Datasets:

---

ftopal

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huggingface-datasets

like 0

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

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sysresearch101/t5-large-finetuned-xsum-cnn\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sysresearch101 for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sysresearch101/t5-large-finetuned-xsum-cnn\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sysresearch101 for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sysresearch101/t5-large-finetuned-xsum\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sysresearch101 for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sysresearch101/t5-large-finetuned-xsum\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sysresearch101 for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: t5-large\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sysresearch101 for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: t5-large\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sysresearch101 for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: t5-base\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sysresearch101 for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: t5-base\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sysresearch101 for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sysresearch101/t5-large-finetuned-xsum\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sysresearch101 for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sysresearch101/t5-large-finetuned-xsum\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sysresearch101 for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: facebook/bart-large-cnn\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sysresearch101 for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: facebook/bart-large-cnn\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sysresearch101 for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sysresearch101/t5-large-finetuned-xsum-cnn\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sysresearch101 for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sysresearch101/t5-large-finetuned-xsum-cnn\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sysresearch101 for evaluating this model." ]

[ "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." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sysresearch101/t5-large-finetuned-xsum\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sysresearch101 for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sysresearch101/t5-large-finetuned-xsum\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sysresearch101 for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sysresearch101/t5-large-finetuned-xsum-cnn\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sysresearch101 for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sysresearch101/t5-large-finetuned-xsum-cnn\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sysresearch101 for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: t5-base\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sysresearch101 for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: t5-base\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sysresearch101 for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: t5-large\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sysresearch101 for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: t5-large\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sysresearch101 for evaluating this model." ]

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

[]

[]

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

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

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: csebuetnlp/mT5_multilingual_XLSum\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @grapplerulrich for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: csebuetnlp/mT5_multilingual_XLSum\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @grapplerulrich for evaluating this model." ]

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

[]

[]

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

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

[ "region:us" ]

[]

[]

[]

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

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: google/bigbird-pegasus-large-arxiv\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @grapplerulrich for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: google/bigbird-pegasus-large-arxiv\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @grapplerulrich for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: google/bigbird-pegasus-large-arxiv\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @grapplerulrich for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: google/bigbird-pegasus-large-arxiv\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @grapplerulrich for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: facebook/bart-large-cnn\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @xarymast for evaluating this model." ]

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

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

[]

[]

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

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

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sshleifer/distilbart-cnn-12-6\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @xarymast for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sshleifer/distilbart-cnn-12-6\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @xarymast for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sshleifer/distilbart-xsum-12-6\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @xarymast for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sshleifer/distilbart-xsum-12-6\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @xarymast for evaluating this model." ]

[ "task_categories:image-classification", "annotations_creators:crowdsourced", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:apache-2.0", "arxiv:2205.00363", "arxiv:1908.03557", "arxiv:1908.07490", "arxiv:2102.03334", "region:us" ]

[ "2205.00363", "1908.03557", "1908.07490", "2102.03334" ]

[ "en" ]

[ "### Dataset Summary\n\n\nThe Visual Spatial Reasoning (VSR) corpus is a collection of caption-image pairs with true/false labels. Each caption describes the spatial relation of two individual objects in the image, and a vision-language model (VLM) needs to judge whether the caption is correctly describing the image (True) or not (False).", "### Supported Tasks and Leaderboards\n\n\nWe test three baselines, all supported in huggingface. They are VisualBERT (Li et al. 2019), LXMERT (Tan and Bansal, 2019) and ViLT (Kim et al. 2021). The leaderboard can be checked at Papers With Code.", "### Languages\n\n\nThe language in the dataset is English as spoken by the annotators. The BCP-47 code for English is en. 'meta\\_data.csv' contains meta data of annotators.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nEach line is an individual data point. Each 'jsonl' file is of the following format:", "### Data Fields\n\n\n'image' denotes name of the image in COCO and 'image\\_link' points to the image on the COCO server (so you can also access directly). 'caption' is self-explanatory. 'label' being '0' and '1' corresponds to False and True respectively. 'relation' records the spatial relation used. 'annotator\\_id' points to the annotator who originally wrote the caption. 'vote\\_true\\_validator\\_id' and 'vote\\_false\\_validator\\_id' are annotators who voted True or False in the second phase validation.", "### Data Splits\n\n\nThe VSR corpus, after validation, contains 10,119 data points with high agreement. On top of these, we create two splits (1) random split and (2) zero-shot split. For random split, we randomly split all data points into train, development, and test sets. Zero-shot split makes sure that train, development and test sets have no overlap of concepts (i.e., if *dog* is in test set, it is not used for training and development). Below are some basic statistics of the two splits.\n\n\n\nCheck out 'data/' for more details.\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nUnderstanding spatial relations is fundamental to achieve intelligence. Existing vision-language reasoning datasets are great but they compose multiple types of challenges and can thus conflate different sources of error.\nThe VSR corpus focuses specifically on spatial relations so we can have accurate diagnosis and maximum interpretability.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nImage pair sampling. MS COCO 2017 contains\n123,287 images and has labelled the segmentation and classes of 886,284 instances (individual\nobjects). Leveraging the segmentation, we first\nrandomly select two concepts, then retrieve all images containing the two concepts in COCO 2017 (train and\nvalidation sets). Then images that contain multiple instances of any of the concept are filtered\nout to avoid referencing ambiguity. For the single-instance images, we also filter out any of the images with instance area size < 30, 000, to prevent extremely small instances. After these filtering steps,\nwe randomly sample a pair in the remaining images.\nWe repeat such process to obtain a large number of\nindividual image pairs for caption generation.", "#### Who are the source language producers?", "### Annotations", "#### Annotation process\n\n\nFill in the blank: template-based caption generation. Given a pair of images, the annotator needs to come up with a valid caption that makes it correctly describing one image but incorrect for the other. In this way, the annotator could focus on the key difference of the two images (which should be spatial relation of the two objects of interest) and come up with challenging relation that differentiates the two. Similar paradigms are also used in the annotation of previous vision-language reasoning datasets such as NLVR2 (Suhr et al., 2017,\n2019) and MaRVL (Liu et al., 2021). To regularise annotators from writing modifiers and differentiating the image pair with things beyond accurate spatial relations, we opt for a template-based classification task instead of free-form caption writing. Besides, the template-generated dataset can be easily categorised based on relations and their meta-categories.\n\n\nThe caption template has the format of “The\n'OBJ1' (is) \\_\\_ the 'OBJ2'.”, and the annotators\nare instructed to select a relation from a fixed set\nto fill in the slot. The copula “is” can be omitted\nfor grammaticality. For example, for “contains”,\n“consists of”, and “has as a part”, “is” should be\ndiscarded in the template when extracting the final\ncaption.\n\n\nThe fixed set of spatial relations enable us to obtain the full control of the generation process. The\nfull list of used relations are listed in the table below. It\ncontains 71 spatial relations and is adapted from\nthe summarised relation table of Fagundes et al.\n(2021). We made minor changes to filter out clearly\nunusable relations, made relation names grammatical under our template, and reduced repeated relations.\nIn our final dataset, 65 out of the 71 available relations are actually included (the other 6 are\neither not selected by annotators or are selected but\nthe captions did not pass the validation phase).\n\n\n\nSecond-round Human Validation. Every annotated data point is reviewed by at least\ntwo additional human annotators (validators). In\nvalidation, given a data point (consists of an image\nand a caption), the validator gives either a True or\nFalse label. We exclude data points that have <\n2/3 validators agreeing with the original label.\n\n\nIn the guideline, we communicated to the validators that, for relations such as “left”/“right”, “in\nfront of”/“behind”, they should tolerate different\nreference frame: i.e., if the caption is true from either the object’s or the viewer’s reference, it should\nbe given a True label. Only\nwhen the caption is incorrect under all reference\nframes, a False label is assigned. This adds\ndifficulty to the models since they could not naively\nrely on relative locations of the objects in the images but also need to correctly identify orientations of objects to make the best judgement.", "#### Who are the annotators?\n\n\nAnnotators are hired from URL. We\nrequire them (1) have at least a bachelor’s degree,\n(2) are fluent in English or native speaker, and (3)\nhave a >99% historical approval rate on the platform. All annotators are paid with an hourly salary\nof 12 GBP. Prolific takes an extra 33% of service\ncharge and 20% VAT on the service charge.\n\n\nFor caption generation, we release the task with\nbatches of 200 instances and the annotator is required to finish a batch in 80 minutes. An annotator\ncannot take more than one batch per day. In this\nway we have a diverse set of annotators and can\nalso prevent annotators from being fatigued. For\nsecond round validation, we group 500 data points\nin one batch and an annotator is asked to label each\nbatch in 90 minutes.\n\n\nIn total, 24 annotators participated in caption\ngeneration and 26 participated in validation. The\nannotators have diverse demographic background:\nthey were born in 13 different countries; live in 13\ndifferent couturiers; and have 14 different nationalities. 57.4% of the annotators identify themselves\nas females and 42.6% as males.", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nThis project is licensed under the Apache-2.0 License.", "### Contributions\n\n\nThanks to @juletx for adding this dataset." ]

[ "TAGS\n#task_categories-image-classification #annotations_creators-crowdsourced #language_creators-machine-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-apache-2.0 #arxiv-2205.00363 #arxiv-1908.03557 #arxiv-1908.07490 #arxiv-2102.03334 #region-us \n", "### Dataset Summary\n\n\nThe Visual Spatial Reasoning (VSR) corpus is a collection of caption-image pairs with true/false labels. Each caption describes the spatial relation of two individual objects in the image, and a vision-language model (VLM) needs to judge whether the caption is correctly describing the image (True) or not (False).", "### Supported Tasks and Leaderboards\n\n\nWe test three baselines, all supported in huggingface. They are VisualBERT (Li et al. 2019), LXMERT (Tan and Bansal, 2019) and ViLT (Kim et al. 2021). The leaderboard can be checked at Papers With Code.", "### Languages\n\n\nThe language in the dataset is English as spoken by the annotators. The BCP-47 code for English is en. 'meta\\_data.csv' contains meta data of annotators.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nEach line is an individual data point. Each 'jsonl' file is of the following format:", "### Data Fields\n\n\n'image' denotes name of the image in COCO and 'image\\_link' points to the image on the COCO server (so you can also access directly). 'caption' is self-explanatory. 'label' being '0' and '1' corresponds to False and True respectively. 'relation' records the spatial relation used. 'annotator\\_id' points to the annotator who originally wrote the caption. 'vote\\_true\\_validator\\_id' and 'vote\\_false\\_validator\\_id' are annotators who voted True or False in the second phase validation.", "### Data Splits\n\n\nThe VSR corpus, after validation, contains 10,119 data points with high agreement. On top of these, we create two splits (1) random split and (2) zero-shot split. For random split, we randomly split all data points into train, development, and test sets. Zero-shot split makes sure that train, development and test sets have no overlap of concepts (i.e., if *dog* is in test set, it is not used for training and development). Below are some basic statistics of the two splits.\n\n\n\nCheck out 'data/' for more details.\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nUnderstanding spatial relations is fundamental to achieve intelligence. Existing vision-language reasoning datasets are great but they compose multiple types of challenges and can thus conflate different sources of error.\nThe VSR corpus focuses specifically on spatial relations so we can have accurate diagnosis and maximum interpretability.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nImage pair sampling. MS COCO 2017 contains\n123,287 images and has labelled the segmentation and classes of 886,284 instances (individual\nobjects). Leveraging the segmentation, we first\nrandomly select two concepts, then retrieve all images containing the two concepts in COCO 2017 (train and\nvalidation sets). Then images that contain multiple instances of any of the concept are filtered\nout to avoid referencing ambiguity. For the single-instance images, we also filter out any of the images with instance area size < 30, 000, to prevent extremely small instances. After these filtering steps,\nwe randomly sample a pair in the remaining images.\nWe repeat such process to obtain a large number of\nindividual image pairs for caption generation.", "#### Who are the source language producers?", "### Annotations", "#### Annotation process\n\n\nFill in the blank: template-based caption generation. Given a pair of images, the annotator needs to come up with a valid caption that makes it correctly describing one image but incorrect for the other. In this way, the annotator could focus on the key difference of the two images (which should be spatial relation of the two objects of interest) and come up with challenging relation that differentiates the two. Similar paradigms are also used in the annotation of previous vision-language reasoning datasets such as NLVR2 (Suhr et al., 2017,\n2019) and MaRVL (Liu et al., 2021). To regularise annotators from writing modifiers and differentiating the image pair with things beyond accurate spatial relations, we opt for a template-based classification task instead of free-form caption writing. Besides, the template-generated dataset can be easily categorised based on relations and their meta-categories.\n\n\nThe caption template has the format of “The\n'OBJ1' (is) \\_\\_ the 'OBJ2'.”, and the annotators\nare instructed to select a relation from a fixed set\nto fill in the slot. The copula “is” can be omitted\nfor grammaticality. For example, for “contains”,\n“consists of”, and “has as a part”, “is” should be\ndiscarded in the template when extracting the final\ncaption.\n\n\nThe fixed set of spatial relations enable us to obtain the full control of the generation process. The\nfull list of used relations are listed in the table below. It\ncontains 71 spatial relations and is adapted from\nthe summarised relation table of Fagundes et al.\n(2021). We made minor changes to filter out clearly\nunusable relations, made relation names grammatical under our template, and reduced repeated relations.\nIn our final dataset, 65 out of the 71 available relations are actually included (the other 6 are\neither not selected by annotators or are selected but\nthe captions did not pass the validation phase).\n\n\n\nSecond-round Human Validation. Every annotated data point is reviewed by at least\ntwo additional human annotators (validators). In\nvalidation, given a data point (consists of an image\nand a caption), the validator gives either a True or\nFalse label. We exclude data points that have <\n2/3 validators agreeing with the original label.\n\n\nIn the guideline, we communicated to the validators that, for relations such as “left”/“right”, “in\nfront of”/“behind”, they should tolerate different\nreference frame: i.e., if the caption is true from either the object’s or the viewer’s reference, it should\nbe given a True label. Only\nwhen the caption is incorrect under all reference\nframes, a False label is assigned. This adds\ndifficulty to the models since they could not naively\nrely on relative locations of the objects in the images but also need to correctly identify orientations of objects to make the best judgement.", "#### Who are the annotators?\n\n\nAnnotators are hired from URL. We\nrequire them (1) have at least a bachelor’s degree,\n(2) are fluent in English or native speaker, and (3)\nhave a >99% historical approval rate on the platform. All annotators are paid with an hourly salary\nof 12 GBP. Prolific takes an extra 33% of service\ncharge and 20% VAT on the service charge.\n\n\nFor caption generation, we release the task with\nbatches of 200 instances and the annotator is required to finish a batch in 80 minutes. An annotator\ncannot take more than one batch per day. In this\nway we have a diverse set of annotators and can\nalso prevent annotators from being fatigued. For\nsecond round validation, we group 500 data points\nin one batch and an annotator is asked to label each\nbatch in 90 minutes.\n\n\nIn total, 24 annotators participated in caption\ngeneration and 26 participated in validation. The\nannotators have diverse demographic background:\nthey were born in 13 different countries; live in 13\ndifferent couturiers; and have 14 different nationalities. 57.4% of the annotators identify themselves\nas females and 42.6% as males.", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nThis project is licensed under the Apache-2.0 License.", "### Contributions\n\n\nThanks to @juletx for adding this dataset." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: google/pegasus-large\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @xarymast for evaluating this model." ]

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

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

[]

[]

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

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

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

[]

[]

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

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

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: facebook/bart-large-cnn\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @xarymast for evaluating this model." ]

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

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

[]

[]

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

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

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sshleifer/distilbart-cnn-12-6\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @xarymast for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sshleifer/distilbart-cnn-12-6\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @xarymast for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sshleifer/distilbart-xsum-12-6\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @xarymast for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sshleifer/distilbart-xsum-12-6\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @xarymast for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: google/pegasus-large\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @xarymast for evaluating this model." ]

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

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

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

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

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

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[]

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

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

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

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: facebook/bart-large-cnn\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @xarymast for evaluating this model." ]

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

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

[]

[]

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

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

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sshleifer/distilbart-cnn-12-6\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @xarymast for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sshleifer/distilbart-cnn-12-6\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @xarymast for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sshleifer/distilbart-xsum-12-6\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @xarymast for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sshleifer/distilbart-xsum-12-6\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @xarymast for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: google/pegasus-large\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @xarymast for evaluating this model." ]

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

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

[]

[]

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

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

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sshleifer/distilbart-xsum-12-6\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @grapplerulrich for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sshleifer/distilbart-xsum-12-6\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @grapplerulrich for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sshleifer/distilbart-cnn-12-6\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @grapplerulrich for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sshleifer/distilbart-cnn-12-6\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @grapplerulrich for evaluating this model." ]

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

[]

[]

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

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

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: csebuetnlp/mT5_multilingual_XLSum\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @grapplerulrich for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: csebuetnlp/mT5_multilingual_XLSum\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @grapplerulrich for evaluating this model." ]

[ "task_categories:object-detection", "annotations_creators:expert-generated", "language_creators:expert-generated", "size_categories:n<1K", "license:cc-by-4.0", "manuscripts", "LAM", "arxiv:2207.11230", "region:us" ]

[ "2207.11230" ]

[]

[ "### Dataset Summary\n\n\nThis dataset contains a subset of data used in the paper You Actually Look Twice At it (YALTAi): using an object detection approach instead of region segmentation within the Kraken engine. This paper proposes treating page layout recognition on historical documents as an object detection task (compared to the usual pixel segmentation approach). This dataset contains images from digitised manuscripts and early printed books with the following labels:\n\n\n* DamageZone\n* DigitizationArtefactZone\n* DropCapitalZone\n* GraphicZone\n* MainZone\n* MarginTextZone\n* MusicZone\n* NumberingZone\n* QuireMarksZone\n* RunningTitleZone\n* SealZone\n* StampZone\n* TableZone\n* TitlePageZone", "### Supported Tasks and Leaderboards\n\n\n* 'object-detection': This dataset can be used to train a model for object-detection on historic document images.\n\n\nDataset Structure\n-----------------\n\n\nThis dataset has two configurations. These configurations both cover the same data and annotations but provide these annotations in different forms to make it easier to integrate the data with existing processing pipelines.\n\n\n* The first configuration, 'YOLO', uses the data's original format.\n* The second configuration converts the YOLO format into a format closer to the 'COCO' annotation format. This is done to make it easier to work with the 'feature\\_extractor' from the 'Transformers' models for object detection, which expect data to be in a COCO style format.", "### Data Instances\n\n\nAn example instance from the COCO config:\n\n\nAn example instance from the YOLO config:", "### Data Fields\n\n\nThe fields for the YOLO config:\n\n\n* 'image': the image\n* 'objects': the annotations which consist of:\n\t+ 'bbox': a list of bounding boxes for the image\n\t+ 'label': a list of labels for this image\n\n\nThe fields for the COCO config:\n\n\n* 'height': height of the image\n* 'width': width of the image\n* 'image': image\n* 'image\\_id': id for the image\n* 'objects': annotations in COCO format, consisting of a list containing dictionaries with the following keys:\n\t+ 'bbox': bounding boxes for the images\n\t+ 'category\\_id': a label for the image\n\t+ 'image\\_id': id for the image\n\t+ 'iscrowd': COCO is a crowd flag\n\t+ 'segmentation': COCO segmentation annotations (empty in this case but kept for compatibility with other processing scripts)", "### Data Splits\n\n\nThe dataset contains a train, validation and test split with the following numbers per split:\n\n\n\nA more detailed summary of the dataset (copied from the paper):\n\n\n\nDataset Creation\n----------------\n\n\nThis dataset is derived from:\n\n\n* CREMMA Medieval ( Pinche, A. (2022). Cremma Medieval (Version Bicerin 1.1.0) Data set\n* CREMMA Medieval Lat (Clérice, T. and Vlachou-Efstathiou, M. (2022). Cremma Medieval Latin Data set\n* Eutyches. (Vlachou-Efstathiou, M. Voss.Lat.O.41 - Eutyches \"de uerbo\" glossed Data set\n* Gallicorpora HTR-Incunable-15e-Siecle ( Pinche, A., Gabay, S., Leroy, N., & Christensen, K. Données HTR incunable du 15e siècle Computer software\n* Gallicorpora HTR-MSS-15e-Siecle ( Pinche, A., Gabay, S., Leroy, N., & Christensen, K. Données HTR manuscrits du 15e siècle Computer software\n* Gallicorpora HTR-imprime-gothique-16e-siecle ( Pinche, A., Gabay, S., Vlachou-Efstathiou, M., & Christensen, K. HTR-imprime-gothique-16e-siecle Computer software\n\n\n* a few hundred newly annotated data, specifically the test set which is completely novel and based on early prints and manuscripts.\n\n\nThese additional annotations were created by correcting an early version of the model developed in the paper using the roboflow platform.", "### Curation Rationale\n\n\n[More information needed]", "### Source Data\n\n\nThe sources of the data are described above.", "#### Initial Data Collection and Normalization\n\n\n[More information needed]", "#### Who are the source language producers?\n\n\n[More information needed]", "### Annotations", "#### Annotation process\n\n\nAdditional annotations produced for this dataset were created by correcting an early version of the model developed in the paper using the roboflow platform.", "#### Who are the annotators?\n\n\n[More information needed]", "### Personal and Sensitive Information\n\n\nThis data does not contain information relating to living individuals.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nA growing number of datasets are related to page layout for historical documents. This dataset offers a different approach to annotating these datasets (focusing on object detection rather than pixel-level annotations). Improving document layout recognition can have a positive impact on downstream tasks, in particular Optical Character Recognition.", "### Discussion of Biases\n\n\nHistorical documents contain a wide variety of page layouts. This means that the ability of models trained on this dataset to transfer to documents with very different layouts is not guaranteed.", "### Other Known Limitations\n\n\n[More information needed]\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCreative Commons Attribution 4.0 International\n\n\n![DOI](URL", "### Contributions\n\n\nThanks to @davanstrien for adding this dataset." ]

[ "TAGS\n#task_categories-object-detection #annotations_creators-expert-generated #language_creators-expert-generated #size_categories-n<1K #license-cc-by-4.0 #manuscripts #LAM #arxiv-2207.11230 #region-us \n", "### Dataset Summary\n\n\nThis dataset contains a subset of data used in the paper You Actually Look Twice At it (YALTAi): using an object detection approach instead of region segmentation within the Kraken engine. This paper proposes treating page layout recognition on historical documents as an object detection task (compared to the usual pixel segmentation approach). This dataset contains images from digitised manuscripts and early printed books with the following labels:\n\n\n* DamageZone\n* DigitizationArtefactZone\n* DropCapitalZone\n* GraphicZone\n* MainZone\n* MarginTextZone\n* MusicZone\n* NumberingZone\n* QuireMarksZone\n* RunningTitleZone\n* SealZone\n* StampZone\n* TableZone\n* TitlePageZone", "### Supported Tasks and Leaderboards\n\n\n* 'object-detection': This dataset can be used to train a model for object-detection on historic document images.\n\n\nDataset Structure\n-----------------\n\n\nThis dataset has two configurations. These configurations both cover the same data and annotations but provide these annotations in different forms to make it easier to integrate the data with existing processing pipelines.\n\n\n* The first configuration, 'YOLO', uses the data's original format.\n* The second configuration converts the YOLO format into a format closer to the 'COCO' annotation format. This is done to make it easier to work with the 'feature\\_extractor' from the 'Transformers' models for object detection, which expect data to be in a COCO style format.", "### Data Instances\n\n\nAn example instance from the COCO config:\n\n\nAn example instance from the YOLO config:", "### Data Fields\n\n\nThe fields for the YOLO config:\n\n\n* 'image': the image\n* 'objects': the annotations which consist of:\n\t+ 'bbox': a list of bounding boxes for the image\n\t+ 'label': a list of labels for this image\n\n\nThe fields for the COCO config:\n\n\n* 'height': height of the image\n* 'width': width of the image\n* 'image': image\n* 'image\\_id': id for the image\n* 'objects': annotations in COCO format, consisting of a list containing dictionaries with the following keys:\n\t+ 'bbox': bounding boxes for the images\n\t+ 'category\\_id': a label for the image\n\t+ 'image\\_id': id for the image\n\t+ 'iscrowd': COCO is a crowd flag\n\t+ 'segmentation': COCO segmentation annotations (empty in this case but kept for compatibility with other processing scripts)", "### Data Splits\n\n\nThe dataset contains a train, validation and test split with the following numbers per split:\n\n\n\nA more detailed summary of the dataset (copied from the paper):\n\n\n\nDataset Creation\n----------------\n\n\nThis dataset is derived from:\n\n\n* CREMMA Medieval ( Pinche, A. (2022). Cremma Medieval (Version Bicerin 1.1.0) Data set\n* CREMMA Medieval Lat (Clérice, T. and Vlachou-Efstathiou, M. (2022). Cremma Medieval Latin Data set\n* Eutyches. (Vlachou-Efstathiou, M. Voss.Lat.O.41 - Eutyches \"de uerbo\" glossed Data set\n* Gallicorpora HTR-Incunable-15e-Siecle ( Pinche, A., Gabay, S., Leroy, N., & Christensen, K. Données HTR incunable du 15e siècle Computer software\n* Gallicorpora HTR-MSS-15e-Siecle ( Pinche, A., Gabay, S., Leroy, N., & Christensen, K. Données HTR manuscrits du 15e siècle Computer software\n* Gallicorpora HTR-imprime-gothique-16e-siecle ( Pinche, A., Gabay, S., Vlachou-Efstathiou, M., & Christensen, K. HTR-imprime-gothique-16e-siecle Computer software\n\n\n* a few hundred newly annotated data, specifically the test set which is completely novel and based on early prints and manuscripts.\n\n\nThese additional annotations were created by correcting an early version of the model developed in the paper using the roboflow platform.", "### Curation Rationale\n\n\n[More information needed]", "### Source Data\n\n\nThe sources of the data are described above.", "#### Initial Data Collection and Normalization\n\n\n[More information needed]", "#### Who are the source language producers?\n\n\n[More information needed]", "### Annotations", "#### Annotation process\n\n\nAdditional annotations produced for this dataset were created by correcting an early version of the model developed in the paper using the roboflow platform.", "#### Who are the annotators?\n\n\n[More information needed]", "### Personal and Sensitive Information\n\n\nThis data does not contain information relating to living individuals.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nA growing number of datasets are related to page layout for historical documents. This dataset offers a different approach to annotating these datasets (focusing on object detection rather than pixel-level annotations). Improving document layout recognition can have a positive impact on downstream tasks, in particular Optical Character Recognition.", "### Discussion of Biases\n\n\nHistorical documents contain a wide variety of page layouts. This means that the ability of models trained on this dataset to transfer to documents with very different layouts is not guaranteed.", "### Other Known Limitations\n\n\n[More information needed]\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCreative Commons Attribution 4.0 International\n\n\n![DOI](URL", "### Contributions\n\n\nThanks to @davanstrien for adding this dataset." ]

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

[]

[]

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

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

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

[]

[]

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

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

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

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for [Dataset Name]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n - Contributions\n\nannotations_creators:\n- no-annotation\nlanguage:\n- en\nlanguage_creators:\n- crowdsourced\nlicense:\n- afl-3.0\nmultilinguality:\n- monolingual\npretty_name: Crema D Diarization\nsize_categories:\n- 10M<n<100M\nsource_datasets: []\ntags: []\ntask_categories:\n- audio-classification\n- automatic-speech-recognition\n- voice-activity-detection\ntask_ids:\n- audio-emotion-recognition\n- speaker-identification", "### Contributions\n\nThanks to @EvgeniiPustozerov for adding this dataset." ]

[ "TAGS\n#region-us \n", "# Dataset Card for [Dataset Name]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n - Contributions\n\nannotations_creators:\n- no-annotation\nlanguage:\n- en\nlanguage_creators:\n- crowdsourced\nlicense:\n- afl-3.0\nmultilinguality:\n- monolingual\npretty_name: Crema D Diarization\nsize_categories:\n- 10M<n<100M\nsource_datasets: []\ntags: []\ntask_categories:\n- audio-classification\n- automatic-speech-recognition\n- voice-activity-detection\ntask_ids:\n- audio-emotion-recognition\n- speaker-identification", "### Contributions\n\nThanks to @EvgeniiPustozerov for adding this dataset." ]

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

[]

[]

[ "### Categories \n\n Billboard - includes advertisements on bus benches and shelters, and the posters on building construction (think with scaffolding). \n\n Signage - store names, signs on buildings, lists of businesses at a strip mall, also includes any small standalone advertisements like those campaign signs in people's yards or papers on telephone poles. \n\n Branding - logos and names on products, like a coffee cup or scooter, includes car badges.", "### Seeking Photos on URL\n\n Right now the images have been mostly collected in Los Angeles, CA. We would love some geographical variety! \n\nIf you have any questions about labeling, don't hesitate to leave a comment and check the checkbox to notify the mods. \n\nWe are light on branding photos, so pictures of products with logos and brands on them are greatly appreciated!", "### Version Info" ]

[ "TAGS\n#license-cc-by-nc-sa-4.0 #region-us \n", "### Categories \n\n Billboard - includes advertisements on bus benches and shelters, and the posters on building construction (think with scaffolding). \n\n Signage - store names, signs on buildings, lists of businesses at a strip mall, also includes any small standalone advertisements like those campaign signs in people's yards or papers on telephone poles. \n\n Branding - logos and names on products, like a coffee cup or scooter, includes car badges.", "### Seeking Photos on URL\n\n Right now the images have been mostly collected in Los Angeles, CA. We would love some geographical variety! \n\nIf you have any questions about labeling, don't hesitate to leave a comment and check the checkbox to notify the mods. \n\nWe are light on branding photos, so pictures of products with logos and brands on them are greatly appreciated!", "### Version Info" ]

[ "region:us" ]

[]

[]

[ "# Label2Id\nThis repository contains all the label2id files of tner dataset." ]

[ "TAGS\n#region-us \n", "# Label2Id\nThis repository contains all the label2id files of tner dataset." ]

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

[]

[]

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

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

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:id", "license:cc-by-4.0", "newspapers", "title", "news", "region:us" ]

[]

[ "id" ]

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

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Indonesian #license-cc-by-4.0 #newspapers #title #news #region-us \n", "# Dataset Card for Indonesian News Title Generation", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @andreaschandra for adding this dataset." ]

[ "task_categories:summarization", "multilinguality:monolingual", "language:en", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-summarization #multilinguality-monolingual #language-English #region-us \n" ]

[ "task_categories:summarization", "task_categories:text2text-generation", "task_categories:text-generation", "multilinguality:monolingual", "language:zh", "region:us" ]

[]

[ "zh" ]

[]

[ "TAGS\n#task_categories-summarization #task_categories-text2text-generation #task_categories-text-generation #multilinguality-monolingual #language-Chinese #region-us \n" ]

[ "task_categories:other", "multilinguality:monolingual", "language:en", "commonsense-generation", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-other #multilinguality-monolingual #language-English #commonsense-generation #region-us \n" ]

[ "task_categories:tabular-to-text", "task_categories:table-to-text", "multilinguality:monolingual", "language:en", "data-to-text", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-tabular-to-text #task_categories-table-to-text #multilinguality-monolingual #language-English #data-to-text #region-us \n" ]

[ "task_categories:conversational", "task_ids:dialogue-generation", "multilinguality:monolingual", "language:en", "dialogue-response-generation", "open-dialogue", "dialog-response-generation", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-conversational #task_ids-dialogue-generation #multilinguality-monolingual #language-English #dialogue-response-generation #open-dialogue #dialog-response-generation #region-us \n" ]

[ "task_categories:question-answering", "multilinguality:monolingual", "language:en", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-question-answering #multilinguality-monolingual #language-English #region-us \n" ]

[ "task_categories:text2text-generation", "multilinguality:monolingual", "language:en", "question-generation", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-text2text-generation #multilinguality-monolingual #language-English #question-generation #region-us \n" ]

[ "task_categories:text2text-generation", "task_ids:text-simplification", "multilinguality:monolingual", "language:en", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-text2text-generation #task_ids-text-simplification #multilinguality-monolingual #language-English #region-us \n" ]

[ "task_categories:text-generation", "multilinguality:monolingual", "language:en", "story-generation", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-text-generation #multilinguality-monolingual #language-English #story-generation #region-us \n" ]

[ "task_categories:conversational", "task_ids:dialogue-generation", "multilinguality:monolingual", "language:en", "dialogue-response-generation", "task-dialogue", "dialog-response-generation", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-conversational #task_ids-dialogue-generation #multilinguality-monolingual #language-English #dialogue-response-generation #task-dialogue #dialog-response-generation #region-us \n" ]

[ "task_categories:translation", "multilinguality:translation", "language:en", "language:fr", "language:de", "language:cs", "language:es", "language:zh", "language:ru", "region:us" ]

[]

[ "en", "fr", "de", "cs", "es", "zh", "ru" ]

[]

[ "TAGS\n#task_categories-translation #multilinguality-translation #language-English #language-French #language-German #language-Czech #language-Spanish #language-Chinese #language-Russian #region-us \n" ]

[ "task_categories:tabular-classification", "size_categories:1K<n<10K", "interpretability", "fairness", "region:us" ]

[]

[]

[ "### Sample usage\n\nLoad the data:\n\n\n\nFit a model:\n\n\n\n\nEvaluate:" ]

[ "TAGS\n#task_categories-tabular-classification #size_categories-1K<n<10K #interpretability #fairness #region-us \n", "### Sample usage\n\nLoad the data:\n\n\n\nFit a model:\n\n\n\n\nEvaluate:" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for new_dataset", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL", "### Dataset Summary\n\nThe HumAID Twitter dataset consists of several thousands of manually annotated tweets that has been collected during 19 major natural disaster events including earthquakes, hurricanes, wildfires, and floods, which happened from 2016 to 2019 across different parts of the World. The annotations in the provided datasets consists of following humanitarian categories. The dataset consists only english tweets and it is the largest dataset for crisis informatics so far.\n Humanitarian categories \n- Caution and advice\n- Displaced people and evacuations\n- Dont know cant judge\n- Infrastructure and utility damage\n- Injured or dead people\n- Missing or found people\n- Not humanitarian\n- Other relevant information\n- Requests or urgent needs\n- Rescue volunteering or donation effort\n- Sympathy and support\n\nThe resulting annotated dataset consists of 11 labels.", "### Supported Tasks and Benchmark\nThe dataset can be used to train a model for multiclass tweet classification for disaster response. The benchmark results can be found in URL\n\nDataset is also released with event-wise and JSON objects for further research.\nFull set of the dataset can be found in URL", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances", "### Data Fields\n\n* tweet_text: corresponds to the tweet text.\n* class_label: corresponds to a label assigned to a given tweet text", "### Data Splits\n\n* Train\n* Development\n* Test", "## Dataset Creation", "### Source Data", "#### Initial Data Collection and Normalization\n\nTweets has been collected during several disaster events.", "### Annotations", "#### Annotation process\n\nAMT has been used to annotate the dataset. Please check the paper for a more detail.", "#### Who are the annotators?\n\n- crowdsourced", "## Additional Information", "### Dataset Curators\n\nAuthors of the paper.", "### Licensing Information\n\n- cc-by-nc-4.0" ]

[ "TAGS\n#region-us \n", "# Dataset Card for new_dataset", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL", "### Dataset Summary\n\nThe HumAID Twitter dataset consists of several thousands of manually annotated tweets that has been collected during 19 major natural disaster events including earthquakes, hurricanes, wildfires, and floods, which happened from 2016 to 2019 across different parts of the World. The annotations in the provided datasets consists of following humanitarian categories. The dataset consists only english tweets and it is the largest dataset for crisis informatics so far.\n Humanitarian categories \n- Caution and advice\n- Displaced people and evacuations\n- Dont know cant judge\n- Infrastructure and utility damage\n- Injured or dead people\n- Missing or found people\n- Not humanitarian\n- Other relevant information\n- Requests or urgent needs\n- Rescue volunteering or donation effort\n- Sympathy and support\n\nThe resulting annotated dataset consists of 11 labels.", "### Supported Tasks and Benchmark\nThe dataset can be used to train a model for multiclass tweet classification for disaster response. The benchmark results can be found in URL\n\nDataset is also released with event-wise and JSON objects for further research.\nFull set of the dataset can be found in URL", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances", "### Data Fields\n\n* tweet_text: corresponds to the tweet text.\n* class_label: corresponds to a label assigned to a given tweet text", "### Data Splits\n\n* Train\n* Development\n* Test", "## Dataset Creation", "### Source Data", "#### Initial Data Collection and Normalization\n\nTweets has been collected during several disaster events.", "### Annotations", "#### Annotation process\n\nAMT has been used to annotate the dataset. Please check the paper for a more detail.", "#### Who are the annotators?\n\n- crowdsourced", "## Additional Information", "### Dataset Curators\n\nAuthors of the paper.", "### Licensing Information\n\n- cc-by-nc-4.0" ]

[ "task_categories:summarization", "task_categories:text2text-generation", "size_categories:100K<n<1M", "source_datasets:https://github.com/huggingface/datasets/tree/master/datasets/cnn_dailymail", "language:sv", "license:mit", "conditional-text-generation", "region:us" ]

[]

[ "sv" ]

[ "### 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 Swedish CNN/DailyMail dataset follows the same splits as the original English version and has 3 splits: *train*, *validation*, and *test*." ]

[ "TAGS\n#task_categories-summarization #task_categories-text2text-generation #size_categories-100K<n<1M #source_datasets-https-//github.com/huggingface/datasets/tree/master/datasets/cnn_dailymail #language-Swedish #license-mit #conditional-text-generation #region-us \n", "### 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 Swedish CNN/DailyMail dataset follows the same splits as the original English version and has 3 splits: *train*, *validation*, and *test*." ]

[ "task_categories:text-to-image", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:cc", "curated", "high-quality", "text-to-image", "evaluation", "validation", "region:us" ]

[]

[ "en" ]

[ "# TeTIm-Eval" ]

[ "TAGS\n#task_categories-text-to-image #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #curated #high-quality #text-to-image #evaluation #validation #region-us \n", "# TeTIm-Eval" ]

[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:id", "license:cc-by-nd-4.0", "indoqa", "qa", "question-answering", "indonesian", "region:us" ]

[]

[ "id" ]

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

[ "TAGS\n#task_categories-question-answering #task_ids-extractive-qa #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Indonesian #license-cc-by-nd-4.0 #indoqa #qa #question-answering #indonesian #region-us \n", "# Dataset Card for Indonesian Question Answering Dataset", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @fhrzn@Kalzaik @ibamibrahim @andreaschandra for adding this dataset." ]

[ "task_categories:summarization", "task_categories:text2text-generation", "size_categories:100K<n<1M", "source_datasets:https://github.com/huggingface/datasets/tree/master/datasets/xsum", "language:sv", "license:mit", "conditional-text-generation", "region:us" ]

[]

[ "sv" ]

[ "### Data Fields\n\n\n* 'id': a string containing the heximal formated SHA1 hash of the url where the story was retrieved from\n* 'document': a string containing the body of the news article\n* 'summary': a string containing the summary of the article as written by the article author", "### Data Splits\n\n\nThe Swedish xsum dataset follows the same splits as the original English version and has 3 splits: *train*, *validation*, and *test*." ]

[ "TAGS\n#task_categories-summarization #task_categories-text2text-generation #size_categories-100K<n<1M #source_datasets-https-//github.com/huggingface/datasets/tree/master/datasets/xsum #language-Swedish #license-mit #conditional-text-generation #region-us \n", "### Data Fields\n\n\n* 'id': a string containing the heximal formated SHA1 hash of the url where the story was retrieved from\n* 'document': a string containing the body of the news article\n* 'summary': a string containing the summary of the article as written by the article author", "### Data Splits\n\n\nThe Swedish xsum dataset follows the same splits as the original English version and has 3 splits: *train*, *validation*, and *test*." ]

[ "task_categories:other", "multilinguality:monolingual", "language:en", "style-transfer", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-other #multilinguality-monolingual #language-English #style-transfer #region-us \n" ]

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

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-other #multilinguality-monolingual #language-English #paraphrase #region-us \n" ]

[ "task_categories:summarization", "task_categories:text2text-generation", "size_categories:1M<n<3M", "source_datasets:https://github.com/huggingface/datasets/tree/master/datasets/gigaword", "language:sv", "license:mit", "conditional-text-generation", "region:us" ]

[]

[ "sv" ]

[ "### Data Fields\n\n\n* 'document': a string containing the shorter body\n* 'summary': a string containing the summary of the body", "### Data Splits\n\n\nThe Swedish gigaword dataset follows the same splits as the original English version and has 3 splits: *train*, *validation*, and *test*." ]

[ "TAGS\n#task_categories-summarization #task_categories-text2text-generation #size_categories-1M<n<3M #source_datasets-https-//github.com/huggingface/datasets/tree/master/datasets/gigaword #language-Swedish #license-mit #conditional-text-generation #region-us \n", "### Data Fields\n\n\n* 'document': a string containing the shorter body\n* 'summary': a string containing the summary of the body", "### Data Splits\n\n\nThe Swedish gigaword dataset follows the same splits as the original English version and has 3 splits: *train*, *validation*, and *test*." ]

[ "task_categories:summarization", "task_categories:text2text-generation", "size_categories:10K<n<100K", "source_datasets:https://github.com/morningmoni/CiteSu", "language:sv", "license:cc-by-sa-3.0", "conditional-text-generation", "region:us" ]

[]

[ "sv" ]

[ "### Data details\n\n\n* gem\\_id: the id for the data instance.\n* gem\\_id\\_parent: the id for the data instance.\n* Document: a string containing the document body.\n* Summary: a string containing the summary of the body.", "### Data Splits\n\n\nThe Swedish wiki\\_lingua dataset follows the same splits as the original English version and has 3 splits: *train*, *validation*, and *test*." ]

[ "TAGS\n#task_categories-summarization #task_categories-text2text-generation #size_categories-10K<n<100K #source_datasets-https-//github.com/morningmoni/CiteSu #language-Swedish #license-cc-by-sa-3.0 #conditional-text-generation #region-us \n", "### Data details\n\n\n* gem\\_id: the id for the data instance.\n* gem\\_id\\_parent: the id for the data instance.\n* Document: a string containing the document body.\n* Summary: a string containing the summary of the body.", "### Data Splits\n\n\nThe Swedish wiki\\_lingua dataset follows the same splits as the original English version and has 3 splits: *train*, *validation*, and *test*." ]

[ "task_categories:summarization", "task_categories:text2text-generation", "size_categories:10K<n<100K", "source_datasets:https://github.com/morningmoni/CiteSu", "language:sv", "license:cc-by-nc-4.0", "conditional-text-generation", "arxiv:2205.06207", "region:us" ]

[ "2205.06207" ]

[ "sv" ]

[ "### Paper\n\n\nURL", "### Authors\n\n\nYuning Mao, Ming Zhong, Jiawei Han\nUniversity of Illinois Urbana-Champaign\n{yuningm2, mingz5, hanj}@URL\n\n\nData details\n------------\n\n\n* src (string): source text. long description of paper\n* tgt (string): target text. tldr of paper\n* paper\\_id (string): unique id for the paper\n* title (string): title of the paper\n* discipline (dict):\n\t+ venue (string): Where the paper was published (conference)\n\t+ journal (string): Journal in which the paper was published\n\t+ mag\\_field\\_of\\_study (list[str]): scientific fields that the paper falls under.", "### Data Splits\n\n\nThe Swedish xsum dataset follows the same splits as the original English version and has 3 splits: *train*, *validation*, and *test*." ]

[ "TAGS\n#task_categories-summarization #task_categories-text2text-generation #size_categories-10K<n<100K #source_datasets-https-//github.com/morningmoni/CiteSu #language-Swedish #license-cc-by-nc-4.0 #conditional-text-generation #arxiv-2205.06207 #region-us \n", "### Paper\n\n\nURL", "### Authors\n\n\nYuning Mao, Ming Zhong, Jiawei Han\nUniversity of Illinois Urbana-Champaign\n{yuningm2, mingz5, hanj}@URL\n\n\nData details\n------------\n\n\n* src (string): source text. long description of paper\n* tgt (string): target text. tldr of paper\n* paper\\_id (string): unique id for the paper\n* title (string): title of the paper\n* discipline (dict):\n\t+ venue (string): Where the paper was published (conference)\n\t+ journal (string): Journal in which the paper was published\n\t+ mag\\_field\\_of\\_study (list[str]): scientific fields that the paper falls under.", "### Data Splits\n\n\nThe Swedish xsum dataset follows the same splits as the original English version and has 3 splits: *train*, *validation*, and *test*." ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:sentence-similarity", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:extended|msrp", "language:id", "license:cc-by-4.0", "msrp", "id-msrp", "paraphrase-detection", "region:us" ]

[]

[ "id" ]

[ "# Dataset Card for Indonesian Sentence Paraphrase Detection", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThe dataset is originally from Microsoft Research Paraphrase Corpus. We translated the text into Bahasa using google translate.", "### Supported Tasks and Leaderboards", "### Languages\n\nIndonesian", "## 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 @andreaschandra for adding this dataset." ]

[ "TAGS\n#task_categories-sentence-similarity #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-extended|msrp #language-Indonesian #license-cc-by-4.0 #msrp #id-msrp #paraphrase-detection #region-us \n", "# Dataset Card for Indonesian Sentence Paraphrase Detection", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThe dataset is originally from Microsoft Research Paraphrase Corpus. We translated the text into Bahasa using google translate.", "### Supported Tasks and Leaderboards", "### Languages\n\nIndonesian", "## 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 @andreaschandra for adding this dataset." ]

[ "arxiv:2207.0467", "arxiv:2205.12654", "arxiv:2207.04672", "region:us" ]

[ "2207.0467", "2205.12654", "2207.04672" ]

[]

[ "### Dataset Summary\n\n\nThis dataset was created based on metadata for mined bitext released by Meta AI. It contains bitext for 148 English-centric and 1465 non-English-centric language pairs using the stopes mining library and the LASER3 encoders (Heffernan et al., 2022). The complete dataset is ~450GB.\n\n\nCCMatrix contains previous versions of mined instructions.", "#### How to use the data\n\n\nThere are two ways to access the data:\n\n\n* Via the Hugging Face Python datasets library\n\n\nFor accessing a particular language pair:\n\n\n* Clone the git repo", "### Supported Tasks and Leaderboards\n\n\nN/A", "### Languages\n\n\nLanguage pairs can be found here.\n\n\nDataset Structure\n-----------------\n\n\nThe dataset contains gzipped tab delimited text files for each direction. Each text file contains lines with parallel sentences.", "### Data Instances\n\n\nThe number of instances for each language pair can be found in the dataset\\_infos.json file.", "### Data Fields\n\n\nEvery instance for a language pair contains the following fields: 'translation' (containing sentence pairs), 'laser\\_score', 'source\\_sentence\\_lid', 'target\\_sentence\\_lid', where 'lid' is language classification probability, 'source\\_sentence\\_source', 'source\\_sentence\\_url', 'target\\_sentence\\_source', 'target\\_sentence\\_url'.\n\n\n* Sentence in first language\n* Sentence in second language\n* LASER score\n* Language ID score for first sentence\n* Language ID score for second sentence\n* First sentence source (See Source Data Table)\n* First sentence URL if the source is crawl-data/\\*; \\_ otherwise\n* Second sentence source\n* Second sentence URL if the source is crawl-data/\\*; \\_ otherwise\n\n\nThe lines are sorted by LASER3 score in decreasing order.\n\n\nExample:", "### Data Splits\n\n\nThe data is not split. Given the noisy nature of the overall process, we recommend using the data only for training and use other datasets like Flores-200 for the evaluation. The data includes some development and test sets from other datasets, such as xlsum. In addition, sourcing data from multiple web crawls is likely to produce incidental overlap with other test sets.\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nData was filtered based on language identification, emoji based filtering, and for some high-resource languages using a language model. For more details on data filtering please refer to Section 5.2 (NLLB Team et al., 2022).", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nMonolingual data was collected from the following sources:", "#### Who are the source language producers?\n\n\nText was collected from the web and various monolingual data sets, many of which are also web crawls. This may have been written by people, generated by templates, or in some cases be machine translation output.", "### Annotations", "#### Annotation process\n\n\nParallel sentences in the monolingual data were identified using LASER3 encoders. (Heffernan et al., 2022)", "#### Who are the annotators?\n\n\nThe data was not human annotated.", "### Personal and Sensitive Information\n\n\nData may contain personally identifiable information, sensitive content, or toxic content that was publicly shared on the Internet.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThis dataset provides data for training machine learning systems for many languages that have low resources available for NLP.", "### Discussion of Biases\n\n\nBiases in the data have not been specifically studied, however as the original source of data is World Wide Web it is likely that the data has biases similar to those prevalent in the Internet. The data may also exhibit biases introduced by language identification and data filtering techniques; lower resource languages generally have lower accuracy.", "### Other Known Limitations\n\n\nSome of the translations are in fact machine translations. While some website machine translation tools are identifiable from HTML source, these tools were not filtered out en mass because raw HTML was not available from some sources and CommonCrawl processing started from WET files.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe data was not curated.", "### Licensing Information\n\n\nThe dataset is released under the terms of ODC-BY. By using this, you are also bound to the respective Terms of Use and License of the original source.\n\n\nSchwenk et al, CCMatrix: Mining Billions of High-Quality Parallel Sentences on the Web. ACL URL\nHefferman et al, Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages. Arxiv URL 2022. \n\nNLLB Team et al, No Language Left Behind: Scaling Human-Centered Machine Translation, Arxiv URL 2022.", "### Contributions\n\n\nWe thank the NLLB Meta AI team for open sourcing the meta data and instructions on how to use it with special thanks to Bapi Akula, Pierre Andrews, Onur Çelebi, Sergey Edunov, Kenneth Heafield, Philipp Koehn, Alex Mourachko, Safiyyah Saleem, Holger Schwenk, and Guillaume Wenzek. We also thank the AllenNLP team at AI2 for hosting and releasing this data, including Akshita Bhagia (for engineering efforts to host the data, and create the huggingface dataset), and Jesse Dodge (for organizing the connection)." ]

[ "TAGS\n#arxiv-2207.0467 #arxiv-2205.12654 #arxiv-2207.04672 #region-us \n", "### Dataset Summary\n\n\nThis dataset was created based on metadata for mined bitext released by Meta AI. It contains bitext for 148 English-centric and 1465 non-English-centric language pairs using the stopes mining library and the LASER3 encoders (Heffernan et al., 2022). The complete dataset is ~450GB.\n\n\nCCMatrix contains previous versions of mined instructions.", "#### How to use the data\n\n\nThere are two ways to access the data:\n\n\n* Via the Hugging Face Python datasets library\n\n\nFor accessing a particular language pair:\n\n\n* Clone the git repo", "### Supported Tasks and Leaderboards\n\n\nN/A", "### Languages\n\n\nLanguage pairs can be found here.\n\n\nDataset Structure\n-----------------\n\n\nThe dataset contains gzipped tab delimited text files for each direction. Each text file contains lines with parallel sentences.", "### Data Instances\n\n\nThe number of instances for each language pair can be found in the dataset\\_infos.json file.", "### Data Fields\n\n\nEvery instance for a language pair contains the following fields: 'translation' (containing sentence pairs), 'laser\\_score', 'source\\_sentence\\_lid', 'target\\_sentence\\_lid', where 'lid' is language classification probability, 'source\\_sentence\\_source', 'source\\_sentence\\_url', 'target\\_sentence\\_source', 'target\\_sentence\\_url'.\n\n\n* Sentence in first language\n* Sentence in second language\n* LASER score\n* Language ID score for first sentence\n* Language ID score for second sentence\n* First sentence source (See Source Data Table)\n* First sentence URL if the source is crawl-data/\\*; \\_ otherwise\n* Second sentence source\n* Second sentence URL if the source is crawl-data/\\*; \\_ otherwise\n\n\nThe lines are sorted by LASER3 score in decreasing order.\n\n\nExample:", "### Data Splits\n\n\nThe data is not split. Given the noisy nature of the overall process, we recommend using the data only for training and use other datasets like Flores-200 for the evaluation. The data includes some development and test sets from other datasets, such as xlsum. In addition, sourcing data from multiple web crawls is likely to produce incidental overlap with other test sets.\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nData was filtered based on language identification, emoji based filtering, and for some high-resource languages using a language model. For more details on data filtering please refer to Section 5.2 (NLLB Team et al., 2022).", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nMonolingual data was collected from the following sources:", "#### Who are the source language producers?\n\n\nText was collected from the web and various monolingual data sets, many of which are also web crawls. This may have been written by people, generated by templates, or in some cases be machine translation output.", "### Annotations", "#### Annotation process\n\n\nParallel sentences in the monolingual data were identified using LASER3 encoders. (Heffernan et al., 2022)", "#### Who are the annotators?\n\n\nThe data was not human annotated.", "### Personal and Sensitive Information\n\n\nData may contain personally identifiable information, sensitive content, or toxic content that was publicly shared on the Internet.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThis dataset provides data for training machine learning systems for many languages that have low resources available for NLP.", "### Discussion of Biases\n\n\nBiases in the data have not been specifically studied, however as the original source of data is World Wide Web it is likely that the data has biases similar to those prevalent in the Internet. The data may also exhibit biases introduced by language identification and data filtering techniques; lower resource languages generally have lower accuracy.", "### Other Known Limitations\n\n\nSome of the translations are in fact machine translations. While some website machine translation tools are identifiable from HTML source, these tools were not filtered out en mass because raw HTML was not available from some sources and CommonCrawl processing started from WET files.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe data was not curated.", "### Licensing Information\n\n\nThe dataset is released under the terms of ODC-BY. By using this, you are also bound to the respective Terms of Use and License of the original source.\n\n\nSchwenk et al, CCMatrix: Mining Billions of High-Quality Parallel Sentences on the Web. ACL URL\nHefferman et al, Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages. Arxiv URL 2022. \n\nNLLB Team et al, No Language Left Behind: Scaling Human-Centered Machine Translation, Arxiv URL 2022.", "### Contributions\n\n\nWe thank the NLLB Meta AI team for open sourcing the meta data and instructions on how to use it with special thanks to Bapi Akula, Pierre Andrews, Onur Çelebi, Sergey Edunov, Kenneth Heafield, Philipp Koehn, Alex Mourachko, Safiyyah Saleem, Holger Schwenk, and Guillaume Wenzek. We also thank the AllenNLP team at AI2 for hosting and releasing this data, including Akshita Bhagia (for engineering efforts to host the data, and create the huggingface dataset), and Jesse Dodge (for organizing the connection)." ]

[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:cc-by-4.0", "aspect-based-sentiment-analysis", "semeval", "semeval2015", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for SemEval Task 12: Aspect-based Sentiment Analysis", "## 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 orignally from SemEval-2015 Task 12.\nFrom the page:\n> SE-ABSA15 will focus on the same domains as SE-ABSA14 (restaurants and laptops). However, unlike SE-ABSA14, the input datasets of SE-ABSA15 will contain entire reviews, not isolated (potentially out of context) sentences. SE-ABSA15 consolidates the four subtasks of SE-ABSA14 within a unified framework. In addition, SE-ABSA15 will include an out-of-domain ABSA subtask, involving test data from a domain unknown to the participants, other than the domains that will be considered during training. In particular, SE-ABSA15 consists of the following two subtasks.", "### 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 @andreaschandra for adding this dataset." ]

[ "TAGS\n#task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc-by-4.0 #aspect-based-sentiment-analysis #semeval #semeval2015 #region-us \n", "# Dataset Card for SemEval Task 12: Aspect-based Sentiment Analysis", "## 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 orignally from SemEval-2015 Task 12.\nFrom the page:\n> SE-ABSA15 will focus on the same domains as SE-ABSA14 (restaurants and laptops). However, unlike SE-ABSA14, the input datasets of SE-ABSA15 will contain entire reviews, not isolated (potentially out of context) sentences. SE-ABSA15 consolidates the four subtasks of SE-ABSA14 within a unified framework. In addition, SE-ABSA15 will include an out-of-domain ABSA subtask, involving test data from a domain unknown to the participants, other than the domains that will be considered during training. In particular, SE-ABSA15 consists of the following two subtasks.", "### 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 @andreaschandra for adding this dataset." ]

[ "task_categories:automatic-speech-recognition", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended|kresnik/zeroth_korean", "language:ko", "license:cc-by-4.0", "region:us" ]

[]

[ "ko" ]

[ "### License\n\n\nCC BY 4.0\n\n\nDataset Structure\n-----------------", "### Data Instance", "### Data Size\n\n\ndownload: 2.68 GiB \n\ngenerated: 2.85 GiB \n\ntotal: 5.52 GiB", "### Data Fields\n\n\n* audio: 'audio', sampling rate = 16000\n\t+ A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.\n\t+ Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling\\_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the \"audio\" column, i.e. 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n* text: 'string'", "### Data Splits\n\n\ntrain: # of data, test: 22263" ]

[ "TAGS\n#task_categories-automatic-speech-recognition #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|kresnik/zeroth_korean #language-Korean #license-cc-by-4.0 #region-us \n", "### License\n\n\nCC BY 4.0\n\n\nDataset Structure\n-----------------", "### Data Instance", "### Data Size\n\n\ndownload: 2.68 GiB \n\ngenerated: 2.85 GiB \n\ntotal: 5.52 GiB", "### Data Fields\n\n\n* audio: 'audio', sampling rate = 16000\n\t+ A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.\n\t+ Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling\\_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the \"audio\" column, i.e. 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n* text: 'string'", "### Data Splits\n\n\ntrain: # of data, test: 22263" ]

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

[]

[ "en" ]

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

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

[ "task_categories:summarization", "task_categories:text2text-generation", "size_categories:10K<n<100K", "source_datasets:https://github.com/huggingface/datasets/tree/master/datasets/pubmed", "language:sv", "license:other", "conditional-text-generation", "region:us" ]

[]

[ "sv" ]

[ "### Data Fields\n\n\n* 'document': a string containing the body of the paper\n* 'summary': a string containing the abstract of the paper", "### Data Splits\n\n\nThe Swedish pubmed dataset follows the same splits as the original English version and has 1 splits: *train*." ]

[ "TAGS\n#task_categories-summarization #task_categories-text2text-generation #size_categories-10K<n<100K #source_datasets-https-//github.com/huggingface/datasets/tree/master/datasets/pubmed #language-Swedish #license-other #conditional-text-generation #region-us \n", "### Data Fields\n\n\n* 'document': a string containing the body of the paper\n* 'summary': a string containing the abstract of the paper", "### Data Splits\n\n\nThe Swedish pubmed dataset follows the same splits as the original English version and has 1 splits: *train*." ]

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

[]

[]

[]

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

[ "task_categories:tabular-classification", "size_categories:100K<n<1M", "interpretability", "fairness", "medicine", "region:us" ]

[]

[]

[ "### Sample usage\n\nLoad the data:\n\n\n\nFit a model:\n\n\n\n\nEvaluate:" ]

[ "TAGS\n#task_categories-tabular-classification #size_categories-100K<n<1M #interpretability #fairness #medicine #region-us \n", "### Sample usage\n\nLoad the data:\n\n\n\nFit a model:\n\n\n\n\nEvaluate:" ]

[ "task_categories:tabular-classification", "size_categories:10K<n<100K", "interpretability", "fairness", "medicine", "region:us" ]

[]

[]

[ "### Sample usage\n\nLoad the data:\n\n\n\nFit a model:\n\n\n\n\nEvaluate:" ]

[ "TAGS\n#task_categories-tabular-classification #size_categories-10K<n<100K #interpretability #fairness #medicine #region-us \n", "### Sample usage\n\nLoad the data:\n\n\n\nFit a model:\n\n\n\n\nEvaluate:" ]

[ "region:us" ]

[]

[]

[]

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

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

[]

[]

[ "### Dataset Summary", "### Content", "### Acknowledgements\n\n\nI'd like to thank URL Salaries for aggregating this data!", "### Supported Tasks and Leaderboards", "### Languages\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields", "### Data Splits\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\n\n\nThis dataset was shared by @ruchi798", "### Licensing Information\n\n\nThe license for this dataset is cc0-1.0", "### Contributions" ]

[ "TAGS\n#license-cc0-1.0 #region-us \n", "### Dataset Summary", "### Content", "### Acknowledgements\n\n\nI'd like to thank URL Salaries for aggregating this data!", "### Supported Tasks and Leaderboards", "### Languages\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields", "### Data Splits\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\n\n\nThis dataset was shared by @ruchi798", "### Licensing Information\n\n\nThe license for this dataset is cc0-1.0", "### Contributions" ]

[ "license:cc-by-nc-sa-4.0", "arxiv:1906.02059", "region:us" ]

[ "1906.02059" ]

[]

[ "# ECHR Cases\n\nThe original data from Chalkidis et al., sourced from URL.", "## Preprocessing\n\n* Order is shuffled\n* Fact numbers preceeding each fact are removed (using the python regex '^[0-9]+\\. '), as some cases didn't have fact numbers to begin with\n* Everything else is the same" ]

[ "TAGS\n#license-cc-by-nc-sa-4.0 #arxiv-1906.02059 #region-us \n", "# ECHR Cases\n\nThe original data from Chalkidis et al., sourced from URL.", "## Preprocessing\n\n* Order is shuffled\n* Fact numbers preceeding each fact are removed (using the python regex '^[0-9]+\\. '), as some cases didn't have fact numbers to begin with\n* Everything else is the same" ]

[ "task_categories:token-classification", "task_categories:text-classification", "task_ids:named-entity-recognition", "size_categories:List[str]", "source_datasets:List[str]", "license:mit", "multilingual", "RNN", "name", "tagging", "nlp", "transliterated", "character-level", "text-tagging", "bias", "classification", "language model", "surname", "ethnicity", "multilabel classification", "natural language", "region:us" ]

[]

[]

[ "# Popular Surname Nationality Mapping\n\nSample of popular surnames for 30+ countries labeled with nationality (language)" ]

[ "TAGS\n#task_categories-token-classification #task_categories-text-classification #task_ids-named-entity-recognition #size_categories-List[str] #source_datasets-List[str] #license-mit #multilingual #RNN #name #tagging #nlp #transliterated #character-level #text-tagging #bias #classification #language model #surname #ethnicity #multilabel classification #natural language #region-us \n", "# Popular Surname Nationality Mapping\n\nSample of popular surnames for 30+ countries labeled with nationality (language)" ]

[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:sl", "license:cc-by-sa-4.0", "slovenian sentiment", "news articles", "region:us" ]

[]

[ "sl" ]

[ "# Dataset Card for SentiNews", "## Dataset Description\n\n- Homepage: URL \n- Paper: Bučar, J., Žnidaršič, M. & Povh, J. Annotated news corpora and a lexicon for sentiment analysis in Slovene. Lang Resources & Evaluation 52, 895–919 (2018). URL", "### Dataset Summary\n\nSentiNews is a Slovenian sentiment classification dataset, consisting of news articles manually annotated with their sentiment by between two and six annotators.\nIt is annotated at three granularities: \n- document-level (config 'document_level', 10 427 documents), \n- paragraph-level (config 'paragraph_level', 89 999 paragraphs), and \n- sentence-level (config 'sentence_level', 168 899 sentences).", "### Supported Tasks and Leaderboards\n\nSentiment classification, three classes (negative, neutral, positive).", "### Languages\n\nSlovenian.", "## Dataset Structure", "### Data Instances\n\nA sample instance from the sentence-level config:", "### Data Fields\n\nThe data fields are similar among all three configs, with the only difference being the IDs.\n\n- 'nid': a uint16 containing a unique ID of the news article (document). \n- 'content': a string containing the body of the news article \n- 'sentiment': the sentiment of the instance\n- 'pid': a uint8 containing the consecutive number of the paragraph inside the current news article, not unique (present in the configs 'paragraph_level' and 'sentence_level')\n- 'sid': a uint8 containing the consecutive number of the sentence inside the current paragraph, not unique (present in the config 'sentence_level')", "## Additional Information", "### Dataset Curators\n\nJože Bučar, Martin Žnidaršič, Janez Povh.", "### Licensing Information\n\nCC BY-SA 4.0", "### Contributions\n\nThanks to @matejklemen for adding this dataset." ]

[ "TAGS\n#task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #source_datasets-original #language-Slovenian #license-cc-by-sa-4.0 #slovenian sentiment #news articles #region-us \n", "# Dataset Card for SentiNews", "## Dataset Description\n\n- Homepage: URL \n- Paper: Bučar, J., Žnidaršič, M. & Povh, J. Annotated news corpora and a lexicon for sentiment analysis in Slovene. Lang Resources & Evaluation 52, 895–919 (2018). URL", "### Dataset Summary\n\nSentiNews is a Slovenian sentiment classification dataset, consisting of news articles manually annotated with their sentiment by between two and six annotators.\nIt is annotated at three granularities: \n- document-level (config 'document_level', 10 427 documents), \n- paragraph-level (config 'paragraph_level', 89 999 paragraphs), and \n- sentence-level (config 'sentence_level', 168 899 sentences).", "### Supported Tasks and Leaderboards\n\nSentiment classification, three classes (negative, neutral, positive).", "### Languages\n\nSlovenian.", "## Dataset Structure", "### Data Instances\n\nA sample instance from the sentence-level config:", "### Data Fields\n\nThe data fields are similar among all three configs, with the only difference being the IDs.\n\n- 'nid': a uint16 containing a unique ID of the news article (document). \n- 'content': a string containing the body of the news article \n- 'sentiment': the sentiment of the instance\n- 'pid': a uint8 containing the consecutive number of the paragraph inside the current news article, not unique (present in the configs 'paragraph_level' and 'sentence_level')\n- 'sid': a uint8 containing the consecutive number of the sentence inside the current paragraph, not unique (present in the config 'sentence_level')", "## Additional Information", "### Dataset Curators\n\nJože Bučar, Martin Žnidaršič, Janez Povh.", "### Licensing Information\n\nCC BY-SA 4.0", "### Contributions\n\nThanks to @matejklemen for adding this dataset." ]

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:cc-by-4.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nThis is the Broad Twitter corpus, a dataset of tweets collected over stratified times, places and social uses. The goal is to represent a broad range of activities, giving a dataset more representative of the language used in this hardest of social media formats to process. Further, the BTC is annotated for named entities.\n\n\nSee the paper, Broad Twitter Corpus: A Diverse Named Entity Recognition Resource, for details.", "### Supported Tasks and Leaderboards\n\n\n* Named Entity Recognition\n* On PWC: Named Entity Recognition on Broad Twitter Corpus", "### Languages\n\n\nEnglish from UK, US, Australia, Canada, Ireland, New Zealand; 'bcp47:en'\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields\n\n\nEach tweet contains an ID, a list of tokens, and a list of NER tags\n\n\n* 'id': a 'string' feature.\n* 'tokens': a 'list' of 'strings'\n* 'ner\\_tags': a 'list' of class IDs ('int's) representing the NER class:", "### Data Splits\n\n\n\nThe most varied parts of the BTC are sections F and H. However, each of the remaining four sections has some specific readily-identifiable bias. So, we propose that one uses half of section H for evaluation and leaves the other half in the training data. Section H should be partitioned in the order of the JSON-format lines. Note that the CoNLL-format data is readily reconstructible from the JSON format, which is the authoritative data format from which others are derived.\n\n\nTest: Section F\n\n\nDevelopment: Section H (the paper says \"second half of Section H\" but ordinality could be ambiguous, so it all goes in. Bonne chance)\n\n\nTraining: everything else\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCreative Commons Attribution 4.0 International (CC BY 4.0)", "### Contributions\n\n\nAuthor-added dataset @leondz" ]

[ "TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-cc-by-4.0 #region-us \n", "### Dataset Summary\n\n\nThis is the Broad Twitter corpus, a dataset of tweets collected over stratified times, places and social uses. The goal is to represent a broad range of activities, giving a dataset more representative of the language used in this hardest of social media formats to process. Further, the BTC is annotated for named entities.\n\n\nSee the paper, Broad Twitter Corpus: A Diverse Named Entity Recognition Resource, for details.", "### Supported Tasks and Leaderboards\n\n\n* Named Entity Recognition\n* On PWC: Named Entity Recognition on Broad Twitter Corpus", "### Languages\n\n\nEnglish from UK, US, Australia, Canada, Ireland, New Zealand; 'bcp47:en'\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields\n\n\nEach tweet contains an ID, a list of tokens, and a list of NER tags\n\n\n* 'id': a 'string' feature.\n* 'tokens': a 'list' of 'strings'\n* 'ner\\_tags': a 'list' of class IDs ('int's) representing the NER class:", "### Data Splits\n\n\n\nThe most varied parts of the BTC are sections F and H. However, each of the remaining four sections has some specific readily-identifiable bias. So, we propose that one uses half of section H for evaluation and leaves the other half in the training data. Section H should be partitioned in the order of the JSON-format lines. Note that the CoNLL-format data is readily reconstructible from the JSON format, which is the authoritative data format from which others are derived.\n\n\nTest: Section F\n\n\nDevelopment: Section H (the paper says \"second half of Section H\" but ordinality could be ambiguous, so it all goes in. Bonne chance)\n\n\nTraining: everything else\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCreative Commons Attribution 4.0 International (CC BY 4.0)", "### Contributions\n\n\nAuthor-added dataset @leondz" ]

[ "task_categories:sentence-similarity", "annotations_creators:no-annotation", "language_creators:other", "size_categories:1M<n<10M", "source_datasets:extended|oscar", "language:en", "license:afl-3.0", "embeddings", "vector search", "semantic similarity", "semantic search", "sentence transformers", "sentence similarity", "region:us" ]

[]

[ "en" ]

[ "# Oscar EN 2M Embeddings\n\nThis dataset contains 2M sentences extracted from the English subset of the OSCAR dataset, and encoded into sentence embeddings using the 'sentence-transformers/all-MiniLM-L6-v2' model." ]

[ "TAGS\n#task_categories-sentence-similarity #annotations_creators-no-annotation #language_creators-other #size_categories-1M<n<10M #source_datasets-extended|oscar #language-English #license-afl-3.0 #embeddings #vector search #semantic similarity #semantic search #sentence transformers #sentence similarity #region-us \n", "# Oscar EN 2M Embeddings\n\nThis dataset contains 2M sentences extracted from the English subset of the OSCAR dataset, and encoded into sentence embeddings using the 'sentence-transformers/all-MiniLM-L6-v2' model." ]

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

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for myds", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\ndataset for Tunisian dialect", "### Supported Tasks and Leaderboards", "### Languages\n\ntuanisian arabic dialect", "## Dataset Structure", "### Data Instances\n\nSize of downloaded dataset files: 4.63 MB\nSize of the generated dataset: 9.78 MB\nTotal amount of disk used: 14.41 MB", "### Data Fields\n\ndsfsergrth", "### Data Splits\n\nrtsert", "## Dataset Creation", "### Curation Rationale\n\nlink", "### Source Data", "#### Initial Data Collection and Normalization\n\nkink", "#### Who are the source language producers?\n\nlink", "### Annotations", "#### Annotation process\n\ntool", "#### Who are the annotators?\n\nme", "### Personal and Sensitive Information\n\nok", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information" ]

[ "TAGS\n#region-us \n", "# Dataset Card for myds", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\ndataset for Tunisian dialect", "### Supported Tasks and Leaderboards", "### Languages\n\ntuanisian arabic dialect", "## Dataset Structure", "### Data Instances\n\nSize of downloaded dataset files: 4.63 MB\nSize of the generated dataset: 9.78 MB\nTotal amount of disk used: 14.41 MB", "### Data Fields\n\ndsfsergrth", "### Data Splits\n\nrtsert", "## Dataset Creation", "### Curation Rationale\n\nlink", "### Source Data", "#### Initial Data Collection and Normalization\n\nkink", "#### Who are the source language producers?\n\nlink", "### Annotations", "#### Annotation process\n\ntool", "#### Who are the annotators?\n\nme", "### Personal and Sensitive Information\n\nok", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information" ]

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

[]

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

[ "### Dataset Summary\n\n\nTyDi QA is a question answering dataset covering 11 typologically diverse languages.\nAnswerable TyDi QA is an extension of the GoldP subtask of the original TyDi QA dataset to also include unanswertable questions.\n\n\nDataset Structure\n-----------------\n\n\nThe dataset contains a train and a validation set, with 116067 and 13325 examples, respectively. Access them with", "### Data Instances\n\n\nHere is an example of an instance of the dataset:\n\n\nDescription of the dataset columns:\n\n\nColumn name: document\\_title, type: str, Description: The title of the Wikipedia article from which the data instance was generated\nColumn name: document\\_url, type: str, Description: The URL of said article\nColumn name: language, type: str, Description: The language of the data instance\nColumn name: question\\_text, type: str, Description: The question to answer\nColumn name: document\\_plaintext, type: str, Description: The context, a Wikipedia paragraph that might or might not contain the answer to the question\nColumn name: annotations[\"answer\\_start\"], type: list[int], Description: The char index in 'document\\_plaintext' where the answer starts. If the question is unanswerable - [-1]\nColumn name: annotations[\"answer\\_text\"], type: list[str], Description: The answer, a span of text from 'document\\_plaintext'. If the question is unanswerable - ['']\n\n\nNotice: If the question is *answerable*, annotations[\"answer\\_start\"] and annotations[\"answer\\_text\"] contain a list of length 1 \n\n(In some variations of the dataset the lists might be longer, e.g. if more than one person annotated the instance, but not in our case).\nIf the question is *unanswerable*, annotations[\"answer\\_start\"] will have \"-1\", while annotations[\"answer\\_text\"] contain a list with an empty sring.\n\n\nUseful stuff\n------------\n\n\nCheck out the datasets ducumentations to learn how to manipulate and use the dataset. Specifically, you might find the following functions useful:\n\n\n'URL', for filtering out data (useful for keeping instances of specific languages, for example).\n\n\n'URL', for manipulating the dataset.\n\n\n'dataset.to\\_pandas', to convert the dataset into a pandas.DataFrame format.", "### Contributions\n\n\nThanks to @thomwolf, @albertvillanova, @lewtun, @patrickvonplaten for adding this dataset." ]

[ "TAGS\n#task_categories-question-answering #task_ids-extractive-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-multilingual #source_datasets-extended|wikipedia #language-English #language-Arabic #language-Bengali #language-Finnish #language-Indonesian #language-Japanese #language-Swahili (macrolanguage) #language-Korean #language-Russian #language-Telugu #language-Thai #license-apache-2.0 #region-us \n", "### Dataset Summary\n\n\nTyDi QA is a question answering dataset covering 11 typologically diverse languages.\nAnswerable TyDi QA is an extension of the GoldP subtask of the original TyDi QA dataset to also include unanswertable questions.\n\n\nDataset Structure\n-----------------\n\n\nThe dataset contains a train and a validation set, with 116067 and 13325 examples, respectively. Access them with", "### Data Instances\n\n\nHere is an example of an instance of the dataset:\n\n\nDescription of the dataset columns:\n\n\nColumn name: document\\_title, type: str, Description: The title of the Wikipedia article from which the data instance was generated\nColumn name: document\\_url, type: str, Description: The URL of said article\nColumn name: language, type: str, Description: The language of the data instance\nColumn name: question\\_text, type: str, Description: The question to answer\nColumn name: document\\_plaintext, type: str, Description: The context, a Wikipedia paragraph that might or might not contain the answer to the question\nColumn name: annotations[\"answer\\_start\"], type: list[int], Description: The char index in 'document\\_plaintext' where the answer starts. If the question is unanswerable - [-1]\nColumn name: annotations[\"answer\\_text\"], type: list[str], Description: The answer, a span of text from 'document\\_plaintext'. If the question is unanswerable - ['']\n\n\nNotice: If the question is *answerable*, annotations[\"answer\\_start\"] and annotations[\"answer\\_text\"] contain a list of length 1 \n\n(In some variations of the dataset the lists might be longer, e.g. if more than one person annotated the instance, but not in our case).\nIf the question is *unanswerable*, annotations[\"answer\\_start\"] will have \"-1\", while annotations[\"answer\\_text\"] contain a list with an empty sring.\n\n\nUseful stuff\n------------\n\n\nCheck out the datasets ducumentations to learn how to manipulate and use the dataset. Specifically, you might find the following functions useful:\n\n\n'URL', for filtering out data (useful for keeping instances of specific languages, for example).\n\n\n'URL', for manipulating the dataset.\n\n\n'dataset.to\\_pandas', to convert the dataset into a pandas.DataFrame format.", "### Contributions\n\n\nThanks to @thomwolf, @albertvillanova, @lewtun, @patrickvonplaten for adding this dataset." ]

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

[]

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

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

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

[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:id", "license:cc-by-4.0", "movie", "subtitle", "indonesian", "region:us" ]

[]

[ "id" ]

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

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Indonesian #license-cc-by-4.0 #movie #subtitle #indonesian #region-us \n", "# Dataset Card for Indonesian Movie Subtitle", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @andreaschandra for adding this dataset." ]

[ "task_categories:text-generation", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-4.0", "arxiv:2101.11718", "region:us" ]

[ "2101.11718" ]

[ "en" ]

[ "### Data Instances\n\n\nAn example of the 'gender\\_prompt.json' looks as follows.\n\n\nDataset Creation\n----------------\n\n\nBOLD consists of language generation prompts extracted from English Wikipedia sentences.\n\n\nConsiderations for Using the Data\n---------------------------------\n\n\nFrom the original BOLD paper:\n\n\n\n> \n> BOLD considers a limited set of demographic domains and a specific subset of groups within each domain. The gender domain is limited to binary gender and the race domain is limited to a small subset of racial identities as conceptualized within the American culture. We note that the groups considered in this study do not cover an entire spectrum of the real-world diversity [ 21]. There are various other groups, languages, types of social biases and cultural contexts that are beyond the scope of BOLD; benchmarking on BOLD provides an indication of whether a model is biased in the categories considered in BOLD, however, it is not an indication that a model is completely fair. One important and immediate future direction is to expand BOLD by adding data from additional domains and by including diverse groups within each domain.\n> \n> \n> \n\n\n\n> \n> Several works have shown that the distribution of demographics of Wikipedia authors is highly skewed resulting in various types of biases [ 9 , 19, 36 ]. Therefore, we caution users of BOLD against a comparison with Wikipedia sentences as a fair baseline. Our experiments on comparing Wikipedia sentences with texts generated by LMs also show that the Wikipedia is not free from biases and the biases it exhibits resemble the biases exposed in the texts generated by LMs.\n> \n> \n>", "### Licensing Information\n\n\nThis project is licensed under the Creative Commons Attribution Share Alike 4.0 International license." ]

[ "TAGS\n#task_categories-text-generation #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-4.0 #arxiv-2101.11718 #region-us \n", "### Data Instances\n\n\nAn example of the 'gender\\_prompt.json' looks as follows.\n\n\nDataset Creation\n----------------\n\n\nBOLD consists of language generation prompts extracted from English Wikipedia sentences.\n\n\nConsiderations for Using the Data\n---------------------------------\n\n\nFrom the original BOLD paper:\n\n\n\n> \n> BOLD considers a limited set of demographic domains and a specific subset of groups within each domain. The gender domain is limited to binary gender and the race domain is limited to a small subset of racial identities as conceptualized within the American culture. We note that the groups considered in this study do not cover an entire spectrum of the real-world diversity [ 21]. There are various other groups, languages, types of social biases and cultural contexts that are beyond the scope of BOLD; benchmarking on BOLD provides an indication of whether a model is biased in the categories considered in BOLD, however, it is not an indication that a model is completely fair. One important and immediate future direction is to expand BOLD by adding data from additional domains and by including diverse groups within each domain.\n> \n> \n> \n\n\n\n> \n> Several works have shown that the distribution of demographics of Wikipedia authors is highly skewed resulting in various types of biases [ 9 , 19, 36 ]. Therefore, we caution users of BOLD against a comparison with Wikipedia sentences as a fair baseline. Our experiments on comparing Wikipedia sentences with texts generated by LMs also show that the Wikipedia is not free from biases and the biases it exhibits resemble the biases exposed in the texts generated by LMs.\n> \n> \n>", "### Licensing Information\n\n\nThis project is licensed under the Creative Commons Attribution Share Alike 4.0 International license." ]

[ "task_categories:automatic-speech-recognition", "annotations_creators:crowdsourced", "annotations_creators:machine-generated", "language_creators:crowdsourced", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:1T<n", "source_datasets:original", "language:en", "license:cc-by-2.0", "license:cc-by-2.5", "license:cc-by-3.0", "license:cc-by-4.0", "license:cc-by-sa-3.0", "license:cc-by-sa-4.0", "robust-speech-recognition", "noisy-speech-recognition", "speech-recognition", "arxiv:2111.09344", "region:us" ]

[ "2111.09344" ]

[ "en" ]

[ "# Dataset Card for People's Speech", "## 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: URL\n- Leaderboard: \n- Point of Contact: datasets@URL", "### Dataset Summary\n\nThe People's Speech Dataset is among the world's largest English speech recognition corpus today that is licensed for academic and commercial usage under CC-BY-SA and CC-BY 4.0. It includes 30,000+ hours of transcribed speech in English languages with a diverse set of speakers. This open dataset is large enough to train speech-to-text systems and crucially is available with a permissive license.", "### Supported Tasks and Leaderboards", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances\n\n{\n \"id\": \"gov_DOT_uscourts_DOT_scotus_DOT_19-161/gov_DOT_uscourts_DOT_scotus_DOT_19-161_DOT_2020-03-02_DOT_mp3_00002.flac\",\n \"audio\": {\n \"path\": \"gov_DOT_uscourts_DOT_scotus_DOT_19-161/gov_DOT_uscourts_DOT_scotus_DOT_19-161_DOT_2020-03-02_DOT_mp3_00002.flac\"\n \"array\": array([-6.10351562e-05, ...]),\n \"sampling_rate\": 16000\n }\n \"duration_ms\": 14490,\n \"text\": \"contends that the suspension clause requires a [...]\"\n}", "### Data Fields\n\n{\n \"id\": datasets.Value(\"string\"),\n \"audio\": datasets.Audio(sampling_rate=16_000),\n \"duration_ms\": datasets.Value(\"int32\"),\n \"text\": datasets.Value(\"string\"),\n}", "### Data Splits\n\nWe provide the following configurations for the dataset: 'cc-by-clean', 'cc-by-dirty', 'cc-by-sa-clean', 'cc-by-sa-dirty', and 'microset'. We don't provide splits for any of the configurations.", "## Dataset Creation", "### Curation Rationale\n\nSee our paper.", "### Source Data", "#### Initial Data Collection and Normalization\n\nData was downloaded via the URL API. No data inference was done.", "#### Who are the source language producers?", "### Annotations", "#### Annotation process\n\nNo manual annotation is done. We download only source audio with already existing transcripts.", "#### Who are the annotators?\n\nFor the test and dev sets, we paid native American English speakers to do transcriptions. We do not know the identities of the transcriptionists for data in the training set. For the training set, we have noticed that some transcriptions are likely to be the output of automatic speech recognition systems.", "### Personal and Sensitive Information\n\nSeveral of our sources are legal and government proceedings, spoken histories, speeches, and so on. Given that these were intended as public documents and licensed as such, it is natural that the involved individuals are aware of this.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe dataset could be used for speech synthesis. However, this requires careful cleaning of the dataset, as background noise is not tolerable for speech synthesis.\n\nThe dataset could be used for keyword spotting tasks as well. In particular, this is good use case for the non-English audio in the dataset.\n\nOur sincere hope is that the large breadth of sources our dataset incorporates reduces existing quality of service issues today, like speech recognition system’s poor understanding of non-native English accents. We cannot think of any unfair treatment that come from using this dataset at this time.", "### Discussion of Biases\n\nOur data is downloaded from URL. As such, the data is biased towards whatever users decide to upload there.\n\nAlmost all of our data is American accented English.", "### Other Known Limitations\n\nAs of version 1.0, a portion of data in the training, test, and dev sets is poorly aligned. Specifically, some words appear in the transcript, but not the audio, or some words appear in the audio, but not the transcript. We are working on it.", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nWe provide CC-BY and CC-BY-SA subsets of the dataset.\n\n\n\nPlease cite:" ]

[ "TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-crowdsourced #annotations_creators-machine-generated #language_creators-crowdsourced #language_creators-machine-generated #multilinguality-monolingual #size_categories-1T<n #source_datasets-original #language-English #license-cc-by-2.0 #license-cc-by-2.5 #license-cc-by-3.0 #license-cc-by-4.0 #license-cc-by-sa-3.0 #license-cc-by-sa-4.0 #robust-speech-recognition #noisy-speech-recognition #speech-recognition #arxiv-2111.09344 #region-us \n", "# Dataset Card for People's Speech", "## 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: URL\n- Leaderboard: \n- Point of Contact: datasets@URL", "### Dataset Summary\n\nThe People's Speech Dataset is among the world's largest English speech recognition corpus today that is licensed for academic and commercial usage under CC-BY-SA and CC-BY 4.0. It includes 30,000+ hours of transcribed speech in English languages with a diverse set of speakers. This open dataset is large enough to train speech-to-text systems and crucially is available with a permissive license.", "### Supported Tasks and Leaderboards", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances\n\n{\n \"id\": \"gov_DOT_uscourts_DOT_scotus_DOT_19-161/gov_DOT_uscourts_DOT_scotus_DOT_19-161_DOT_2020-03-02_DOT_mp3_00002.flac\",\n \"audio\": {\n \"path\": \"gov_DOT_uscourts_DOT_scotus_DOT_19-161/gov_DOT_uscourts_DOT_scotus_DOT_19-161_DOT_2020-03-02_DOT_mp3_00002.flac\"\n \"array\": array([-6.10351562e-05, ...]),\n \"sampling_rate\": 16000\n }\n \"duration_ms\": 14490,\n \"text\": \"contends that the suspension clause requires a [...]\"\n}", "### Data Fields\n\n{\n \"id\": datasets.Value(\"string\"),\n \"audio\": datasets.Audio(sampling_rate=16_000),\n \"duration_ms\": datasets.Value(\"int32\"),\n \"text\": datasets.Value(\"string\"),\n}", "### Data Splits\n\nWe provide the following configurations for the dataset: 'cc-by-clean', 'cc-by-dirty', 'cc-by-sa-clean', 'cc-by-sa-dirty', and 'microset'. We don't provide splits for any of the configurations.", "## Dataset Creation", "### Curation Rationale\n\nSee our paper.", "### Source Data", "#### Initial Data Collection and Normalization\n\nData was downloaded via the URL API. No data inference was done.", "#### Who are the source language producers?", "### Annotations", "#### Annotation process\n\nNo manual annotation is done. We download only source audio with already existing transcripts.", "#### Who are the annotators?\n\nFor the test and dev sets, we paid native American English speakers to do transcriptions. We do not know the identities of the transcriptionists for data in the training set. For the training set, we have noticed that some transcriptions are likely to be the output of automatic speech recognition systems.", "### Personal and Sensitive Information\n\nSeveral of our sources are legal and government proceedings, spoken histories, speeches, and so on. Given that these were intended as public documents and licensed as such, it is natural that the involved individuals are aware of this.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe dataset could be used for speech synthesis. However, this requires careful cleaning of the dataset, as background noise is not tolerable for speech synthesis.\n\nThe dataset could be used for keyword spotting tasks as well. In particular, this is good use case for the non-English audio in the dataset.\n\nOur sincere hope is that the large breadth of sources our dataset incorporates reduces existing quality of service issues today, like speech recognition system’s poor understanding of non-native English accents. We cannot think of any unfair treatment that come from using this dataset at this time.", "### Discussion of Biases\n\nOur data is downloaded from URL. As such, the data is biased towards whatever users decide to upload there.\n\nAlmost all of our data is American accented English.", "### Other Known Limitations\n\nAs of version 1.0, a portion of data in the training, test, and dev sets is poorly aligned. Specifically, some words appear in the transcript, but not the audio, or some words appear in the audio, but not the transcript. We are working on it.", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nWe provide CC-BY and CC-BY-SA subsets of the dataset.\n\n\n\nPlease cite:" ]