Datasets:

---

ftopal

/

huggingface-datasets

like 0

[ "task_categories:fill-mask", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:fr", "license:cc-by-nc-4.0", "region:us" ]

[]

[ "fr" ]

[ "# Dataset Card for CoFiF", "## Dataset Description\n\n- Repository: URL\n\n- Paper: URL\n\n- Point of Contact: Sina Ahmadi (URL@URL) and Tobias Daudert (tobias.daudert@URL)", "### Dataset Summary\n\nCoFiF is the first corpus comprising company reports in the French language. It contains over 188 million tokens in 2655 reports, covering four types of documents:\n\n- Reference documents (documents de référence) published annually, usually in the months following the end of the calendar year, and contain information regarding the financial situation and perspectives of a company\n- Annual report (résultats annuels) which summarises a company’s business and activities throughout the previous year\n- Semestrial (résultats semestriels): similar to annual reports in content but published every 6 months\n- Trimestrial reports (résultats trimestriels): similar to annual reports but published every 3 months\n\nThese documents are collected from the 60 largest French companies listed in France’s main stock indices CAC40 and CAC Next 20. The corpus spans over 20 years, ranging from 1995 to 2018.", "### Supported Tasks and Leaderboards\nLanguage modeling : can be used to train a French model (CamemBERT, FlauBERT, BARTHez, etc.) on data of financial types", "### Languages\nFrench", "## Dataset Structure\nRaw text", "## Dataset Creation", "### Curation Rationale\nNo French language datasets related to finance were available.", "### Source Data\n- Reference documents (documents de référence) published annually, usually in the months following the end of the calendar year, and contain information regarding the financial situation and perspectives of a company\n- Annual report (résultats annuels) which summarises a company’s business and activities throughout the previous year\n- Semestrial (résultats semestriels): similar to annual reports in content but published every 6 months\n- Trimestrial reports (résultats trimestriels): similar to annual reports but published every 3 months\n\nThese documents are collected from the 60 largest French companies listed in France’s main stock indices CAC40 and CAC Next 20. The corpus spans over 20 years, ranging from 1995 to 2018.", "#### Initial Data Collection and Normalization\nThe authors have done a cleanup but have not detailed it in their publication or on their GitHub directory.", "#### Who are the source language producers?\nPublic administrative reports written by humans.", "### Annotations\nNo annotations.", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\nThis publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289, co-funded by the European Regional Development Fund.", "### Licensing Information\nThe authors of the publication (Tobias Daudert and Sina Ahmadi) do not acknowledge any third party, so it appears that they were the only ones involved in the data collection.\n\nThis corpus is openly available for non-commercial use under the Attribution-NonCommercial-ShareAlike 4.0 International.\n\n\n\n\nIf you're using CoFiF in your researches, please don't forget to cite this paper:\n\n~~~\n@inproceedings{daudert-ahmadi-2019-cofif,\n title = \"{C}o{F}i{F}: A Corpus of Financial Reports in {F}rench Language\",\n author = \"Daudert, Tobias and Ahmadi, Sina\",\n booktitle = \"Proceedings of the First Workshop on Financial Technology and Natural Language Processing\",\n month = \"12 \" # aug,\n year = \"2019\",\n address = \"Macao, China\",\n url = \"URL\n pages = \"21--26\",\n}\n~~~" ]

[ "TAGS\n#task_categories-fill-mask #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #source_datasets-original #language-French #license-cc-by-nc-4.0 #region-us \n", "# Dataset Card for CoFiF", "## Dataset Description\n\n- Repository: URL\n\n- Paper: URL\n\n- Point of Contact: Sina Ahmadi (URL@URL) and Tobias Daudert (tobias.daudert@URL)", "### Dataset Summary\n\nCoFiF is the first corpus comprising company reports in the French language. It contains over 188 million tokens in 2655 reports, covering four types of documents:\n\n- Reference documents (documents de référence) published annually, usually in the months following the end of the calendar year, and contain information regarding the financial situation and perspectives of a company\n- Annual report (résultats annuels) which summarises a company’s business and activities throughout the previous year\n- Semestrial (résultats semestriels): similar to annual reports in content but published every 6 months\n- Trimestrial reports (résultats trimestriels): similar to annual reports but published every 3 months\n\nThese documents are collected from the 60 largest French companies listed in France’s main stock indices CAC40 and CAC Next 20. The corpus spans over 20 years, ranging from 1995 to 2018.", "### Supported Tasks and Leaderboards\nLanguage modeling : can be used to train a French model (CamemBERT, FlauBERT, BARTHez, etc.) on data of financial types", "### Languages\nFrench", "## Dataset Structure\nRaw text", "## Dataset Creation", "### Curation Rationale\nNo French language datasets related to finance were available.", "### Source Data\n- Reference documents (documents de référence) published annually, usually in the months following the end of the calendar year, and contain information regarding the financial situation and perspectives of a company\n- Annual report (résultats annuels) which summarises a company’s business and activities throughout the previous year\n- Semestrial (résultats semestriels): similar to annual reports in content but published every 6 months\n- Trimestrial reports (résultats trimestriels): similar to annual reports but published every 3 months\n\nThese documents are collected from the 60 largest French companies listed in France’s main stock indices CAC40 and CAC Next 20. The corpus spans over 20 years, ranging from 1995 to 2018.", "#### Initial Data Collection and Normalization\nThe authors have done a cleanup but have not detailed it in their publication or on their GitHub directory.", "#### Who are the source language producers?\nPublic administrative reports written by humans.", "### Annotations\nNo annotations.", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\nThis publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289, co-funded by the European Regional Development Fund.", "### Licensing Information\nThe authors of the publication (Tobias Daudert and Sina Ahmadi) do not acknowledge any third party, so it appears that they were the only ones involved in the data collection.\n\nThis corpus is openly available for non-commercial use under the Attribution-NonCommercial-ShareAlike 4.0 International.\n\n\n\n\nIf you're using CoFiF in your researches, please don't forget to cite this paper:\n\n~~~\n@inproceedings{daudert-ahmadi-2019-cofif,\n title = \"{C}o{F}i{F}: A Corpus of Financial Reports in {F}rench Language\",\n author = \"Daudert, Tobias and Ahmadi, Sina\",\n booktitle = \"Proceedings of the First Workshop on Financial Technology and Natural Language Processing\",\n month = \"12 \" # aug,\n year = \"2019\",\n address = \"Macao, China\",\n url = \"URL\n pages = \"21--26\",\n}\n~~~" ]

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

[]

[]

[ "# Dataset Card for 'lbox_open'", "## Dataset Description\n\n- Homepage: 'URL'\n- Repository: 'URL\n- Point of Contact: Wonseok Hwang", "### Dataset Summary\n\nA Legal AI Benchmark Dataset from Korean Legal Cases.", "### Languages\n\nKorean", "### How to use\n\n\nFor more information about the dataset, please visit <URL", "## Licensing Information\nCopyright 2022-present LBox Co. Ltd.\n\nLicensed under the CC BY-NC 4.0" ]

[ "TAGS\n#license-cc-by-nc-4.0 #region-us \n", "# Dataset Card for 'lbox_open'", "## Dataset Description\n\n- Homepage: 'URL'\n- Repository: 'URL\n- Point of Contact: Wonseok Hwang", "### Dataset Summary\n\nA Legal AI Benchmark Dataset from Korean Legal Cases.", "### Languages\n\nKorean", "### How to use\n\n\nFor more information about the dataset, please visit <URL", "## Licensing Information\nCopyright 2022-present LBox Co. Ltd.\n\nLicensed under the CC BY-NC 4.0" ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[]

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

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

[]

[]

[ "# Dummy dataset to test evaluation framework for SUPERB." ]

[ "TAGS\n#benchmark-superb #region-us \n", "# Dummy dataset to test evaluation framework for SUPERB." ]

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

[]

[]

[ "# Batch job\n\nmodel_id: lewtun/superb-s3prl-osanseviero__hubert_base-asr-cbcd177a\ndataset_name: superb\ndataset_config: asr\ndataset_split: test\ndataset_column: file" ]

[ "TAGS\n#benchmark-superb #region-us \n", "# Batch job\n\nmodel_id: lewtun/superb-s3prl-osanseviero__hubert_base-asr-cbcd177a\ndataset_name: superb\ndataset_config: asr\ndataset_split: test\ndataset_column: file" ]

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

[]

[]

[ "# GEM submissions for gem-sub-03", "## Submitting to the benchmark\n\nFILL ME IN", "### Submission file format\n\nPlease follow this format for your 'URL' file:\n\n\n\nIn this case, 'dataset_identifier' is the identifier of the dataset followed by an identifier of the set the outputs were created from, for example '_validation' or '_test'. That means, the 'mlsum_de' test set would have the identifier 'mlsum_de_test'.\n\nThe 'keys' field can be set to avoid accidental shuffling to impact your metrics. Simply add a list of the 'gem_id' for each output example in the same order as your values.", "### Validate your submission\n\nTo ensure that your submission files are correctly formatted, run the following command from the root of the repository:\n\n\n\nIf everything is correct, you should see the following message:", "### Push your submission to the Hugging Face Hub!\n\nThe final step is to commit your files and push them to the Hub:\n\n\n\nIf there are no errors, you should see the following message:\n\n\n\nwhere the evaluation is run every Sunday and your results will be visible on the leaderboard." ]

[ "TAGS\n#benchmark-gem #region-us \n", "# GEM submissions for gem-sub-03", "## Submitting to the benchmark\n\nFILL ME IN", "### Submission file format\n\nPlease follow this format for your 'URL' file:\n\n\n\nIn this case, 'dataset_identifier' is the identifier of the dataset followed by an identifier of the set the outputs were created from, for example '_validation' or '_test'. That means, the 'mlsum_de' test set would have the identifier 'mlsum_de_test'.\n\nThe 'keys' field can be set to avoid accidental shuffling to impact your metrics. Simply add a list of the 'gem_id' for each output example in the same order as your values.", "### Validate your submission\n\nTo ensure that your submission files are correctly formatted, run the following command from the root of the repository:\n\n\n\nIf everything is correct, you should see the following message:", "### Push your submission to the Hugging Face Hub!\n\nThe final step is to commit your files and push them to the Hub:\n\n\n\nIf there are no errors, you should see the following message:\n\n\n\nwhere the evaluation is run every Sunday and your results will be visible on the leaderboard." ]

[ "region:us" ]

[]

[]

[ "# GitHub Issues Dataset" ]

[ "TAGS\n#region-us \n", "# GitHub Issues Dataset" ]

[ "arxiv:2005.00614", "region:us" ]

[ "2005.00614" ]

[]

[ "### Dataset Summary\n\n\nGitHub Issues is a dataset consisting of GitHub issues and pull requests associated with the Datasets repository. It is intended for educational purposes and can be used for semantic search or multilabel text classification. The contents of each GitHub issue are in English and concern the domain of datasets for NLP, computer vision, and beyond.", "### Supported Tasks and Leaderboards\n\n\nFor each of the tasks tagged for this dataset, give a brief description of the tag, metrics, and suggested models (with a link to their HuggingFace implementation if available). Give a similar description of tasks that were not covered by the structured tag set (repace the 'task-category-tag' with an appropriate 'other:other-task-name').\n\n\n* 'task-category-tag': The dataset can be used to train a model for [TASK NAME], which consists in [TASK DESCRIPTION]. Success on this task is typically measured by achieving a *high/low* metric name. The (model name or model class) model currently achieves the following score. *[IF A LEADERBOARD IS AVAILABLE]:* This task has an active leaderboard which can be found at leaderboard url and ranks models based on metric name while also reporting other metric name.", "### Languages\n\n\nProvide a brief overview of the languages represented in the dataset. Describe relevant details about specifics of the language such as whether it is social media text, African American English,...\n\n\nWhen relevant, please provide BCP-47 codes, which consist of a primary language subtag, with a script subtag and/or region subtag if available.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nProvide an JSON-formatted example and brief description of a typical instance in the dataset. If available, provide a link to further examples.\n\n\nProvide any additional information that is not covered in the other sections about the data here. In particular describe any relationships between data points and if these relationships are made explicit.", "### Data Fields\n\n\nList and describe the fields present in the dataset. Mention their data type, and whether they are used as input or output in any of the tasks the dataset currently supports. If the data has span indices, describe their attributes, such as whether they are at the character level or word level, whether they are contiguous or not, etc. If the datasets contains example IDs, state whether they have an inherent meaning, such as a mapping to other datasets or pointing to relationships between data points.\n\n\n* 'example\\_field': description of 'example\\_field'\n\n\nNote that the descriptions can be initialized with the Show Markdown Data Fields output of the tagging app, you will then only need to refine the generated descriptions.", "### Data Splits\n\n\nDescribe and name the splits in the dataset if there are more than one.\n\n\nDescribe any criteria for splitting the data, if used. If their are differences between the splits (e.g. if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here.\n\n\nProvide the sizes of each split. As appropriate, provide any descriptive statistics for the features, such as average length. For example:\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nWhat need motivated the creation of this dataset? What are some of the reasons underlying the major choices involved in putting it together?", "### Source Data\n\n\nThis section describes the source data (e.g. news text and headlines, social media posts, translated sentences,...)", "#### Initial Data Collection and Normalization\n\n\nDescribe the data collection process. Describe any criteria for data selection or filtering. List any key words or search terms used. If possible, include runtime information for the collection process.\n\n\nIf data was collected from other pre-existing datasets, link to source here and to their Hugging Face version.\n\n\nIf the data was modified or normalized after being collected (e.g. if the data is word-tokenized), describe the process and the tools used.", "#### Who are the source language producers?\n\n\nState whether the data was produced by humans or machine generated. Describe the people or systems who originally created the data.\n\n\nIf available, include self-reported demographic or identity information for the source data creators, but avoid inferring this information. Instead state that this information is unknown. See Larson 2017 for using identity categories as a variables, particularly gender.\n\n\nDescribe the conditions under which the data was created (for example, if the producers were crowdworkers, state what platform was used, or if the data was found, what website the data was found on). If compensation was provided, include that information here.\n\n\nDescribe other people represented or mentioned in the data. Where possible, link to references for the information.", "### Annotations\n\n\nIf the dataset contains annotations which are not part of the initial data collection, describe them in the following paragraphs.", "#### Annotation process\n\n\nIf applicable, describe the annotation process and any tools used, or state otherwise. Describe the amount of data annotated, if not all. Describe or reference annotation guidelines provided to the annotators. If available, provide interannotator statistics. Describe any annotation validation processes.", "#### Who are the annotators?\n\n\nIf annotations were collected for the source data (such as class labels or syntactic parses), state whether the annotations were produced by humans or machine generated.\n\n\nDescribe the people or systems who originally created the annotations and their selection criteria if applicable.\n\n\nIf available, include self-reported demographic or identity information for the annotators, but avoid inferring this information. Instead state that this information is unknown. See Larson 2017 for using identity categories as a variables, particularly gender.\n\n\nDescribe the conditions under which the data was annotated (for example, if the annotators were crowdworkers, state what platform was used, or if the data was found, what website the data was found on). If compensation was provided, include that information here.", "### Personal and Sensitive Information\n\n\nState whether the dataset uses identity categories and, if so, how the information is used. Describe where this information comes from (i.e. self-reporting, collecting from profiles, inferring, etc.). See Larson 2017 for using identity categories as a variables, particularly gender. State whether the data is linked to individuals and whether those individuals can be identified in the dataset, either directly or indirectly (i.e., in combination with other data).\n\n\nState whether the dataset contains other data that might be considered sensitive (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history).\n\n\nIf efforts were made to anonymize the data, describe the anonymization process.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nPlease discuss some of the ways you believe the use of this dataset will impact society.\n\n\nThe statement should include both positive outlooks, such as outlining how technologies developed through its use may improve people's lives, and discuss the accompanying risks. These risks may range from making important decisions more opaque to people who are affected by the technology, to reinforcing existing harmful biases (whose specifics should be discussed in the next section), among other considerations.\n\n\nAlso describe in this section if the proposed dataset contains a low-resource or under-represented language. If this is the case or if this task has any impact on underserved communities, please elaborate here.", "### Discussion of Biases\n\n\nProvide descriptions of specific biases that are likely to be reflected in the data, and state whether any steps were taken to reduce their impact.\n\n\nFor Wikipedia text, see for example Dinan et al 2020 on biases in Wikipedia (esp. Table 1), or Blodgett et al 2020 for a more general discussion of the topic.\n\n\nIf analyses have been run quantifying these biases, please add brief summaries and links to the studies here.", "### Other Known Limitations\n\n\nIf studies of the datasets have outlined other limitations of the dataset, such as annotation artifacts, please outline and cite them here.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nList the people involved in collecting the dataset and their affiliation(s). If funding information is known, include it here.", "### Licensing Information\n\n\nProvide the license and link to the license webpage if available.\n\n\nProvide the BibTex-formatted reference for the dataset. For example:\n\n\nIf the dataset has a DOI, please provide it here.", "### Contributions\n\n\nThanks to @lewtun for adding this dataset." ]

[ "TAGS\n#arxiv-2005.00614 #region-us \n", "### Dataset Summary\n\n\nGitHub Issues is a dataset consisting of GitHub issues and pull requests associated with the Datasets repository. It is intended for educational purposes and can be used for semantic search or multilabel text classification. The contents of each GitHub issue are in English and concern the domain of datasets for NLP, computer vision, and beyond.", "### Supported Tasks and Leaderboards\n\n\nFor each of the tasks tagged for this dataset, give a brief description of the tag, metrics, and suggested models (with a link to their HuggingFace implementation if available). Give a similar description of tasks that were not covered by the structured tag set (repace the 'task-category-tag' with an appropriate 'other:other-task-name').\n\n\n* 'task-category-tag': The dataset can be used to train a model for [TASK NAME], which consists in [TASK DESCRIPTION]. Success on this task is typically measured by achieving a *high/low* metric name. The (model name or model class) model currently achieves the following score. *[IF A LEADERBOARD IS AVAILABLE]:* This task has an active leaderboard which can be found at leaderboard url and ranks models based on metric name while also reporting other metric name.", "### Languages\n\n\nProvide a brief overview of the languages represented in the dataset. Describe relevant details about specifics of the language such as whether it is social media text, African American English,...\n\n\nWhen relevant, please provide BCP-47 codes, which consist of a primary language subtag, with a script subtag and/or region subtag if available.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nProvide an JSON-formatted example and brief description of a typical instance in the dataset. If available, provide a link to further examples.\n\n\nProvide any additional information that is not covered in the other sections about the data here. In particular describe any relationships between data points and if these relationships are made explicit.", "### Data Fields\n\n\nList and describe the fields present in the dataset. Mention their data type, and whether they are used as input or output in any of the tasks the dataset currently supports. If the data has span indices, describe their attributes, such as whether they are at the character level or word level, whether they are contiguous or not, etc. If the datasets contains example IDs, state whether they have an inherent meaning, such as a mapping to other datasets or pointing to relationships between data points.\n\n\n* 'example\\_field': description of 'example\\_field'\n\n\nNote that the descriptions can be initialized with the Show Markdown Data Fields output of the tagging app, you will then only need to refine the generated descriptions.", "### Data Splits\n\n\nDescribe and name the splits in the dataset if there are more than one.\n\n\nDescribe any criteria for splitting the data, if used. If their are differences between the splits (e.g. if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here.\n\n\nProvide the sizes of each split. As appropriate, provide any descriptive statistics for the features, such as average length. For example:\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nWhat need motivated the creation of this dataset? What are some of the reasons underlying the major choices involved in putting it together?", "### Source Data\n\n\nThis section describes the source data (e.g. news text and headlines, social media posts, translated sentences,...)", "#### Initial Data Collection and Normalization\n\n\nDescribe the data collection process. Describe any criteria for data selection or filtering. List any key words or search terms used. If possible, include runtime information for the collection process.\n\n\nIf data was collected from other pre-existing datasets, link to source here and to their Hugging Face version.\n\n\nIf the data was modified or normalized after being collected (e.g. if the data is word-tokenized), describe the process and the tools used.", "#### Who are the source language producers?\n\n\nState whether the data was produced by humans or machine generated. Describe the people or systems who originally created the data.\n\n\nIf available, include self-reported demographic or identity information for the source data creators, but avoid inferring this information. Instead state that this information is unknown. See Larson 2017 for using identity categories as a variables, particularly gender.\n\n\nDescribe the conditions under which the data was created (for example, if the producers were crowdworkers, state what platform was used, or if the data was found, what website the data was found on). If compensation was provided, include that information here.\n\n\nDescribe other people represented or mentioned in the data. Where possible, link to references for the information.", "### Annotations\n\n\nIf the dataset contains annotations which are not part of the initial data collection, describe them in the following paragraphs.", "#### Annotation process\n\n\nIf applicable, describe the annotation process and any tools used, or state otherwise. Describe the amount of data annotated, if not all. Describe or reference annotation guidelines provided to the annotators. If available, provide interannotator statistics. Describe any annotation validation processes.", "#### Who are the annotators?\n\n\nIf annotations were collected for the source data (such as class labels or syntactic parses), state whether the annotations were produced by humans or machine generated.\n\n\nDescribe the people or systems who originally created the annotations and their selection criteria if applicable.\n\n\nIf available, include self-reported demographic or identity information for the annotators, but avoid inferring this information. Instead state that this information is unknown. See Larson 2017 for using identity categories as a variables, particularly gender.\n\n\nDescribe the conditions under which the data was annotated (for example, if the annotators were crowdworkers, state what platform was used, or if the data was found, what website the data was found on). If compensation was provided, include that information here.", "### Personal and Sensitive Information\n\n\nState whether the dataset uses identity categories and, if so, how the information is used. Describe where this information comes from (i.e. self-reporting, collecting from profiles, inferring, etc.). See Larson 2017 for using identity categories as a variables, particularly gender. State whether the data is linked to individuals and whether those individuals can be identified in the dataset, either directly or indirectly (i.e., in combination with other data).\n\n\nState whether the dataset contains other data that might be considered sensitive (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history).\n\n\nIf efforts were made to anonymize the data, describe the anonymization process.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nPlease discuss some of the ways you believe the use of this dataset will impact society.\n\n\nThe statement should include both positive outlooks, such as outlining how technologies developed through its use may improve people's lives, and discuss the accompanying risks. These risks may range from making important decisions more opaque to people who are affected by the technology, to reinforcing existing harmful biases (whose specifics should be discussed in the next section), among other considerations.\n\n\nAlso describe in this section if the proposed dataset contains a low-resource or under-represented language. If this is the case or if this task has any impact on underserved communities, please elaborate here.", "### Discussion of Biases\n\n\nProvide descriptions of specific biases that are likely to be reflected in the data, and state whether any steps were taken to reduce their impact.\n\n\nFor Wikipedia text, see for example Dinan et al 2020 on biases in Wikipedia (esp. Table 1), or Blodgett et al 2020 for a more general discussion of the topic.\n\n\nIf analyses have been run quantifying these biases, please add brief summaries and links to the studies here.", "### Other Known Limitations\n\n\nIf studies of the datasets have outlined other limitations of the dataset, such as annotation artifacts, please outline and cite them here.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nList the people involved in collecting the dataset and their affiliation(s). If funding information is known, include it here.", "### Licensing Information\n\n\nProvide the license and link to the license webpage if available.\n\n\nProvide the BibTex-formatted reference for the dataset. For example:\n\n\nIf the dataset has a DOI, please provide it here.", "### Contributions\n\n\nThanks to @lewtun for adding this dataset." ]

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

[]

[]

[ "# Dataset Card for RAFT Submission" ]

[ "TAGS\n#benchmark-test #region-us \n", "# Dataset Card for RAFT Submission" ]

[ "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:apache-2.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for Demo", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThis is a demo dataset with two files 'URL' and 'URL'.\n\nLoad it by:", "### 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#annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-apache-2.0 #region-us \n", "# Dataset Card for Demo", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThis is a demo dataset with two files 'URL' and 'URL'.\n\nLoad it by:", "### 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", "language_creators:crowdsourced", "multilinguality:multilingual", "size_categories:100k<n<1M", "source_datasets:original", "language:en", "language:fr", "license:cc-by-4.0", "region:us" ]

[]

[ "en", "fr" ]

[ "## Dataset Description", "### Dataset Summary\n\nThis is a raw text corpus, mainly intended for testing purposes.", "### Languages\n\n- French\n- English", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Source Data\n\nIt is a mashup including the following CC-BY-SA 4.0 licenced texts\n\n - *Rayons émis par les composés de l’uranium et du\n thorium*,\n Maria Skłodowska Curie\n - *Frankenstein, or the Modern\n Prometheus*),\n Mary Wollstonecraft Shelley\n - *Les maîtres sonneurs*, George Sand\n\n It also includes the text of *Sketch of The Analytical Engine Invented by Charles Babbage With\n notes upon the Memoir by the Translator* by Luigi Menabrea and Ada Lovelace, which to the best of\n my knowledge should be public domain.", "## Considerations for Using the Data\n\nThis really should not be used for anything but testing purposes", "## Licence\n\nThis corpus is available under the Creative Commons Attribution-ShareAlike 4.0 License" ]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #language_creators-crowdsourced #multilinguality-multilingual #size_categories-100k<n<1M #source_datasets-original #language-English #language-French #license-cc-by-4.0 #region-us \n", "## Dataset Description", "### Dataset Summary\n\nThis is a raw text corpus, mainly intended for testing purposes.", "### Languages\n\n- French\n- English", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Source Data\n\nIt is a mashup including the following CC-BY-SA 4.0 licenced texts\n\n - *Rayons émis par les composés de l’uranium et du\n thorium*,\n Maria Skłodowska Curie\n - *Frankenstein, or the Modern\n Prometheus*),\n Mary Wollstonecraft Shelley\n - *Les maîtres sonneurs*, George Sand\n\n It also includes the text of *Sketch of The Analytical Engine Invented by Charles Babbage With\n notes upon the Memoir by the Translator* by Luigi Menabrea and Ada Lovelace, which to the best of\n my knowledge should be public domain.", "## Considerations for Using the Data\n\nThis really should not be used for anything but testing purposes", "## Licence\n\nThis corpus is available under the Creative Commons Attribution-ShareAlike 4.0 License" ]

[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended|wikipedia", "language:en", "license:cc-by-4.0", "arxiv:1606.05250", "region:us" ]

[ "1606.05250" ]

[ "en" ]

[ "### Dataset Summary\n\n\nThis dataset is a custom copy of the original SQuAD dataset. It is used to showcase dataset repositories. Data are the same as the original dataset.\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.", "### Supported Tasks", "### Languages\n\n\nDataset Structure\n-----------------\n\n\nWe show detailed information for up to 5 configurations of the dataset.", "### Data Instances", "#### plain\\_text\n\n\n* Size of downloaded dataset files: 33.51 MB\n* Size of the generated dataset: 85.75 MB\n* Total amount of disk used: 119.27 MB\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### plain\\_text\n\n\n* 'id': a 'string' feature.\n* 'title': a 'string' feature.\n* 'context': a 'string' feature.\n* 'question': a 'string' feature.\n* 'answers': a dictionary feature containing:\n\t+ 'text': a 'string' feature.\n\t+ 'answer\\_start': a 'int32' feature.", "### Data Splits Sample Size\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "### Annotations", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions\n\n\nThanks to @lewtun, @albertvillanova, @patrickvonplaten, @thomwolf for adding this dataset." ]

[ "TAGS\n#task_categories-question-answering #task_ids-extractive-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|wikipedia #language-English #license-cc-by-4.0 #arxiv-1606.05250 #region-us \n", "### Dataset Summary\n\n\nThis dataset is a custom copy of the original SQuAD dataset. It is used to showcase dataset repositories. Data are the same as the original dataset.\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.", "### Supported Tasks", "### Languages\n\n\nDataset Structure\n-----------------\n\n\nWe show detailed information for up to 5 configurations of the dataset.", "### Data Instances", "#### plain\\_text\n\n\n* Size of downloaded dataset files: 33.51 MB\n* Size of the generated dataset: 85.75 MB\n* Total amount of disk used: 119.27 MB\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### plain\\_text\n\n\n* 'id': a 'string' feature.\n* 'title': a 'string' feature.\n* 'context': a 'string' feature.\n* 'question': a 'string' feature.\n* 'answers': a dictionary feature containing:\n\t+ 'text': a 'string' feature.\n\t+ 'answer\\_start': a 'int32' feature.", "### Data Splits Sample Size\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "### Annotations", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions\n\n\nThanks to @lewtun, @albertvillanova, @patrickvonplaten, @thomwolf for adding this dataset." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Demo1", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThis is a demo dataset. It consists in two files 'data/URL' and 'data/URL'\n\nYou can load it with", "### 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 Demo1", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThis is a demo dataset. It consists in two files 'data/URL' and 'data/URL'\n\nYou can load it with", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @github-username for adding this dataset." ]

[ "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:en", "license:mit", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-mit #region-us \n" ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:question-answering", "task_ids:extractive-qa", "task_ids:open-domain-qa", "annotations_creators:private", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "source_datasets:newspaper", "source_datasets:online", "license:cc-by-nc-sa-4.0", "region:us" ]

[]

[ "fr-FR" ]

[ "### Dataset Summary\n\n\nnewsquadfr is a small dataset created for Question Answering task. Contexts are paragraphs of articles extracted from nine online french newspaper during year 2020/2021. newsquadfr stands for Newspaper question answering dataset in french. inspired by Piaf and Squad dataset. 2 520 triplets context - question - answer.\n\n\n(train set)", "### Supported Tasks and Leaderboards\n\n\n* extractive-qa\n* open-domain-qa", "### Languages\n\n\nFr-fr\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields\n\n\n* 'answers': a dictionary feature containing:\n\t+ 'text': a 'string' feature.\n\t+ 'answer\\_start': a 'int64' feature.\n* 'article\\_id': a 'int64' feature.\n* 'article\\_title': a string feature.\n* 'article\\_url': a string feature.\n* 'context': a 'string' feature.\n* 'id': a 'string' feature.\n* 'paragraph\\_id': a 'int64' feature.\n* 'question': a 'string' feature.\n* 'website': a 'string' feature.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nParagraphs were chosen according to theses rules:\n\n\n* parent article must have more than 71% ASCII characters\n* paragraphs size must be between 170 and 670 characters\n* paragraphs shouldn't contain \"A LIRE\" or \"A VOIR AUSSI\"\n\n\nThen, we stratified our original dataset to create this dataset according to :\n\n\n* website\n* number of named entities\n* paragraph size", "#### Who are the source language producers?", "### Annotations", "#### Annotation process\n\n\nUsing Piaf annotation tools. Three different persons mostly.", "#### Who are the annotators?\n\n\nLincoln", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases\n\n\n* Annotation is not well controlled\n* asking question on news is biaised", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nURL" ]

[ "TAGS\n#task_categories-question-answering #task_ids-extractive-qa #task_ids-open-domain-qa #annotations_creators-private #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #source_datasets-newspaper #source_datasets-online #license-cc-by-nc-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nnewsquadfr is a small dataset created for Question Answering task. Contexts are paragraphs of articles extracted from nine online french newspaper during year 2020/2021. newsquadfr stands for Newspaper question answering dataset in french. inspired by Piaf and Squad dataset. 2 520 triplets context - question - answer.\n\n\n(train set)", "### Supported Tasks and Leaderboards\n\n\n* extractive-qa\n* open-domain-qa", "### Languages\n\n\nFr-fr\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields\n\n\n* 'answers': a dictionary feature containing:\n\t+ 'text': a 'string' feature.\n\t+ 'answer\\_start': a 'int64' feature.\n* 'article\\_id': a 'int64' feature.\n* 'article\\_title': a string feature.\n* 'article\\_url': a string feature.\n* 'context': a 'string' feature.\n* 'id': a 'string' feature.\n* 'paragraph\\_id': a 'int64' feature.\n* 'question': a 'string' feature.\n* 'website': a 'string' feature.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nParagraphs were chosen according to theses rules:\n\n\n* parent article must have more than 71% ASCII characters\n* paragraphs size must be between 170 and 670 characters\n* paragraphs shouldn't contain \"A LIRE\" or \"A VOIR AUSSI\"\n\n\nThen, we stratified our original dataset to create this dataset according to :\n\n\n* website\n* number of named entities\n* paragraph size", "#### Who are the source language producers?", "### Annotations", "#### Annotation process\n\n\nUsing Piaf annotation tools. Three different persons mostly.", "#### Who are the annotators?\n\n\nLincoln", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases\n\n\n* Annotation is not well controlled\n* asking question on news is biaised", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nURL" ]

[ "size_categories:10M<n<100M", "language:es", "language:en", "language:fr", "language:it", "language:cs", "language:pt", "language:ar", "language:zh", "language:fi", "language:de", "language:hu", "language:ru", "poetry", "arxiv:2307.01387", "region:us" ]

[ "2307.01387" ]

[ "es", "en", "fr", "it", "cs", "pt", "ar", "zh", "fi", "de", "hu", "ru" ]

[ "## PULPO\n\nPULPO, the Prolific Unannotated Literary Poetry Corpus, is a set of multilingual corpora of verses and stanzas with over 95M words.\n\nSee URL\n\nThe following corpora has been downloaded using the Averell tool, developed by the POSTDATA team:", "### Spanish\n- Disco v3\n- Corpus of Spanish Golden-Age Sonnets\n- Corpus general de poesía lírica castellana del Siglo de Oro\n- Gongocorpus - source", "### English\n- Eighteenth-Century Poetry Archive (ECPA)\n- For better for verse", "### French\n- Métrique en Ligne - source", "### Italian\n- Biblioteca italiana - source", "### Czech\n- Corpus of Czech Verse", "### Portuguese\n- Stichotheque\n\nAlso, we obtained the following corpora from these sources:", "### Spanish \n- URL - source", "### English\n- A Gutenberg Poetry Corpus", "### Arabic\n- Arabic Poetry dataset", "### Chinese\n- THU Chinese Classical Poetry Corpus", "### Finnish\n- SKVR", "### German\n- TextGrid Poetry Corpus - source\n- German Rhyme Corpus", "### Hungarian\n- verskorpusz", "### Portuguese\n- Poems in Portuguese", "### Russian\n- 19 000 Russian poems" ]

[ "TAGS\n#size_categories-10M<n<100M #language-Spanish #language-English #language-French #language-Italian #language-Czech #language-Portuguese #language-Arabic #language-Chinese #language-Finnish #language-German #language-Hungarian #language-Russian #poetry #arxiv-2307.01387 #region-us \n", "## PULPO\n\nPULPO, the Prolific Unannotated Literary Poetry Corpus, is a set of multilingual corpora of verses and stanzas with over 95M words.\n\nSee URL\n\nThe following corpora has been downloaded using the Averell tool, developed by the POSTDATA team:", "### Spanish\n- Disco v3\n- Corpus of Spanish Golden-Age Sonnets\n- Corpus general de poesía lírica castellana del Siglo de Oro\n- Gongocorpus - source", "### English\n- Eighteenth-Century Poetry Archive (ECPA)\n- For better for verse", "### French\n- Métrique en Ligne - source", "### Italian\n- Biblioteca italiana - source", "### Czech\n- Corpus of Czech Verse", "### Portuguese\n- Stichotheque\n\nAlso, we obtained the following corpora from these sources:", "### Spanish \n- URL - source", "### English\n- A Gutenberg Poetry Corpus", "### Arabic\n- Arabic Poetry dataset", "### Chinese\n- THU Chinese Classical Poetry Corpus", "### Finnish\n- SKVR", "### German\n- TextGrid Poetry Corpus - source\n- German Rhyme Corpus", "### Hungarian\n- verskorpusz", "### Portuguese\n- Poems in Portuguese", "### Russian\n- 19 000 Russian poems" ]

[ "task_categories:text-generation", "source_datasets:allenai/c4", "language:en", "grammatical-error-correction", "region:us" ]

[]

[ "en" ]

[ "# C4 200M", "# Dataset Summary\nc4_200m is a collection of 185 million sentence pairs generated from the cleaned English dataset from C4. This dataset can be used in grammatical error correction (GEC) tasks.\n\nThe corruption edits and scripts used to synthesize this dataset is referenced from: C4_200M Synthetic Dataset", "# Description\nAs discussed before, this dataset contains 185 million sentence pairs. Each article has these two attributes: 'input' and 'output'. Here is a sample of dataset:" ]

[ "TAGS\n#task_categories-text-generation #source_datasets-allenai/c4 #language-English #grammatical-error-correction #region-us \n", "# C4 200M", "# Dataset Summary\nc4_200m is a collection of 185 million sentence pairs generated from the cleaned English dataset from C4. This dataset can be used in grammatical error correction (GEC) tasks.\n\nThe corruption edits and scripts used to synthesize this dataset is referenced from: C4_200M Synthetic Dataset", "# Description\nAs discussed before, this dataset contains 185 million sentence pairs. Each article has these two attributes: 'input' and 'output'. Here is a sample of dataset:" ]

[ "task_categories:text-classification", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:en", "license:cc-by-4.0", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-text-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-unknown #source_datasets-original #language-English #license-cc-by-4.0 #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# spoken-punctuation\nSpoken punctuation for Speech-to-Text by language and locale.", "## Disclaimer\nData collected from Google Cloud Speech-to-Text \"Supported spoken punctuation\" documentation: URL" ]

[ "TAGS\n#region-us \n", "# spoken-punctuation\nSpoken punctuation for Speech-to-Text by language and locale.", "## Disclaimer\nData collected from Google Cloud Speech-to-Text \"Supported spoken punctuation\" documentation: URL" ]

[ "task_categories:text-classification", "task_ids:language-modeling", "task_ids:sentiment-classification", "task_ids:sentiment-scoring", "task_ids:topic-classification", "annotations_creators:found", "language_creators:found", "source_datasets:original", "language:es", "language:pt", "license:other", "region:us" ]

[]

[ "es", "pt" ]

[ "### Dataset Summary\n\n\nWe provide a Mercado Libre product reviews dataset for spanish and portuguese text classification. The dataset contains reviews in these two languages collected between August 2020 and January 2021. Each record in the dataset contains the review content and title, the star rating, the country where it was pubilshed and the product category (arts, technology, etc.). The corpus is roughly balanced across stars, so each star rating constitutes approximately 20% of the reviews in each language.\n\n\n\nTable shows the number of samples per star rate in each split. There is a total of 442.108 training samples in spanish and 253.955 in portuguese. We limited the number of reviews per product to 30 and we perform a ranked inclusion of the downloaded reviews to include those with rich semantic content. In these ranking, the lenght of the review content and the valorization (difference between likes and dislikes) was prioritized. For more details on this process, see (CITATION).\n\n\nReviews in spanish were obtained from 8 different Latin Amercian countries (Argentina, Colombia, Peru, Uruguay, Chile, Venezuela and Mexico), and portuguese reviews were extracted from Brasil. To match the language with its respective country, we applied a language detection algorithm based on the works of Joulin et al. (2016a and 2016b) to determine the language of the review text and we removed reviews that were not written in the expected language.", "### Languages\n\n\nThe dataset contains reviews in Latin American Spanish and Portuguese.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nEach data instance corresponds to a review. Each split is stored in a separated '.csv' file, so every row in each file consists on a review. For example, here we show a snippet of the spanish training split:", "### Data Fields\n\n\n* 'country': The string identifier of the country. It could be one of the following: 'MLA' (Argentina), 'MCO' (Colombia), 'MPE' (Peru), 'MLU' (Uruguay), 'MLC' (Chile), 'MLV' (Venezuela), 'MLM' (Mexico) or 'MLB' (Brasil).\n* 'category': String representation of the product's category. It could be one of the following:\n\t+ Hogar / Casa\n\t+ Tecnologı́a y electrónica / Tecnologia e electronica\n\t+ Salud, ropa y cuidado personal / Saúde, roupas e cuidado pessoal\n\t+ Arte y entretenimiento / Arte e Entretenimiento\n\t+ Alimentos y Bebidas / Alimentos e Bebidas\n* 'review\\_content': The text content of the review.\n* 'review\\_title': The text title of the review.\n* 'review\\_rate': An int between 1-5 indicating the number of stars.", "### Data Splits\n\n\nEach language configuration comes with it's own 'train', 'validation', and 'test' splits. The 'all\\_languages' split is simply a concatenation of the corresponding split across all languages. That is, the 'train' split for 'all\\_languages' is a concatenation of the 'train' splits for each of the languages and likewise for 'validation' and 'test'.\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThe dataset is motivated by the desire to advance sentiment analysis and text classification in Latin American Spanish and Portuguese.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe authors gathered the reviews from the marketplaces in Argentina, Colombia, Peru, Uruguay, Chile, Venezuela and Mexico for the Spanish language and from Brasil for Portuguese. They prioritized reviews that contained relevant semantic content by applying a ranking filter based in the lenght and the valorization (difference betweent the number of likes and dislikes) of the review. They then ensured the correct language by applying a semi-automatic language detection algorithm, only retaining those of the target language. No normalization was applied to the review content or title.\n\n\nOriginal products categories were grouped in higher level categories, resulting in five different types of products: \"Home\" (Hogar / Casa), \"Technology and electronics\" (Tecnologı́a y electrónica\n/ Tecnologia e electronica), \"Health, Dress and Personal Care\" (Salud, ropa y cuidado personal / Saúde, roupas e cuidado pessoal) and \"Arts and Entertainment\" (Arte y entretenimiento / Arte e Entretenimiento).", "#### Who are the source language producers?\n\n\nThe original text comes from Mercado Libre customers reviewing products on the marketplace across a variety of product categories.", "### Annotations", "#### Annotation process\n\n\nEach of the fields included are submitted by the user with the review or otherwise associated with the review. No manual or machine-driven annotation was necessary.", "#### Who are the annotators?\n\n\nN/A", "### Personal and Sensitive Information\n\n\nMercado Libre Reviews are submitted by users with the knowledge and attention of being public. The reviewer ID's included in this dataset are anonymized, meaning that they are disassociated from the original user profiles. However, these fields would likely be easy to deannoymize given the public and identifying nature of free-form text responses.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nAlthough Spanish and Portuguese languages are relatively high resource, most of the data is collected from European or United State users. This dataset is part of an effort to encourage text classification research in languages other than English and European Spanish and Portuguese. Such work increases the accessibility of natural language technology to more regions and cultures.", "### Discussion of Biases\n\n\nThe data included here are from unverified consumers. Some percentage of these reviews may be fake or contain misleading or offensive language.", "### Other Known Limitations\n\n\nThe dataset is constructed so that the distribution of star ratings is roughly balanced. This feature has some advantages for purposes of classification, but some types of language may be over or underrepresented relative to the original distribution of reviews to acheive this balance.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nPublished by Lautaro Estienne, Matías Vera and Leonardo Rey Vega. Managed by the Signal Processing in Comunications Laboratory of the Electronic Department at the Engeneering School of the Buenos Aires University (UBA).", "### Licensing Information\n\n\nAmazon has licensed this dataset under its own agreement, to be found at the dataset webpage here:\nURL\n\n\nPlease cite the following paper if you found this dataset useful:\n\n\n(CITATION)", "### Contributions" ]

[ "TAGS\n#task_categories-text-classification #task_ids-language-modeling #task_ids-sentiment-classification #task_ids-sentiment-scoring #task_ids-topic-classification #annotations_creators-found #language_creators-found #source_datasets-original #language-Spanish #language-Portuguese #license-other #region-us \n", "### Dataset Summary\n\n\nWe provide a Mercado Libre product reviews dataset for spanish and portuguese text classification. The dataset contains reviews in these two languages collected between August 2020 and January 2021. Each record in the dataset contains the review content and title, the star rating, the country where it was pubilshed and the product category (arts, technology, etc.). The corpus is roughly balanced across stars, so each star rating constitutes approximately 20% of the reviews in each language.\n\n\n\nTable shows the number of samples per star rate in each split. There is a total of 442.108 training samples in spanish and 253.955 in portuguese. We limited the number of reviews per product to 30 and we perform a ranked inclusion of the downloaded reviews to include those with rich semantic content. In these ranking, the lenght of the review content and the valorization (difference between likes and dislikes) was prioritized. For more details on this process, see (CITATION).\n\n\nReviews in spanish were obtained from 8 different Latin Amercian countries (Argentina, Colombia, Peru, Uruguay, Chile, Venezuela and Mexico), and portuguese reviews were extracted from Brasil. To match the language with its respective country, we applied a language detection algorithm based on the works of Joulin et al. (2016a and 2016b) to determine the language of the review text and we removed reviews that were not written in the expected language.", "### Languages\n\n\nThe dataset contains reviews in Latin American Spanish and Portuguese.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nEach data instance corresponds to a review. Each split is stored in a separated '.csv' file, so every row in each file consists on a review. For example, here we show a snippet of the spanish training split:", "### Data Fields\n\n\n* 'country': The string identifier of the country. It could be one of the following: 'MLA' (Argentina), 'MCO' (Colombia), 'MPE' (Peru), 'MLU' (Uruguay), 'MLC' (Chile), 'MLV' (Venezuela), 'MLM' (Mexico) or 'MLB' (Brasil).\n* 'category': String representation of the product's category. It could be one of the following:\n\t+ Hogar / Casa\n\t+ Tecnologı́a y electrónica / Tecnologia e electronica\n\t+ Salud, ropa y cuidado personal / Saúde, roupas e cuidado pessoal\n\t+ Arte y entretenimiento / Arte e Entretenimiento\n\t+ Alimentos y Bebidas / Alimentos e Bebidas\n* 'review\\_content': The text content of the review.\n* 'review\\_title': The text title of the review.\n* 'review\\_rate': An int between 1-5 indicating the number of stars.", "### Data Splits\n\n\nEach language configuration comes with it's own 'train', 'validation', and 'test' splits. The 'all\\_languages' split is simply a concatenation of the corresponding split across all languages. That is, the 'train' split for 'all\\_languages' is a concatenation of the 'train' splits for each of the languages and likewise for 'validation' and 'test'.\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThe dataset is motivated by the desire to advance sentiment analysis and text classification in Latin American Spanish and Portuguese.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe authors gathered the reviews from the marketplaces in Argentina, Colombia, Peru, Uruguay, Chile, Venezuela and Mexico for the Spanish language and from Brasil for Portuguese. They prioritized reviews that contained relevant semantic content by applying a ranking filter based in the lenght and the valorization (difference betweent the number of likes and dislikes) of the review. They then ensured the correct language by applying a semi-automatic language detection algorithm, only retaining those of the target language. No normalization was applied to the review content or title.\n\n\nOriginal products categories were grouped in higher level categories, resulting in five different types of products: \"Home\" (Hogar / Casa), \"Technology and electronics\" (Tecnologı́a y electrónica\n/ Tecnologia e electronica), \"Health, Dress and Personal Care\" (Salud, ropa y cuidado personal / Saúde, roupas e cuidado pessoal) and \"Arts and Entertainment\" (Arte y entretenimiento / Arte e Entretenimiento).", "#### Who are the source language producers?\n\n\nThe original text comes from Mercado Libre customers reviewing products on the marketplace across a variety of product categories.", "### Annotations", "#### Annotation process\n\n\nEach of the fields included are submitted by the user with the review or otherwise associated with the review. No manual or machine-driven annotation was necessary.", "#### Who are the annotators?\n\n\nN/A", "### Personal and Sensitive Information\n\n\nMercado Libre Reviews are submitted by users with the knowledge and attention of being public. The reviewer ID's included in this dataset are anonymized, meaning that they are disassociated from the original user profiles. However, these fields would likely be easy to deannoymize given the public and identifying nature of free-form text responses.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nAlthough Spanish and Portuguese languages are relatively high resource, most of the data is collected from European or United State users. This dataset is part of an effort to encourage text classification research in languages other than English and European Spanish and Portuguese. Such work increases the accessibility of natural language technology to more regions and cultures.", "### Discussion of Biases\n\n\nThe data included here are from unverified consumers. Some percentage of these reviews may be fake or contain misleading or offensive language.", "### Other Known Limitations\n\n\nThe dataset is constructed so that the distribution of star ratings is roughly balanced. This feature has some advantages for purposes of classification, but some types of language may be over or underrepresented relative to the original distribution of reviews to acheive this balance.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nPublished by Lautaro Estienne, Matías Vera and Leonardo Rey Vega. Managed by the Signal Processing in Comunications Laboratory of the Electronic Department at the Engeneering School of the Buenos Aires University (UBA).", "### Licensing Information\n\n\nAmazon has licensed this dataset under its own agreement, to be found at the dataset webpage here:\nURL\n\n\nPlease cite the following paper if you found this dataset useful:\n\n\n(CITATION)", "### Contributions" ]

[ "arxiv:1803.09010", "region:us" ]

[ "1803.09010" ]

[]

[ "# Million English Numbers\nA list of a million American English numbers, under a AGPL 3.0 license. This datasheet is inspired by Datasheets for Datasets.", "## Sample", "## Motivation\n\nThis dataset was created as a toy sample of text for use in natural language processing, in machine learning.\n\nThe goal was to create small samples of text with minimal variation and results that could be easily audited (observe how often the model predicts \"eighty twenty hundred three ten forty\").\n\nThis is original research, produced by the linguistic model in the NodeJS package 'written-number' by Pedro Tacla Yamada, freely available on npm.\n\nThe estimated cost of creating the dataset is minimal, and subsidized with private funds.", "## Composition\n\nThe instances that comprise the dataset are spelled-out integers, in colloquial Mid-Atlantic American English, identifiable to a speaker born around the year 2000.\n\nThere are one million instances, from 0 to 999999 consecutively.\n\nThe instances consist of ASCII text, delimited by line feeds.\n\nCounting lines from zero, the line number of each instance is its integer value.\n\nNo information is missing from each instance.\n\nIn the related _fast.ai_ 'HUMAN_NUMBERS' dataset, the split is between 1-7999, and 8001-9999. A user may elect to split this dataset similarly, with the last percentages of lines used for validation or testing.\n\nThere are no known errors or sources of noise or redundancies in the dataset.\n\nThe dataset is self-contained.\n\nThe dataset is not confidential, and its method of generation is public as well.\n\nThe dataset will probably not be offensive / insulting / threatening / anxiety-inducing to many people. The numerologically-minded may wish to exercise discernment when choosing which numbers to use: all of the auspicious numbers, all of the inauspicious numbers, all of the meaningful numbers, for all numerological traditions, are included in this dataset, without any emphasis or warnings besides sequential ordering.\n\nThe dataset does not relate to people, except by using human language to express integers.", "## Collection\n\nThe data was directly observed from the 'written-number' npm package.\n\nTo rebuild this dataset, run 'docker run -e MAXINT=1000000 -e WN=written-number -w /x node sh -c 'npm i $WN 2>1 >/dev/null; node -e \"const w=require(URL.WN);for(i=0;i<URL.MAXINT;i++) URL(w(i,{noAnd: true}))\" | tr \"-\" \" \"'' on any x86 machine with Docker. Manual spot-checking confirmed the results.\n\nThis is a subset of the set of integers, in increasing order, with no omissions, starting from zero.\n\nThis was collected by one individual, writing minimal code, using free time donated to the project.\n\nThe data was collected at one point in time, using colloquial Mid-Atlantic American English. The idea of integers including zero is long-standing, and dates back to Babylonians in 700 BCE, the Olmec and Maya in 100 BCE, Brahmagupta in 628 CE.\n\nThere was no IRB involved in the making of this data product.\n\nThe instances individually do not relate to people.", "## Preprocessing\n\nThe default output of the version of 'written-number' puts a hyphen between the tens and ones place, and this hyphen was translated into a space in the output.\nFurther, the default conjunction _'and'_ between the hundreds and tens place was removed, as visible above in the sample (_'nine hundred ninety'_).\n\nThis raw data was not saved.\n\nThe code to regenerate the raw data, and the code to run the preprocessing, is available in this datasheet.", "## Uses\n\nThis dataset has not been used for any tasks already. It has been inspired by a smaller _fast.ai_ dataset used pedagogically for training NLP models.\n\nThe dataset could also be used in place of the original code that generated it, if someone desired a list of human-readable numbers in this dialect of English.\nThe dataset could also be used as a normative spelling of integers (to correct someone writing \"fourty\" for instance).\nThe dataset could also be used, as an artifact of language, could be used to establish normative language for reading integers.\n\nThe dataset is composed of only one of the many languages and dialects that 'written-number' produces.\nA native user of another dialect might elect to change language or dialect, for easier auditing of the output of the language model trained on the numbers.\nSpecifically, someone might expect to see _'nine lakh ninety nine thousand nine hundred ninety nine'_ instead of _'nine hundred ninety nine thousand nine hundred ninety nine'_ as the last line of the sample above.\n\nIt is important to not use this dataset as a normative spelling of integers, especially to impose American English readings of integers on speakers of other dialects of English.", "## Distribution\n\nThis dataset is distributed worldwide.\n\nIt is available on Huggingface, at URL .\n\nIt is currently available.\n\nThe license is AGPL 3.0.\n\nThe library 'written-number' is available under the MIT license, and its output is not currently restricted by license.\n\nNo third parties have imposed any restrictions on the data associated with these instance of written numbers.\n\nNo export controls or other regulatory restrictions currently apply to the dataset or to individual instances in the dataset.", "## Maintenance\n\nHuggingface is currently hosting the dataset, and @lsb is maintaining the dataset.\n\nContact is available via pull-request, and via email at 'hi@URL' .\n\nThere are currently no errata, and the full edit history of the dataset is available in the 'git' repository in which this datasheet is included.\n\nThis dataset is not expected to frequently update. Any users of the dataset may elect to 'git pull' any updates.\n\nThe data does not relate to people, and there are no limits on the retention of the data associated with the instances.\n\nOlder versions of the dataset continue to be supported and hosted and maintained, through the 'git' repository that includes the full edit history of the dataset.\n\nIf others wish to extend or augment or build on or contribute to the dataset, a mechanism available is to upload additional datasets to Huggingface." ]

[ "TAGS\n#arxiv-1803.09010 #region-us \n", "# Million English Numbers\nA list of a million American English numbers, under a AGPL 3.0 license. This datasheet is inspired by Datasheets for Datasets.", "## Sample", "## Motivation\n\nThis dataset was created as a toy sample of text for use in natural language processing, in machine learning.\n\nThe goal was to create small samples of text with minimal variation and results that could be easily audited (observe how often the model predicts \"eighty twenty hundred three ten forty\").\n\nThis is original research, produced by the linguistic model in the NodeJS package 'written-number' by Pedro Tacla Yamada, freely available on npm.\n\nThe estimated cost of creating the dataset is minimal, and subsidized with private funds.", "## Composition\n\nThe instances that comprise the dataset are spelled-out integers, in colloquial Mid-Atlantic American English, identifiable to a speaker born around the year 2000.\n\nThere are one million instances, from 0 to 999999 consecutively.\n\nThe instances consist of ASCII text, delimited by line feeds.\n\nCounting lines from zero, the line number of each instance is its integer value.\n\nNo information is missing from each instance.\n\nIn the related _fast.ai_ 'HUMAN_NUMBERS' dataset, the split is between 1-7999, and 8001-9999. A user may elect to split this dataset similarly, with the last percentages of lines used for validation or testing.\n\nThere are no known errors or sources of noise or redundancies in the dataset.\n\nThe dataset is self-contained.\n\nThe dataset is not confidential, and its method of generation is public as well.\n\nThe dataset will probably not be offensive / insulting / threatening / anxiety-inducing to many people. The numerologically-minded may wish to exercise discernment when choosing which numbers to use: all of the auspicious numbers, all of the inauspicious numbers, all of the meaningful numbers, for all numerological traditions, are included in this dataset, without any emphasis or warnings besides sequential ordering.\n\nThe dataset does not relate to people, except by using human language to express integers.", "## Collection\n\nThe data was directly observed from the 'written-number' npm package.\n\nTo rebuild this dataset, run 'docker run -e MAXINT=1000000 -e WN=written-number -w /x node sh -c 'npm i $WN 2>1 >/dev/null; node -e \"const w=require(URL.WN);for(i=0;i<URL.MAXINT;i++) URL(w(i,{noAnd: true}))\" | tr \"-\" \" \"'' on any x86 machine with Docker. Manual spot-checking confirmed the results.\n\nThis is a subset of the set of integers, in increasing order, with no omissions, starting from zero.\n\nThis was collected by one individual, writing minimal code, using free time donated to the project.\n\nThe data was collected at one point in time, using colloquial Mid-Atlantic American English. The idea of integers including zero is long-standing, and dates back to Babylonians in 700 BCE, the Olmec and Maya in 100 BCE, Brahmagupta in 628 CE.\n\nThere was no IRB involved in the making of this data product.\n\nThe instances individually do not relate to people.", "## Preprocessing\n\nThe default output of the version of 'written-number' puts a hyphen between the tens and ones place, and this hyphen was translated into a space in the output.\nFurther, the default conjunction _'and'_ between the hundreds and tens place was removed, as visible above in the sample (_'nine hundred ninety'_).\n\nThis raw data was not saved.\n\nThe code to regenerate the raw data, and the code to run the preprocessing, is available in this datasheet.", "## Uses\n\nThis dataset has not been used for any tasks already. It has been inspired by a smaller _fast.ai_ dataset used pedagogically for training NLP models.\n\nThe dataset could also be used in place of the original code that generated it, if someone desired a list of human-readable numbers in this dialect of English.\nThe dataset could also be used as a normative spelling of integers (to correct someone writing \"fourty\" for instance).\nThe dataset could also be used, as an artifact of language, could be used to establish normative language for reading integers.\n\nThe dataset is composed of only one of the many languages and dialects that 'written-number' produces.\nA native user of another dialect might elect to change language or dialect, for easier auditing of the output of the language model trained on the numbers.\nSpecifically, someone might expect to see _'nine lakh ninety nine thousand nine hundred ninety nine'_ instead of _'nine hundred ninety nine thousand nine hundred ninety nine'_ as the last line of the sample above.\n\nIt is important to not use this dataset as a normative spelling of integers, especially to impose American English readings of integers on speakers of other dialects of English.", "## Distribution\n\nThis dataset is distributed worldwide.\n\nIt is available on Huggingface, at URL .\n\nIt is currently available.\n\nThe license is AGPL 3.0.\n\nThe library 'written-number' is available under the MIT license, and its output is not currently restricted by license.\n\nNo third parties have imposed any restrictions on the data associated with these instance of written numbers.\n\nNo export controls or other regulatory restrictions currently apply to the dataset or to individual instances in the dataset.", "## Maintenance\n\nHuggingface is currently hosting the dataset, and @lsb is maintaining the dataset.\n\nContact is available via pull-request, and via email at 'hi@URL' .\n\nThere are currently no errata, and the full edit history of the dataset is available in the 'git' repository in which this datasheet is included.\n\nThis dataset is not expected to frequently update. Any users of the dataset may elect to 'git pull' any updates.\n\nThe data does not relate to people, and there are no limits on the retention of the data associated with the instances.\n\nOlder versions of the dataset continue to be supported and hosted and maintained, through the 'git' repository that includes the full edit history of the dataset.\n\nIf others wish to extend or augment or build on or contribute to the dataset, a mechanism available is to upload additional datasets to Huggingface." ]

[ "task_categories:table-to-text", "task_categories:question-answering", "task_categories:summarization", "task_categories:text-generation", "task_ids:abstractive-qa", "task_ids:closed-domain-qa", "task_ids:extractive-qa", "task_ids:language-modeling", "task_ids:named-entity-recognition", "task_ids:text-simplification", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:en", "license:other", "conditional-text-generation", "structure-prediction", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for World Bank Project Documents", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository: URL\n- Paper: Forthcoming\n- Point of Contact: Luke Jordan (lukej at mit)", "### Dataset Summary\n\nThis is a dataset of documents related to World Bank development projects in the period 1947-2020. The dataset includes\nthe documents used to propose or describe projects when they are launched, and those in the review. The documents are indexed\nby the World Bank project ID, which can be used to obtain features from multiple publicly available tabular datasets.", "### Supported Tasks and Leaderboards\n\nNo leaderboard yet. A wide range of possible supported tasks, including varieties of summarization, QA, and language modelling. To date, the datasets have been used primarily in conjunction with tabular data (via BERT embeddings) to predict project outcomes.", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances", "### Data Fields\n\n* World Bank project ID\n* Document text\n* Document type: \"APPROVAL\" for documents written at the beginning of a project, when it is approved; and \"REVIEW\" for documents written at the end of a project", "### Data Splits\n\nTo allow for open exploration, and since different applications will want to do splits based on different sampling weights, we have not done a train test split but left all files in the train branch.", "## Dataset Creation", "### Source Data\n\nDocuments were scraped from the World Bank's public project archive, following links through to specific project pages and then collecting the text files made available by the World Bank.", "### Annotations\n\nThis dataset is not annotated.", "### Personal and Sensitive Information\n\nNone.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nAffects development projects, which can have large-scale consequences for many millions of people.", "### Discussion of Biases\n\nThe documents reflect the history of development, which has well-documented and well-studied issues with the imposition of developed world ideas on developing world countries. The documents provide a way to study those in the field of development, but should not be used for their description of the recipient countries, since that language will reflect a multitude of biases, especially in the earlier reaches of the historical projects.", "## Additional Information", "### Dataset Curators\n\nLuke Jordan, Busani Ndlovu.", "### Licensing Information\n\nMIT +no-false-attribs license (MITNFA).\n\n\n\n@dataset{world-bank-project-documents,\n author = {Jordan, Luke and Ndlovu, Busani and Shenk, Justin},\n title = {World Bank Project Documents Dataset},\n year = {2021}\n}", "### Contributions\n\nThanks to @luke-grassroot, @FRTNX and @justinshenk for adding this dataset." ]

[ "TAGS\n#task_categories-table-to-text #task_categories-question-answering #task_categories-summarization #task_categories-text-generation #task_ids-abstractive-qa #task_ids-closed-domain-qa #task_ids-extractive-qa #task_ids-language-modeling #task_ids-named-entity-recognition #task_ids-text-simplification #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-unknown #source_datasets-original #language-English #license-other #conditional-text-generation #structure-prediction #region-us \n", "# Dataset Card for World Bank Project Documents", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository: URL\n- Paper: Forthcoming\n- Point of Contact: Luke Jordan (lukej at mit)", "### Dataset Summary\n\nThis is a dataset of documents related to World Bank development projects in the period 1947-2020. The dataset includes\nthe documents used to propose or describe projects when they are launched, and those in the review. The documents are indexed\nby the World Bank project ID, which can be used to obtain features from multiple publicly available tabular datasets.", "### Supported Tasks and Leaderboards\n\nNo leaderboard yet. A wide range of possible supported tasks, including varieties of summarization, QA, and language modelling. To date, the datasets have been used primarily in conjunction with tabular data (via BERT embeddings) to predict project outcomes.", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances", "### Data Fields\n\n* World Bank project ID\n* Document text\n* Document type: \"APPROVAL\" for documents written at the beginning of a project, when it is approved; and \"REVIEW\" for documents written at the end of a project", "### Data Splits\n\nTo allow for open exploration, and since different applications will want to do splits based on different sampling weights, we have not done a train test split but left all files in the train branch.", "## Dataset Creation", "### Source Data\n\nDocuments were scraped from the World Bank's public project archive, following links through to specific project pages and then collecting the text files made available by the World Bank.", "### Annotations\n\nThis dataset is not annotated.", "### Personal and Sensitive Information\n\nNone.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nAffects development projects, which can have large-scale consequences for many millions of people.", "### Discussion of Biases\n\nThe documents reflect the history of development, which has well-documented and well-studied issues with the imposition of developed world ideas on developing world countries. The documents provide a way to study those in the field of development, but should not be used for their description of the recipient countries, since that language will reflect a multitude of biases, especially in the earlier reaches of the historical projects.", "## Additional Information", "### Dataset Curators\n\nLuke Jordan, Busani Ndlovu.", "### Licensing Information\n\nMIT +no-false-attribs license (MITNFA).\n\n\n\n@dataset{world-bank-project-documents,\n author = {Jordan, Luke and Ndlovu, Busani and Shenk, Justin},\n title = {World Bank Project Documents Dataset},\n year = {2021}\n}", "### Contributions\n\nThanks to @luke-grassroot, @FRTNX and @justinshenk for adding this dataset." ]

[ "region:us" ]

[]

[]

[ "# dureader\n\n数据来自千言DuReader数据集，这里是原始地址 千言数据集：阅读理解。\n\n> 本数据集只用作学术研究使用。如果本仓库涉及侵权行为，会立即删除。\n\n目前包含以下两个子集：\n\n* DuReader-robust\n* DuReader-checklist" ]

[ "TAGS\n#region-us \n", "# dureader\n\n数据来自千言DuReader数据集，这里是原始地址 千言数据集：阅读理解。\n\n> 本数据集只用作学术研究使用。如果本仓库涉及侵权行为，会立即删除。\n\n目前包含以下两个子集：\n\n* DuReader-robust\n* DuReader-checklist" ]

[ "region:us" ]

[]

[]

[ "# KgCLUE-Knowledge\n\nThe original data is from CLUEbenchmark/KgCLUE.\n\nHere is a JSON version of the original knowledge base.", "## Usage" ]

[ "TAGS\n#region-us \n", "# KgCLUE-Knowledge\n\nThe original data is from CLUEbenchmark/KgCLUE.\n\nHere is a JSON version of the original knowledge base.", "## Usage" ]

[ "region:us" ]

[]

[]

[ "# question-answering-datasets\n\n\nDatasets for Question Answering task!" ]

[ "TAGS\n#region-us \n", "# question-answering-datasets\n\n\nDatasets for Question Answering task!" ]

[ "region:us" ]

[]

[]

[ "# CodeParrot Dataset Cleaned (train)\n\nTrain split of CodeParrot Dataset Cleaned.", "## Dataset structure" ]

[ "TAGS\n#region-us \n", "# CodeParrot Dataset Cleaned (train)\n\nTrain split of CodeParrot Dataset Cleaned.", "## Dataset structure" ]

[ "region:us" ]

[]

[]

[ "# CodeParrot Dataset Cleaned (valid)\n\nTrain split of CodeParrot Dataset Cleaned.", "## Dataset structure" ]

[ "TAGS\n#region-us \n", "# CodeParrot Dataset Cleaned (valid)\n\nTrain split of CodeParrot Dataset Cleaned.", "## Dataset structure" ]

[ "python", "code", "region:us" ]

[]

[]

[ "# CodeParrot Dataset Cleaned", "## What is it?\n\nA dataset of Python files from Github. This is the deduplicated version of the codeparrot.", "## Processing\nThe original dataset contains a lot of duplicated and noisy data. Therefore, the dataset was cleaned with the following steps:\n- Deduplication \n - Remove exact matches\n- Filtering\n - Average line length < 100\n - Maximum line length < 1000\n - Alpha numeric characters fraction > 0.25\n - Remove auto-generated files (keyword search)\n \nFor more details see the preprocessing script in the transformers repository here.", "## Splits\nThe dataset is split in a train and validation split used for training and evaluation.", "## Structure\nThis dataset has ~50GB of code and 5361373 files." ]

[ "TAGS\n#python #code #region-us \n", "# CodeParrot Dataset Cleaned", "## What is it?\n\nA dataset of Python files from Github. This is the deduplicated version of the codeparrot.", "## Processing\nThe original dataset contains a lot of duplicated and noisy data. Therefore, the dataset was cleaned with the following steps:\n- Deduplication \n - Remove exact matches\n- Filtering\n - Average line length < 100\n - Maximum line length < 1000\n - Alpha numeric characters fraction > 0.25\n - Remove auto-generated files (keyword search)\n \nFor more details see the preprocessing script in the transformers repository here.", "## Splits\nThe dataset is split in a train and validation split used for training and evaluation.", "## Structure\nThis dataset has ~50GB of code and 5361373 files." ]

[ "task_categories:text-generation", "task_ids:language-modeling", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:unknown", "language:code", "license:other", "region:us" ]

[]

[ "code" ]

[ "### How to use it\n\n\nThe GitHub Code dataset is a very large dataset so for most use cases it is recommended to make use of the streaming API of 'datasets'. You can load and iterate through the dataset with the following two lines of code:\n\n\nYou can see that besides the code, repo name, and path also the programming language, license, and the size of the file are part of the dataset. You can also filter the dataset for any subset of the 30 included languages (see the full list below) in the dataset. Just pass the list of languages as a list. E.g. if your dream is to build a Codex model for Dockerfiles use the following configuration:\n\n\nWe also have access to the license of the origin repo of a file so we can filter for licenses in the same way we filtered for languages:\n\n\nNaturally, you can also download the full dataset. Note that this will download ~300GB compressed text data and the uncompressed dataset will take up ~1TB of storage:\n\n\nData Structure\n--------------", "### Data Instances", "### Data Fields\n\n\nField: code, Type: string, Description: content of source file\nField: repo\\_name, Type: string, Description: name of the GitHub repository\nField: path, Type: string, Description: path of file in GitHub repository\nField: language, Type: string, Description: programming language as inferred by extension\nField: license, Type: string, Description: license of GitHub repository\nField: size, Type: int, Description: size of source file in bytes", "### Data Splits\n\n\nThe dataset only contains a train split.\n\n\nLanguages\n---------\n\n\nThe dataset contains 30 programming languages with over 60 extensions:\n\n\nLicenses\n--------\n\n\nEach example is also annotated with the license of the associated repository. There are in total 15 licenses:\n\n\nDataset Statistics\n------------------\n\n\nThe dataset contains 115M files and the sum of all the source code file sizes is 873 GB (note that the size of the dataset is larger due to the extra fields). A breakdown per language is given in the plot and table below:\n\n\n!dataset-statistics\n\n\n\nDataset Creation\n----------------\n\n\nThe dataset was created in two steps:\n\n\n1. Files of with the extensions given in the list above were retrieved from the GitHub dataset on BigQuery (full query here). The query was executed on *Mar 16, 2022, 6:23:39 PM UTC+1*.\n2. Files with lines longer than 1000 characters and duplicates (exact duplicates ignoring whitespaces) were dropped (full preprocessing script here).\n\n\nConsiderations for Using the Data\n---------------------------------\n\n\nThe dataset consists of source code from a wide range of repositories. As such they can potentially include harmful or biased code as well as sensitive information like passwords or usernames.\n\n\nReleases\n--------\n\n\nYou can load any older version of the dataset with the 'revision' argument:", "### v1.0\n\n\n* Initial release of dataset\n* The query was executed on *Feb 14, 2022, 12:03:16 PM UTC+1*", "### v1.1\n\n\n* Fix missing Scala/TypeScript\n* Fix deduplication issue with inconsistent Python 'hash'\n* The query was executed on *Mar 16, 2022, 6:23:39 PM UTC+1*" ]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-multilingual #size_categories-unknown #language-code #license-other #region-us \n", "### How to use it\n\n\nThe GitHub Code dataset is a very large dataset so for most use cases it is recommended to make use of the streaming API of 'datasets'. You can load and iterate through the dataset with the following two lines of code:\n\n\nYou can see that besides the code, repo name, and path also the programming language, license, and the size of the file are part of the dataset. You can also filter the dataset for any subset of the 30 included languages (see the full list below) in the dataset. Just pass the list of languages as a list. E.g. if your dream is to build a Codex model for Dockerfiles use the following configuration:\n\n\nWe also have access to the license of the origin repo of a file so we can filter for licenses in the same way we filtered for languages:\n\n\nNaturally, you can also download the full dataset. Note that this will download ~300GB compressed text data and the uncompressed dataset will take up ~1TB of storage:\n\n\nData Structure\n--------------", "### Data Instances", "### Data Fields\n\n\nField: code, Type: string, Description: content of source file\nField: repo\\_name, Type: string, Description: name of the GitHub repository\nField: path, Type: string, Description: path of file in GitHub repository\nField: language, Type: string, Description: programming language as inferred by extension\nField: license, Type: string, Description: license of GitHub repository\nField: size, Type: int, Description: size of source file in bytes", "### Data Splits\n\n\nThe dataset only contains a train split.\n\n\nLanguages\n---------\n\n\nThe dataset contains 30 programming languages with over 60 extensions:\n\n\nLicenses\n--------\n\n\nEach example is also annotated with the license of the associated repository. There are in total 15 licenses:\n\n\nDataset Statistics\n------------------\n\n\nThe dataset contains 115M files and the sum of all the source code file sizes is 873 GB (note that the size of the dataset is larger due to the extra fields). A breakdown per language is given in the plot and table below:\n\n\n!dataset-statistics\n\n\n\nDataset Creation\n----------------\n\n\nThe dataset was created in two steps:\n\n\n1. Files of with the extensions given in the list above were retrieved from the GitHub dataset on BigQuery (full query here). The query was executed on *Mar 16, 2022, 6:23:39 PM UTC+1*.\n2. Files with lines longer than 1000 characters and duplicates (exact duplicates ignoring whitespaces) were dropped (full preprocessing script here).\n\n\nConsiderations for Using the Data\n---------------------------------\n\n\nThe dataset consists of source code from a wide range of repositories. As such they can potentially include harmful or biased code as well as sensitive information like passwords or usernames.\n\n\nReleases\n--------\n\n\nYou can load any older version of the dataset with the 'revision' argument:", "### v1.0\n\n\n* Initial release of dataset\n* The query was executed on *Feb 14, 2022, 12:03:16 PM UTC+1*", "### v1.1\n\n\n* Fix missing Scala/TypeScript\n* Fix deduplication issue with inconsistent Python 'hash'\n* The query was executed on *Mar 16, 2022, 6:23:39 PM UTC+1*" ]

[ "region:us" ]

[]

[]

[ "# Red Wine Dataset \n\nThis dataset contains the red wine dataset found here. See also this example of a Scikit-Learn model trained on this dataset." ]

[ "TAGS\n#region-us \n", "# Red Wine Dataset \n\nThis dataset contains the red wine dataset found here. See also this example of a Scikit-Learn model trained on this dataset." ]

[ "region:us" ]

[]

[]

[ "# RecipeNLG: A Cooking Recipes Dataset\nRecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation - Lite version\n\nThe dataset contains '7,198' cooking recipes ('>7K'). \nIt's processed in more careful way and provides more samples than any other dataset in the area.", "## How to use\n\n\n\nLoad 'm3hrdadfi/recipe_nlg_lite' dataset using 'load_dataset':\n\n\n\nOutput:", "## Examples" ]

[ "TAGS\n#region-us \n", "# RecipeNLG: A Cooking Recipes Dataset\nRecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation - Lite version\n\nThe dataset contains '7,198' cooking recipes ('>7K'). \nIt's processed in more careful way and provides more samples than any other dataset in the area.", "## How to use\n\n\n\nLoad 'm3hrdadfi/recipe_nlg_lite' dataset using 'load_dataset':\n\n\n\nOutput:", "## Examples" ]

[ "region:us" ]

[]

[]

[ "# Indonesia News Dataset", "## Source \n\n- Kompas\n- Detik (Cooming Soon)\n- Liputan6 (Cooming Soon)\n- Tempo (Cooming Soon)\n- Okezone (Cooming Soon)\n- CNN Indonesia (Cooming Soon)\n- CNBC Indonesia (Cooming Soon)", "## Roadmap \n\n1. Web Data Crawling and Indexing \n2. NER Model and Sentiment Analysis Model \n3. Data Visualization", "## Contact \n\n- You Can Contact Me at twitter" ]

[ "TAGS\n#region-us \n", "# Indonesia News Dataset", "## Source \n\n- Kompas\n- Detik (Cooming Soon)\n- Liputan6 (Cooming Soon)\n- Tempo (Cooming Soon)\n- Okezone (Cooming Soon)\n- CNN Indonesia (Cooming Soon)\n- CNBC Indonesia (Cooming Soon)", "## Roadmap \n\n1. Web Data Crawling and Indexing \n2. NER Model and Sentiment Analysis Model \n3. Data Visualization", "## Contact \n\n- You Can Contact Me at twitter" ]

[ "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:es", "license:cc-by-nc-nd-4.0", "region:us" ]

[]

[ "es" ]

[ "# mammut-corpus-venezuela\n\nHuggingFace Dataset for testing purposes. The train dataset is 'mammut/mammut-corpus-venezuela'.", "## 1. How to use\n\nHow to load this dataset directly with the datasets library:\n\n'>>> from datasets import load_dataset' \n'>>> dataset = load_dataset(\"mammut/mammut-corpus-venezuela\")'", "## 2. Dataset Summary\n\nmammut-corpus-venezuela is a dataset for Spanish language modeling. This dataset comprises a large number of Venezuelan and Latin-American Spanish texts, manually selected and collected in 2021. The data was collected by a process of web scraping from different portals, downloading of Telegram group chats' history, and selecting of Venezuelan and Latin-American Spanish corpus available online. The texts come from Venezuelan Spanish speakers, subtitlers, journalists, politicians, doctors, writers, and online sellers. Social biases may be present, and a percentage of the texts may be fake or contain misleading or offensive language.\n\nEach record in the dataset contains the author of the text (anonymized for conversation authors), the date on which the text entered in the corpus, the text which was automatically tokenized at sentence level for sources other than conversations, the source of the text, the title of the text, the number of tokens (excluding punctuation marks) of the text, and the linguistic register of the text.\n\nThis is the test set for 'mammut/mammut-corpus-venezuela' dataset.", "## 3. Supported Tasks and Leaderboards\n\nThis dataset can be used for language modeling testing.", "## 4. Languages\n\nThe dataset contains Venezuelan and Latin-American Spanish.", "## 5. Dataset Structure\n\nDataset structure features.", "### 5.1 Data Instances\n\nAn example from the dataset:\n \n \n \"AUTHOR\":\"author in title\", \n \"TITLE\":\"Luis Alberto Buttó: Hecho en socialismo\", \n \"SENTENCE\":\"Históricamente, siempre fue así.\",\n \"DATE\":\"2021-07-04 07:18:46.918253\", \n \"SOURCE\":\"la patilla\", \n \"TOKENS\":\"4\", \n \"TYPE\":\"opinion/news\", \n\n\nThe average word token count are provided below:", "### 5.2 Total of tokens (no spelling marks)\n\nTest: 4,876,739.", "### 5.3 Data Fields\n\nThe data have several fields:\n\n AUTHOR: author of the text. It is anonymized for conversation authors. \n DATE: date on which the text was entered in the corpus. \n SENTENCE: text. It was automatically tokenized for sources other than conversations. \n SOURCE: source of the texts. \n TITLE: title of the text from which SENTENCE originates. \n TOKENS: number of tokens (excluding punctuation marks) of SENTENCE. \n TYPE: linguistic register of the text.", "### 5.4 Data Splits\n\nThe mammut-corpus-venezuela dataset has 2 splits: train and test. Below are the statistics:\n\nNumber of Instances in Split. \n\nTest: 157,011.", "## 6. Dataset Creation", "### 6.1 Curation Rationale\n\nThe purpose of the mammut-corpus-venezuela dataset is language modeling. It can be used for pre-training a model from scratch or for fine-tuning on another pre-trained model.", "### 6.2 Source Data\n\n6.2.1 Initial Data Collection and Normalization\n\nThe data consists of opinion articles and text messages. It was collected by a process of web scraping from different portals, downloading of Telegram group chats’ history and selecting of Venezuelan and Latin-American Spanish corpus available online.\n\nThe text from the web scraping process was separated in sentences and was automatically tokenized for sources other than conversations.\n\nAn arrow parquet file was created.\n\nText sources: El Estímulo (website), cinco8 (website), csm-1990 (oral speaking corpus), \"El atajo más largo\" (blog), El Pitazo (website), La Patilla (website), Venezuelan movies subtitles, Preseea Mérida (oral speaking corpus), Prodavinci (website), Runrunes (website), and Telegram group chats.\n\n6.2.2 Who are the source language producers?\n\nThe texts come from Venezuelan Spanish speakers, subtitlers, journalists, politicians, doctors, writers, and online sellers.", "## 6.3 Annotations\n\n6.3.1 Annotation process\n\nAt the moment the dataset does not contain any additional annotations.\n\n6.3.2 Who are the annotators?\n\nNot applicable.", "### 6.4 Personal and Sensitive Information\n\nThe data is partially anonymized. Also, there are messages from Telegram selling chats, some percentage of these messages may be fake or contain misleading or offensive language.", "## 7. Considerations for Using the Data", "### 7.1 Social Impact of Dataset\n\nThe purpose of this dataset is to help the development of language modeling models (pre-training or fine-tuning) in Venezuelan Spanish.", "### 7.2 Discussion of Biases\n\nMost of the content comes from political, economical and sociological opinion articles. Social biases may be present.", "### 7.3 Other Known Limitations\n\n(If applicable, description of the other limitations in the data.)\n\nNot applicable.", "## 8. Additional Information", "### 8.1 Dataset Curators\n\nThe data was originally collected by Lino Urdaneta and Miguel Riveros from URL.", "### 8.2 Licensing Information\n\nNot applicable.", "### 8.3 Citation Information\n\nNot applicable.", "### 8.4 Contributions\n\nNot applicable." ]

[ "TAGS\n#task_ids-language-modeling #annotations_creators-no-annotation #language_creators-expert-generated #multilinguality-monolingual #size_categories-unknown #source_datasets-original #language-Spanish #license-cc-by-nc-nd-4.0 #region-us \n", "# mammut-corpus-venezuela\n\nHuggingFace Dataset for testing purposes. The train dataset is 'mammut/mammut-corpus-venezuela'.", "## 1. How to use\n\nHow to load this dataset directly with the datasets library:\n\n'>>> from datasets import load_dataset' \n'>>> dataset = load_dataset(\"mammut/mammut-corpus-venezuela\")'", "## 2. Dataset Summary\n\nmammut-corpus-venezuela is a dataset for Spanish language modeling. This dataset comprises a large number of Venezuelan and Latin-American Spanish texts, manually selected and collected in 2021. The data was collected by a process of web scraping from different portals, downloading of Telegram group chats' history, and selecting of Venezuelan and Latin-American Spanish corpus available online. The texts come from Venezuelan Spanish speakers, subtitlers, journalists, politicians, doctors, writers, and online sellers. Social biases may be present, and a percentage of the texts may be fake or contain misleading or offensive language.\n\nEach record in the dataset contains the author of the text (anonymized for conversation authors), the date on which the text entered in the corpus, the text which was automatically tokenized at sentence level for sources other than conversations, the source of the text, the title of the text, the number of tokens (excluding punctuation marks) of the text, and the linguistic register of the text.\n\nThis is the test set for 'mammut/mammut-corpus-venezuela' dataset.", "## 3. Supported Tasks and Leaderboards\n\nThis dataset can be used for language modeling testing.", "## 4. Languages\n\nThe dataset contains Venezuelan and Latin-American Spanish.", "## 5. Dataset Structure\n\nDataset structure features.", "### 5.1 Data Instances\n\nAn example from the dataset:\n \n \n \"AUTHOR\":\"author in title\", \n \"TITLE\":\"Luis Alberto Buttó: Hecho en socialismo\", \n \"SENTENCE\":\"Históricamente, siempre fue así.\",\n \"DATE\":\"2021-07-04 07:18:46.918253\", \n \"SOURCE\":\"la patilla\", \n \"TOKENS\":\"4\", \n \"TYPE\":\"opinion/news\", \n\n\nThe average word token count are provided below:", "### 5.2 Total of tokens (no spelling marks)\n\nTest: 4,876,739.", "### 5.3 Data Fields\n\nThe data have several fields:\n\n AUTHOR: author of the text. It is anonymized for conversation authors. \n DATE: date on which the text was entered in the corpus. \n SENTENCE: text. It was automatically tokenized for sources other than conversations. \n SOURCE: source of the texts. \n TITLE: title of the text from which SENTENCE originates. \n TOKENS: number of tokens (excluding punctuation marks) of SENTENCE. \n TYPE: linguistic register of the text.", "### 5.4 Data Splits\n\nThe mammut-corpus-venezuela dataset has 2 splits: train and test. Below are the statistics:\n\nNumber of Instances in Split. \n\nTest: 157,011.", "## 6. Dataset Creation", "### 6.1 Curation Rationale\n\nThe purpose of the mammut-corpus-venezuela dataset is language modeling. It can be used for pre-training a model from scratch or for fine-tuning on another pre-trained model.", "### 6.2 Source Data\n\n6.2.1 Initial Data Collection and Normalization\n\nThe data consists of opinion articles and text messages. It was collected by a process of web scraping from different portals, downloading of Telegram group chats’ history and selecting of Venezuelan and Latin-American Spanish corpus available online.\n\nThe text from the web scraping process was separated in sentences and was automatically tokenized for sources other than conversations.\n\nAn arrow parquet file was created.\n\nText sources: El Estímulo (website), cinco8 (website), csm-1990 (oral speaking corpus), \"El atajo más largo\" (blog), El Pitazo (website), La Patilla (website), Venezuelan movies subtitles, Preseea Mérida (oral speaking corpus), Prodavinci (website), Runrunes (website), and Telegram group chats.\n\n6.2.2 Who are the source language producers?\n\nThe texts come from Venezuelan Spanish speakers, subtitlers, journalists, politicians, doctors, writers, and online sellers.", "## 6.3 Annotations\n\n6.3.1 Annotation process\n\nAt the moment the dataset does not contain any additional annotations.\n\n6.3.2 Who are the annotators?\n\nNot applicable.", "### 6.4 Personal and Sensitive Information\n\nThe data is partially anonymized. Also, there are messages from Telegram selling chats, some percentage of these messages may be fake or contain misleading or offensive language.", "## 7. Considerations for Using the Data", "### 7.1 Social Impact of Dataset\n\nThe purpose of this dataset is to help the development of language modeling models (pre-training or fine-tuning) in Venezuelan Spanish.", "### 7.2 Discussion of Biases\n\nMost of the content comes from political, economical and sociological opinion articles. Social biases may be present.", "### 7.3 Other Known Limitations\n\n(If applicable, description of the other limitations in the data.)\n\nNot applicable.", "## 8. Additional Information", "### 8.1 Dataset Curators\n\nThe data was originally collected by Lino Urdaneta and Miguel Riveros from URL.", "### 8.2 Licensing Information\n\nNot applicable.", "### 8.3 Citation Information\n\nNot applicable.", "### 8.4 Contributions\n\nNot applicable." ]

[ "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:es", "license:cc-by-nc-nd-4.0", "region:us" ]

[]

[ "es" ]

[ "# mammut-corpus-venezuela\n\nHuggingFace Dataset", "## 1. How to use\n\nHow to load this dataset directly with the datasets library:\n\n'>>> from datasets import load_dataset' \n'>>> dataset = load_dataset(\"mammut-corpus-venezuela\")'", "## 2. Dataset Summary\n\nmammut-corpus-venezuela is a dataset for Spanish language modeling. This dataset comprises a large number of Venezuelan and Latin-American Spanish texts, manually selected and collected in 2021. The data was collected by a process of web scraping from different portals, downloading of Telegram group chats' history, and selecting of Venezuelan and Latin-American Spanish corpus available online. The texts come from Venezuelan Spanish speakers, subtitlers, journalists, politicians, doctors, writers, and online sellers. Social biases may be present, and a percentage of the texts may be fake or contain misleading or offensive language.\n\nEach record in the dataset contains the author of the text (anonymized for conversation authors), the date on which the text entered in the corpus, the text which was automatically tokenized at sentence level for sources other than conversations, the source of the text, the title of the text, the number of tokens (excluding punctuation marks) of the text, and the linguistic register of the text.\n\nThe dataset counts with a train split and a test split.", "## 3. Supported Tasks and Leaderboards\n\nThis dataset can be used for language modeling.", "## 4. Languages\n\nThe dataset contains Venezuelan and Latin-American Spanish.", "## 5. Dataset Structure\n\nDataset structure features.", "### 5.1 Data Instances\n\nAn example from the dataset:\n \n \n \"AUTHOR\":\"author in title\", \n \"TITLE\":\"Luis Alberto Buttó: Hecho en socialismo\", \n \"SENTENCE\":\"Históricamente, siempre fue así.\",\n \"DATE\":\"2021-07-04 07:18:46.918253\", \n \"SOURCE\":\"la patilla\", \n \"TOKENS\":\"4\", \n \"TYPE\":\"opinion/news\", \n\n\nThe average word token count are provided below:", "### 5.2 Total of tokens (no spelling marks)\n\nTrain: 92,431,194. \nTest: 4,876,739 (in another file).", "### 5.3 Data Fields\n\nThe data have several fields:\n\n AUTHOR: author of the text. It is anonymized for conversation authors. \n DATE: date on which the text was entered in the corpus. \n SENTENCE: text. It was automatically tokenized for sources other than conversations. \n SOURCE: source of the texts. \n TITLE: title of the text from which SENTENCE originates. \n TOKENS: number of tokens (excluding punctuation marks) of SENTENCE. \n TYPE: linguistic register of the text.", "### 5.4 Data Splits\n\nThe mammut-corpus-venezuela dataset has 2 splits: train and test. Below are the statistics:\n\nNumber of Instances in Split. \n\nTrain: 2,983,302. \nTest: 157,011.", "## 6. Dataset Creation", "### 6.1 Curation Rationale\n\nThe purpose of the mammut-corpus-venezuela dataset is language modeling. It can be used for pre-training a model from scratch or for fine-tuning on another pre-trained model.", "### 6.2 Source Data\n\n6.2.1 Initial Data Collection and Normalization\n\nThe data consists of opinion articles and text messages. It was collected by a process of web scraping from different portals, downloading of Telegram group chats’ history and selecting of Venezuelan and Latin-American Spanish corpus available online.\n\nThe text from the web scraping process was separated in sentences and was automatically tokenized for sources other than conversations.\n\nAn arrow parquet file was created.\n\nText sources: El Estímulo (website), cinco8 (website), csm-1990 (oral speaking corpus), \"El atajo más largo\" (blog), El Pitazo (website), La Patilla (website), Venezuelan movies subtitles, Preseea Mérida (oral speaking corpus), Prodavinci (website), Runrunes (website), and Telegram group chats.\n\n6.2.2 Who are the source language producers?\n\nThe texts come from Venezuelan Spanish speakers, subtitlers, journalists, politicians, doctors, writers, and online sellers.", "## 6.3 Annotations\n\n6.3.1 Annotation process\n\nAt the moment the dataset does not contain any additional annotations.\n\n6.3.2 Who are the annotators?\n\nNot applicable.", "### 6.4 Personal and Sensitive Information\n\nThe data is partially anonymized. Also, there are messages from Telegram selling chats, some percentage of these messages may be fake or contain misleading or offensive language.", "## 7. Considerations for Using the Data", "### 7.1 Social Impact of Dataset\n\nThe purpose of this dataset is to help the development of language modeling models (pre-training or fine-tuning) in Venezuelan Spanish.", "### 7.2 Discussion of Biases\n\nMost of the content comes from political, economical and sociological opinion articles. Social biases may be present.", "### 7.3 Other Known Limitations\n\n(If applicable, description of the other limitations in the data.)\n\nNot applicable.", "## 8. Additional Information", "### 8.1 Dataset Curators\n\nThe data was originally collected by Lino Urdaneta and Miguel Riveros from URL.", "### 8.2 Licensing Information\n\nNot applicable.", "### 8.3 Citation Information\n\nNot applicable.", "### 8.4 Contributions\n\nNot applicable." ]

[ "TAGS\n#task_ids-language-modeling #annotations_creators-no-annotation #language_creators-expert-generated #multilinguality-monolingual #size_categories-unknown #source_datasets-original #language-Spanish #license-cc-by-nc-nd-4.0 #region-us \n", "# mammut-corpus-venezuela\n\nHuggingFace Dataset", "## 1. How to use\n\nHow to load this dataset directly with the datasets library:\n\n'>>> from datasets import load_dataset' \n'>>> dataset = load_dataset(\"mammut-corpus-venezuela\")'", "## 2. Dataset Summary\n\nmammut-corpus-venezuela is a dataset for Spanish language modeling. This dataset comprises a large number of Venezuelan and Latin-American Spanish texts, manually selected and collected in 2021. The data was collected by a process of web scraping from different portals, downloading of Telegram group chats' history, and selecting of Venezuelan and Latin-American Spanish corpus available online. The texts come from Venezuelan Spanish speakers, subtitlers, journalists, politicians, doctors, writers, and online sellers. Social biases may be present, and a percentage of the texts may be fake or contain misleading or offensive language.\n\nEach record in the dataset contains the author of the text (anonymized for conversation authors), the date on which the text entered in the corpus, the text which was automatically tokenized at sentence level for sources other than conversations, the source of the text, the title of the text, the number of tokens (excluding punctuation marks) of the text, and the linguistic register of the text.\n\nThe dataset counts with a train split and a test split.", "## 3. Supported Tasks and Leaderboards\n\nThis dataset can be used for language modeling.", "## 4. Languages\n\nThe dataset contains Venezuelan and Latin-American Spanish.", "## 5. Dataset Structure\n\nDataset structure features.", "### 5.1 Data Instances\n\nAn example from the dataset:\n \n \n \"AUTHOR\":\"author in title\", \n \"TITLE\":\"Luis Alberto Buttó: Hecho en socialismo\", \n \"SENTENCE\":\"Históricamente, siempre fue así.\",\n \"DATE\":\"2021-07-04 07:18:46.918253\", \n \"SOURCE\":\"la patilla\", \n \"TOKENS\":\"4\", \n \"TYPE\":\"opinion/news\", \n\n\nThe average word token count are provided below:", "### 5.2 Total of tokens (no spelling marks)\n\nTrain: 92,431,194. \nTest: 4,876,739 (in another file).", "### 5.3 Data Fields\n\nThe data have several fields:\n\n AUTHOR: author of the text. It is anonymized for conversation authors. \n DATE: date on which the text was entered in the corpus. \n SENTENCE: text. It was automatically tokenized for sources other than conversations. \n SOURCE: source of the texts. \n TITLE: title of the text from which SENTENCE originates. \n TOKENS: number of tokens (excluding punctuation marks) of SENTENCE. \n TYPE: linguistic register of the text.", "### 5.4 Data Splits\n\nThe mammut-corpus-venezuela dataset has 2 splits: train and test. Below are the statistics:\n\nNumber of Instances in Split. \n\nTrain: 2,983,302. \nTest: 157,011.", "## 6. Dataset Creation", "### 6.1 Curation Rationale\n\nThe purpose of the mammut-corpus-venezuela dataset is language modeling. It can be used for pre-training a model from scratch or for fine-tuning on another pre-trained model.", "### 6.2 Source Data\n\n6.2.1 Initial Data Collection and Normalization\n\nThe data consists of opinion articles and text messages. It was collected by a process of web scraping from different portals, downloading of Telegram group chats’ history and selecting of Venezuelan and Latin-American Spanish corpus available online.\n\nThe text from the web scraping process was separated in sentences and was automatically tokenized for sources other than conversations.\n\nAn arrow parquet file was created.\n\nText sources: El Estímulo (website), cinco8 (website), csm-1990 (oral speaking corpus), \"El atajo más largo\" (blog), El Pitazo (website), La Patilla (website), Venezuelan movies subtitles, Preseea Mérida (oral speaking corpus), Prodavinci (website), Runrunes (website), and Telegram group chats.\n\n6.2.2 Who are the source language producers?\n\nThe texts come from Venezuelan Spanish speakers, subtitlers, journalists, politicians, doctors, writers, and online sellers.", "## 6.3 Annotations\n\n6.3.1 Annotation process\n\nAt the moment the dataset does not contain any additional annotations.\n\n6.3.2 Who are the annotators?\n\nNot applicable.", "### 6.4 Personal and Sensitive Information\n\nThe data is partially anonymized. Also, there are messages from Telegram selling chats, some percentage of these messages may be fake or contain misleading or offensive language.", "## 7. Considerations for Using the Data", "### 7.1 Social Impact of Dataset\n\nThe purpose of this dataset is to help the development of language modeling models (pre-training or fine-tuning) in Venezuelan Spanish.", "### 7.2 Discussion of Biases\n\nMost of the content comes from political, economical and sociological opinion articles. Social biases may be present.", "### 7.3 Other Known Limitations\n\n(If applicable, description of the other limitations in the data.)\n\nNot applicable.", "## 8. Additional Information", "### 8.1 Dataset Curators\n\nThe data was originally collected by Lino Urdaneta and Miguel Riveros from URL.", "### 8.2 Licensing Information\n\nNot applicable.", "### 8.3 Citation Information\n\nNot applicable.", "### 8.4 Contributions\n\nNot applicable." ]

[ "region:us" ]

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

[ "region:us" ]

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

[ "region:us" ]

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

[ "region:us" ]

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

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

[ "region:us" ]

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

[ "region:us" ]

[]

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

[ "arxiv:2005.00614", "region:us" ]

[ "2005.00614" ]

[]

[ "### Dataset Summary\n\n\nGitHub Issues is a dataset consisting of GitHub issues and pull requests associated with the 🤗 Datasets repository. It is intended for educational purposes and can be used for semantic search or multilabel text classification. The contents of each GitHub issue are in English and concern the domain of datasets for NLP, computer vision, and beyond.", "### Supported Tasks and Leaderboards\n\n\nFor each of the tasks tagged for this dataset, give a brief description of the tag, metrics, and suggested models (with a link to their HuggingFace implementation if available). Give a similar description of tasks that were not covered by the structured tag set (repace the 'task-category-tag' with an appropriate 'other:other-task-name').\n\n\n* 'task-category-tag': The dataset can be used to train a model for [TASK NAME], which consists in [TASK DESCRIPTION]. Success on this task is typically measured by achieving a *high/low* metric name. The (model name or model class) model currently achieves the following score. *[IF A LEADERBOARD IS AVAILABLE]:* This task has an active leaderboard which can be found at leaderboard url and ranks models based on metric name while also reporting other metric name.", "### Languages\n\n\nProvide a brief overview of the languages represented in the dataset. Describe relevant details about specifics of the language such as whether it is social media text, African American English,...\n\n\nWhen relevant, please provide BCP-47 codes, which consist of a primary language subtag, with a script subtag and/or region subtag if available.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nProvide an JSON-formatted example and brief description of a typical instance in the dataset. If available, provide a link to further examples.\n\n\nProvide any additional information that is not covered in the other sections about the data here. In particular describe any relationships between data points and if these relationships are made explicit.", "### Data Fields\n\n\nList and describe the fields present in the dataset. Mention their data type, and whether they are used as input or output in any of the tasks the dataset currently supports. If the data has span indices, describe their attributes, such as whether they are at the character level or word level, whether they are contiguous or not, etc. If the datasets contains example IDs, state whether they have an inherent meaning, such as a mapping to other datasets or pointing to relationships between data points.\n\n\n* 'example\\_field': description of 'example\\_field'\n\n\nNote that the descriptions can be initialized with the Show Markdown Data Fields output of the tagging app, you will then only need to refine the generated descriptions.", "### Data Splits\n\n\nDescribe and name the splits in the dataset if there are more than one.\n\n\nDescribe any criteria for splitting the data, if used. If their are differences between the splits (e.g. if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here.\n\n\nProvide the sizes of each split. As appropriate, provide any descriptive statistics for the features, such as average length. For example:\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nWhat need motivated the creation of this dataset? What are some of the reasons underlying the major choices involved in putting it together?", "### Source Data\n\n\nThis section describes the source data (e.g. news text and headlines, social media posts, translated sentences,...)", "#### Initial Data Collection and Normalization\n\n\nDescribe the data collection process. Describe any criteria for data selection or filtering. List any key words or search terms used. If possible, include runtime information for the collection process.\n\n\nIf data was collected from other pre-existing datasets, link to source here and to their Hugging Face version.\n\n\nIf the data was modified or normalized after being collected (e.g. if the data is word-tokenized), describe the process and the tools used.", "#### Who are the source language producers?\n\n\nState whether the data was produced by humans or machine generated. Describe the people or systems who originally created the data.\n\n\nIf available, include self-reported demographic or identity information for the source data creators, but avoid inferring this information. Instead state that this information is unknown. See Larson 2017 for using identity categories as a variables, particularly gender.\n\n\nDescribe the conditions under which the data was created (for example, if the producers were crowdworkers, state what platform was used, or if the data was found, what website the data was found on). If compensation was provided, include that information here.\n\n\nDescribe other people represented or mentioned in the data. Where possible, link to references for the information.", "### Annotations\n\n\nIf the dataset contains annotations which are not part of the initial data collection, describe them in the following paragraphs.", "#### Annotation process\n\n\nIf applicable, describe the annotation process and any tools used, or state otherwise. Describe the amount of data annotated, if not all. Describe or reference annotation guidelines provided to the annotators. If available, provide interannotator statistics. Describe any annotation validation processes.", "#### Who are the annotators?\n\n\nIf annotations were collected for the source data (such as class labels or syntactic parses), state whether the annotations were produced by humans or machine generated.\n\n\nDescribe the people or systems who originally created the annotations and their selection criteria if applicable.\n\n\nIf available, include self-reported demographic or identity information for the annotators, but avoid inferring this information. Instead state that this information is unknown. See Larson 2017 for using identity categories as a variables, particularly gender.\n\n\nDescribe the conditions under which the data was annotated (for example, if the annotators were crowdworkers, state what platform was used, or if the data was found, what website the data was found on). If compensation was provided, include that information here.", "### Personal and Sensitive Information\n\n\nState whether the dataset uses identity categories and, if so, how the information is used. Describe where this information comes from (i.e. self-reporting, collecting from profiles, inferring, etc.). See Larson 2017 for using identity categories as a variables, particularly gender. State whether the data is linked to individuals and whether those individuals can be identified in the dataset, either directly or indirectly (i.e., in combination with other data).\n\n\nState whether the dataset contains other data that might be considered sensitive (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history).\n\n\nIf efforts were made to anonymize the data, describe the anonymization process.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nPlease discuss some of the ways you believe the use of this dataset will impact society.\n\n\nThe statement should include both positive outlooks, such as outlining how technologies developed through its use may improve people's lives, and discuss the accompanying risks. These risks may range from making important decisions more opaque to people who are affected by the technology, to reinforcing existing harmful biases (whose specifics should be discussed in the next section), among other considerations.\n\n\nAlso describe in this section if the proposed dataset contains a low-resource or under-represented language. If this is the case or if this task has any impact on underserved communities, please elaborate here.", "### Discussion of Biases\n\n\nProvide descriptions of specific biases that are likely to be reflected in the data, and state whether any steps were taken to reduce their impact.\n\n\nFor Wikipedia text, see for example Dinan et al 2020 on biases in Wikipedia (esp. Table 1), or Blodgett et al 2020 for a more general discussion of the topic.\n\n\nIf analyses have been run quantifying these biases, please add brief summaries and links to the studies here.", "### Other Known Limitations\n\n\nIf studies of the datasets have outlined other limitations of the dataset, such as annotation artifacts, please outline and cite them here.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nList the people involved in collecting the dataset and their affiliation(s). If funding information is known, include it here.", "### Licensing Information\n\n\nProvide the license and link to the license webpage if available.\n\n\nProvide the BibTex-formatted reference for the dataset. For example:\n\n\nIf the dataset has a DOI, please provide it here.", "### Contributions\n\n\nThanks to @mbateman for adding this dataset." ]

[ "TAGS\n#arxiv-2005.00614 #region-us \n", "### Dataset Summary\n\n\nGitHub Issues is a dataset consisting of GitHub issues and pull requests associated with the 🤗 Datasets repository. It is intended for educational purposes and can be used for semantic search or multilabel text classification. The contents of each GitHub issue are in English and concern the domain of datasets for NLP, computer vision, and beyond.", "### Supported Tasks and Leaderboards\n\n\nFor each of the tasks tagged for this dataset, give a brief description of the tag, metrics, and suggested models (with a link to their HuggingFace implementation if available). Give a similar description of tasks that were not covered by the structured tag set (repace the 'task-category-tag' with an appropriate 'other:other-task-name').\n\n\n* 'task-category-tag': The dataset can be used to train a model for [TASK NAME], which consists in [TASK DESCRIPTION]. Success on this task is typically measured by achieving a *high/low* metric name. The (model name or model class) model currently achieves the following score. *[IF A LEADERBOARD IS AVAILABLE]:* This task has an active leaderboard which can be found at leaderboard url and ranks models based on metric name while also reporting other metric name.", "### Languages\n\n\nProvide a brief overview of the languages represented in the dataset. Describe relevant details about specifics of the language such as whether it is social media text, African American English,...\n\n\nWhen relevant, please provide BCP-47 codes, which consist of a primary language subtag, with a script subtag and/or region subtag if available.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nProvide an JSON-formatted example and brief description of a typical instance in the dataset. If available, provide a link to further examples.\n\n\nProvide any additional information that is not covered in the other sections about the data here. In particular describe any relationships between data points and if these relationships are made explicit.", "### Data Fields\n\n\nList and describe the fields present in the dataset. Mention their data type, and whether they are used as input or output in any of the tasks the dataset currently supports. If the data has span indices, describe their attributes, such as whether they are at the character level or word level, whether they are contiguous or not, etc. If the datasets contains example IDs, state whether they have an inherent meaning, such as a mapping to other datasets or pointing to relationships between data points.\n\n\n* 'example\\_field': description of 'example\\_field'\n\n\nNote that the descriptions can be initialized with the Show Markdown Data Fields output of the tagging app, you will then only need to refine the generated descriptions.", "### Data Splits\n\n\nDescribe and name the splits in the dataset if there are more than one.\n\n\nDescribe any criteria for splitting the data, if used. If their are differences between the splits (e.g. if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here.\n\n\nProvide the sizes of each split. As appropriate, provide any descriptive statistics for the features, such as average length. For example:\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nWhat need motivated the creation of this dataset? What are some of the reasons underlying the major choices involved in putting it together?", "### Source Data\n\n\nThis section describes the source data (e.g. news text and headlines, social media posts, translated sentences,...)", "#### Initial Data Collection and Normalization\n\n\nDescribe the data collection process. Describe any criteria for data selection or filtering. List any key words or search terms used. If possible, include runtime information for the collection process.\n\n\nIf data was collected from other pre-existing datasets, link to source here and to their Hugging Face version.\n\n\nIf the data was modified or normalized after being collected (e.g. if the data is word-tokenized), describe the process and the tools used.", "#### Who are the source language producers?\n\n\nState whether the data was produced by humans or machine generated. Describe the people or systems who originally created the data.\n\n\nIf available, include self-reported demographic or identity information for the source data creators, but avoid inferring this information. Instead state that this information is unknown. See Larson 2017 for using identity categories as a variables, particularly gender.\n\n\nDescribe the conditions under which the data was created (for example, if the producers were crowdworkers, state what platform was used, or if the data was found, what website the data was found on). If compensation was provided, include that information here.\n\n\nDescribe other people represented or mentioned in the data. Where possible, link to references for the information.", "### Annotations\n\n\nIf the dataset contains annotations which are not part of the initial data collection, describe them in the following paragraphs.", "#### Annotation process\n\n\nIf applicable, describe the annotation process and any tools used, or state otherwise. Describe the amount of data annotated, if not all. Describe or reference annotation guidelines provided to the annotators. If available, provide interannotator statistics. Describe any annotation validation processes.", "#### Who are the annotators?\n\n\nIf annotations were collected for the source data (such as class labels or syntactic parses), state whether the annotations were produced by humans or machine generated.\n\n\nDescribe the people or systems who originally created the annotations and their selection criteria if applicable.\n\n\nIf available, include self-reported demographic or identity information for the annotators, but avoid inferring this information. Instead state that this information is unknown. See Larson 2017 for using identity categories as a variables, particularly gender.\n\n\nDescribe the conditions under which the data was annotated (for example, if the annotators were crowdworkers, state what platform was used, or if the data was found, what website the data was found on). If compensation was provided, include that information here.", "### Personal and Sensitive Information\n\n\nState whether the dataset uses identity categories and, if so, how the information is used. Describe where this information comes from (i.e. self-reporting, collecting from profiles, inferring, etc.). See Larson 2017 for using identity categories as a variables, particularly gender. State whether the data is linked to individuals and whether those individuals can be identified in the dataset, either directly or indirectly (i.e., in combination with other data).\n\n\nState whether the dataset contains other data that might be considered sensitive (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history).\n\n\nIf efforts were made to anonymize the data, describe the anonymization process.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nPlease discuss some of the ways you believe the use of this dataset will impact society.\n\n\nThe statement should include both positive outlooks, such as outlining how technologies developed through its use may improve people's lives, and discuss the accompanying risks. These risks may range from making important decisions more opaque to people who are affected by the technology, to reinforcing existing harmful biases (whose specifics should be discussed in the next section), among other considerations.\n\n\nAlso describe in this section if the proposed dataset contains a low-resource or under-represented language. If this is the case or if this task has any impact on underserved communities, please elaborate here.", "### Discussion of Biases\n\n\nProvide descriptions of specific biases that are likely to be reflected in the data, and state whether any steps were taken to reduce their impact.\n\n\nFor Wikipedia text, see for example Dinan et al 2020 on biases in Wikipedia (esp. Table 1), or Blodgett et al 2020 for a more general discussion of the topic.\n\n\nIf analyses have been run quantifying these biases, please add brief summaries and links to the studies here.", "### Other Known Limitations\n\n\nIf studies of the datasets have outlined other limitations of the dataset, such as annotation artifacts, please outline and cite them here.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nList the people involved in collecting the dataset and their affiliation(s). If funding information is known, include it here.", "### Licensing Information\n\n\nProvide the license and link to the license webpage if available.\n\n\nProvide the BibTex-formatted reference for the dataset. For example:\n\n\nIf the dataset has a DOI, please provide it here.", "### Contributions\n\n\nThanks to @mbateman for adding this dataset." ]

[ "annotations_creators:other", "language_creators:other", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:hi", "license:other", "region:us" ]

[]

[ "hi" ]

[ "# 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", "## 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", "## Licensing information\n\nAcademic Free License v1.2." ]

[ "TAGS\n#annotations_creators-other #language_creators-other #multilinguality-monolingual #size_categories-unknown #source_datasets-original #language-Hindi #license-other #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", "## 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", "## Licensing information\n\nAcademic Free License v1.2." ]

[ "annotations_creators:other", "language_creators:other", "multilinguality:multilingual", "size_categories:unknown", "source_datasets:original", "language:hi", "language:ta", "license:other", "region:us" ]

[]

[ "hi", "ta" ]

[ "# 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", "## 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", "# Licensing information\n\nAcademic Free License v1.2." ]

[ "TAGS\n#annotations_creators-other #language_creators-other #multilinguality-multilingual #size_categories-unknown #source_datasets-original #language-Hindi #language-Tamil #license-other #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", "## 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", "# Licensing information\n\nAcademic Free License v1.2." ]

[ "annotations_creators:other", "language_creators:other", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:ta", "license:other", "region:us" ]

[]

[ "ta" ]

[ "# 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", "## 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", "## Licensing information\n\nAcademic Free License v1.2." ]

[ "TAGS\n#annotations_creators-other #language_creators-other #multilinguality-monolingual #size_categories-unknown #source_datasets-original #language-Tamil #license-other #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", "## 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", "## Licensing information\n\nAcademic Free License v1.2." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for folk-mythology-tales", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nFolklore and Mythology Electronic Texts dataset, original dataset is found URL", "### Supported Tasks and Leaderboards", "### Languages\n\nen", "## Dataset Structure", "### Data Instances\n\nPlain text with no json structure", "### Data Fields\n\nNo fields", "### Data Splits\n\nOnly training set", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\n\n\n\n\n---\nannotations_creators:\n- found\nlanguage_creators:\n- found\nlanguages:\n- en\nlicenses:\n- cc0-1.0\nmultilinguality:\n- monolingual\npretty_name: Folklore and Mythology Electronic Texts\nsize_categories:\n- unknown\nsource_datasets: []\ntask_categories: []\ntask_ids: []\n---" ]

[ "TAGS\n#region-us \n", "# Dataset Card for folk-mythology-tales", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nFolklore and Mythology Electronic Texts dataset, original dataset is found URL", "### Supported Tasks and Leaderboards", "### Languages\n\nen", "## Dataset Structure", "### Data Instances\n\nPlain text with no json structure", "### Data Fields\n\nNo fields", "### Data Splits\n\nOnly training set", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\n\n\n\n\n---\nannotations_creators:\n- found\nlanguage_creators:\n- found\nlanguages:\n- en\nlicenses:\n- cc0-1.0\nmultilinguality:\n- monolingual\npretty_name: Folklore and Mythology Electronic Texts\nsize_categories:\n- unknown\nsource_datasets: []\ntask_categories: []\ntask_ids: []\n---" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for poetry", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nIt contains poems from subjects: Love, Nature and Mythology & Folklore that belong to two periods namely Renaissance and Modern", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields\n\nHas 5 columns:\n- Content\n- Author\n- Poem name\n- Age\n- Type", "### Data Splits\n\nOnly training set", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\n\n\n\n\n\n\n---\nannotations_creators:\n- found\nlanguage_creators:\n- found\nlanguage:\n- en\nlicense:\n- unknown\nmultilinguality:\n- monolingual\npretty_name: poetry\nsize_categories:\n- unknown\nsource_datasets:\n- original\ntask_categories:\n- text-classification\ntask_ids: []\n---" ]

[ "TAGS\n#region-us \n", "# Dataset Card for poetry", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nIt contains poems from subjects: Love, Nature and Mythology & Folklore that belong to two periods namely Renaissance and Modern", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields\n\nHas 5 columns:\n- Content\n- Author\n- Poem name\n- Age\n- Type", "### Data Splits\n\nOnly training set", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\n\n\n\n\n\n\n---\nannotations_creators:\n- found\nlanguage_creators:\n- found\nlanguage:\n- en\nlicense:\n- unknown\nmultilinguality:\n- monolingual\npretty_name: poetry\nsize_categories:\n- unknown\nsource_datasets:\n- original\ntask_categories:\n- text-classification\ntask_ids: []\n---" ]

[ "task_categories:text-classification", "task_ids:acceptability-classification", "size_categories:10K<n<100K", "language:en", "license:apache-2.0", "cola", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-text-classification #task_ids-acceptability-classification #size_categories-10K<n<100K #language-English #license-apache-2.0 #cola #region-us \n" ]

[ "task_categories:text-classification", "task_ids:sentiment-classification", "task_ids:fact-checking", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:unknown", "language:en", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-text-classification #task_ids-sentiment-classification #task_ids-fact-checking #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-unknown #language-English #region-us \n" ]

[ "task_categories:text-classification", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:unknown", "language:en", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-text-classification #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-unknown #language-English #region-us \n" ]

[ "task_categories:text-classification", "task_ids:natural-language-inference", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:apache-2.0", "nli", "natural-language-inference", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-text-classification #task_ids-natural-language-inference #annotations_creators-expert-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-apache-2.0 #nli #natural-language-inference #region-us \n" ]

[ "region:us" ]

[]

[]

[ "### Data Fields\n\n\n* id: unique identifier of the document.\n* document: Whitespace separated list of words in the document.\n* doc\\_bio\\_tags: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.\n* extractive\\_keyphrases: List of all the present keyphrases.\n* abstractive\\_keyphrase: List of all the absent keyphrases.", "### Data Splits\n\n\n\nUsage\n-----", "### Full Dataset\n\n\nOutput", "### Keyphrase Extraction", "### Keyphrase Generation\n\n\nContributions\n-------------\n\n\nThanks to @debanjanbhucs, @dibyaaaaax and @ad6398 for adding this dataset" ]

[ "TAGS\n#region-us \n", "### Data Fields\n\n\n* id: unique identifier of the document.\n* document: Whitespace separated list of words in the document.\n* doc\\_bio\\_tags: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.\n* extractive\\_keyphrases: List of all the present keyphrases.\n* abstractive\\_keyphrase: List of all the absent keyphrases.", "### Data Splits\n\n\n\nUsage\n-----", "### Full Dataset\n\n\nOutput", "### Keyphrase Extraction", "### Keyphrase Generation\n\n\nContributions\n-------------\n\n\nThanks to @debanjanbhucs, @dibyaaaaax and @ad6398 for adding this dataset" ]

[ "region:us" ]

[]

[]

[ "### Data Fields\n\n\n* id: unique identifier of the document.\n* document: Whitespace separated list of words in the document.\n* doc\\_bio\\_tags: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.\n* extractive\\_keyphrases: List of all the present keyphrases.\n* abstractive\\_keyphrase: List of all the absent keyphrases.", "### Data Splits\n\n\n\nTrain\n\n\n* Percentage of keyphrases that are named entities: 69.49% (named entities detected using scispacy - en-core-sci-lg model)\n* Percentage of keyphrases that are noun phrases: 81.26% (noun phrases detected using spacy en-core-web-lg after removing determiners)\n\n\nTest\n\n\n* Percentage of keyphrases that are named entities: 70.79% (named entities detected using scispacy - en-core-sci-lg model)\n* Percentage of keyphrases that are noun phrases: 82.74% (noun phrases detected using spacy en-core-web-lg after removing determiners)\n\n\nUsage\n-----", "### Full Dataset\n\n\nOutput", "### Keyphrase Extraction", "### Keyphrase Generation\n\n\nContributions\n-------------\n\n\nThanks to @debanjanbhucs, @dibyaaaaax and @ad6398 for adding this dataset" ]

[ "TAGS\n#region-us \n", "### Data Fields\n\n\n* id: unique identifier of the document.\n* document: Whitespace separated list of words in the document.\n* doc\\_bio\\_tags: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.\n* extractive\\_keyphrases: List of all the present keyphrases.\n* abstractive\\_keyphrase: List of all the absent keyphrases.", "### Data Splits\n\n\n\nTrain\n\n\n* Percentage of keyphrases that are named entities: 69.49% (named entities detected using scispacy - en-core-sci-lg model)\n* Percentage of keyphrases that are noun phrases: 81.26% (noun phrases detected using spacy en-core-web-lg after removing determiners)\n\n\nTest\n\n\n* Percentage of keyphrases that are named entities: 70.79% (named entities detected using scispacy - en-core-sci-lg model)\n* Percentage of keyphrases that are noun phrases: 82.74% (noun phrases detected using spacy en-core-web-lg after removing determiners)\n\n\nUsage\n-----", "### Full Dataset\n\n\nOutput", "### Keyphrase Extraction", "### Keyphrase Generation\n\n\nContributions\n-------------\n\n\nThanks to @debanjanbhucs, @dibyaaaaax and @ad6398 for adding this dataset" ]

[ "region:us" ]

[]

[]

[ "### Data Fields\n\n\n* id: unique identifier of the document.\n* document: Whitespace separated list of words in the document.\n* doc\\_bio\\_tags: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.\n* extractive\\_keyphrases: List of all the present keyphrases.\n* abstractive\\_keyphrase: List of all the absent keyphrases.", "### Data Splits\n\n\n\nUsage\n-----", "### Full Dataset\n\n\nOutput", "### Keyphrase Extraction", "### Keyphrase Generation\n\n\nContributions\n-------------\n\n\nThanks to @debanjanbhucs, @dibyaaaaax and @ad6398 for adding this dataset" ]

[ "TAGS\n#region-us \n", "### Data Fields\n\n\n* id: unique identifier of the document.\n* document: Whitespace separated list of words in the document.\n* doc\\_bio\\_tags: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.\n* extractive\\_keyphrases: List of all the present keyphrases.\n* abstractive\\_keyphrase: List of all the absent keyphrases.", "### Data Splits\n\n\n\nUsage\n-----", "### Full Dataset\n\n\nOutput", "### Keyphrase Extraction", "### Keyphrase Generation\n\n\nContributions\n-------------\n\n\nThanks to @debanjanbhucs, @dibyaaaaax and @ad6398 for adding this dataset" ]

[ "arxiv:1910.08840", "region:us" ]

[ "1910.08840" ]

[]

[ "### Data Fields\n\n\n* id: unique identifier of the document.\n* document: Whitespace separated list of words in the document.\n* doc\\_bio\\_tags: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.\n* extractive\\_keyphrases: List of all the present keyphrases.\n* abstractive\\_keyphrase: List of all the absent keyphrases.", "### Data Splits\n\n\n\nUsage\n-----", "### Full Dataset\n\n\nOutput", "### Keyphrase Extraction", "### Keyphrase Generation\n\n\nPlease cite the works below if you use this dataset in your work.\n\n\nand\n\n\nand\n\n\nContributions\n-------------\n\n\nThanks to @debanjanbhucs, @dibyaaaaax, @UmaGunturi and @ad6398 for adding this dataset" ]

[ "TAGS\n#arxiv-1910.08840 #region-us \n", "### Data Fields\n\n\n* id: unique identifier of the document.\n* document: Whitespace separated list of words in the document.\n* doc\\_bio\\_tags: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.\n* extractive\\_keyphrases: List of all the present keyphrases.\n* abstractive\\_keyphrase: List of all the absent keyphrases.", "### Data Splits\n\n\n\nUsage\n-----", "### Full Dataset\n\n\nOutput", "### Keyphrase Extraction", "### Keyphrase Generation\n\n\nPlease cite the works below if you use this dataset in your work.\n\n\nand\n\n\nand\n\n\nContributions\n-------------\n\n\nThanks to @debanjanbhucs, @dibyaaaaax, @UmaGunturi and @ad6398 for adding this dataset" ]

[ "region:us" ]

[]

[]

[ "### Data Fields\n\n\n* id: unique identifier of the document.\n* document: Whitespace separated list of words in the document.\n* doc\\_bio\\_tags: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.\n* extractive\\_keyphrases: List of all the present keyphrases.\n* abstractive\\_keyphrase: List of all the absent keyphrases.", "### Data Splits\n\n\n\n* Percentage of keyphrases that are named entities: 56.99% (named entities detected using scispacy - en-core-sci-lg model)\n* Percentage of keyphrases that are noun phrases: 54.99% (noun phrases detected using spacy en-core-web-lg after removing determiners)\n\n\nUsage\n-----", "### Full Dataset\n\n\nOutput", "### Keyphrase Extraction", "### Keyphrase Generation\n\n\nContributions\n-------------\n\n\nThanks to @debanjanbhucs, @dibyaaaaax and @ad6398 for adding this dataset" ]

[ "TAGS\n#region-us \n", "### Data Fields\n\n\n* id: unique identifier of the document.\n* document: Whitespace separated list of words in the document.\n* doc\\_bio\\_tags: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.\n* extractive\\_keyphrases: List of all the present keyphrases.\n* abstractive\\_keyphrase: List of all the absent keyphrases.", "### Data Splits\n\n\n\n* Percentage of keyphrases that are named entities: 56.99% (named entities detected using scispacy - en-core-sci-lg model)\n* Percentage of keyphrases that are noun phrases: 54.99% (noun phrases detected using spacy en-core-web-lg after removing determiners)\n\n\nUsage\n-----", "### Full Dataset\n\n\nOutput", "### Keyphrase Extraction", "### Keyphrase Generation\n\n\nContributions\n-------------\n\n\nThanks to @debanjanbhucs, @dibyaaaaax and @ad6398 for adding this dataset" ]

[ "region:us" ]

[]

[]

[ "### Data Fields\n\n\n* id: unique identifier of the document.\n* document: Whitespace separated list of words in the document.\n* doc\\_bio\\_tags: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.\n* extractive\\_keyphrases: List of all the present keyphrases.\n* abstractive\\_keyphrase: List of all the absent keyphrases.", "### Data Splits\n\n\n\nUsage\n-----", "### Full Dataset\n\n\nOutput", "### Keyphrase Extraction", "### Keyphrase Generation\n\n\nPlease cite the works below if you use this dataset in your work.\n\n\nContributions\n-------------\n\n\nThanks to @debanjanbhucs, @dibyaaaaax, @UmaGunturi and @ad6398 for adding this dataset" ]

[ "TAGS\n#region-us \n", "### Data Fields\n\n\n* id: unique identifier of the document.\n* document: Whitespace separated list of words in the document.\n* doc\\_bio\\_tags: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.\n* extractive\\_keyphrases: List of all the present keyphrases.\n* abstractive\\_keyphrase: List of all the absent keyphrases.", "### Data Splits\n\n\n\nUsage\n-----", "### Full Dataset\n\n\nOutput", "### Keyphrase Extraction", "### Keyphrase Generation\n\n\nPlease cite the works below if you use this dataset in your work.\n\n\nContributions\n-------------\n\n\nThanks to @debanjanbhucs, @dibyaaaaax, @UmaGunturi and @ad6398 for adding this dataset" ]

[ "arxiv:1306.4886", "region:us" ]

[ "1306.4886" ]

[]

[ "### Dataset Statistics\n\n\nTable 1: Statistics on the length of the extractive keyphrases for Train, Test splits of kpcrowd dataset.\n\n\n\nTable 2: Statistics on the length of the abstractive keyphrases for Train, Test splits of kpcrowd dataset.\n\n\n\nTable 3: General statistics of the kpcrowd dataset.", "### Data Fields\n\n\n* id: unique identifier of the document.\n* document: Whitespace separated list of words in the document.\n* doc\\_bio\\_tags: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.\n* extractive\\_keyphrases: List of all the present keyphrases.\n* abstractive\\_keyphrase: List of all the absent keyphrases.", "### Data Splits\n\n\n\nUsage\n-----", "### Full Dataset\n\n\nOutput", "### Keyphrase Extraction", "### Keyphrase Generation\n\n\nContributions\n-------------\n\n\nThanks to @debanjanbhucs, @dibyaaaaax and @ad6398 for adding this dataset" ]

[ "TAGS\n#arxiv-1306.4886 #region-us \n", "### Dataset Statistics\n\n\nTable 1: Statistics on the length of the extractive keyphrases for Train, Test splits of kpcrowd dataset.\n\n\n\nTable 2: Statistics on the length of the abstractive keyphrases for Train, Test splits of kpcrowd dataset.\n\n\n\nTable 3: General statistics of the kpcrowd dataset.", "### Data Fields\n\n\n* id: unique identifier of the document.\n* document: Whitespace separated list of words in the document.\n* doc\\_bio\\_tags: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.\n* extractive\\_keyphrases: List of all the present keyphrases.\n* abstractive\\_keyphrase: List of all the absent keyphrases.", "### Data Splits\n\n\n\nUsage\n-----", "### Full Dataset\n\n\nOutput", "### Keyphrase Extraction", "### Keyphrase Generation\n\n\nContributions\n-------------\n\n\nThanks to @debanjanbhucs, @dibyaaaaax and @ad6398 for adding this dataset" ]

[ "region:us" ]

[]

[]

[ "### Data Fields\n\n\n* id: unique identifier of the document.\n* document: Whitespace separated list of words in the document.\n* doc\\_bio\\_tags: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.\n* extractive\\_keyphrases: List of all the present keyphrases.\n* abstractive\\_keyphrase: List of all the absent keyphrases.\n* other metadata: Additional information present in the original dataset.\n\t+ id : unique identifier for the document\n\t+ date : publishing date (YYYY/MM/DD)\n\t+ categories : categories of the article (1 or 2 categories)\n\t+ title : title of the document\n\t+ abstract : content of the article\n\t+ keyword : list of keywords", "### Data Splits\n\n\n\nUsage\n-----", "### Full Dataset\n\n\nOutput", "### Keyphrase Extraction", "### Keyphrase Generation\n\n\nContributions\n-------------\n\n\nThanks to @debanjanbhucs, @dibyaaaaax, @UmaGunturi and @ad6398 for adding this dataset" ]

[ "TAGS\n#region-us \n", "### Data Fields\n\n\n* id: unique identifier of the document.\n* document: Whitespace separated list of words in the document.\n* doc\\_bio\\_tags: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.\n* extractive\\_keyphrases: List of all the present keyphrases.\n* abstractive\\_keyphrase: List of all the absent keyphrases.\n* other metadata: Additional information present in the original dataset.\n\t+ id : unique identifier for the document\n\t+ date : publishing date (YYYY/MM/DD)\n\t+ categories : categories of the article (1 or 2 categories)\n\t+ title : title of the document\n\t+ abstract : content of the article\n\t+ keyword : list of keywords", "### Data Splits\n\n\n\nUsage\n-----", "### Full Dataset\n\n\nOutput", "### Keyphrase Extraction", "### Keyphrase Generation\n\n\nContributions\n-------------\n\n\nThanks to @debanjanbhucs, @dibyaaaaax, @UmaGunturi and @ad6398 for adding this dataset" ]

[ "region:us" ]

[]

[]

[ "### Data Fields\n\n\n* id: unique identifier of the document.\n* document: Whitespace separated list of words in the document.\n* doc\\_bio\\_tags: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.\n* extractive\\_keyphrases: List of all the present keyphrases.\n* abstractive\\_keyphrase: List of all the absent keyphrases.", "### Data Splits\n\n\n\nUsage\n-----", "### Full Dataset\n\n\nOutput", "### Keyphrase Extraction", "### Keyphrase Generation\n\n\nContributions\n-------------\n\n\nThanks to @debanjanbhucs, @dibyaaaaax and @ad6398 for adding this dataset" ]

[ "TAGS\n#region-us \n", "### Data Fields\n\n\n* id: unique identifier of the document.\n* document: Whitespace separated list of words in the document.\n* doc\\_bio\\_tags: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.\n* extractive\\_keyphrases: List of all the present keyphrases.\n* abstractive\\_keyphrase: List of all the absent keyphrases.", "### Data Splits\n\n\n\nUsage\n-----", "### Full Dataset\n\n\nOutput", "### Keyphrase Extraction", "### Keyphrase Generation\n\n\nContributions\n-------------\n\n\nThanks to @debanjanbhucs, @dibyaaaaax and @ad6398 for adding this dataset" ]

[ "region:us" ]

[]

[]

[ "### Data Fields\n\n\n* id: unique identifier of the document.\n* sections: list of all the sections present in the document.\n* sec\\_text: list of white space separated list of words present in each section.\n* sec\\_bio\\_tags: list of BIO tags of white space separated list of words present in each section.\n* extractive\\_keyphrases: List of all the present keyphrases.\n* abstractive\\_keyphrase: List of all the absent keyphrases.", "### Data Splits\n\n\n\nUsage\n-----", "### Small Dataset\n\n\nOutput", "### Medium Dataset", "### Large Dataset\n\n\nPlease cite the works below if you use this dataset in your work.\n\n\nContributions\n-------------\n\n\nThanks to @debanjanbhucs, @dibyaaaaax, @UmaGunturi and @ad6398 for adding this dataset" ]

[ "TAGS\n#region-us \n", "### Data Fields\n\n\n* id: unique identifier of the document.\n* sections: list of all the sections present in the document.\n* sec\\_text: list of white space separated list of words present in each section.\n* sec\\_bio\\_tags: list of BIO tags of white space separated list of words present in each section.\n* extractive\\_keyphrases: List of all the present keyphrases.\n* abstractive\\_keyphrase: List of all the absent keyphrases.", "### Data Splits\n\n\n\nUsage\n-----", "### Small Dataset\n\n\nOutput", "### Medium Dataset", "### Large Dataset\n\n\nPlease cite the works below if you use this dataset in your work.\n\n\nContributions\n-------------\n\n\nThanks to @debanjanbhucs, @dibyaaaaax, @UmaGunturi and @ad6398 for adding this dataset" ]

[ "region:us" ]

[]

[]

[ "### Data Fields\n\n\n* id: unique identifier of the document.\n* sections: list of all the sections present in the document.\n* sec\\_text: list of white space separated list of words present in each section.\n* sec\\_bio\\_tags: list of BIO tags of white space separated list of words present in each section.\n* extractive\\_keyphrases: List of all the present keyphrases.\n* abstractive\\_keyphrase: List of all the absent keyphrases.", "### Data Splits\n\n\n\nUsage\n-----", "### Small Dataset\n\n\nOutput", "### Medium Dataset", "### Large Dataset\n\n\nPlease cite the works below if you use this dataset in your work.\n\n\nContributions\n-------------\n\n\nThanks to @debanjanbhucs, @dibyaaaaax, @UmaGunturi and @ad6398 for adding this dataset" ]

[ "TAGS\n#region-us \n", "### Data Fields\n\n\n* id: unique identifier of the document.\n* sections: list of all the sections present in the document.\n* sec\\_text: list of white space separated list of words present in each section.\n* sec\\_bio\\_tags: list of BIO tags of white space separated list of words present in each section.\n* extractive\\_keyphrases: List of all the present keyphrases.\n* abstractive\\_keyphrase: List of all the absent keyphrases.", "### Data Splits\n\n\n\nUsage\n-----", "### Small Dataset\n\n\nOutput", "### Medium Dataset", "### Large Dataset\n\n\nPlease cite the works below if you use this dataset in your work.\n\n\nContributions\n-------------\n\n\nThanks to @debanjanbhucs, @dibyaaaaax, @UmaGunturi and @ad6398 for adding this dataset" ]

[ "region:us" ]

[]

[]

[ "### Data Fields\n\n\n* id: unique identifier of the document.\n* document: Whitespace separated list of words in the document.\n* doc\\_bio\\_tags: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.\n* extractive\\_keyphrases: List of all the present keyphrases.\n* abstractive\\_keyphrase: List of all the absent keyphrases.", "### Data Splits\n\n\n\n* Percentage of keyphrases that are named entities: 67.95% (named entities detected using scispacy - en-core-sci-lg model)\n* Percentage of keyphrases that are noun phrases: 82.16% (noun phrases detected using spacy en-core-web-lg after removing determiners)\n\n\nUsage\n-----", "### Full Dataset\n\n\nOutput", "### Keyphrase Extraction", "### Keyphrase Generation\n\n\nContributions\n-------------\n\n\nThanks to @debanjanbhucs, @dibyaaaaax and @ad6398 for adding this dataset" ]

[ "TAGS\n#region-us \n", "### Data Fields\n\n\n* id: unique identifier of the document.\n* document: Whitespace separated list of words in the document.\n* doc\\_bio\\_tags: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.\n* extractive\\_keyphrases: List of all the present keyphrases.\n* abstractive\\_keyphrase: List of all the absent keyphrases.", "### Data Splits\n\n\n\n* Percentage of keyphrases that are named entities: 67.95% (named entities detected using scispacy - en-core-sci-lg model)\n* Percentage of keyphrases that are noun phrases: 82.16% (noun phrases detected using spacy en-core-web-lg after removing determiners)\n\n\nUsage\n-----", "### Full Dataset\n\n\nOutput", "### Keyphrase Extraction", "### Keyphrase Generation\n\n\nContributions\n-------------\n\n\nThanks to @debanjanbhucs, @dibyaaaaax and @ad6398 for adding this dataset" ]

[ "region:us" ]

[]

[]

[ "### Data Fields\n\n\n* id: unique identifier of the document.\n* document: Whitespace separated list of words in the document.\n* doc\\_bio\\_tags: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.\n* extractive\\_keyphrases: List of all the present keyphrases.\n* abstractive\\_keyphrase: List of all the absent keyphrases.", "### Data Splits\n\n\n\nUsage\n-----", "### Full Dataset\n\n\nOutput", "### Keyphrase Extraction", "### Keyphrase Generation\n\n\nContributions\n-------------\n\n\nThanks to @debanjanbhucs, @dibyaaaaax and @ad6398 for adding this dataset" ]

[ "TAGS\n#region-us \n", "### Data Fields\n\n\n* id: unique identifier of the document.\n* document: Whitespace separated list of words in the document.\n* doc\\_bio\\_tags: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.\n* extractive\\_keyphrases: List of all the present keyphrases.\n* abstractive\\_keyphrase: List of all the absent keyphrases.", "### Data Splits\n\n\n\nUsage\n-----", "### Full Dataset\n\n\nOutput", "### Keyphrase Extraction", "### Keyphrase Generation\n\n\nContributions\n-------------\n\n\nThanks to @debanjanbhucs, @dibyaaaaax and @ad6398 for adding this dataset" ]

[ "region:us" ]

[]

[]

[ "### Data Fields\n\n\n* id: unique identifier of the document.\n* document: Whitespace separated list of words in the document.\n* doc\\_bio\\_tags: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.\n* extractive\\_keyphrases: List of all the present keyphrases.\n* abstractive\\_keyphrase: List of all the absent keyphrases.", "### Data Splits\n\n\n\n* Percentage of keyphrases that are named entities: 84.94% (named entities detected using scispacy - en-core-sci-lg model)\n* Percentage of keyphrases that are noun phrases: 81.54% (noun phrases detected using spacy en-core-web-lg after removing determiners)\n\n\nUsage\n-----", "### Full Dataset\n\n\nOutput", "### Keyphrase Extraction", "### Keyphrase Generation\n\n\nContributions\n-------------\n\n\nThanks to @debanjanbhucs, @dibyaaaaax and @ad6398 for adding this dataset" ]

[ "TAGS\n#region-us \n", "### Data Fields\n\n\n* id: unique identifier of the document.\n* document: Whitespace separated list of words in the document.\n* doc\\_bio\\_tags: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.\n* extractive\\_keyphrases: List of all the present keyphrases.\n* abstractive\\_keyphrase: List of all the absent keyphrases.", "### Data Splits\n\n\n\n* Percentage of keyphrases that are named entities: 84.94% (named entities detected using scispacy - en-core-sci-lg model)\n* Percentage of keyphrases that are noun phrases: 81.54% (noun phrases detected using spacy en-core-web-lg after removing determiners)\n\n\nUsage\n-----", "### Full Dataset\n\n\nOutput", "### Keyphrase Extraction", "### Keyphrase Generation\n\n\nContributions\n-------------\n\n\nThanks to @debanjanbhucs, @dibyaaaaax and @ad6398 for adding this dataset" ]

[ "arxiv:1910.08840", "region:us" ]

[ "1910.08840" ]

[]

[ "### Data Fields\n\n\n* id: unique identifier of the document.\n* document: Whitespace separated list of words in the document.\n* doc\\_bio\\_tags: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.\n* extractive\\_keyphrases: List of all the present keyphrases.\n* abstractive\\_keyphrase: List of all the absent keyphrases.", "### Data Splits\n\n\n\nTrain\n\n\n* Percentage of keyphrases that are named entities: 63.01% (named entities detected using scispacy - en-core-sci-lg model)\n* Percentage of keyphrases that are noun phrases: 82.50% (noun phrases detected using spacy en-core-web-lg after removing determiners)\n\n\nTest\n\n\n* Percentage of keyphrases that are named entities: 62.06% (named entities detected using scispacy - en-core-sci-lg model)\n* Percentage of keyphrases that are noun phrases: 78.36% (noun phrases detected using spacy after removing determiners)\n\n\nUsage\n-----", "### Full Dataset\n\n\nOutput", "### Keyphrase Extraction", "### Keyphrase Generation\n\n\nContributions\n-------------\n\n\nThanks to @debanjanbhucs, @dibyaaaaax and @ad6398 for adding this dataset" ]

[ "TAGS\n#arxiv-1910.08840 #region-us \n", "### Data Fields\n\n\n* id: unique identifier of the document.\n* document: Whitespace separated list of words in the document.\n* doc\\_bio\\_tags: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.\n* extractive\\_keyphrases: List of all the present keyphrases.\n* abstractive\\_keyphrase: List of all the absent keyphrases.", "### Data Splits\n\n\n\nTrain\n\n\n* Percentage of keyphrases that are named entities: 63.01% (named entities detected using scispacy - en-core-sci-lg model)\n* Percentage of keyphrases that are noun phrases: 82.50% (noun phrases detected using spacy en-core-web-lg after removing determiners)\n\n\nTest\n\n\n* Percentage of keyphrases that are named entities: 62.06% (named entities detected using scispacy - en-core-sci-lg model)\n* Percentage of keyphrases that are noun phrases: 78.36% (noun phrases detected using spacy after removing determiners)\n\n\nUsage\n-----", "### Full Dataset\n\n\nOutput", "### Keyphrase Extraction", "### Keyphrase Generation\n\n\nContributions\n-------------\n\n\nThanks to @debanjanbhucs, @dibyaaaaax and @ad6398 for adding this dataset" ]

[ "arxiv:1704.02853", "arxiv:1910.08840", "region:us" ]

[ "1704.02853", "1910.08840" ]

[]

[ "### Data Fields\n\n\n* id: unique identifier of the document.\n* document: Whitespace separated list of words in the document.\n* doc\\_bio\\_tags: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.\n* extractive\\_keyphrases: List of all the present keyphrases.\n* abstractive\\_keyphrase: List of all the absent keyphrases.", "### Data Splits\n\n\n\nUsage\n-----", "### Full Dataset\n\n\nOutput", "### Keyphrase Extraction", "### Keyphrase Generation\n\n\nContributions\n-------------\n\n\nThanks to @debanjanbhucs, @dibyaaaaax and @ad6398 for adding this dataset" ]

[ "TAGS\n#arxiv-1704.02853 #arxiv-1910.08840 #region-us \n", "### Data Fields\n\n\n* id: unique identifier of the document.\n* document: Whitespace separated list of words in the document.\n* doc\\_bio\\_tags: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.\n* extractive\\_keyphrases: List of all the present keyphrases.\n* abstractive\\_keyphrase: List of all the absent keyphrases.", "### Data Splits\n\n\n\nUsage\n-----", "### Full Dataset\n\n\nOutput", "### Keyphrase Extraction", "### Keyphrase Generation\n\n\nContributions\n-------------\n\n\nThanks to @debanjanbhucs, @dibyaaaaax and @ad6398 for adding this dataset" ]

[ "region:us" ]

[]

[]

[ "### Data Fields\n\n\n* id: unique identifier of the document.\n* document: Whitespace separated list of words in the document.\n* doc\\_bio\\_tags: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.\n* extractive\\_keyphrases: List of all the present keyphrases.\n* abstractive\\_keyphrase: List of all the absent keyphrases.", "### Data Splits\n\n\n\nUsage\n-----", "### Full Dataset\n\n\nOutput", "### Keyphrase Extraction", "### Keyphrase Generation\n\n\nContributions\n-------------\n\n\nThanks to @debanjanbhucs, @dibyaaaaax and @ad6398 for adding this dataset" ]

[ "TAGS\n#region-us \n", "### Data Fields\n\n\n* id: unique identifier of the document.\n* document: Whitespace separated list of words in the document.\n* doc\\_bio\\_tags: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.\n* extractive\\_keyphrases: List of all the present keyphrases.\n* abstractive\\_keyphrase: List of all the absent keyphrases.", "### Data Splits\n\n\n\nUsage\n-----", "### Full Dataset\n\n\nOutput", "### Keyphrase Extraction", "### Keyphrase Generation\n\n\nContributions\n-------------\n\n\nThanks to @debanjanbhucs, @dibyaaaaax and @ad6398 for adding this dataset" ]

[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:is", "license:unknown", "region:us" ]

[]

[ "is" ]

[]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-unknown #source_datasets-original #language-Icelandic #license-unknown #region-us \n" ]

[ "annotations_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:is", "license:cc-by-4.0", "region:us" ]

[]

[ "is" ]

[ "# Icelandic Error Corpus\n\nRefer to URL for a description of the dataset.\n\nPlease cite the dataset as follows if you use it." ]

[ "TAGS\n#annotations_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Icelandic #license-cc-by-4.0 #region-us \n", "# Icelandic Error Corpus\n\nRefer to URL for a description of the dataset.\n\nPlease cite the dataset as follows if you use it." ]

[ "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:https://github.com/huggingface/datasets/tree/master/datasets/cnn_dailymail", "language:nl", "license:mit", "region:us" ]

[]

[ "nl" ]

[ "### Dataset Summary\n\n\nThe Dutch CNN / DailyMail Dataset is a machine-translated version of the English CNN / Dailymail dataset containing just over 300k unique news aticles as written by journalists at CNN and the Daily Mail.\n\n\nMost information about the dataset can be found on the HuggingFace page of the original English version.\n\n\nThese are the basic steps used to create this dataset (+ some chunking):\n\n\nAnd this is the HuggingFace translation pipeline:", "### Data Fields\n\n\n* 'id': a string containing the heximal formated SHA1 hash of the url where the story was retrieved from\n* 'article': a string containing the body of the news article\n* 'highlights': a string containing the highlight of the article as written by the article author", "### Data Splits\n\n\nThe Dutch CNN/DailyMail dataset follows the same splits as the original English version and has 3 splits: *train*, *validation*, and *test*." ]

[ "TAGS\n#annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-https-//github.com/huggingface/datasets/tree/master/datasets/cnn_dailymail #language-Dutch #license-mit #region-us \n", "### Dataset Summary\n\n\nThe Dutch CNN / DailyMail Dataset is a machine-translated version of the English CNN / Dailymail dataset containing just over 300k unique news aticles as written by journalists at CNN and the Daily Mail.\n\n\nMost information about the dataset can be found on the HuggingFace page of the original English version.\n\n\nThese are the basic steps used to create this dataset (+ some chunking):\n\n\nAnd this is the HuggingFace translation pipeline:", "### Data Fields\n\n\n* 'id': a string containing the heximal formated SHA1 hash of the url where the story was retrieved from\n* 'article': a string containing the body of the news article\n* 'highlights': a string containing the highlight of the article as written by the article author", "### Data Splits\n\n\nThe Dutch CNN/DailyMail dataset follows the same splits as the original English version and has 3 splits: *train*, *validation*, and *test*." ]

[ "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:extended|xsum", "language:nl", "license:unknown", "region:us" ]

[]

[ "nl" ]

[ "# Dataset Card for XSum NL", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThis dataset is a machine translated dataset. It's the XSum dataset translated with this model from English to Dutch.\n\nSee the Hugginface page of the original dataset for more information on the format of this dataset.\n\nUse with:", "### Languages\n\nDutch", "## Dataset Structure", "### Data Instances", "### Data Fields\n\n- 'id': BBC ID of the article.\n- 'document': a string containing the body of the news article \n- 'summary': a string containing a one sentence summary of the article.", "### Data Splits\n\n- 'train'\n- 'test'\n- 'validation'", "## 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#annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-unknown #source_datasets-extended|xsum #language-Dutch #license-unknown #region-us \n", "# Dataset Card for XSum NL", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThis dataset is a machine translated dataset. It's the XSum dataset translated with this model from English to Dutch.\n\nSee the Hugginface page of the original dataset for more information on the format of this dataset.\n\nUse with:", "### Languages\n\nDutch", "## Dataset Structure", "### Data Instances", "### Data Fields\n\n- 'id': BBC ID of the article.\n- 'document': a string containing the body of the news article \n- 'summary': a string containing a one sentence summary of the article.", "### Data Splits\n\n- 'train'\n- 'test'\n- 'validation'", "## 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" ]

[]

[]

[]

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

[ "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:100K<n<1M", "language:en", "license:apache-2.0", "arxiv:1810.09164", "region:us" ]

[ "1810.09164" ]

[ "en" ]

[ "### Supported Tasks and Leaderboards", "### Languages\n\n\nenglish\n\n\nDataset Structure\n-----------------\n\n\nWe show detailed information for up to 5 configurations of the dataset.", "### Data Instances", "#### plain\\_text\n\n\n* Size of downloaded dataset files: 46.64 MB\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### plain\\_text\n\n\n* 'example\\_id': an 'int32' feature,\n* 'string': a 'string' feature,\n* 'text': a 'string' feature,\n* 'correct\\_id': a 'string' feature,\n* 'wrong\\_id': a 'string' feature,\n* 'correct\\_description': a 'string' feature,\n* 'wrong\\_description': a 'string' feature,", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ "TAGS\n#annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-100K<n<1M #language-English #license-apache-2.0 #arxiv-1810.09164 #region-us \n", "### Supported Tasks and Leaderboards", "### Languages\n\n\nenglish\n\n\nDataset Structure\n-----------------\n\n\nWe show detailed information for up to 5 configurations of the dataset.", "### Data Instances", "#### plain\\_text\n\n\n* Size of downloaded dataset files: 46.64 MB\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### plain\\_text\n\n\n* 'example\\_id': an 'int32' feature,\n* 'string': a 'string' feature,\n* 'text': a 'string' feature,\n* 'correct\\_id': a 'string' feature,\n* 'wrong\\_id': a 'string' feature,\n* 'correct\\_description': a 'string' feature,\n* 'wrong\\_description': a 'string' feature,", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

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

[]

[]

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

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

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:automatic-speech-recognition", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:multilingual", "source_datasets:extended|common_voice", "license:cc0-1.0", "arxiv:1912.06670", "region:us" ]

[ "1912.06670" ]

[]

[ "# Dataset Card for Common Voice Corpus 1", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: URL\n- Point of Contact: Anton Lozhkov", "### Dataset Summary\n\nThe Common Voice dataset consists of a unique MP3 and corresponding text file. \nMany of the 1368 recorded hours in the dataset also include demographic metadata like age, sex, and accent \nthat can help improve the accuracy of speech recognition engines.\n\nThe dataset currently consists of 1096 validated hours in 19 languages, but more voices and languages are always added. \nTake a look at the Languages page to request a language or start contributing.", "### Supported Tasks and Leaderboards\n\nThe results for models trained on the Common Voice datasets are available via the \n Speech Bench", "### Languages", "## Dataset Structure", "### Data Instances\n\nA typical data point comprises the 'path' to the audio file and its 'sentence'. \nAdditional fields include 'accent', 'age', 'client_id', 'up_votes', 'down_votes', 'gender', 'locale' and 'segment'.", "### Data Fields\n\n'client_id' ('string'): An id for which client (voice) made the recording\n\n'path' ('string'): The path to the audio file\n\n'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n\n'sentence' ('string'): The sentence the user was prompted to speak\n\n'up_votes' ('int64'): How many upvotes the audio file has received from reviewers\n\n'down_votes' ('int64'): How many downvotes the audio file has received from reviewers\n\n'age' ('string'): The age of the speaker (e.g. 'teens', 'twenties', 'fifties')\n\n'gender' ('string'): The gender of the speaker\n\n'accent' ('string'): Accent of the speaker\n\n'locale' ('string'): The locale of the speaker\n\n'segment' ('string'): Usually an empty field", "### Data Splits\n\nThe speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.\n\nThe validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.\n\nThe invalidated data is data has been invalidated by reviewers\nand received downvotes indicating that the data is of low quality.\n\nThe reported data is data that has been reported, for different reasons.\n\nThe other data is data that has not yet been reviewed.\n\nThe dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.", "## Data Preprocessing Recommended by Hugging Face\n\nThe following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. \n\nMany examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.\n\nIn addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, almost all sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.", "## 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\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nPublic Domain, CC-0" ]

[ "TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-multilingual #source_datasets-extended|common_voice #license-cc0-1.0 #arxiv-1912.06670 #region-us \n", "# Dataset Card for Common Voice Corpus 1", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: URL\n- Point of Contact: Anton Lozhkov", "### Dataset Summary\n\nThe Common Voice dataset consists of a unique MP3 and corresponding text file. \nMany of the 1368 recorded hours in the dataset also include demographic metadata like age, sex, and accent \nthat can help improve the accuracy of speech recognition engines.\n\nThe dataset currently consists of 1096 validated hours in 19 languages, but more voices and languages are always added. \nTake a look at the Languages page to request a language or start contributing.", "### Supported Tasks and Leaderboards\n\nThe results for models trained on the Common Voice datasets are available via the \n Speech Bench", "### Languages", "## Dataset Structure", "### Data Instances\n\nA typical data point comprises the 'path' to the audio file and its 'sentence'. \nAdditional fields include 'accent', 'age', 'client_id', 'up_votes', 'down_votes', 'gender', 'locale' and 'segment'.", "### Data Fields\n\n'client_id' ('string'): An id for which client (voice) made the recording\n\n'path' ('string'): The path to the audio file\n\n'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n\n'sentence' ('string'): The sentence the user was prompted to speak\n\n'up_votes' ('int64'): How many upvotes the audio file has received from reviewers\n\n'down_votes' ('int64'): How many downvotes the audio file has received from reviewers\n\n'age' ('string'): The age of the speaker (e.g. 'teens', 'twenties', 'fifties')\n\n'gender' ('string'): The gender of the speaker\n\n'accent' ('string'): Accent of the speaker\n\n'locale' ('string'): The locale of the speaker\n\n'segment' ('string'): Usually an empty field", "### Data Splits\n\nThe speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.\n\nThe validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.\n\nThe invalidated data is data has been invalidated by reviewers\nand received downvotes indicating that the data is of low quality.\n\nThe reported data is data that has been reported, for different reasons.\n\nThe other data is data that has not yet been reviewed.\n\nThe dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.", "## Data Preprocessing Recommended by Hugging Face\n\nThe following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. \n\nMany examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.\n\nIn addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, almost all sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.", "## 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\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nPublic Domain, CC-0" ]

[ "task_categories:automatic-speech-recognition", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:multilingual", "source_datasets:extended|common_voice", "license:cc0-1.0", "arxiv:1912.06670", "region:us" ]

[ "1912.06670" ]

[]

[ "# Dataset Card for Common Voice Corpus 2", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: URL\n- Point of Contact: Anton Lozhkov", "### Dataset Summary\n\nThe Common Voice dataset consists of a unique MP3 and corresponding text file. \nMany of the 2366 recorded hours in the dataset also include demographic metadata like age, sex, and accent \nthat can help improve the accuracy of speech recognition engines.\n\nThe dataset currently consists of 1872 validated hours in 28 languages, but more voices and languages are always added. \nTake a look at the Languages page to request a language or start contributing.", "### Supported Tasks and Leaderboards\n\nThe results for models trained on the Common Voice datasets are available via the \n Speech Bench", "### Languages", "## Dataset Structure", "### Data Instances\n\nA typical data point comprises the 'path' to the audio file and its 'sentence'. \nAdditional fields include 'accent', 'age', 'client_id', 'up_votes', 'down_votes', 'gender', 'locale' and 'segment'.", "### Data Fields\n\n'client_id' ('string'): An id for which client (voice) made the recording\n\n'path' ('string'): The path to the audio file\n\n'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n\n'sentence' ('string'): The sentence the user was prompted to speak\n\n'up_votes' ('int64'): How many upvotes the audio file has received from reviewers\n\n'down_votes' ('int64'): How many downvotes the audio file has received from reviewers\n\n'age' ('string'): The age of the speaker (e.g. 'teens', 'twenties', 'fifties')\n\n'gender' ('string'): The gender of the speaker\n\n'accent' ('string'): Accent of the speaker\n\n'locale' ('string'): The locale of the speaker\n\n'segment' ('string'): Usually an empty field", "### Data Splits\n\nThe speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.\n\nThe validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.\n\nThe invalidated data is data has been invalidated by reviewers\nand received downvotes indicating that the data is of low quality.\n\nThe reported data is data that has been reported, for different reasons.\n\nThe other data is data that has not yet been reviewed.\n\nThe dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.", "## Data Preprocessing Recommended by Hugging Face\n\nThe following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. \n\nMany examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.\n\nIn addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, almost all sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.", "## 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\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nPublic Domain, CC-0" ]

[ "TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-multilingual #source_datasets-extended|common_voice #license-cc0-1.0 #arxiv-1912.06670 #region-us \n", "# Dataset Card for Common Voice Corpus 2", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: URL\n- Point of Contact: Anton Lozhkov", "### Dataset Summary\n\nThe Common Voice dataset consists of a unique MP3 and corresponding text file. \nMany of the 2366 recorded hours in the dataset also include demographic metadata like age, sex, and accent \nthat can help improve the accuracy of speech recognition engines.\n\nThe dataset currently consists of 1872 validated hours in 28 languages, but more voices and languages are always added. \nTake a look at the Languages page to request a language or start contributing.", "### Supported Tasks and Leaderboards\n\nThe results for models trained on the Common Voice datasets are available via the \n Speech Bench", "### Languages", "## Dataset Structure", "### Data Instances\n\nA typical data point comprises the 'path' to the audio file and its 'sentence'. \nAdditional fields include 'accent', 'age', 'client_id', 'up_votes', 'down_votes', 'gender', 'locale' and 'segment'.", "### Data Fields\n\n'client_id' ('string'): An id for which client (voice) made the recording\n\n'path' ('string'): The path to the audio file\n\n'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n\n'sentence' ('string'): The sentence the user was prompted to speak\n\n'up_votes' ('int64'): How many upvotes the audio file has received from reviewers\n\n'down_votes' ('int64'): How many downvotes the audio file has received from reviewers\n\n'age' ('string'): The age of the speaker (e.g. 'teens', 'twenties', 'fifties')\n\n'gender' ('string'): The gender of the speaker\n\n'accent' ('string'): Accent of the speaker\n\n'locale' ('string'): The locale of the speaker\n\n'segment' ('string'): Usually an empty field", "### Data Splits\n\nThe speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.\n\nThe validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.\n\nThe invalidated data is data has been invalidated by reviewers\nand received downvotes indicating that the data is of low quality.\n\nThe reported data is data that has been reported, for different reasons.\n\nThe other data is data that has not yet been reviewed.\n\nThe dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.", "## Data Preprocessing Recommended by Hugging Face\n\nThe following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. \n\nMany examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.\n\nIn addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, almost all sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.", "## 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\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nPublic Domain, CC-0" ]

[ "task_categories:automatic-speech-recognition", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:multilingual", "source_datasets:extended|common_voice", "license:cc0-1.0", "arxiv:1912.06670", "region:us" ]

[ "1912.06670" ]

[]

[ "# Dataset Card for Common Voice Corpus 3", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: URL\n- Point of Contact: Anton Lozhkov", "### Dataset Summary\n\nThe Common Voice dataset consists of a unique MP3 and corresponding text file. \nMany of the 2454 recorded hours in the dataset also include demographic metadata like age, sex, and accent \nthat can help improve the accuracy of speech recognition engines.\n\nThe dataset currently consists of 1979 validated hours in 29 languages, but more voices and languages are always added. \nTake a look at the Languages page to request a language or start contributing.", "### Supported Tasks and Leaderboards\n\nThe results for models trained on the Common Voice datasets are available via the \n Speech Bench", "### Languages", "## Dataset Structure", "### Data Instances\n\nA typical data point comprises the 'path' to the audio file and its 'sentence'. \nAdditional fields include 'accent', 'age', 'client_id', 'up_votes', 'down_votes', 'gender', 'locale' and 'segment'.", "### Data Fields\n\n'client_id' ('string'): An id for which client (voice) made the recording\n\n'path' ('string'): The path to the audio file\n\n'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n\n'sentence' ('string'): The sentence the user was prompted to speak\n\n'up_votes' ('int64'): How many upvotes the audio file has received from reviewers\n\n'down_votes' ('int64'): How many downvotes the audio file has received from reviewers\n\n'age' ('string'): The age of the speaker (e.g. 'teens', 'twenties', 'fifties')\n\n'gender' ('string'): The gender of the speaker\n\n'accent' ('string'): Accent of the speaker\n\n'locale' ('string'): The locale of the speaker\n\n'segment' ('string'): Usually an empty field", "### Data Splits\n\nThe speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.\n\nThe validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.\n\nThe invalidated data is data has been invalidated by reviewers\nand received downvotes indicating that the data is of low quality.\n\nThe reported data is data that has been reported, for different reasons.\n\nThe other data is data that has not yet been reviewed.\n\nThe dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.", "## Data Preprocessing Recommended by Hugging Face\n\nThe following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. \n\nMany examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.\n\nIn addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, almost all sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.", "## 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\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nPublic Domain, CC-0" ]

[ "TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-multilingual #source_datasets-extended|common_voice #license-cc0-1.0 #arxiv-1912.06670 #region-us \n", "# Dataset Card for Common Voice Corpus 3", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: URL\n- Point of Contact: Anton Lozhkov", "### Dataset Summary\n\nThe Common Voice dataset consists of a unique MP3 and corresponding text file. \nMany of the 2454 recorded hours in the dataset also include demographic metadata like age, sex, and accent \nthat can help improve the accuracy of speech recognition engines.\n\nThe dataset currently consists of 1979 validated hours in 29 languages, but more voices and languages are always added. \nTake a look at the Languages page to request a language or start contributing.", "### Supported Tasks and Leaderboards\n\nThe results for models trained on the Common Voice datasets are available via the \n Speech Bench", "### Languages", "## Dataset Structure", "### Data Instances\n\nA typical data point comprises the 'path' to the audio file and its 'sentence'. \nAdditional fields include 'accent', 'age', 'client_id', 'up_votes', 'down_votes', 'gender', 'locale' and 'segment'.", "### Data Fields\n\n'client_id' ('string'): An id for which client (voice) made the recording\n\n'path' ('string'): The path to the audio file\n\n'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n\n'sentence' ('string'): The sentence the user was prompted to speak\n\n'up_votes' ('int64'): How many upvotes the audio file has received from reviewers\n\n'down_votes' ('int64'): How many downvotes the audio file has received from reviewers\n\n'age' ('string'): The age of the speaker (e.g. 'teens', 'twenties', 'fifties')\n\n'gender' ('string'): The gender of the speaker\n\n'accent' ('string'): Accent of the speaker\n\n'locale' ('string'): The locale of the speaker\n\n'segment' ('string'): Usually an empty field", "### Data Splits\n\nThe speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.\n\nThe validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.\n\nThe invalidated data is data has been invalidated by reviewers\nand received downvotes indicating that the data is of low quality.\n\nThe reported data is data that has been reported, for different reasons.\n\nThe other data is data that has not yet been reviewed.\n\nThe dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.", "## Data Preprocessing Recommended by Hugging Face\n\nThe following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. \n\nMany examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.\n\nIn addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, almost all sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.", "## 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\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nPublic Domain, CC-0" ]

[ "task_categories:automatic-speech-recognition", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:multilingual", "source_datasets:extended|common_voice", "license:cc0-1.0", "arxiv:1912.06670", "region:us" ]

[ "1912.06670" ]

[]

[ "# Dataset Card for Common Voice Corpus 4", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: URL\n- Point of Contact: Anton Lozhkov", "### Dataset Summary\n\nThe Common Voice dataset consists of a unique MP3 and corresponding text file. \nMany of the 4257 recorded hours in the dataset also include demographic metadata like age, sex, and accent \nthat can help improve the accuracy of speech recognition engines.\n\nThe dataset currently consists of 3401 validated hours in 40 languages, but more voices and languages are always added. \nTake a look at the Languages page to request a language or start contributing.", "### Supported Tasks and Leaderboards\n\nThe results for models trained on the Common Voice datasets are available via the \n Speech Bench", "### Languages", "## Dataset Structure", "### Data Instances\n\nA typical data point comprises the 'path' to the audio file and its 'sentence'. \nAdditional fields include 'accent', 'age', 'client_id', 'up_votes', 'down_votes', 'gender', 'locale' and 'segment'.", "### Data Fields\n\n'client_id' ('string'): An id for which client (voice) made the recording\n\n'path' ('string'): The path to the audio file\n\n'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n\n'sentence' ('string'): The sentence the user was prompted to speak\n\n'up_votes' ('int64'): How many upvotes the audio file has received from reviewers\n\n'down_votes' ('int64'): How many downvotes the audio file has received from reviewers\n\n'age' ('string'): The age of the speaker (e.g. 'teens', 'twenties', 'fifties')\n\n'gender' ('string'): The gender of the speaker\n\n'accent' ('string'): Accent of the speaker\n\n'locale' ('string'): The locale of the speaker\n\n'segment' ('string'): Usually an empty field", "### Data Splits\n\nThe speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.\n\nThe validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.\n\nThe invalidated data is data has been invalidated by reviewers\nand received downvotes indicating that the data is of low quality.\n\nThe reported data is data that has been reported, for different reasons.\n\nThe other data is data that has not yet been reviewed.\n\nThe dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.", "## Data Preprocessing Recommended by Hugging Face\n\nThe following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. \n\nMany examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.\n\nIn addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, almost all sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.", "## 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\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nPublic Domain, CC-0" ]

[ "TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-multilingual #source_datasets-extended|common_voice #license-cc0-1.0 #arxiv-1912.06670 #region-us \n", "# Dataset Card for Common Voice Corpus 4", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: URL\n- Point of Contact: Anton Lozhkov", "### Dataset Summary\n\nThe Common Voice dataset consists of a unique MP3 and corresponding text file. \nMany of the 4257 recorded hours in the dataset also include demographic metadata like age, sex, and accent \nthat can help improve the accuracy of speech recognition engines.\n\nThe dataset currently consists of 3401 validated hours in 40 languages, but more voices and languages are always added. \nTake a look at the Languages page to request a language or start contributing.", "### Supported Tasks and Leaderboards\n\nThe results for models trained on the Common Voice datasets are available via the \n Speech Bench", "### Languages", "## Dataset Structure", "### Data Instances\n\nA typical data point comprises the 'path' to the audio file and its 'sentence'. \nAdditional fields include 'accent', 'age', 'client_id', 'up_votes', 'down_votes', 'gender', 'locale' and 'segment'.", "### Data Fields\n\n'client_id' ('string'): An id for which client (voice) made the recording\n\n'path' ('string'): The path to the audio file\n\n'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n\n'sentence' ('string'): The sentence the user was prompted to speak\n\n'up_votes' ('int64'): How many upvotes the audio file has received from reviewers\n\n'down_votes' ('int64'): How many downvotes the audio file has received from reviewers\n\n'age' ('string'): The age of the speaker (e.g. 'teens', 'twenties', 'fifties')\n\n'gender' ('string'): The gender of the speaker\n\n'accent' ('string'): Accent of the speaker\n\n'locale' ('string'): The locale of the speaker\n\n'segment' ('string'): Usually an empty field", "### Data Splits\n\nThe speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.\n\nThe validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.\n\nThe invalidated data is data has been invalidated by reviewers\nand received downvotes indicating that the data is of low quality.\n\nThe reported data is data that has been reported, for different reasons.\n\nThe other data is data that has not yet been reviewed.\n\nThe dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.", "## Data Preprocessing Recommended by Hugging Face\n\nThe following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. \n\nMany examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.\n\nIn addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, almost all sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.", "## 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\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nPublic Domain, CC-0" ]

[ "task_categories:automatic-speech-recognition", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:multilingual", "source_datasets:extended|common_voice", "license:cc0-1.0", "arxiv:1912.06670", "region:us" ]

[ "1912.06670" ]

[]

[ "# Dataset Card for Common Voice Corpus 5", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: URL\n- Point of Contact: Anton Lozhkov", "### Dataset Summary\n\nThe Common Voice dataset consists of a unique MP3 and corresponding text file. \nMany of the 7226 recorded hours in the dataset also include demographic metadata like age, sex, and accent \nthat can help improve the accuracy of speech recognition engines.\n\nThe dataset currently consists of 5591 validated hours in 54 languages, but more voices and languages are always added. \nTake a look at the Languages page to request a language or start contributing.", "### Supported Tasks and Leaderboards\n\nThe results for models trained on the Common Voice datasets are available via the \n Speech Bench", "### Languages", "## Dataset Structure", "### Data Instances\n\nA typical data point comprises the 'path' to the audio file and its 'sentence'. \nAdditional fields include 'accent', 'age', 'client_id', 'up_votes', 'down_votes', 'gender', 'locale' and 'segment'.", "### Data Fields\n\n'client_id' ('string'): An id for which client (voice) made the recording\n\n'path' ('string'): The path to the audio file\n\n'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n\n'sentence' ('string'): The sentence the user was prompted to speak\n\n'up_votes' ('int64'): How many upvotes the audio file has received from reviewers\n\n'down_votes' ('int64'): How many downvotes the audio file has received from reviewers\n\n'age' ('string'): The age of the speaker (e.g. 'teens', 'twenties', 'fifties')\n\n'gender' ('string'): The gender of the speaker\n\n'accent' ('string'): Accent of the speaker\n\n'locale' ('string'): The locale of the speaker\n\n'segment' ('string'): Usually an empty field", "### Data Splits\n\nThe speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.\n\nThe validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.\n\nThe invalidated data is data has been invalidated by reviewers\nand received downvotes indicating that the data is of low quality.\n\nThe reported data is data that has been reported, for different reasons.\n\nThe other data is data that has not yet been reviewed.\n\nThe dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.", "## Data Preprocessing Recommended by Hugging Face\n\nThe following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. \n\nMany examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.\n\nIn addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, almost all sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.", "## 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\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nPublic Domain, CC-0" ]

[ "TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-multilingual #source_datasets-extended|common_voice #license-cc0-1.0 #arxiv-1912.06670 #region-us \n", "# Dataset Card for Common Voice Corpus 5", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: URL\n- Point of Contact: Anton Lozhkov", "### Dataset Summary\n\nThe Common Voice dataset consists of a unique MP3 and corresponding text file. \nMany of the 7226 recorded hours in the dataset also include demographic metadata like age, sex, and accent \nthat can help improve the accuracy of speech recognition engines.\n\nThe dataset currently consists of 5591 validated hours in 54 languages, but more voices and languages are always added. \nTake a look at the Languages page to request a language or start contributing.", "### Supported Tasks and Leaderboards\n\nThe results for models trained on the Common Voice datasets are available via the \n Speech Bench", "### Languages", "## Dataset Structure", "### Data Instances\n\nA typical data point comprises the 'path' to the audio file and its 'sentence'. \nAdditional fields include 'accent', 'age', 'client_id', 'up_votes', 'down_votes', 'gender', 'locale' and 'segment'.", "### Data Fields\n\n'client_id' ('string'): An id for which client (voice) made the recording\n\n'path' ('string'): The path to the audio file\n\n'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n\n'sentence' ('string'): The sentence the user was prompted to speak\n\n'up_votes' ('int64'): How many upvotes the audio file has received from reviewers\n\n'down_votes' ('int64'): How many downvotes the audio file has received from reviewers\n\n'age' ('string'): The age of the speaker (e.g. 'teens', 'twenties', 'fifties')\n\n'gender' ('string'): The gender of the speaker\n\n'accent' ('string'): Accent of the speaker\n\n'locale' ('string'): The locale of the speaker\n\n'segment' ('string'): Usually an empty field", "### Data Splits\n\nThe speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.\n\nThe validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.\n\nThe invalidated data is data has been invalidated by reviewers\nand received downvotes indicating that the data is of low quality.\n\nThe reported data is data that has been reported, for different reasons.\n\nThe other data is data that has not yet been reviewed.\n\nThe dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.", "## Data Preprocessing Recommended by Hugging Face\n\nThe following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. \n\nMany examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.\n\nIn addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, almost all sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.", "## 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\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nPublic Domain, CC-0" ]

[ "task_categories:automatic-speech-recognition", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:multilingual", "source_datasets:extended|common_voice", "license:cc0-1.0", "arxiv:1912.06670", "region:us" ]

[ "1912.06670" ]

[]

[ "# Dataset Card for Common Voice Corpus 5.1", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: URL\n- Point of Contact: Anton Lozhkov", "### Dataset Summary\n\nThe Common Voice dataset consists of a unique MP3 and corresponding text file. \nMany of the 7226 recorded hours in the dataset also include demographic metadata like age, sex, and accent \nthat can help improve the accuracy of speech recognition engines.\n\nThe dataset currently consists of 5671 validated hours in 54 languages, but more voices and languages are always added. \nTake a look at the Languages page to request a language or start contributing.", "### Supported Tasks and Leaderboards\n\nThe results for models trained on the Common Voice datasets are available via the \n Speech Bench", "### Languages", "## Dataset Structure", "### Data Instances\n\nA typical data point comprises the 'path' to the audio file and its 'sentence'. \nAdditional fields include 'accent', 'age', 'client_id', 'up_votes', 'down_votes', 'gender', 'locale' and 'segment'.", "### Data Fields\n\n'client_id' ('string'): An id for which client (voice) made the recording\n\n'path' ('string'): The path to the audio file\n\n'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n\n'sentence' ('string'): The sentence the user was prompted to speak\n\n'up_votes' ('int64'): How many upvotes the audio file has received from reviewers\n\n'down_votes' ('int64'): How many downvotes the audio file has received from reviewers\n\n'age' ('string'): The age of the speaker (e.g. 'teens', 'twenties', 'fifties')\n\n'gender' ('string'): The gender of the speaker\n\n'accent' ('string'): Accent of the speaker\n\n'locale' ('string'): The locale of the speaker\n\n'segment' ('string'): Usually an empty field", "### Data Splits\n\nThe speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.\n\nThe validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.\n\nThe invalidated data is data has been invalidated by reviewers\nand received downvotes indicating that the data is of low quality.\n\nThe reported data is data that has been reported, for different reasons.\n\nThe other data is data that has not yet been reviewed.\n\nThe dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.", "## Data Preprocessing Recommended by Hugging Face\n\nThe following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. \n\nMany examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.\n\nIn addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, almost all sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.", "## 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\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nPublic Domain, CC-0" ]

[ "TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-multilingual #source_datasets-extended|common_voice #license-cc0-1.0 #arxiv-1912.06670 #region-us \n", "# Dataset Card for Common Voice Corpus 5.1", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: URL\n- Point of Contact: Anton Lozhkov", "### Dataset Summary\n\nThe Common Voice dataset consists of a unique MP3 and corresponding text file. \nMany of the 7226 recorded hours in the dataset also include demographic metadata like age, sex, and accent \nthat can help improve the accuracy of speech recognition engines.\n\nThe dataset currently consists of 5671 validated hours in 54 languages, but more voices and languages are always added. \nTake a look at the Languages page to request a language or start contributing.", "### Supported Tasks and Leaderboards\n\nThe results for models trained on the Common Voice datasets are available via the \n Speech Bench", "### Languages", "## Dataset Structure", "### Data Instances\n\nA typical data point comprises the 'path' to the audio file and its 'sentence'. \nAdditional fields include 'accent', 'age', 'client_id', 'up_votes', 'down_votes', 'gender', 'locale' and 'segment'.", "### Data Fields\n\n'client_id' ('string'): An id for which client (voice) made the recording\n\n'path' ('string'): The path to the audio file\n\n'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n\n'sentence' ('string'): The sentence the user was prompted to speak\n\n'up_votes' ('int64'): How many upvotes the audio file has received from reviewers\n\n'down_votes' ('int64'): How many downvotes the audio file has received from reviewers\n\n'age' ('string'): The age of the speaker (e.g. 'teens', 'twenties', 'fifties')\n\n'gender' ('string'): The gender of the speaker\n\n'accent' ('string'): Accent of the speaker\n\n'locale' ('string'): The locale of the speaker\n\n'segment' ('string'): Usually an empty field", "### Data Splits\n\nThe speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.\n\nThe validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.\n\nThe invalidated data is data has been invalidated by reviewers\nand received downvotes indicating that the data is of low quality.\n\nThe reported data is data that has been reported, for different reasons.\n\nThe other data is data that has not yet been reviewed.\n\nThe dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.", "## Data Preprocessing Recommended by Hugging Face\n\nThe following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. \n\nMany examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.\n\nIn addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, almost all sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.", "## 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\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nPublic Domain, CC-0" ]

[ "task_categories:automatic-speech-recognition", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:multilingual", "source_datasets:extended|common_voice", "license:cc0-1.0", "arxiv:1912.06670", "region:us" ]

[ "1912.06670" ]

[]

[ "# Dataset Card for Common Voice Corpus 6.0", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: URL\n- Point of Contact: Anton Lozhkov", "### Dataset Summary\n\nThe Common Voice dataset consists of a unique MP3 and corresponding text file. \nMany of the 9261 recorded hours in the dataset also include demographic metadata like age, sex, and accent \nthat can help improve the accuracy of speech recognition engines.\n\nThe dataset currently consists of 7327 validated hours in 60 languages, but more voices and languages are always added. \nTake a look at the Languages page to request a language or start contributing.", "### Supported Tasks and Leaderboards\n\nThe results for models trained on the Common Voice datasets are available via the \n Speech Bench", "### Languages", "## Dataset Structure", "### Data Instances\n\nA typical data point comprises the 'path' to the audio file and its 'sentence'. \nAdditional fields include 'accent', 'age', 'client_id', 'up_votes', 'down_votes', 'gender', 'locale' and 'segment'.", "### Data Fields\n\n'client_id' ('string'): An id for which client (voice) made the recording\n\n'path' ('string'): The path to the audio file\n\n'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n\n'sentence' ('string'): The sentence the user was prompted to speak\n\n'up_votes' ('int64'): How many upvotes the audio file has received from reviewers\n\n'down_votes' ('int64'): How many downvotes the audio file has received from reviewers\n\n'age' ('string'): The age of the speaker (e.g. 'teens', 'twenties', 'fifties')\n\n'gender' ('string'): The gender of the speaker\n\n'accent' ('string'): Accent of the speaker\n\n'locale' ('string'): The locale of the speaker\n\n'segment' ('string'): Usually an empty field", "### Data Splits\n\nThe speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.\n\nThe validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.\n\nThe invalidated data is data has been invalidated by reviewers\nand received downvotes indicating that the data is of low quality.\n\nThe reported data is data that has been reported, for different reasons.\n\nThe other data is data that has not yet been reviewed.\n\nThe dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.", "## Data Preprocessing Recommended by Hugging Face\n\nThe following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. \n\nMany examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.\n\nIn addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, almost all sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.", "## 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\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nPublic Domain, CC-0" ]

[ "TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-multilingual #source_datasets-extended|common_voice #license-cc0-1.0 #arxiv-1912.06670 #region-us \n", "# Dataset Card for Common Voice Corpus 6.0", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: URL\n- Point of Contact: Anton Lozhkov", "### Dataset Summary\n\nThe Common Voice dataset consists of a unique MP3 and corresponding text file. \nMany of the 9261 recorded hours in the dataset also include demographic metadata like age, sex, and accent \nthat can help improve the accuracy of speech recognition engines.\n\nThe dataset currently consists of 7327 validated hours in 60 languages, but more voices and languages are always added. \nTake a look at the Languages page to request a language or start contributing.", "### Supported Tasks and Leaderboards\n\nThe results for models trained on the Common Voice datasets are available via the \n Speech Bench", "### Languages", "## Dataset Structure", "### Data Instances\n\nA typical data point comprises the 'path' to the audio file and its 'sentence'. \nAdditional fields include 'accent', 'age', 'client_id', 'up_votes', 'down_votes', 'gender', 'locale' and 'segment'.", "### Data Fields\n\n'client_id' ('string'): An id for which client (voice) made the recording\n\n'path' ('string'): The path to the audio file\n\n'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n\n'sentence' ('string'): The sentence the user was prompted to speak\n\n'up_votes' ('int64'): How many upvotes the audio file has received from reviewers\n\n'down_votes' ('int64'): How many downvotes the audio file has received from reviewers\n\n'age' ('string'): The age of the speaker (e.g. 'teens', 'twenties', 'fifties')\n\n'gender' ('string'): The gender of the speaker\n\n'accent' ('string'): Accent of the speaker\n\n'locale' ('string'): The locale of the speaker\n\n'segment' ('string'): Usually an empty field", "### Data Splits\n\nThe speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.\n\nThe validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.\n\nThe invalidated data is data has been invalidated by reviewers\nand received downvotes indicating that the data is of low quality.\n\nThe reported data is data that has been reported, for different reasons.\n\nThe other data is data that has not yet been reviewed.\n\nThe dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.", "## Data Preprocessing Recommended by Hugging Face\n\nThe following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. \n\nMany examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.\n\nIn addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, almost all sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.", "## 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\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nPublic Domain, CC-0" ]

[ "task_categories:automatic-speech-recognition", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:multilingual", "source_datasets:extended|common_voice", "license:cc0-1.0", "arxiv:1912.06670", "region:us" ]

[ "1912.06670" ]

[]

[ "# Dataset Card for Common Voice Corpus 6.1", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: URL\n- Point of Contact: Anton Lozhkov", "### Dataset Summary\n\nThe Common Voice dataset consists of a unique MP3 and corresponding text file. \nMany of the 9283 recorded hours in the dataset also include demographic metadata like age, sex, and accent \nthat can help improve the accuracy of speech recognition engines.\n\nThe dataset currently consists of 7335 validated hours in 60 languages, but more voices and languages are always added. \nTake a look at the Languages page to request a language or start contributing.", "### Supported Tasks and Leaderboards\n\nThe results for models trained on the Common Voice datasets are available via the \n Speech Bench", "### Languages", "## Dataset Structure", "### Data Instances\n\nA typical data point comprises the 'path' to the audio file and its 'sentence'. \nAdditional fields include 'accent', 'age', 'client_id', 'up_votes', 'down_votes', 'gender', 'locale' and 'segment'.", "### Data Fields\n\n'client_id' ('string'): An id for which client (voice) made the recording\n\n'path' ('string'): The path to the audio file\n\n'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n\n'sentence' ('string'): The sentence the user was prompted to speak\n\n'up_votes' ('int64'): How many upvotes the audio file has received from reviewers\n\n'down_votes' ('int64'): How many downvotes the audio file has received from reviewers\n\n'age' ('string'): The age of the speaker (e.g. 'teens', 'twenties', 'fifties')\n\n'gender' ('string'): The gender of the speaker\n\n'accent' ('string'): Accent of the speaker\n\n'locale' ('string'): The locale of the speaker\n\n'segment' ('string'): Usually an empty field", "### Data Splits\n\nThe speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.\n\nThe validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.\n\nThe invalidated data is data has been invalidated by reviewers\nand received downvotes indicating that the data is of low quality.\n\nThe reported data is data that has been reported, for different reasons.\n\nThe other data is data that has not yet been reviewed.\n\nThe dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.", "## Data Preprocessing Recommended by Hugging Face\n\nThe following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. \n\nMany examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.\n\nIn addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, almost all sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.", "## 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\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nPublic Domain, CC-0" ]

[ "TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-multilingual #source_datasets-extended|common_voice #license-cc0-1.0 #arxiv-1912.06670 #region-us \n", "# Dataset Card for Common Voice Corpus 6.1", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: URL\n- Point of Contact: Anton Lozhkov", "### Dataset Summary\n\nThe Common Voice dataset consists of a unique MP3 and corresponding text file. \nMany of the 9283 recorded hours in the dataset also include demographic metadata like age, sex, and accent \nthat can help improve the accuracy of speech recognition engines.\n\nThe dataset currently consists of 7335 validated hours in 60 languages, but more voices and languages are always added. \nTake a look at the Languages page to request a language or start contributing.", "### Supported Tasks and Leaderboards\n\nThe results for models trained on the Common Voice datasets are available via the \n Speech Bench", "### Languages", "## Dataset Structure", "### Data Instances\n\nA typical data point comprises the 'path' to the audio file and its 'sentence'. \nAdditional fields include 'accent', 'age', 'client_id', 'up_votes', 'down_votes', 'gender', 'locale' and 'segment'.", "### Data Fields\n\n'client_id' ('string'): An id for which client (voice) made the recording\n\n'path' ('string'): The path to the audio file\n\n'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n\n'sentence' ('string'): The sentence the user was prompted to speak\n\n'up_votes' ('int64'): How many upvotes the audio file has received from reviewers\n\n'down_votes' ('int64'): How many downvotes the audio file has received from reviewers\n\n'age' ('string'): The age of the speaker (e.g. 'teens', 'twenties', 'fifties')\n\n'gender' ('string'): The gender of the speaker\n\n'accent' ('string'): Accent of the speaker\n\n'locale' ('string'): The locale of the speaker\n\n'segment' ('string'): Usually an empty field", "### Data Splits\n\nThe speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.\n\nThe validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.\n\nThe invalidated data is data has been invalidated by reviewers\nand received downvotes indicating that the data is of low quality.\n\nThe reported data is data that has been reported, for different reasons.\n\nThe other data is data that has not yet been reviewed.\n\nThe dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.", "## Data Preprocessing Recommended by Hugging Face\n\nThe following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. \n\nMany examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.\n\nIn addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, almost all sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.", "## 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\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nPublic Domain, CC-0" ]

[ "task_categories:automatic-speech-recognition", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:multilingual", "source_datasets:extended|common_voice", "license:cc0-1.0", "arxiv:1912.06670", "region:us" ]

[ "1912.06670" ]

[]

[ "# Dataset Card for Common Voice Corpus 7.0", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: URL\n- Point of Contact: Anton Lozhkov", "### Dataset Summary\n\nThe Common Voice dataset consists of a unique MP3 and corresponding text file. \nMany of the 13905 recorded hours in the dataset also include demographic metadata like age, sex, and accent \nthat can help improve the accuracy of speech recognition engines.\n\nThe dataset currently consists of 11192 validated hours in 76 languages, but more voices and languages are always added. \nTake a look at the Languages page to request a language or start contributing.", "### Supported Tasks and Leaderboards\n\nThe results for models trained on the Common Voice datasets are available via the \n Speech Bench", "### Languages", "## Dataset Structure", "### Data Instances\n\nA typical data point comprises the 'path' to the audio file and its 'sentence'. \nAdditional fields include 'accent', 'age', 'client_id', 'up_votes', 'down_votes', 'gender', 'locale' and 'segment'.", "### Data Fields\n\n'client_id' ('string'): An id for which client (voice) made the recording\n\n'path' ('string'): The path to the audio file\n\n'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n\n'sentence' ('string'): The sentence the user was prompted to speak\n\n'up_votes' ('int64'): How many upvotes the audio file has received from reviewers\n\n'down_votes' ('int64'): How many downvotes the audio file has received from reviewers\n\n'age' ('string'): The age of the speaker (e.g. 'teens', 'twenties', 'fifties')\n\n'gender' ('string'): The gender of the speaker\n\n'accent' ('string'): Accent of the speaker\n\n'locale' ('string'): The locale of the speaker\n\n'segment' ('string'): Usually an empty field", "### Data Splits\n\nThe speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.\n\nThe validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.\n\nThe invalidated data is data has been invalidated by reviewers\nand received downvotes indicating that the data is of low quality.\n\nThe reported data is data that has been reported, for different reasons.\n\nThe other data is data that has not yet been reviewed.\n\nThe dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.", "## Data Preprocessing Recommended by Hugging Face\n\nThe following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. \n\nMany examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.\n\nIn addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, almost all sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.", "## 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\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nPublic Domain, CC-0" ]

[ "TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-multilingual #source_datasets-extended|common_voice #license-cc0-1.0 #arxiv-1912.06670 #region-us \n", "# Dataset Card for Common Voice Corpus 7.0", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: URL\n- Point of Contact: Anton Lozhkov", "### Dataset Summary\n\nThe Common Voice dataset consists of a unique MP3 and corresponding text file. \nMany of the 13905 recorded hours in the dataset also include demographic metadata like age, sex, and accent \nthat can help improve the accuracy of speech recognition engines.\n\nThe dataset currently consists of 11192 validated hours in 76 languages, but more voices and languages are always added. \nTake a look at the Languages page to request a language or start contributing.", "### Supported Tasks and Leaderboards\n\nThe results for models trained on the Common Voice datasets are available via the \n Speech Bench", "### Languages", "## Dataset Structure", "### Data Instances\n\nA typical data point comprises the 'path' to the audio file and its 'sentence'. \nAdditional fields include 'accent', 'age', 'client_id', 'up_votes', 'down_votes', 'gender', 'locale' and 'segment'.", "### Data Fields\n\n'client_id' ('string'): An id for which client (voice) made the recording\n\n'path' ('string'): The path to the audio file\n\n'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n\n'sentence' ('string'): The sentence the user was prompted to speak\n\n'up_votes' ('int64'): How many upvotes the audio file has received from reviewers\n\n'down_votes' ('int64'): How many downvotes the audio file has received from reviewers\n\n'age' ('string'): The age of the speaker (e.g. 'teens', 'twenties', 'fifties')\n\n'gender' ('string'): The gender of the speaker\n\n'accent' ('string'): Accent of the speaker\n\n'locale' ('string'): The locale of the speaker\n\n'segment' ('string'): Usually an empty field", "### Data Splits\n\nThe speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.\n\nThe validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.\n\nThe invalidated data is data has been invalidated by reviewers\nand received downvotes indicating that the data is of low quality.\n\nThe reported data is data that has been reported, for different reasons.\n\nThe other data is data that has not yet been reviewed.\n\nThe dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.", "## Data Preprocessing Recommended by Hugging Face\n\nThe following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. \n\nMany examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.\n\nIn addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, almost all sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.", "## 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\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nPublic Domain, CC-0" ]

[ "task_categories:automatic-speech-recognition", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:multilingual", "source_datasets:extended|common_voice", "license:cc0-1.0", "arxiv:1912.06670", "region:us" ]

[ "1912.06670" ]

[]

[ "# Dataset Card for Common Voice Corpus 8.0", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: URL\n- Point of Contact: Anton Lozhkov", "### Dataset Summary\n\nThe Common Voice dataset consists of a unique MP3 and corresponding text file. \nMany of the 18243 recorded hours in the dataset also include demographic metadata like age, sex, and accent \nthat can help improve the accuracy of speech recognition engines.\n\nThe dataset currently consists of 14122 validated hours in 87 languages, but more voices and languages are always added. \nTake a look at the Languages page to request a language or start contributing.", "### Supported Tasks and Leaderboards\n\nThe results for models trained on the Common Voice datasets are available via the \n Speech Bench", "### Languages", "## Dataset Structure", "### Data Instances\n\nA typical data point comprises the 'path' to the audio file and its 'sentence'. \nAdditional fields include 'accent', 'age', 'client_id', 'up_votes', 'down_votes', 'gender', 'locale' and 'segment'.", "### Data Fields\n\n'client_id' ('string'): An id for which client (voice) made the recording\n\n'path' ('string'): The path to the audio file\n\n'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n\n'sentence' ('string'): The sentence the user was prompted to speak\n\n'up_votes' ('int64'): How many upvotes the audio file has received from reviewers\n\n'down_votes' ('int64'): How many downvotes the audio file has received from reviewers\n\n'age' ('string'): The age of the speaker (e.g. 'teens', 'twenties', 'fifties')\n\n'gender' ('string'): The gender of the speaker\n\n'accent' ('string'): Accent of the speaker\n\n'locale' ('string'): The locale of the speaker\n\n'segment' ('string'): Usually an empty field", "### Data Splits\n\nThe speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.\n\nThe validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.\n\nThe invalidated data is data has been invalidated by reviewers\nand received downvotes indicating that the data is of low quality.\n\nThe reported data is data that has been reported, for different reasons.\n\nThe other data is data that has not yet been reviewed.\n\nThe dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.", "## Data Preprocessing Recommended by Hugging Face\n\nThe following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. \n\nMany examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.\n\nIn addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, almost all sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.", "## 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\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nPublic Domain, CC-0" ]

[ "TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-multilingual #source_datasets-extended|common_voice #license-cc0-1.0 #arxiv-1912.06670 #region-us \n", "# Dataset Card for Common Voice Corpus 8.0", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: URL\n- Point of Contact: Anton Lozhkov", "### Dataset Summary\n\nThe Common Voice dataset consists of a unique MP3 and corresponding text file. \nMany of the 18243 recorded hours in the dataset also include demographic metadata like age, sex, and accent \nthat can help improve the accuracy of speech recognition engines.\n\nThe dataset currently consists of 14122 validated hours in 87 languages, but more voices and languages are always added. \nTake a look at the Languages page to request a language or start contributing.", "### Supported Tasks and Leaderboards\n\nThe results for models trained on the Common Voice datasets are available via the \n Speech Bench", "### Languages", "## Dataset Structure", "### Data Instances\n\nA typical data point comprises the 'path' to the audio file and its 'sentence'. \nAdditional fields include 'accent', 'age', 'client_id', 'up_votes', 'down_votes', 'gender', 'locale' and 'segment'.", "### Data Fields\n\n'client_id' ('string'): An id for which client (voice) made the recording\n\n'path' ('string'): The path to the audio file\n\n'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n\n'sentence' ('string'): The sentence the user was prompted to speak\n\n'up_votes' ('int64'): How many upvotes the audio file has received from reviewers\n\n'down_votes' ('int64'): How many downvotes the audio file has received from reviewers\n\n'age' ('string'): The age of the speaker (e.g. 'teens', 'twenties', 'fifties')\n\n'gender' ('string'): The gender of the speaker\n\n'accent' ('string'): Accent of the speaker\n\n'locale' ('string'): The locale of the speaker\n\n'segment' ('string'): Usually an empty field", "### Data Splits\n\nThe speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.\n\nThe validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.\n\nThe invalidated data is data has been invalidated by reviewers\nand received downvotes indicating that the data is of low quality.\n\nThe reported data is data that has been reported, for different reasons.\n\nThe other data is data that has not yet been reviewed.\n\nThe dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.", "## Data Preprocessing Recommended by Hugging Face\n\nThe following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. \n\nMany examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.\n\nIn addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, almost all sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.", "## 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\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nPublic Domain, CC-0" ]

[ "region:us" ]

[]

[]

[]

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

[ "arxiv:2005.00547", "region:us" ]

[ "2005.00547" ]

[]

[ "### Data Format\n\n\nOur raw dataset, split into three csv files, includes all annotations as well as metadata on the comments. Each row represents a single rater's annotation for a single example. This file includes the following columns:\n\n\n* 'text': The text of the comment (with masked tokens, as described in the paper).\n* 'id': The unique id of the comment.\n* 'author': The Reddit username of the comment's author.\n* 'subreddit': The subreddit that the comment belongs to.\n* 'link\\_id': The link id of the comment.\n* 'parent\\_id': The parent id of the comment.\n* 'created\\_utc': The timestamp of the comment.\n* 'rater\\_id': The unique id of the annotator.\n* 'example\\_very\\_unclear': Whether the annotator marked the example as being very unclear or difficult to label (in this case they did not choose any emotion labels).\n* separate columns representing each of the emotion categories, with binary labels (0 or 1)\n\n\nThe data we used for training the models includes examples where there is agreement between at least 2 raters. Our data includes 43,410 training examples ('URL'), 5426 dev examples ('URL') and 5427 test examples ('URL'). These files have *no header row* and have the following columns:\n\n\n1. text\n2. comma-separated list of emotion ids (the ids are indexed based on the order of emotions in 'URL')\n3. id of the comment", "### Visualization\n\n\nHere you can view a TSNE projection showing a random sample of the data. The plot is generated using PPCA (see scripts below). Each point in the plot represents a single example and the text and the labels are shown on mouse-hover. The color of each point is the weighted average of the RGB values of the those emotions.\n\n\nData Analysis\n-------------\n\n\nSee each script for more documentation and descriptive command line flags.\n\n\n* 'python3 -m analyze\\_data': get high-level statistics of the\ndata and correlation among emotion ratings.\n* 'python3 -m extract\\_words': get the words that are significantly\nassociated with each emotion, in contrast to the other emotions, based on\ntheir log odds ratio.\n* 'python3 -m ppca': run PPCA\n(Cowen et al., 2019) on\nthe data and generate plots.", "### Tutorial\n\n\nWe released a detailed tutorial\nfor training a neural emotion prediction model. In it, we work through training\na model architecture available on TensorFlow Model Garden using GoEmotions and\napplying it for the task of suggesting emojis based on conversational text.\n\n\nIf you use this code for your publication, please cite the original paper:\n\n\nContact\n-------\n\n\nDora Demszky\n\n\nDisclaimer\n----------\n\n\n* We are aware that the dataset contains biases and is not representative of global diversity.\n* We are aware that the dataset contains potentially problematic content.\n* Potential biases in the data include: Inherent biases in Reddit and user base biases, the offensive/vulgar word lists used for data filtering, inherent or unconscious bias in assessment of offensive identity labels, annotators were all native English speakers from India. All these likely affect labelling, precision, and recall for a trained model.\n* The emotion pilot model used for sentiment labeling, was trained on examples reviewed by the research team.\n* Anyone using this dataset should be aware of these limitations of the dataset.\n\n\nDataset Metadata\n----------------\n\n\nThe following table is necessary for this dataset to be indexed by search\nengines such as [Google Dataset Search](https://g.co/datasetsearch).\n\n\n<div itemscope itemtype=\"URL\n <p>" ]

[ "TAGS\n#arxiv-2005.00547 #region-us \n", "### Data Format\n\n\nOur raw dataset, split into three csv files, includes all annotations as well as metadata on the comments. Each row represents a single rater's annotation for a single example. This file includes the following columns:\n\n\n* 'text': The text of the comment (with masked tokens, as described in the paper).\n* 'id': The unique id of the comment.\n* 'author': The Reddit username of the comment's author.\n* 'subreddit': The subreddit that the comment belongs to.\n* 'link\\_id': The link id of the comment.\n* 'parent\\_id': The parent id of the comment.\n* 'created\\_utc': The timestamp of the comment.\n* 'rater\\_id': The unique id of the annotator.\n* 'example\\_very\\_unclear': Whether the annotator marked the example as being very unclear or difficult to label (in this case they did not choose any emotion labels).\n* separate columns representing each of the emotion categories, with binary labels (0 or 1)\n\n\nThe data we used for training the models includes examples where there is agreement between at least 2 raters. Our data includes 43,410 training examples ('URL'), 5426 dev examples ('URL') and 5427 test examples ('URL'). These files have *no header row* and have the following columns:\n\n\n1. text\n2. comma-separated list of emotion ids (the ids are indexed based on the order of emotions in 'URL')\n3. id of the comment", "### Visualization\n\n\nHere you can view a TSNE projection showing a random sample of the data. The plot is generated using PPCA (see scripts below). Each point in the plot represents a single example and the text and the labels are shown on mouse-hover. The color of each point is the weighted average of the RGB values of the those emotions.\n\n\nData Analysis\n-------------\n\n\nSee each script for more documentation and descriptive command line flags.\n\n\n* 'python3 -m analyze\\_data': get high-level statistics of the\ndata and correlation among emotion ratings.\n* 'python3 -m extract\\_words': get the words that are significantly\nassociated with each emotion, in contrast to the other emotions, based on\ntheir log odds ratio.\n* 'python3 -m ppca': run PPCA\n(Cowen et al., 2019) on\nthe data and generate plots.", "### Tutorial\n\n\nWe released a detailed tutorial\nfor training a neural emotion prediction model. In it, we work through training\na model architecture available on TensorFlow Model Garden using GoEmotions and\napplying it for the task of suggesting emojis based on conversational text.\n\n\nIf you use this code for your publication, please cite the original paper:\n\n\nContact\n-------\n\n\nDora Demszky\n\n\nDisclaimer\n----------\n\n\n* We are aware that the dataset contains biases and is not representative of global diversity.\n* We are aware that the dataset contains potentially problematic content.\n* Potential biases in the data include: Inherent biases in Reddit and user base biases, the offensive/vulgar word lists used for data filtering, inherent or unconscious bias in assessment of offensive identity labels, annotators were all native English speakers from India. All these likely affect labelling, precision, and recall for a trained model.\n* The emotion pilot model used for sentiment labeling, was trained on examples reviewed by the research team.\n* Anyone using this dataset should be aware of these limitations of the dataset.\n\n\nDataset Metadata\n----------------\n\n\nThe following table is necessary for this dataset to be indexed by search\nengines such as [Google Dataset Search](https://g.co/datasetsearch).\n\n\n<div itemscope itemtype=\"URL\n <p>" ]

[ "region:us" ]

[]

[]

[]

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

[ "annotations_creators:expert-generated", "language_creators:other", "multilinguality:translation", "multilinguality:multilingual", "language:en", "language:ko", "license:cc-by-nc-nd-4.0", "region:us" ]

[]

[ "en", "ko" ]

[ "# Dataset Card for english-korean-multitarget-ted-talks-task", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL", "### Dataset Summary\n\n- Parallel English-Korean Text Corpus\n- Text was originally transcribed to English from various Ted Talks, then translated to Korean by TED translators\n- Approximately 166k train, 2k validation, and 2k test sentence pairs.", "### Supported Tasks and Leaderboards\n\n- Machine Translation", "### Languages\n\n- English\n- Korean", "## Additional Information", "### Dataset Curators\n\nKevin Duh, \"The Multitarget TED Talks Task\", URL 2018", "### Licensing Information\n\nTED makes its collection available under the Creative Commons BY-NC-ND license. Please acknowledge TED when using this data. We acknowledge the authorship of TED Talks (BY condition). We are not redistributing the transcripts for commercial purposes (NC condition) nor making derivative works of the original contents (ND condition).\n\n\n\n@misc{duh18multitarget,\n author = {Kevin Duh},\n title = {The Multitarget TED Talks Task},\n howpublished = {\\url{URL\n year = {2018},\n}" ]

[ "TAGS\n#annotations_creators-expert-generated #language_creators-other #multilinguality-translation #multilinguality-multilingual #language-English #language-Korean #license-cc-by-nc-nd-4.0 #region-us \n", "# Dataset Card for english-korean-multitarget-ted-talks-task", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL", "### Dataset Summary\n\n- Parallel English-Korean Text Corpus\n- Text was originally transcribed to English from various Ted Talks, then translated to Korean by TED translators\n- Approximately 166k train, 2k validation, and 2k test sentence pairs.", "### Supported Tasks and Leaderboards\n\n- Machine Translation", "### Languages\n\n- English\n- Korean", "## Additional Information", "### Dataset Curators\n\nKevin Duh, \"The Multitarget TED Talks Task\", URL 2018", "### Licensing Information\n\nTED makes its collection available under the Creative Commons BY-NC-ND license. Please acknowledge TED when using this data. We acknowledge the authorship of TED Talks (BY condition). We are not redistributing the transcripts for commercial purposes (NC condition) nor making derivative works of the original contents (ND condition).\n\n\n\n@misc{duh18multitarget,\n author = {Kevin Duh},\n title = {The Multitarget TED Talks Task},\n howpublished = {\\url{URL\n year = {2018},\n}" ]

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:text-classification", "task_ids:text-scoring", "language:de", "region:us" ]

[]

[ "de" ]

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

[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "source_datasets:extended", "language:id", "license:odc-by", "arxiv:1910.10683", "region:us" ]

[ "1910.10683" ]

[ "id" ]

[ "# Dataset Card for Clean(maybe) Indonesia mC4", "## Dataset Description\n\n- Original Homepage: HF Hub\n- Paper: ArXiv", "### Dataset Summary\n\nA thoroughly cleaned version of the Indonesia split of the multilingual colossal, cleaned version of Common Crawl's web crawl corpus (mC4). Based on the Common Crawl dataset. The original version was prepared by AllenAI, hosted at the address URL", "### Data Fields\n\nThe data contains the following fields:\n\n- 'url': url of the source as a string\n- 'text': text content as a string\n- 'timestamp': timestamp of extraction as a string", "### Data Splits\n\n\nYou can load any subset like this:\n\n\n\nSince splits are quite large, you may want to traverse them using the streaming mode available starting from Datasets v1.9.0:", "## Dataset Creation\n\nRefer to the original paper for more considerations regarding the choice of sources and the scraping process for creating 'mC4'.", "## Considerations for Using the Data", "### Discussion of Biases\n\nDespite the cleaning procedure aimed at removing vulgarity and profanity, it must be considered that model trained on this scraped corpus will inevitably reflect biases present in blog articles and comments on the Internet. This makes the corpus especially interesting in the context of studying data biases and how to limit their impacts.", "## Additional Information", "### Dataset Curators\n\nAuthors at AllenAI are the original curators for the 'mc4' corpus.", "### Licensing Information\n\nAllenAI are releasing this dataset under the terms of ODC-BY. By using this, you are also bound by the Common Crawl terms of use in respect of the content contained in the dataset.\n\n\n\nIf you use this dataset in your work, please cite us and the original mC4 authors as:", "### Contributions\n\nThanks to @dirkgr and @lhoestq for adding this dataset." ]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #source_datasets-extended #language-Indonesian #license-odc-by #arxiv-1910.10683 #region-us \n", "# Dataset Card for Clean(maybe) Indonesia mC4", "## Dataset Description\n\n- Original Homepage: HF Hub\n- Paper: ArXiv", "### Dataset Summary\n\nA thoroughly cleaned version of the Indonesia split of the multilingual colossal, cleaned version of Common Crawl's web crawl corpus (mC4). Based on the Common Crawl dataset. The original version was prepared by AllenAI, hosted at the address URL", "### Data Fields\n\nThe data contains the following fields:\n\n- 'url': url of the source as a string\n- 'text': text content as a string\n- 'timestamp': timestamp of extraction as a string", "### Data Splits\n\n\nYou can load any subset like this:\n\n\n\nSince splits are quite large, you may want to traverse them using the streaming mode available starting from Datasets v1.9.0:", "## Dataset Creation\n\nRefer to the original paper for more considerations regarding the choice of sources and the scraping process for creating 'mC4'.", "## Considerations for Using the Data", "### Discussion of Biases\n\nDespite the cleaning procedure aimed at removing vulgarity and profanity, it must be considered that model trained on this scraped corpus will inevitably reflect biases present in blog articles and comments on the Internet. This makes the corpus especially interesting in the context of studying data biases and how to limit their impacts.", "## Additional Information", "### Dataset Curators\n\nAuthors at AllenAI are the original curators for the 'mc4' corpus.", "### Licensing Information\n\nAllenAI are releasing this dataset under the terms of ODC-BY. By using this, you are also bound by the Common Crawl terms of use in respect of the content contained in the dataset.\n\n\n\nIf you use this dataset in your work, please cite us and the original mC4 authors as:", "### Contributions\n\nThanks to @dirkgr and @lhoestq for adding this dataset." ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "annotations_creators:machine-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:extended|wikipedia", "license:cc-by-4.0", "arxiv:2110.08228", "region:us" ]

[ "2110.08228" ]

[ "en-US", "en" ]

[ "### Dataset Summary\n\n\nMedWiki is a large sentence dataset collected from a medically-relevant subset of Wikipedia and annotated with biomedical entities in the Unified Medical Language System (UMLS) knowledge base. For each entity, we include a rich set of types sourced from both UMLS and WikiData. Consisting of over 13 million sentences and 17 million entity annotations, MedWiki can be utilized as a pretraining resource for language models and can improve performance of medical named entity recognition and disambiguation systems, especially on rare entities.\n\n\nHere, we include two configurations of MedWiki (further details in Dataset Creation):\n\n\n* 'MedWiki-Full' is a large sentence dataset with UMLS medical entity annotations generated through the following two steps: (1) a weak labeling proecedure to annotate WikiData entities in sentences and (2) a data integration approach that maps WikiData entities to their counterparts in UMLS.\n* 'MedWiki-HQ' is a subset of MedWiki-Full with higher quality labels designed to limit noise that arises from the annotation procedure listed above.", "### Languages\n\n\nThe text in the dataset is in English and was obtained from English Wikipedia.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA typical data point includes a sentence collected from Wikipedia annotated with UMLS medical entities and associated titles and types.\n\n\nAn example from the MedWiki test set looks as follows:", "### Data Fields\n\n\n* 'sent\\_idx\\_unq': a unique integer identifier for the data instance\n* 'sentence': a string sentence collected from English Wikipedia. Punctuation is separated from words, and the sentence can be tokenized into word-pieces with the .split() method.\n* 'mentions': list of medical mentions in the sentence.\n* 'entities': list of UMLS medical entity identifiers corresponding to mentions. There is exactly one entity for each mention, and the length of the 'entities' list is equal to the length of the 'mentions' list.\n* 'entity\\_titles': List of English titles collected from UMLS that describe each entity. The length of the 'entity\\_titles' list is equal to the length of the 'entities' list.\n* 'types': List of category types associated with each entity, including types collected from UMLS and WikiData.\n* 'spans': List of integer pairs representing the word span of each mention in the sentence.", "### Data Splits\n\n\nMedWiki includes two configurations: MedWiki-Full and MedWiki-HQ (described further in Dataset Creation). For each configuration, data is split into training, development, and test sets. The split sizes are as follow:\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nExisting medical text datasets are generally limited in scope, often obtaining low coverage over the entities and structural resources in the UMLS medical knowledge base. When language models are trained across such datasets, the lack of adequate examples may prevent models from learning the complex reasoning patterns that are necessary for performing effective entity linking or disambiguation, especially for rare entities as shown in prior work by Orr et al.. Wikipedia, which is often utilized as a rich knowledge source in general text settings, contains references to medical terms and can help address this issue. Here, we curate the MedWiki dataset, which is a large-scale, weakly-labeled dataset that consists of sentences from Wikipedia annotated with medical entities in the UMLS knowledge base. MedWiki can serve as a pretraining dataset for language models and holds potential for improving performance on medical named entity recognition tasks, especially on rare entities.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nMedWiki consists of sentences obtained from the November 2019 dump of English Wikipedia. We split pages into an 80/10/10 train/dev/test split and then segment each page at the sentence-level. This ensures that all sentences associated with a single Wikipedia page are placed in the same split.", "#### Who are the source language producers?\n\n\nThe source language producers are editors on English Wikipedia.", "### Annotations", "#### Annotation process\n\n\nWe create two configurations of our dataset: MedWiki-Full and MedWiki-HQ. We label MedWiki-Full by first annotating all English Wikipedia articles with textual mentions and corresponding WikiData entities; we do so by obtaining gold entity labels from internal page links as well as generating weak labels based on pronouns and alternative entity names (see Orr et al. 2020 for additional information). Then, we use the off-the-shelf entity linker Bootleg to map entities in WikiData to their counterparts in the 2017AA release of the Unified Medical Language System (UMLS), a standard knowledge base for biomedical entities (additional implementation details in forthcoming publication). Any sentence containing at least one UMLS entity is included in MedWiki-Full. We also include types associated with each entity, which are collected from both WikiData and UMLS using the generated UMLS-Wikidata mapping. It is important to note that types obtained from WikiData are filtered according to methods described in Orr et al. 2020.\n\n\nSince our labeling procedure introduces some noise into annotations, we also release the MedWiki-HQ dataset configuration with higher-quality labels. To generate MedWiki-HQ, we filtered the UMLS-Wikidata mappings to only include pairs of UMLS medical entities and WikiData items that share a high textual overlap between titles. MedWiki-HQ is a subset of MedWiki-Full.\n\n\nTo evaluate the quality of our UMLS-Wikidata mappings, we find that WikiData includes a small set of \"true\" labeled mappings between UMLS entities and WikiData items. (Note that we only include WikiData items associated with linked Wikipedia pages.) This set comprises approximately 9.3k UMLS entities in the original UMLS-Wikidata mapping (used for MedWiki-Full) and 5.6k entities in the filtered UMLS-Wikidata mapping (used for MedWiki-HQ). Using these labeled sets, we find that our mapping accuracy is 80.2% for the original UMLS-Wikidata mapping and 94.5% for the filtered UMLS-Wikidata mapping. We also evaluate integration performance on this segment as the proportion of mapped WikiData entities that share a WikiData type with the true entity, suggesting the predicted mapping adds relevant structural resources. Integration performance is 85.4% for the original UMLS-Wikidata mapping and 95.9% for the filtered UMLS-Wikidata mapping. The remainder of items in UMLS have no “true” mappings to WikiData.", "#### Who are the annotators?\n\n\nThe dataset was labeled using weak-labeling techniques as described above.", "### Personal and Sensitive Information\n\n\nNo personal or sensitive information is included in MedWiki.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThe purpose of this dataset is to enable the creation of better named entity recognition systems for biomedical text. MedWiki encompasses a large set of entities in the UMLS knowledge base and includes a rich set of types associated with each entity, which can enable the creation of models that achieve high performance on named entity recognition tasks, especially on rare or unpopular entities. Such systems hold potential for improving automated parsing and information retrieval from large quantities of biomedical text.", "### Discussion of Biases\n\n\nThe data included in MedWiki comes from English Wikipedia. Generally, Wikipedia articles are neutral in point of view and aim to avoid bias. However, some prior work has shown that ideological biases may exist within some Wikipedia articles, especially those that are focused on political issues or those that are written by fewer authors. We anticipate that such biases are rare for medical articles, which are typically comprised of scientific facts. However, it is important to note that bias encoded in Wikipedia is likely to be reflected by MedWiki.", "### Other Known Limitations\n\n\nSince MedWiki was annotated using weak labeling techniques, there is likely some noise in entity annotations. (Note that to address this, we include the MedWiki-HQ configuration, which is a subset of MedWiki-Full with higher quality labels. Additional details in Dataset Creation).\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nMedWiki was curated by Maya Varma, Laurel Orr, Sen Wu, Megan Leszczynski, Xiao Ling, and Chris Ré.", "### Licensing Information\n\n\nDataset licensed under CC BY 4.0.", "### Contributions\n\n\nThanks to @maya124 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #annotations_creators-machine-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-unknown #source_datasets-extended|wikipedia #license-cc-by-4.0 #arxiv-2110.08228 #region-us \n", "### Dataset Summary\n\n\nMedWiki is a large sentence dataset collected from a medically-relevant subset of Wikipedia and annotated with biomedical entities in the Unified Medical Language System (UMLS) knowledge base. For each entity, we include a rich set of types sourced from both UMLS and WikiData. Consisting of over 13 million sentences and 17 million entity annotations, MedWiki can be utilized as a pretraining resource for language models and can improve performance of medical named entity recognition and disambiguation systems, especially on rare entities.\n\n\nHere, we include two configurations of MedWiki (further details in Dataset Creation):\n\n\n* 'MedWiki-Full' is a large sentence dataset with UMLS medical entity annotations generated through the following two steps: (1) a weak labeling proecedure to annotate WikiData entities in sentences and (2) a data integration approach that maps WikiData entities to their counterparts in UMLS.\n* 'MedWiki-HQ' is a subset of MedWiki-Full with higher quality labels designed to limit noise that arises from the annotation procedure listed above.", "### Languages\n\n\nThe text in the dataset is in English and was obtained from English Wikipedia.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA typical data point includes a sentence collected from Wikipedia annotated with UMLS medical entities and associated titles and types.\n\n\nAn example from the MedWiki test set looks as follows:", "### Data Fields\n\n\n* 'sent\\_idx\\_unq': a unique integer identifier for the data instance\n* 'sentence': a string sentence collected from English Wikipedia. Punctuation is separated from words, and the sentence can be tokenized into word-pieces with the .split() method.\n* 'mentions': list of medical mentions in the sentence.\n* 'entities': list of UMLS medical entity identifiers corresponding to mentions. There is exactly one entity for each mention, and the length of the 'entities' list is equal to the length of the 'mentions' list.\n* 'entity\\_titles': List of English titles collected from UMLS that describe each entity. The length of the 'entity\\_titles' list is equal to the length of the 'entities' list.\n* 'types': List of category types associated with each entity, including types collected from UMLS and WikiData.\n* 'spans': List of integer pairs representing the word span of each mention in the sentence.", "### Data Splits\n\n\nMedWiki includes two configurations: MedWiki-Full and MedWiki-HQ (described further in Dataset Creation). For each configuration, data is split into training, development, and test sets. The split sizes are as follow:\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nExisting medical text datasets are generally limited in scope, often obtaining low coverage over the entities and structural resources in the UMLS medical knowledge base. When language models are trained across such datasets, the lack of adequate examples may prevent models from learning the complex reasoning patterns that are necessary for performing effective entity linking or disambiguation, especially for rare entities as shown in prior work by Orr et al.. Wikipedia, which is often utilized as a rich knowledge source in general text settings, contains references to medical terms and can help address this issue. Here, we curate the MedWiki dataset, which is a large-scale, weakly-labeled dataset that consists of sentences from Wikipedia annotated with medical entities in the UMLS knowledge base. MedWiki can serve as a pretraining dataset for language models and holds potential for improving performance on medical named entity recognition tasks, especially on rare entities.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nMedWiki consists of sentences obtained from the November 2019 dump of English Wikipedia. We split pages into an 80/10/10 train/dev/test split and then segment each page at the sentence-level. This ensures that all sentences associated with a single Wikipedia page are placed in the same split.", "#### Who are the source language producers?\n\n\nThe source language producers are editors on English Wikipedia.", "### Annotations", "#### Annotation process\n\n\nWe create two configurations of our dataset: MedWiki-Full and MedWiki-HQ. We label MedWiki-Full by first annotating all English Wikipedia articles with textual mentions and corresponding WikiData entities; we do so by obtaining gold entity labels from internal page links as well as generating weak labels based on pronouns and alternative entity names (see Orr et al. 2020 for additional information). Then, we use the off-the-shelf entity linker Bootleg to map entities in WikiData to their counterparts in the 2017AA release of the Unified Medical Language System (UMLS), a standard knowledge base for biomedical entities (additional implementation details in forthcoming publication). Any sentence containing at least one UMLS entity is included in MedWiki-Full. We also include types associated with each entity, which are collected from both WikiData and UMLS using the generated UMLS-Wikidata mapping. It is important to note that types obtained from WikiData are filtered according to methods described in Orr et al. 2020.\n\n\nSince our labeling procedure introduces some noise into annotations, we also release the MedWiki-HQ dataset configuration with higher-quality labels. To generate MedWiki-HQ, we filtered the UMLS-Wikidata mappings to only include pairs of UMLS medical entities and WikiData items that share a high textual overlap between titles. MedWiki-HQ is a subset of MedWiki-Full.\n\n\nTo evaluate the quality of our UMLS-Wikidata mappings, we find that WikiData includes a small set of \"true\" labeled mappings between UMLS entities and WikiData items. (Note that we only include WikiData items associated with linked Wikipedia pages.) This set comprises approximately 9.3k UMLS entities in the original UMLS-Wikidata mapping (used for MedWiki-Full) and 5.6k entities in the filtered UMLS-Wikidata mapping (used for MedWiki-HQ). Using these labeled sets, we find that our mapping accuracy is 80.2% for the original UMLS-Wikidata mapping and 94.5% for the filtered UMLS-Wikidata mapping. We also evaluate integration performance on this segment as the proportion of mapped WikiData entities that share a WikiData type with the true entity, suggesting the predicted mapping adds relevant structural resources. Integration performance is 85.4% for the original UMLS-Wikidata mapping and 95.9% for the filtered UMLS-Wikidata mapping. The remainder of items in UMLS have no “true” mappings to WikiData.", "#### Who are the annotators?\n\n\nThe dataset was labeled using weak-labeling techniques as described above.", "### Personal and Sensitive Information\n\n\nNo personal or sensitive information is included in MedWiki.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThe purpose of this dataset is to enable the creation of better named entity recognition systems for biomedical text. MedWiki encompasses a large set of entities in the UMLS knowledge base and includes a rich set of types associated with each entity, which can enable the creation of models that achieve high performance on named entity recognition tasks, especially on rare or unpopular entities. Such systems hold potential for improving automated parsing and information retrieval from large quantities of biomedical text.", "### Discussion of Biases\n\n\nThe data included in MedWiki comes from English Wikipedia. Generally, Wikipedia articles are neutral in point of view and aim to avoid bias. However, some prior work has shown that ideological biases may exist within some Wikipedia articles, especially those that are focused on political issues or those that are written by fewer authors. We anticipate that such biases are rare for medical articles, which are typically comprised of scientific facts. However, it is important to note that bias encoded in Wikipedia is likely to be reflected by MedWiki.", "### Other Known Limitations\n\n\nSince MedWiki was annotated using weak labeling techniques, there is likely some noise in entity annotations. (Note that to address this, we include the MedWiki-HQ configuration, which is a subset of MedWiki-Full with higher quality labels. Additional details in Dataset Creation).\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nMedWiki was curated by Maya Varma, Laurel Orr, Sen Wu, Megan Leszczynski, Xiao Ling, and Chris Ré.", "### Licensing Information\n\n\nDataset licensed under CC BY 4.0.", "### Contributions\n\n\nThanks to @maya124 for adding this dataset." ]