Datasets:

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ftopal

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huggingface-datasets

like 0

[ "region:us" ]

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

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

[ "region:us" ]

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

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

[ "region:us" ]

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[ "# Dataset Card for \"test_push4\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"test_push4\"\n\nMore Information needed" ]

[ "region:us" ]

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

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

[ "region:us" ]

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

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

[ "region:us" ]

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

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

[ "region:us" ]

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

[ "region:us" ]

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

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

[ "region:us" ]

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[ "# Dataset Card for \"vira-intents-live\"\n\nMore Information needed" ]

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

[ "region:us" ]

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

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

[ "region:us" ]

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[ "# Dataset Card for \"artificial-unbalanced-500Kb\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"artificial-unbalanced-500Kb\"\n\nMore Information needed" ]

[ "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 \"dataset_from_synthea_for_NER_with_train_val_test_splits\"\n\nMore Information needed" ]

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

[ "region:us" ]

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[ "### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\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#region-us \n", "### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\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:" ]

[ "region:us" ]

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

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

[ "region:us" ]

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

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

[ "region:us" ]

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[ "# 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#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-to-image", "size_categories:10K<n<100K", "audio", "spectrograms", "region:us" ]

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[ "TAGS\n#task_categories-image-to-image #size_categories-10K<n<100K #audio #spectrograms #region-us \n" ]

[ "region:us" ]

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

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

[ "region:us" ]

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[ "# Dataset Card for \"zhou_h1n1_human\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"zhou_h1n1_human\"\n\nMore Information needed" ]

[ "region:us" ]

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

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

[ "task_categories:summarization", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "bills", "legal", "region:us" ]

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[ "en" ]

[ "# Dataset Card for \"bill_summary_us\"", "## 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: BillML\n- Repository: BillML\n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nDataset for summarization of summarization of US Congressional bills (bill_summary_us).", "### Supported Tasks and Leaderboards", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances", "#### default", "### Data Fields\n\n- id: id of the bill in format(congress number + bill type + bill number + bill version).\n- congress: number of the congress.\n- bill_type: type of the bill.\n- bill_number: number of the bill.\n- bill_version: version of the bill.\n- sections: list of bill sections with section_id, text and header.\n- sections_length: number with lenght of the sections list.\n- text: bill text.\n- text_length: number of characters in the text.\n- summary: summary of the bill.\n- summary_length: number of characters in the summary.\n- title: official title of the bill.", "### Data Splits\n\ntrain", "## Dataset Creation", "### Curation Rationale\n\nBills (proposed laws) are specialized, structured documents with great public significance. Often, the language of a bill may not directly explain the potential impact of the legislation. For bills in the U.S. Congress, the Congressional Research Service of the Library of Congress provides professional, non-partisan summaries of bills. These are valuable for public understanding of the bills and are serve as an essential part of the lawmaking process to understand the meaning and potential legislative impact.\n\nThis dataset collects the text of bills, some metadata, as well as the CRS summaries. In order to build more accurate ML models for bill summarization it is important to have a clean dataset, alongside the professionally-written CRS summaries. ML summarization models built on generic data are bound to produce less accurate results (sometimes creating summaries that describe the opposite of a bill's actual effect). In addition, models that attempt to summarize all bills (some of which may reach 4000 pages long) may also be inaccurate due to the current limitations of summarization on long texts.\n\nAs a result, this dataset collects bill and summary information; it provides text as a list of sections with the text and header. This could be used to create a summary of sections and then a summary of summaries.", "### Source Data\n\nURL", "#### Initial Data Collection and Normalization\n\nThe data consists of the US congress bills that were collected from the URL service provided by the United States Government Publishing Office (GPO) under CC0-1.0 license.", "#### 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\n\nURL", "### Licensing Information\n\nBill and summary information are public and are unlicensed, as it is data produced by government entities. The collection and enhancement work that we provide for this dataset, to the degree it may be covered by copyright, is released under CC0.", "### Contributions\n\nThanks to @aih @BorodaUA, @alexbojko for adding this dataset." ]

[ "TAGS\n#task_categories-summarization #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #bills #legal #region-us \n", "# Dataset Card for \"bill_summary_us\"", "## 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: BillML\n- Repository: BillML\n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nDataset for summarization of summarization of US Congressional bills (bill_summary_us).", "### Supported Tasks and Leaderboards", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances", "#### default", "### Data Fields\n\n- id: id of the bill in format(congress number + bill type + bill number + bill version).\n- congress: number of the congress.\n- bill_type: type of the bill.\n- bill_number: number of the bill.\n- bill_version: version of the bill.\n- sections: list of bill sections with section_id, text and header.\n- sections_length: number with lenght of the sections list.\n- text: bill text.\n- text_length: number of characters in the text.\n- summary: summary of the bill.\n- summary_length: number of characters in the summary.\n- title: official title of the bill.", "### Data Splits\n\ntrain", "## Dataset Creation", "### Curation Rationale\n\nBills (proposed laws) are specialized, structured documents with great public significance. Often, the language of a bill may not directly explain the potential impact of the legislation. For bills in the U.S. Congress, the Congressional Research Service of the Library of Congress provides professional, non-partisan summaries of bills. These are valuable for public understanding of the bills and are serve as an essential part of the lawmaking process to understand the meaning and potential legislative impact.\n\nThis dataset collects the text of bills, some metadata, as well as the CRS summaries. In order to build more accurate ML models for bill summarization it is important to have a clean dataset, alongside the professionally-written CRS summaries. ML summarization models built on generic data are bound to produce less accurate results (sometimes creating summaries that describe the opposite of a bill's actual effect). In addition, models that attempt to summarize all bills (some of which may reach 4000 pages long) may also be inaccurate due to the current limitations of summarization on long texts.\n\nAs a result, this dataset collects bill and summary information; it provides text as a list of sections with the text and header. This could be used to create a summary of sections and then a summary of summaries.", "### Source Data\n\nURL", "#### Initial Data Collection and Normalization\n\nThe data consists of the US congress bills that were collected from the URL service provided by the United States Government Publishing Office (GPO) under CC0-1.0 license.", "#### 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\n\nURL", "### Licensing Information\n\nBill and summary information are public and are unlicensed, as it is data produced by government entities. The collection and enhancement work that we provide for this dataset, to the degree it may be covered by copyright, is released under CC0.", "### Contributions\n\nThanks to @aih @BorodaUA, @alexbojko for adding this dataset." ]

[ "task_categories:text2text-generation", "annotations_creators:expert-generated", "language_creators:other", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:de", "license:cc-by-4.0", "short answer feedback", "legal domain", "region:us" ]

[]

[ "de" ]

[ "### Dataset Summary\n\n\nThis Short Answer Feedback (SAF) dataset contains 19 German questions in the domain of the German social law (with reference answers). The idea of constructing a bilingual (English and German) short answer dataset as a way to remedy the lack of content-focused feedback datasets was introduced in Your Answer is Incorrect... Would you like to know why? Introducing a Bilingual Short Answer Feedback Dataset (Filighera et al., ACL 2022). Please refer to saf\\_micro\\_job\\_german and saf\\_communication\\_networks\\_english for similarly constructed datasets that can be used for SAF tasks.", "### Supported Tasks and Leaderboards\n\n\n* 'short\\_answer\\_feedback': The dataset can be used to train a Text2Text Generation model from HuggingFace transformers in order to generate automatic short answer feedback.", "### Languages\n\n\nThe questions, reference answers, provided answers and the answer feedback in the dataset are written in German.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of an entry of the training split looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'id': a 'string' feature (UUID4 in HEX format).\n* 'question': a 'string' feature representing a question.\n* 'reference\\_answer': a 'string' feature representing a reference answer to the question.\n* 'provided\\_answer': a 'string' feature representing an answer that was provided for a particular question.\n* 'answer\\_feedback': a 'string' feature representing the feedback given to the provided answers.\n* 'verification\\_feedback': a 'string' feature representing an automatic labeling of the score. It can be 'Correct' ('score' = 1), 'Incorrect' ('score' = 0) or 'Partially correct' (all intermediate scores).\n* 'error\\_class': a 'string' feature representing the type of error identified in the case of a not completely correct answer.\n* 'score': a 'float64' feature (between 0 and 1) representing the score given to the provided answer.", "### Data Splits\n\n\nThe dataset is comprised of four data splits.\n\n\n* 'train': used for training, contains a set of questions and the provided answers to them.\n* 'validation': used for validation, contains a set of questions and the provided answers to them (derived from the original training set from which the data came from).\n* 'test\\_unseen\\_answers': used for testing, contains unseen answers to the questions present in the 'train' split.\n* 'test\\_unseen\\_questions': used for testing, contains unseen questions that do not appear in the 'train' split.\n\n\n\nAdditional Information\n----------------------", "### Contributions\n\n\nThanks to @JohnnyBoy2103 for adding this dataset." ]

[ "TAGS\n#task_categories-text2text-generation #annotations_creators-expert-generated #language_creators-other #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-German #license-cc-by-4.0 #short answer feedback #legal domain #region-us \n", "### Dataset Summary\n\n\nThis Short Answer Feedback (SAF) dataset contains 19 German questions in the domain of the German social law (with reference answers). The idea of constructing a bilingual (English and German) short answer dataset as a way to remedy the lack of content-focused feedback datasets was introduced in Your Answer is Incorrect... Would you like to know why? Introducing a Bilingual Short Answer Feedback Dataset (Filighera et al., ACL 2022). Please refer to saf\\_micro\\_job\\_german and saf\\_communication\\_networks\\_english for similarly constructed datasets that can be used for SAF tasks.", "### Supported Tasks and Leaderboards\n\n\n* 'short\\_answer\\_feedback': The dataset can be used to train a Text2Text Generation model from HuggingFace transformers in order to generate automatic short answer feedback.", "### Languages\n\n\nThe questions, reference answers, provided answers and the answer feedback in the dataset are written in German.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of an entry of the training split looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'id': a 'string' feature (UUID4 in HEX format).\n* 'question': a 'string' feature representing a question.\n* 'reference\\_answer': a 'string' feature representing a reference answer to the question.\n* 'provided\\_answer': a 'string' feature representing an answer that was provided for a particular question.\n* 'answer\\_feedback': a 'string' feature representing the feedback given to the provided answers.\n* 'verification\\_feedback': a 'string' feature representing an automatic labeling of the score. It can be 'Correct' ('score' = 1), 'Incorrect' ('score' = 0) or 'Partially correct' (all intermediate scores).\n* 'error\\_class': a 'string' feature representing the type of error identified in the case of a not completely correct answer.\n* 'score': a 'float64' feature (between 0 and 1) representing the score given to the provided answer.", "### Data Splits\n\n\nThe dataset is comprised of four data splits.\n\n\n* 'train': used for training, contains a set of questions and the provided answers to them.\n* 'validation': used for validation, contains a set of questions and the provided answers to them (derived from the original training set from which the data came from).\n* 'test\\_unseen\\_answers': used for testing, contains unseen answers to the questions present in the 'train' split.\n* 'test\\_unseen\\_questions': used for testing, contains unseen questions that do not appear in the 'train' split.\n\n\n\nAdditional Information\n----------------------", "### Contributions\n\n\nThanks to @JohnnyBoy2103 for adding this dataset." ]

[ "region:us" ]

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

[ "region:us" ]

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[ "# Dataset Card for \"multi-label-classification-test\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

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[]

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

[ "task_categories:text-classification", "size_categories:1K<n<10K", "language:sv", "license:cc-by-nc-4.0", "region:us" ]

[]

[ "sv" ]

[ "# Dataset Card for \"swerec-mini\"\n\nMore Information needed" ]

[ "TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-Swedish #license-cc-by-nc-4.0 #region-us \n", "# Dataset Card for \"swerec-mini\"\n\nMore Information needed" ]

[ "region:us" ]

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

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

[ "task_categories:question-answering", "task_ids:extractive-qa", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended|wikipedia", "language:en", "license:cc-by-4.0", "arxiv:2210.03992", "region:us" ]

[ "2210.03992" ]

[ "en" ]

[ "### Dataset Summary\n\n\nThis is a synthetic QA dataset generated with fine-tuned QG models over 'lmqg/qa\\_harvesting\\_from\\_wikipedia', 1 million paragraph and answer pairs collected in Du and Cardie, 2018, made for question-answering based evaluation (QAE) for question generation model proposed by Zhang and Bansal, 2019.\nThe 'train' split is the synthetic data and the 'validation' split is the original validation set of SQuAD, where the model should be evaluate on.\n\n\nThis contains synthetic QA datasets created with the following QG models:\n\n\n* lmqg/bart-base-squad\n* lmqg/bart-large-squad\n* lmqg/t5-small-squad\n* lmqg/t5-base-squad\n* lmqg/t5-large-squad\n\n\nSee more detail about the QAE at URL", "### Supported Tasks and Leaderboards\n\n\n* 'question-answering'", "### Languages\n\n\nEnglish (en)\n\n\nDataset Structure\n-----------------", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### plain\\_text\n\n\n* 'id': a 'string' feature of id\n* 'title': a 'string' feature of title of the paragraph\n* 'context': a 'string' feature of paragraph\n* 'question': a 'string' feature of question\n* 'answers': a 'json' feature of answers", "### Data Splits" ]

[ "TAGS\n#task_categories-question-answering #task_ids-extractive-qa #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|wikipedia #language-English #license-cc-by-4.0 #arxiv-2210.03992 #region-us \n", "### Dataset Summary\n\n\nThis is a synthetic QA dataset generated with fine-tuned QG models over 'lmqg/qa\\_harvesting\\_from\\_wikipedia', 1 million paragraph and answer pairs collected in Du and Cardie, 2018, made for question-answering based evaluation (QAE) for question generation model proposed by Zhang and Bansal, 2019.\nThe 'train' split is the synthetic data and the 'validation' split is the original validation set of SQuAD, where the model should be evaluate on.\n\n\nThis contains synthetic QA datasets created with the following QG models:\n\n\n* lmqg/bart-base-squad\n* lmqg/bart-large-squad\n* lmqg/t5-small-squad\n* lmqg/t5-base-squad\n* lmqg/t5-large-squad\n\n\nSee more detail about the QAE at URL", "### Supported Tasks and Leaderboards\n\n\n* 'question-answering'", "### Languages\n\n\nEnglish (en)\n\n\nDataset Structure\n-----------------", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### plain\\_text\n\n\n* 'id': a 'string' feature of id\n* 'title': a 'string' feature of title of the paragraph\n* 'context': a 'string' feature of paragraph\n* 'question': a 'string' feature of question\n* 'answers': a 'json' feature of answers", "### Data Splits" ]

[ "region:us" ]

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

[ "region:us" ]

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

[ "task_categories:text-generation", "task_categories:feature-extraction", "multilinguality:monolingual", "source_datasets:bartman081523/stable-diffusion-discord-prompts", "source_datasets:succinctly/midjourney-prompts", "source_datasets:Gustavosta/Stable-Diffusion-Prompts", "language:en", "license:apache-2.0", "text generation", "region:us" ]

[]

[ "en" ]

[ "# text2image multi-prompt(s): a dataset collection\n\n- collection of several text2image prompt datasets\n- data was cleaned/normalized with the goal of removing \"model specific APIs\" like the \"--ar\" for Midjourney and so on\n- data de-duplicated on a basic level: exactly duplicate prompts were dropped (_after cleaning and normalization_)", "## updates\n\n- Oct 2023: the 'default' config has been updated with better deduplication. It was deduplicated with minhash (_params: n-gram size set to 3, deduplication threshold at 0.6, hash function chosen as xxh3 with 32-bit hash bits, and 128 permutations with a batch size of 10,000._) which drops 2+ million rows.\n - original version is still available under 'config_name=\"original\"'", "## contents\n\n\n\ndefault:\n\n\n\nFor 'original' config:\n\n\n_NOTE: as the other two datasets did not have a 'validation' split, the validation split of 'succinctly/midjourney-prompts' was merged into 'train'._" ]

[ "TAGS\n#task_categories-text-generation #task_categories-feature-extraction #multilinguality-monolingual #source_datasets-bartman081523/stable-diffusion-discord-prompts #source_datasets-succinctly/midjourney-prompts #source_datasets-Gustavosta/Stable-Diffusion-Prompts #language-English #license-apache-2.0 #text generation #region-us \n", "# text2image multi-prompt(s): a dataset collection\n\n- collection of several text2image prompt datasets\n- data was cleaned/normalized with the goal of removing \"model specific APIs\" like the \"--ar\" for Midjourney and so on\n- data de-duplicated on a basic level: exactly duplicate prompts were dropped (_after cleaning and normalization_)", "## updates\n\n- Oct 2023: the 'default' config has been updated with better deduplication. It was deduplicated with minhash (_params: n-gram size set to 3, deduplication threshold at 0.6, hash function chosen as xxh3 with 32-bit hash bits, and 128 permutations with a batch size of 10,000._) which drops 2+ million rows.\n - original version is still available under 'config_name=\"original\"'", "## contents\n\n\n\ndefault:\n\n\n\nFor 'original' config:\n\n\n_NOTE: as the other two datasets did not have a 'validation' split, the validation split of 'succinctly/midjourney-prompts' was merged into 'train'._" ]

[ "region:us" ]

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[ "# Dataset Card for \"quick-captioning-dataset-test\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"quick-captioning-dataset-test\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ "license:cc", "doi:10.57967/hf/0749", "region:us" ]

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[ "## Description\n\nThe Spam SMS is a set of SMS-tagged messages that have been collected for SMS Spam research. It contains one set of SMS messages in English of 5,574 messages, tagged according to being ham (legitimate) or spam.\n\nSource: uciml/sms-spam-collection-dataset" ]

[ "TAGS\n#license-cc #doi-10.57967/hf/0749 #region-us \n", "## Description\n\nThe Spam SMS is a set of SMS-tagged messages that have been collected for SMS Spam research. It contains one set of SMS messages in English of 5,574 messages, tagged according to being ham (legitimate) or spam.\n\nSource: uciml/sms-spam-collection-dataset" ]

[ "task_categories:text-generation", "task_categories:text2text-generation", "task_ids:language-modeling", "task_ids:explanation-generation", "annotations_creators:no-annotation", "language_creators:expert-generated", "multilinguality:other-en-prolog", "size_categories:n<1K", "source_datasets:original", "language:en", "license:gpl-2.0", "geo", "prolog", "semantic-parsing", "code-generation", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-text-generation #task_categories-text2text-generation #task_ids-language-modeling #task_ids-explanation-generation #annotations_creators-no-annotation #language_creators-expert-generated #multilinguality-other-en-prolog #size_categories-n<1K #source_datasets-original #language-English #license-gpl-2.0 #geo #prolog #semantic-parsing #code-generation #region-us \n" ]

[ "task_categories:text-generation", "task_ids:language-modeling", "multilinguality:other-en-python", "size_categories:n<1K", "language:en", "license:mit", "code-synthesis", "semantic-parsing", "python", "hearthstone", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #multilinguality-other-en-python #size_categories-n<1K #language-English #license-mit #code-synthesis #semantic-parsing #python #hearthstone #region-us \n" ]

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

[]

[]

[ "# Stable Diffusion Prompts 200m \n\nBecause Diffusion-DB dataset is too big. So I extracted the prompts out for prompt study. \n\nThe file introduction:\n- sd_promts_2m.txt : the main dataset.\n- sd_top5000.URL: the top 5000 frequent key words or phrase.\n-" ]

[ "TAGS\n#license-cc0-1.0 #region-us \n", "# Stable Diffusion Prompts 200m \n\nBecause Diffusion-DB dataset is too big. So I extracted the prompts out for prompt study. \n\nThe file introduction:\n- sd_promts_2m.txt : the main dataset.\n- sd_top5000.URL: the top 5000 frequent key words or phrase.\n-" ]

[ "task_categories:image-classification", "task_ids:multi-label-image-classification", "annotations_creators:no-annotation", "language_creators:other", "multilinguality:monolingual", "size_categories:100M<n<1B", "source_datasets:original", "language:en", "license:cc-by-4.0", "image-labeled pairs", "arxiv:2010.11929", "region:us" ]

[ "2010.11929" ]

[ "en" ]

[ "### Dataset Summary\n\n\nCOYO-Labeled-300M is a dataset of machine-labeled 300M images-multi-label pairs. We labeled subset of COYO-700M with a large model (efficientnetv2-xl) trained on imagenet-21k. We followed the same evaluation pipeline as in efficientnet-v2. The labels are top 50 most likely labels out of 21,841 classes from imagenet-21k. The label probabilies are provided rather than label so that the user can select threshold of their choice for multi-label classification use or can take top-1 class for single class classification use.\n\n\nIn other words, COYO-Labeled-300M is a ImageNet-like dataset. Instead of human labeled 1.25 million samples, it's machine-labeled 300 million samples. This dataset is similar to JFT-300M which is not released to the public.", "### Supported Tasks and Leaderboards\n\n\nWe empirically validated the quality of COYO-Labeled-300M dataset by re-implementing popular model, ViT.\nWe found that our ViT implementation trained on COYO-Labeled-300M performs similar to the performance numbers in the ViT paper trained on JFT-300M.\nWe also provide weights for the pretrained ViT model on COYO-Labeled-300M as well as its training & fine-tuning code.", "### Languages\n\n\nThe labels in the COYO-Labeled-300M dataset consist of English.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nEach instance in COYO-Labeled-300M represents multi-labels and image pair information with meta-attributes. \n\nAnd we also provide label information, imagenet21k\\_tree.pickle.", "### Data Fields\n\n\nname: id, type: long, description: Unique 64-bit integer ID generated by monotonically\\_increasing\\_id() which is the same value that is mapped with the existing COYO-700M.\nname: url, type: string, description: The image URL extracted from the 'src' attribute of the '![]()'\nname: imagehash, type: string, description: The perceptual hash(pHash) of the image\nname: labels, type: sequence[integer], description: Inference results of EfficientNetV2-XL model trained on ImageNet-21K dataset (Top 50 indices among 21,841 classes)\nname: label\\_probs, type: sequence[float], description: Inference results of EfficientNetV2-XL model trained on ImageNet-21K dataset (Top 50 indices among 21,841 probabilites)\nname: width, type: integer, description: The width of the image\nname: height, type: integer, description: The height of the image", "### Data Splits\n\n\nData was not split, since the evaluation was expected to be performed on more widely used downstream task(s).\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nWe labeled subset of COYO-700M with a large model (efficientnetv2-xl) trained on imagenet-21k. Data sampling was done with a size similar to jft-300m, filtered by a specific threshold for probabilities for the top-1 label.", "### Source Data\n\n\nCOYO-700M", "#### Who are the source language producers?\n\n\nCommon Crawl is the data source for COYO-700M.", "### Annotations", "#### Annotation process\n\n\nThe dataset was built in a fully automated process that did not require human annotation.", "#### Who are the annotators?\n\n\nNo human annotation", "### Personal and Sensitive Information\n\n\nThe basic instruction, licenses and contributors are the same as for the coyo-700m." ]

[ "TAGS\n#task_categories-image-classification #task_ids-multi-label-image-classification #annotations_creators-no-annotation #language_creators-other #multilinguality-monolingual #size_categories-100M<n<1B #source_datasets-original #language-English #license-cc-by-4.0 #image-labeled pairs #arxiv-2010.11929 #region-us \n", "### Dataset Summary\n\n\nCOYO-Labeled-300M is a dataset of machine-labeled 300M images-multi-label pairs. We labeled subset of COYO-700M with a large model (efficientnetv2-xl) trained on imagenet-21k. We followed the same evaluation pipeline as in efficientnet-v2. The labels are top 50 most likely labels out of 21,841 classes from imagenet-21k. The label probabilies are provided rather than label so that the user can select threshold of their choice for multi-label classification use or can take top-1 class for single class classification use.\n\n\nIn other words, COYO-Labeled-300M is a ImageNet-like dataset. Instead of human labeled 1.25 million samples, it's machine-labeled 300 million samples. This dataset is similar to JFT-300M which is not released to the public.", "### Supported Tasks and Leaderboards\n\n\nWe empirically validated the quality of COYO-Labeled-300M dataset by re-implementing popular model, ViT.\nWe found that our ViT implementation trained on COYO-Labeled-300M performs similar to the performance numbers in the ViT paper trained on JFT-300M.\nWe also provide weights for the pretrained ViT model on COYO-Labeled-300M as well as its training & fine-tuning code.", "### Languages\n\n\nThe labels in the COYO-Labeled-300M dataset consist of English.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nEach instance in COYO-Labeled-300M represents multi-labels and image pair information with meta-attributes. \n\nAnd we also provide label information, imagenet21k\\_tree.pickle.", "### Data Fields\n\n\nname: id, type: long, description: Unique 64-bit integer ID generated by monotonically\\_increasing\\_id() which is the same value that is mapped with the existing COYO-700M.\nname: url, type: string, description: The image URL extracted from the 'src' attribute of the '![]()'\nname: imagehash, type: string, description: The perceptual hash(pHash) of the image\nname: labels, type: sequence[integer], description: Inference results of EfficientNetV2-XL model trained on ImageNet-21K dataset (Top 50 indices among 21,841 classes)\nname: label\\_probs, type: sequence[float], description: Inference results of EfficientNetV2-XL model trained on ImageNet-21K dataset (Top 50 indices among 21,841 probabilites)\nname: width, type: integer, description: The width of the image\nname: height, type: integer, description: The height of the image", "### Data Splits\n\n\nData was not split, since the evaluation was expected to be performed on more widely used downstream task(s).\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nWe labeled subset of COYO-700M with a large model (efficientnetv2-xl) trained on imagenet-21k. Data sampling was done with a size similar to jft-300m, filtered by a specific threshold for probabilities for the top-1 label.", "### Source Data\n\n\nCOYO-700M", "#### Who are the source language producers?\n\n\nCommon Crawl is the data source for COYO-700M.", "### Annotations", "#### Annotation process\n\n\nThe dataset was built in a fully automated process that did not require human annotation.", "#### Who are the annotators?\n\n\nNo human annotation", "### Personal and Sensitive Information\n\n\nThe basic instruction, licenses and contributors are the same as for the coyo-700m." ]

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

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[ "# 1. Overview\nThis dataset is a collection of 5,000+ images of clothing & apparels set that are ready to use for optimizing the accuracy of computer vision models. All of the contents is sourced from PIXTA's stock library of 100M+ Asian-featured images and videos. PIXTA is the largest platform of visual materials in the Asia Pacific region offering fully-managed services, high quality contents and data, and powerful tools for businesses & organisations to enable their creative and machine learning projects.", "# 2. Use case\nThe e-commerce apparel dataset could be used for various AI & Computer Vision models: Product Visual Search, Similar Product Recommendation, Product Catalog,... Each data set is supported by both AI and human review process to ensure labelling consistency and accuracy. Contact us for more custom datasets.", "# 3. About PIXTA\nPIXTASTOCK is the largest Asian-featured stock platform providing data, contents, tools and services since 2005. PIXTA experiences 15 years of integrating advanced AI technology in managing, curating, processing over 100M visual materials and serving global leading brands for their creative and data demands. Visit us at URL or contact via our email contact@URL.\"" ]

[ "TAGS\n#license-other #region-us \n", "# 1. Overview\nThis dataset is a collection of 5,000+ images of clothing & apparels set that are ready to use for optimizing the accuracy of computer vision models. All of the contents is sourced from PIXTA's stock library of 100M+ Asian-featured images and videos. PIXTA is the largest platform of visual materials in the Asia Pacific region offering fully-managed services, high quality contents and data, and powerful tools for businesses & organisations to enable their creative and machine learning projects.", "# 2. Use case\nThe e-commerce apparel dataset could be used for various AI & Computer Vision models: Product Visual Search, Similar Product Recommendation, Product Catalog,... Each data set is supported by both AI and human review process to ensure labelling consistency and accuracy. Contact us for more custom datasets.", "# 3. About PIXTA\nPIXTASTOCK is the largest Asian-featured stock platform providing data, contents, tools and services since 2005. PIXTA experiences 15 years of integrating advanced AI technology in managing, curating, processing over 100M visual materials and serving global leading brands for their creative and data demands. Visit us at URL or contact via our email contact@URL.\"" ]

[ "region:us" ]

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

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

[ "region:us" ]

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[ "# Dataset Card for \"dalio-handwritten-io\"\n\nMore Information needed" ]

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

[ "region:us" ]

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[ "# Dataset Card for \"dalio-handwritten-complete\"\n\nMore Information needed" ]

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

[ "region:us" ]

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[ "# Dataset Card for \"dalio-synthetic-io\"\n\nMore Information needed" ]

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

[ "region:us" ]

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[ "# Dataset Card for \"dalio-synthetic-complete\"\n\nMore Information needed" ]

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

[ "region:us" ]

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[ "# Dataset Card for \"dalio-all-io\"\n\nMore Information needed" ]

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

[ "region:us" ]

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[ "# Dataset Card for \"dalio-all-complete\"\n\nMore Information needed" ]

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

[ "region:us" ]

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[]

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

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

[ "region:us" ]

[]

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

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

[ "region:us" ]

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

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

[ "region:us" ]

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

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

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

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

[ "region:us" ]

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

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

[ "region:us" ]

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

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

[ "region:us" ]

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

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

[ "region:us" ]

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[ "# Dataset Card for \"visual-basic_paths\"\n\nMore Information needed" ]

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

[ "region:us" ]

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

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

[ "region:us" ]

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

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

[ "region:us" ]

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

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

[ "region:us" ]

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

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

[ "region:us" ]

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

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

[ "region:us" ]

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

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

[ "region:us" ]

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

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

[ "region:us" ]

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

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

[ "region:us" ]

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

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

[ "region:us" ]

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

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

[ "region:us" ]

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

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

[ "region:us" ]

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

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

[ "region:us" ]

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

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

[ "region:us" ]

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[ "# Dataset Card for \"c-sharp_paths\"\n\nMore Information needed" ]

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

[ "region:us" ]

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

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

[ "region:us" ]

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

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

[ "region:us" ]

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

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

[ "region:us" ]

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

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

[ "region:us" ]

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

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

[ "region:us" ]

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

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

[ "region:us" ]

[]

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[ "# 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#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." ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

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

[ "TAGS\n#region-us \n", "# Dataset Card for \"powershell_paths\"\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:text2text-generation", "annotations_creators:expert-generated", "language_creators:other", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:de", "license:cc-by-4.0", "short answer feedback", "micro job", "region:us" ]

[]

[ "de" ]

[ "### Dataset Summary\n\n\nShort Answer Feedback (SAF) dataset is a short answer dataset introduced in Your Answer is Incorrect... Would you like to know why? Introducing a Bilingual Short Answer Feedback Dataset (Filighera et al., ACL 2022) as a way to remedy the lack of content-focused feedback datasets. This version of the dataset contains 8 German questions used in micro-job training on the crowd-worker platform appJobber - while the original dataset presented in the paper is comprised of an assortment of both English and German short answer questions (with reference answers). Please refer to the saf\\_communication\\_networks\\_english dataset to examine the English subset of the original dataset. Furthermore, a similarly constructed SAF dataset (covering the German legal domain) can be found at saf\\_legal\\_domain\\_german.", "### Supported Tasks and Leaderboards\n\n\n* 'short\\_answer\\_feedback': The dataset can be used to train a Text2Text Generation model from HuggingFace transformers in order to generate automatic short answer feedback.", "### Languages\n\n\nThe questions, reference answers, provided answers and the answer feedback in the dataset are written in German.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of an entry of the training split looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'id': a 'string' feature (UUID4 in HEX format).\n* 'question': a 'string' feature representing a question.\n* 'reference\\_answer': a 'string' feature representing a reference answer to the question.\n* 'provided\\_answer': a 'string' feature representing an answer that was provided for a particular question.\n* 'answer\\_feedback': a 'string' feature representing the feedback given to the provided answers.\n* 'verification\\_feedback': a 'string' feature representing an automatic labeling of the score. It can be 'Correct' ('score' = 1), 'Incorrect' ('score' = 0) or 'Partially correct' (all intermediate scores).\n* 'score': a 'float64' feature (between 0 and 1) representing the score given to the provided answer.", "### Data Splits\n\n\nThe dataset is comprised of four data splits.\n\n\n* 'train': used for training, contains a set of questions and the provided answers to them.\n* 'validation': used for validation, contains a set of questions and the provided answers to them (derived from the original training set defined in the paper).\n* 'test\\_unseen\\_answers': used for testing, contains unseen answers to the questions present in the 'train' split.\n* 'test\\_unseen\\_questions': used for testing, contains unseen questions that do not appear in the 'train' split.\n\n\n\nDataset Creation\n----------------", "### Annotation Process\n\n\nTwo experienced appJobber employees were selected to evaluate the crowd-worker platform’s answers, and both of them underwent a general annotation guideline training (supervised by a Psychology doctoral student with prior work in the field of feedback). After the training, the annotators individually provided feedback to the answers following an agreed upon scoring rubric and the general annotation guideline. The individually annotated answer files were then combined into a cohesive gold standard after discussing and solving possible disagreements.\n\n\nAdditional Information\n----------------------", "### Contributions\n\n\nThanks to @JohnnyBoy2103 for adding this dataset." ]

[ "TAGS\n#task_categories-text2text-generation #annotations_creators-expert-generated #language_creators-other #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-German #license-cc-by-4.0 #short answer feedback #micro job #region-us \n", "### Dataset Summary\n\n\nShort Answer Feedback (SAF) dataset is a short answer dataset introduced in Your Answer is Incorrect... Would you like to know why? Introducing a Bilingual Short Answer Feedback Dataset (Filighera et al., ACL 2022) as a way to remedy the lack of content-focused feedback datasets. This version of the dataset contains 8 German questions used in micro-job training on the crowd-worker platform appJobber - while the original dataset presented in the paper is comprised of an assortment of both English and German short answer questions (with reference answers). Please refer to the saf\\_communication\\_networks\\_english dataset to examine the English subset of the original dataset. Furthermore, a similarly constructed SAF dataset (covering the German legal domain) can be found at saf\\_legal\\_domain\\_german.", "### Supported Tasks and Leaderboards\n\n\n* 'short\\_answer\\_feedback': The dataset can be used to train a Text2Text Generation model from HuggingFace transformers in order to generate automatic short answer feedback.", "### Languages\n\n\nThe questions, reference answers, provided answers and the answer feedback in the dataset are written in German.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of an entry of the training split looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'id': a 'string' feature (UUID4 in HEX format).\n* 'question': a 'string' feature representing a question.\n* 'reference\\_answer': a 'string' feature representing a reference answer to the question.\n* 'provided\\_answer': a 'string' feature representing an answer that was provided for a particular question.\n* 'answer\\_feedback': a 'string' feature representing the feedback given to the provided answers.\n* 'verification\\_feedback': a 'string' feature representing an automatic labeling of the score. It can be 'Correct' ('score' = 1), 'Incorrect' ('score' = 0) or 'Partially correct' (all intermediate scores).\n* 'score': a 'float64' feature (between 0 and 1) representing the score given to the provided answer.", "### Data Splits\n\n\nThe dataset is comprised of four data splits.\n\n\n* 'train': used for training, contains a set of questions and the provided answers to them.\n* 'validation': used for validation, contains a set of questions and the provided answers to them (derived from the original training set defined in the paper).\n* 'test\\_unseen\\_answers': used for testing, contains unseen answers to the questions present in the 'train' split.\n* 'test\\_unseen\\_questions': used for testing, contains unseen questions that do not appear in the 'train' split.\n\n\n\nDataset Creation\n----------------", "### Annotation Process\n\n\nTwo experienced appJobber employees were selected to evaluate the crowd-worker platform’s answers, and both of them underwent a general annotation guideline training (supervised by a Psychology doctoral student with prior work in the field of feedback). After the training, the annotators individually provided feedback to the answers following an agreed upon scoring rubric and the general annotation guideline. The individually annotated answer files were then combined into a cohesive gold standard after discussing and solving possible disagreements.\n\n\nAdditional Information\n----------------------", "### Contributions\n\n\nThanks to @JohnnyBoy2103 for adding this dataset." ]

[ "task_categories:text2text-generation", "annotations_creators:expert-generated", "language_creators:other", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:cc-by-4.0", "short answer feedback", "communication networks", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nShort Answer Feedback (SAF) dataset is a short answer dataset introduced in Your Answer is Incorrect... Would you like to know why? Introducing a Bilingual Short Answer Feedback Dataset (Filighera et al., ACL 2022) as a way to remedy the lack of content-focused feedback datasets. This version of the dataset contains 31 English questions covering a range of college-level communication networks topics - while the original dataset presented in the paper is comprised of an assortment of both English and German short answer questions (with reference answers). Please refer to the saf\\_micro\\_job\\_german dataset to examine the German subset of the original dataset. Furthermore, a similarly constructed SAF dataset (covering the German legal domain) can be found at saf\\_legal\\_domain\\_german.", "### Supported Tasks and Leaderboards\n\n\n* 'short\\_answer\\_feedback': The dataset can be used to train a Text2Text Generation model from HuggingFace transformers in order to generate automatic short answer feedback.", "### Languages\n\n\nThe questions, reference answers, provided answers and the answer feedback in the dataset are written in English.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of an entry of the training split looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'id': a 'string' feature (UUID4 in HEX format).\n* 'question': a 'string' feature representing a question.\n* 'reference\\_answer': a 'string' feature representing a reference answer to the question.\n* 'provided\\_answer': a 'string' feature representing an answer that was provided for a particular question.\n* 'answer\\_feedback': a 'string' feature representing the feedback given to the provided answers.\n* 'verification\\_feedback': a 'string' feature representing an automatic labeling of the score. It can be 'Correct' ('score' = maximum points achievable), 'Incorrect' ('score' = 0) or 'Partially correct' (all intermediate scores).\n* 'score': a 'float64' feature representing the score given to the provided answer. For most questions it ranges from 0 to 1.", "### Data Splits\n\n\nThe dataset is comprised of four data splits.\n\n\n* 'train': used for training, contains a set of questions and the provided answers to them.\n* 'validation': used for validation, contains a set of questions and the provided answers to them (derived from the original training set defined in the paper).\n* 'test\\_unseen\\_answers': used for testing, contains unseen answers to the questions present in the 'train' split.\n* 'test\\_unseen\\_questions': used for testing, contains unseen questions that do not appear in the 'train' split.\n\n\n\nDataset Creation\n----------------", "### Annotation Process\n\n\nTwo graduate students who had completed the communication networks course were selected to evaluate the answers, and both of them underwent a general annotation guideline training (supervised by a Psychology doctoral student with prior work in the field of feedback). After the training, the annotators individually provided feedback to the answers following an agreed upon scoring rubric and the general annotation guideline. The individually annotated answer files were then combined into a cohesive gold standard after discussing and solving possible disagreements.\n\n\nAdditional Information\n----------------------", "### Contributions\n\n\nThanks to @JohnnyBoy2103 for adding this dataset." ]

[ "TAGS\n#task_categories-text2text-generation #annotations_creators-expert-generated #language_creators-other #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc-by-4.0 #short answer feedback #communication networks #region-us \n", "### Dataset Summary\n\n\nShort Answer Feedback (SAF) dataset is a short answer dataset introduced in Your Answer is Incorrect... Would you like to know why? Introducing a Bilingual Short Answer Feedback Dataset (Filighera et al., ACL 2022) as a way to remedy the lack of content-focused feedback datasets. This version of the dataset contains 31 English questions covering a range of college-level communication networks topics - while the original dataset presented in the paper is comprised of an assortment of both English and German short answer questions (with reference answers). Please refer to the saf\\_micro\\_job\\_german dataset to examine the German subset of the original dataset. Furthermore, a similarly constructed SAF dataset (covering the German legal domain) can be found at saf\\_legal\\_domain\\_german.", "### Supported Tasks and Leaderboards\n\n\n* 'short\\_answer\\_feedback': The dataset can be used to train a Text2Text Generation model from HuggingFace transformers in order to generate automatic short answer feedback.", "### Languages\n\n\nThe questions, reference answers, provided answers and the answer feedback in the dataset are written in English.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of an entry of the training split looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'id': a 'string' feature (UUID4 in HEX format).\n* 'question': a 'string' feature representing a question.\n* 'reference\\_answer': a 'string' feature representing a reference answer to the question.\n* 'provided\\_answer': a 'string' feature representing an answer that was provided for a particular question.\n* 'answer\\_feedback': a 'string' feature representing the feedback given to the provided answers.\n* 'verification\\_feedback': a 'string' feature representing an automatic labeling of the score. It can be 'Correct' ('score' = maximum points achievable), 'Incorrect' ('score' = 0) or 'Partially correct' (all intermediate scores).\n* 'score': a 'float64' feature representing the score given to the provided answer. For most questions it ranges from 0 to 1.", "### Data Splits\n\n\nThe dataset is comprised of four data splits.\n\n\n* 'train': used for training, contains a set of questions and the provided answers to them.\n* 'validation': used for validation, contains a set of questions and the provided answers to them (derived from the original training set defined in the paper).\n* 'test\\_unseen\\_answers': used for testing, contains unseen answers to the questions present in the 'train' split.\n* 'test\\_unseen\\_questions': used for testing, contains unseen questions that do not appear in the 'train' split.\n\n\n\nDataset Creation\n----------------", "### Annotation Process\n\n\nTwo graduate students who had completed the communication networks course were selected to evaluate the answers, and both of them underwent a general annotation guideline training (supervised by a Psychology doctoral student with prior work in the field of feedback). After the training, the annotators individually provided feedback to the answers following an agreed upon scoring rubric and the general annotation guideline. The individually annotated answer files were then combined into a cohesive gold standard after discussing and solving possible disagreements.\n\n\nAdditional Information\n----------------------", "### Contributions\n\n\nThanks to @JohnnyBoy2103 for adding this dataset." ]

[ "task_categories:text-classification", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:multilingual", "size_categories:1K<n<10K", "language:en", "language:hi", "language:ko", "language:es", "language:ta", "language:fr", "language:vi", "language:ru", "language:af", "language:hu", "license:cc-by-4.0", "politeness", "wikipedia", "multilingual", "region:us" ]

[]

[ "en", "hi", "ko", "es", "ta", "fr", "vi", "ru", "af", "hu" ]

[ "# TyDiP: A Dataset for Politeness Classification in Nine Typologically Diverse Languages\nThis repo contains the code and data for the EMNLP 2022 findings paper TyDiP: A Dataset for Politeness Classification in Nine Typologically Diverse Languages which can be found here.", "## Data\nThe TyDiP dataset is licensed under the CC BY 4.0 license.\n\nThe 'data' folder contains the different files we release as part of the TyDiP dataset. The TyDiP dataset comprises of an English train set and English test set that are adapted from the Stanford Politeness Corpus, and test data in 9 more languages (Hindi, Korean, Spanish, Tamil, French, Vietnamese, Russian, Afrikaans, Hungarian) that we annotated.\n\n\n'data/all' consists of the complete train and test sets. \n'data/binary' is a filtered version of the above where sentences from the top and bottom 25 percentile of scores is only present. This is the data that we used for training and evaluation in the paper. \n'data/unlabelled_train_sets'", "## Code\n'politeness_regresor.py' is used for training and evaluation of transformer models\n\nTo train a model\n\n\nTo test this trained model on $lang", "## Pretrained Model\nXLM-Roberta Large finetuned on the English train set (as discussed and evaluated in the paper) can be found here", "## Politeness Strategies\n'strategies' contains the processed strategy lexicon for different languages. 'strategies/learnt_strategies.xlsx' contains the human edited strategies for 4 langauges", "## Annotation Interface\n'URL' contains the UI used for conducting data annotation\n\nIf you use the English train or test data, please cite the Stanford Politeness Dataset\n\nIf you use the test data from the 9 target languages, please cite our paper" ]

[ "TAGS\n#task_categories-text-classification #annotations_creators-crowdsourced #language_creators-found #multilinguality-multilingual #size_categories-1K<n<10K #language-English #language-Hindi #language-Korean #language-Spanish #language-Tamil #language-French #language-Vietnamese #language-Russian #language-Afrikaans #language-Hungarian #license-cc-by-4.0 #politeness #wikipedia #multilingual #region-us \n", "# TyDiP: A Dataset for Politeness Classification in Nine Typologically Diverse Languages\nThis repo contains the code and data for the EMNLP 2022 findings paper TyDiP: A Dataset for Politeness Classification in Nine Typologically Diverse Languages which can be found here.", "## Data\nThe TyDiP dataset is licensed under the CC BY 4.0 license.\n\nThe 'data' folder contains the different files we release as part of the TyDiP dataset. The TyDiP dataset comprises of an English train set and English test set that are adapted from the Stanford Politeness Corpus, and test data in 9 more languages (Hindi, Korean, Spanish, Tamil, French, Vietnamese, Russian, Afrikaans, Hungarian) that we annotated.\n\n\n'data/all' consists of the complete train and test sets. \n'data/binary' is a filtered version of the above where sentences from the top and bottom 25 percentile of scores is only present. This is the data that we used for training and evaluation in the paper. \n'data/unlabelled_train_sets'", "## Code\n'politeness_regresor.py' is used for training and evaluation of transformer models\n\nTo train a model\n\n\nTo test this trained model on $lang", "## Pretrained Model\nXLM-Roberta Large finetuned on the English train set (as discussed and evaluated in the paper) can be found here", "## Politeness Strategies\n'strategies' contains the processed strategy lexicon for different languages. 'strategies/learnt_strategies.xlsx' contains the human edited strategies for 4 langauges", "## Annotation Interface\n'URL' contains the UI used for conducting data annotation\n\nIf you use the English train or test data, please cite the Stanford Politeness Dataset\n\nIf you use the test data from the 9 target languages, please cite our paper" ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"rvl_cdip_400_train_val_test\"\n\nMore Information needed", "### Dataset Format\n\n'" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"rvl_cdip_400_train_val_test\"\n\nMore Information needed", "### Dataset Format\n\n'" ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:machine-generated", "annotations_creators:expert-generated", "language_creators:machine-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:extended|tydiqa", "language:id", "license:cc-by-4.0", "region:us" ]

[]

[ "id" ]

[ "# Dataset Card for IDK-MRC", "## Dataset Description\n\n- Repository: rifkiaputri/IDK-MRC\n- Paper: PDF\n- Point of Contact: rifkiaputri", "### Dataset Summary\n\nI(n)dontKnow-MRC (IDK-MRC) is an Indonesian Machine Reading Comprehension dataset that covers answerable and unanswerable questions. Based on the combination of the existing answerable questions in TyDiQA, the new unanswerable question in IDK-MRC is generated using a question generation model and human-written question. Each paragraph in the dataset has a set of answerable and unanswerable questions with the corresponding answer.", "### Supported Tasks\n\nIDK-MRC is mainly intended to train Machine Reading Comprehension or extractive QA models.", "### Languages\n\nIndonesian", "## Dataset Structure", "### Data Instances", "### Data Fields\n\nEach instance has several fields:\n\n- 'context': context passage/paragraph as a string\n- 'qas': list of questions related to the 'context'\n - 'id': question ID as a string\n - 'is_impossible': whether the question is unanswerable (impossible to answer) or not as a boolean\n - 'question': question as a string\n - 'answers': list of answers\n - 'text': answer as a string\n - 'answer_start': answer start index as an integer", "### Data Splits\n\n- 'train': 9,332 (5,042 answerable, 4,290 unanswerable)\n- 'valid': 764 (382 answerable, 382 unanswerable)\n- 'test': 844 (422 answerable, 422 unanswerable)", "## Dataset Creation", "### Curation Rationale\n\nIDK-MRC dataset is built based on the existing paragraph and answerable questions (ans) in TyDiQA-GoldP (Clark et al., 2020). The new unanswerable questions are automatically generated using the combination of mT5 (Xue et al., 2021) and XLM-R (Conneau et al., 2020) models, which are then manually verified by human annotators (filtered ans and filtered unans). We also asked the annotators to manually write additional unanswerable questions as described in §3.3 (additional unans). Each paragraphs in the final dataset will have a set of filtered ans, filtered unans, and additional unans questions.", "### Annotations", "#### Annotation process\n\nIn our dataset collection pipeline, the annotators are asked to validate the model-generated unanswerable questions and write a new additional unanswerable questions.", "#### Who are the annotators?\n\nWe recruit four annotators with 2+ years of experience in Indonesian NLP annotation using direct recruitment. All of them are Indonesian native speakers who reside in Indonesia (Java Island) and fall under the 18–34 age category. We set the payment to around $7.5 per hour. Given the annotators’ demographic, we ensure that the payment is above the minimum wage rate (as of December 2021). All annotators also have signed the consent form and agreed to participate in this project.", "## Considerations for Using the Data\n\nThe paragraphs and answerable questions that we utilized to build IDK-MRC dataset are taken from Indonesian subset of TyDiQA-GoldP dataset (Clark et al., 2020), which originates from Wikipedia articles. Since those articles are written from a neutral point of view, the risk of harmful content is minimal. Also, all model-generated questions in our dataset have been validated by human annotators to eliminate the risk of harmful questions. During the manual question generation process, the annotators are also encouraged to avoid producing possibly offensive questions.\n\nEven so, we argue that further assessment is needed before using our dataset and models in real-world applications. This measurement is especially required for the pre-trained language models used in our experiments, namely mT5 (Xue et al., 2021), IndoBERT (Wilie et al., 2020), mBERT (Devlin et al., 2019), and XLM-R (Conneau et al., 2020). These language models are mostly pre-trained on the common-crawl dataset, which may contain harmful biases or stereotypes.", "## Additional Information", "### Licensing Information\n\nCC BY-SA 4.0" ]

[ "TAGS\n#task_categories-question-answering #task_ids-extractive-qa #annotations_creators-machine-generated #annotations_creators-expert-generated #language_creators-machine-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-extended|tydiqa #language-Indonesian #license-cc-by-4.0 #region-us \n", "# Dataset Card for IDK-MRC", "## Dataset Description\n\n- Repository: rifkiaputri/IDK-MRC\n- Paper: PDF\n- Point of Contact: rifkiaputri", "### Dataset Summary\n\nI(n)dontKnow-MRC (IDK-MRC) is an Indonesian Machine Reading Comprehension dataset that covers answerable and unanswerable questions. Based on the combination of the existing answerable questions in TyDiQA, the new unanswerable question in IDK-MRC is generated using a question generation model and human-written question. Each paragraph in the dataset has a set of answerable and unanswerable questions with the corresponding answer.", "### Supported Tasks\n\nIDK-MRC is mainly intended to train Machine Reading Comprehension or extractive QA models.", "### Languages\n\nIndonesian", "## Dataset Structure", "### Data Instances", "### Data Fields\n\nEach instance has several fields:\n\n- 'context': context passage/paragraph as a string\n- 'qas': list of questions related to the 'context'\n - 'id': question ID as a string\n - 'is_impossible': whether the question is unanswerable (impossible to answer) or not as a boolean\n - 'question': question as a string\n - 'answers': list of answers\n - 'text': answer as a string\n - 'answer_start': answer start index as an integer", "### Data Splits\n\n- 'train': 9,332 (5,042 answerable, 4,290 unanswerable)\n- 'valid': 764 (382 answerable, 382 unanswerable)\n- 'test': 844 (422 answerable, 422 unanswerable)", "## Dataset Creation", "### Curation Rationale\n\nIDK-MRC dataset is built based on the existing paragraph and answerable questions (ans) in TyDiQA-GoldP (Clark et al., 2020). The new unanswerable questions are automatically generated using the combination of mT5 (Xue et al., 2021) and XLM-R (Conneau et al., 2020) models, which are then manually verified by human annotators (filtered ans and filtered unans). We also asked the annotators to manually write additional unanswerable questions as described in §3.3 (additional unans). Each paragraphs in the final dataset will have a set of filtered ans, filtered unans, and additional unans questions.", "### Annotations", "#### Annotation process\n\nIn our dataset collection pipeline, the annotators are asked to validate the model-generated unanswerable questions and write a new additional unanswerable questions.", "#### Who are the annotators?\n\nWe recruit four annotators with 2+ years of experience in Indonesian NLP annotation using direct recruitment. All of them are Indonesian native speakers who reside in Indonesia (Java Island) and fall under the 18–34 age category. We set the payment to around $7.5 per hour. Given the annotators’ demographic, we ensure that the payment is above the minimum wage rate (as of December 2021). All annotators also have signed the consent form and agreed to participate in this project.", "## Considerations for Using the Data\n\nThe paragraphs and answerable questions that we utilized to build IDK-MRC dataset are taken from Indonesian subset of TyDiQA-GoldP dataset (Clark et al., 2020), which originates from Wikipedia articles. Since those articles are written from a neutral point of view, the risk of harmful content is minimal. Also, all model-generated questions in our dataset have been validated by human annotators to eliminate the risk of harmful questions. During the manual question generation process, the annotators are also encouraged to avoid producing possibly offensive questions.\n\nEven so, we argue that further assessment is needed before using our dataset and models in real-world applications. This measurement is especially required for the pre-trained language models used in our experiments, namely mT5 (Xue et al., 2021), IndoBERT (Wilie et al., 2020), mBERT (Devlin et al., 2019), and XLM-R (Conneau et al., 2020). These language models are mostly pre-trained on the common-crawl dataset, which may contain harmful biases or stereotypes.", "## Additional Information", "### Licensing Information\n\nCC BY-SA 4.0" ]

[ "region:us" ]

[]

[]

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

[ "TAGS\n#region-us \n", "# Dataset Card for \"bayc\"\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:text-generation", "task_ids:language-modeling", "multilinguality:monolingual", "size_categories:1k<n<10K", "source_datasets:tweet_qa", "language:en", "license:cc-by-sa-4.0", "question-generation", "arxiv:2210.03992", "region:us" ]

[ "2210.03992" ]

[ "en" ]

[ "### Dataset Summary\n\n\nThis is the question & answer generation dataset based on the tweet\\_qa. The test set of the original data is not publicly released, so we randomly sampled test questions from the training set.", "### Supported Tasks and Leaderboards\n\n\n* 'question-answer-generation': The dataset is assumed to be used to train a model for question & answer generation.\nSuccess on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail).", "### Languages\n\n\nEnglish (en)\n\n\nDataset Structure\n-----------------\n\n\nAn example of 'train' looks as follows.\n\n\nThe data fields are the same among all splits.\n\n\n* 'questions': a 'list' of 'string' features.\n* 'answers': a 'list' of 'string' features.\n* 'paragraph': a 'string' feature.\n* 'questions\\_answers': a 'string' feature.\n\n\nData Splits\n-----------" ]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #multilinguality-monolingual #size_categories-1k<n<10K #source_datasets-tweet_qa #language-English #license-cc-by-sa-4.0 #question-generation #arxiv-2210.03992 #region-us \n", "### Dataset Summary\n\n\nThis is the question & answer generation dataset based on the tweet\\_qa. The test set of the original data is not publicly released, so we randomly sampled test questions from the training set.", "### Supported Tasks and Leaderboards\n\n\n* 'question-answer-generation': The dataset is assumed to be used to train a model for question & answer generation.\nSuccess on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail).", "### Languages\n\n\nEnglish (en)\n\n\nDataset Structure\n-----------------\n\n\nAn example of 'train' looks as follows.\n\n\nThe data fields are the same among all splits.\n\n\n* 'questions': a 'list' of 'string' features.\n* 'answers': a 'list' of 'string' features.\n* 'paragraph': a 'string' feature.\n* 'questions\\_answers': a 'string' feature.\n\n\nData Splits\n-----------" ]

[ "task_categories:text-generation", "task_ids:language-modeling", "multilinguality:monolingual", "size_categories:1k<n<10K", "source_datasets:lmqg/qg_squad", "language:en", "license:cc-by-sa-4.0", "question-generation", "arxiv:2210.03992", "region:us" ]

[ "2210.03992" ]

[ "en" ]

[ "### Dataset Summary\n\n\nThis is the question & answer generation dataset based on the SQuAD.", "### Supported Tasks and Leaderboards\n\n\n* 'question-answer-generation': The dataset is assumed to be used to train a model for question & answer generation.\nSuccess on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail).", "### Languages\n\n\nEnglish (en)\n\n\nDataset Structure\n-----------------\n\n\nAn example of 'train' looks as follows.\n\n\nThe data fields are the same among all splits.\n\n\n* 'questions': a 'list' of 'string' features.\n* 'answers': a 'list' of 'string' features.\n* 'paragraph': a 'string' feature.\n* 'questions\\_answers': a 'string' feature.\n\n\nData Splits\n-----------" ]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #multilinguality-monolingual #size_categories-1k<n<10K #source_datasets-lmqg/qg_squad #language-English #license-cc-by-sa-4.0 #question-generation #arxiv-2210.03992 #region-us \n", "### Dataset Summary\n\n\nThis is the question & answer generation dataset based on the SQuAD.", "### Supported Tasks and Leaderboards\n\n\n* 'question-answer-generation': The dataset is assumed to be used to train a model for question & answer generation.\nSuccess on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail).", "### Languages\n\n\nEnglish (en)\n\n\nDataset Structure\n-----------------\n\n\nAn example of 'train' looks as follows.\n\n\nThe data fields are the same among all splits.\n\n\n* 'questions': a 'list' of 'string' features.\n* 'answers': a 'list' of 'string' features.\n* 'paragraph': a 'string' feature.\n* 'questions\\_answers': a 'string' feature.\n\n\nData Splits\n-----------" ]

[ "region:us" ]

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[]

[ "# Dataset Card for \"lolita-dress-CHIN\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"olm-wikipedia-20221101-kl-language\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"olm-wikipedia-20221101-kl-language\"\n\nMore Information needed" ]

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

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[]

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

[ "TAGS\n#task_categories-text-classification #region-us \n", "### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\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:" ]

[ "region:us" ]

[]

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

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

[ "region:us" ]

[]

[]

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

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