Forgeries\", Proc. 11th Int. Conference on Document Analysis and Recognition, 2011" ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# Stanford Cars (Test set only)\n\nOriginal paper: 3D Object Representations for Fine-Grained Categorization\n\nHomepage: URL\n\nBibtex:" ]

[ "TAGS\n#region-us \n", "# Stanford Cars (Test set only)\n\nOriginal paper: 3D Object Representations for Fine-Grained Categorization\n\nHomepage: URL\n\nBibtex:" ]

[ "arxiv:1910.04867", "region:us" ]

[ "1910.04867" ]

[]

[ "# Rendered SST2 (Test set only)\n\nOriginal paper: The Visual Task Adaptation Benchmark\n\nHomepage: URL\n\nDerived from SST2: URL\n\nBibtex:" ]

[ "TAGS\n#arxiv-1910.04867 #region-us \n", "# Rendered SST2 (Test set only)\n\nOriginal paper: The Visual Task Adaptation Benchmark\n\nHomepage: URL\n\nDerived from SST2: URL\n\nBibtex:" ]

[ "region:us" ]

[]

[]

[ "# STL-10 (Test set only)\n\nOriginal paper: An Analysis of Single Layer Networks in Unsupervised Feature Learning\n\nHomepage: URL\n\nBibtex:" ]

[ "TAGS\n#region-us \n", "# STL-10 (Test set only)\n\nOriginal paper: An Analysis of Single Layer Networks in Unsupervised Feature Learning\n\nHomepage: URL\n\nBibtex:" ]