Datasets:

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ftopal

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huggingface-datasets-processed

like 0

[ "task_categories:graph-ml", "license:unknown", "region:us" ]

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[ "### Dataset Summary\n\n\nThe 'ZINC' dataset is a \"curated collection of commercially available chemical compounds prepared especially for virtual screening\" (Wikipedia).", "### Supported Tasks and Leaderboards\n\n\n'ZINC' should be used for molecular property prediction (aiming to predict the constrained solubility of the molecules), a graph regression task. The score used is the MAE.\n\n\nThe associated leaderboard is here: Papers with code leaderboard.\n\n\nExternal Use\n------------", "### PyGeometric\n\n\nTo load in PyGeometric, do the following:\n\n\nDataset Structure\n-----------------", "### Data Properties", "### Data Fields\n\n\nEach row of a given file is a graph, with:\n\n\n* 'node\\_feat' (list: #nodes x #node-features): nodes\n* 'edge\\_index' (list: 2 x #edges): pairs of nodes constituting edges\n* 'edge\\_attr' (list: #edges x #edge-features): for the aforementioned edges, contains their features\n* 'y' (list: 1 x #labels): contains the number of labels available to predict (here 1, equal to zero or one)\n* 'num\\_nodes' (int): number of nodes of the graph", "### Data Splits\n\n\nThis data comes from the PyGeometric version of the dataset, and follows the provided data splits.\nThis information can be found back using\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nThe dataset has been released under unknown license. Please open an issue if you know what is the license of this dataset.", "### Contributions\n\n\nThanks to @clefourrier for adding this dataset." ]

[ "TAGS\n#task_categories-graph-ml #license-unknown #region-us \n", "### Dataset Summary\n\n\nThe 'ZINC' dataset is a \"curated collection of commercially available chemical compounds prepared especially for virtual screening\" (Wikipedia).", "### Supported Tasks and Leaderboards\n\n\n'ZINC' should be used for molecular property prediction (aiming to predict the constrained solubility of the molecules), a graph regression task. The score used is the MAE.\n\n\nThe associated leaderboard is here: Papers with code leaderboard.\n\n\nExternal Use\n------------", "### PyGeometric\n\n\nTo load in PyGeometric, do the following:\n\n\nDataset Structure\n-----------------", "### Data Properties", "### Data Fields\n\n\nEach row of a given file is a graph, with:\n\n\n* 'node\\_feat' (list: #nodes x #node-features): nodes\n* 'edge\\_index' (list: 2 x #edges): pairs of nodes constituting edges\n* 'edge\\_attr' (list: #edges x #edge-features): for the aforementioned edges, contains their features\n* 'y' (list: 1 x #labels): contains the number of labels available to predict (here 1, equal to zero or one)\n* 'num\\_nodes' (int): number of nodes of the graph", "### Data Splits\n\n\nThis data comes from the PyGeometric version of the dataset, and follows the provided data splits.\nThis information can be found back using\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nThe dataset has been released under unknown license. Please open an issue if you know what is the license of this dataset.", "### Contributions\n\n\nThanks to @clefourrier for adding this dataset." ]

[ 23, 35, 72, 25, 4, 158, 43, 34, 17 ]

[ "passage: TAGS\n#task_categories-graph-ml #license-unknown #region-us \n### Dataset Summary\n\n\nThe 'ZINC' dataset is a \"curated collection of commercially available chemical compounds prepared especially for virtual screening\" (Wikipedia).### Supported Tasks and Leaderboards\n\n\n'ZINC' should be used for molecular property prediction (aiming to predict the constrained solubility of the molecules), a graph regression task. The score used is the MAE.\n\n\nThe associated leaderboard is here: Papers with code leaderboard.\n\n\nExternal Use\n------------### PyGeometric\n\n\nTo load in PyGeometric, do the following:\n\n\nDataset Structure\n-----------------### Data Properties### Data Fields\n\n\nEach row of a given file is a graph, with:\n\n\n* 'node\\_feat' (list: #nodes x #node-features): nodes\n* 'edge\\_index' (list: 2 x #edges): pairs of nodes constituting edges\n* 'edge\\_attr' (list: #edges x #edge-features): for the aforementioned edges, contains their features\n* 'y' (list: 1 x #labels): contains the number of labels available to predict (here 1, equal to zero or one)\n* 'num\\_nodes' (int): number of nodes of the graph### Data Splits\n\n\nThis data comes from the PyGeometric version of the dataset, and follows the provided data splits.\nThis information can be found back using\n\n\nAdditional Information\n----------------------### Licensing Information\n\n\nThe dataset has been released under unknown license. Please open an issue if you know what is the license of this dataset.### Contributions\n\n\nThanks to @clefourrier for adding this dataset." ]

[ "task_categories:graph-ml", "license:unknown", "region:us" ]

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[ "### Dataset Summary\n\n\nThe 'PROTEINS' dataset is a medium molecular property prediction dataset.", "### Supported Tasks and Leaderboards\n\n\n'PROTEINS' should be used for molecular property prediction (aiming to predict whether molecules are enzymes or not), a binary classification task. The score used is accuracy, using a 10-fold cross-validation.\n\n\nExternal Use\n------------", "### PyGeometric\n\n\nTo load in PyGeometric, do the following:\n\n\nDataset Structure\n-----------------", "### Data Properties", "### Data Fields\n\n\nEach row of a given file is a graph, with:\n\n\n* 'node\\_feat' (list: #nodes x #node-features): nodes\n* 'edge\\_index' (list: 2 x #edges): pairs of nodes constituting edges\n* 'edge\\_attr' (list: #edges x #edge-features): for the aforementioned edges, contains their features\n* 'y' (list: 1 x #labels): contains the number of labels available to predict (here 1, equal to zero or one)\n* 'num\\_nodes' (int): number of nodes of the graph", "### Data Splits\n\n\nThis data comes from the PyGeometric version of the dataset provided by TUDataset.\nThis information can be found back using\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nThe dataset has been released under unknown license, please open an issue if you have info about it.", "### Contributions\n\n\nThanks to @clefourrier for adding this dataset." ]

[ "TAGS\n#task_categories-graph-ml #license-unknown #region-us \n", "### Dataset Summary\n\n\nThe 'PROTEINS' dataset is a medium molecular property prediction dataset.", "### Supported Tasks and Leaderboards\n\n\n'PROTEINS' should be used for molecular property prediction (aiming to predict whether molecules are enzymes or not), a binary classification task. The score used is accuracy, using a 10-fold cross-validation.\n\n\nExternal Use\n------------", "### PyGeometric\n\n\nTo load in PyGeometric, do the following:\n\n\nDataset Structure\n-----------------", "### Data Properties", "### Data Fields\n\n\nEach row of a given file is a graph, with:\n\n\n* 'node\\_feat' (list: #nodes x #node-features): nodes\n* 'edge\\_index' (list: 2 x #edges): pairs of nodes constituting edges\n* 'edge\\_attr' (list: #edges x #edge-features): for the aforementioned edges, contains their features\n* 'y' (list: 1 x #labels): contains the number of labels available to predict (here 1, equal to zero or one)\n* 'num\\_nodes' (int): number of nodes of the graph", "### Data Splits\n\n\nThis data comes from the PyGeometric version of the dataset provided by TUDataset.\nThis information can be found back using\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nThe dataset has been released under unknown license, please open an issue if you have info about it.", "### Contributions\n\n\nThanks to @clefourrier for adding this dataset." ]

[ 23, 24, 67, 25, 4, 158, 39, 29, 17 ]

[ "passage: TAGS\n#task_categories-graph-ml #license-unknown #region-us \n### Dataset Summary\n\n\nThe 'PROTEINS' dataset is a medium molecular property prediction dataset.### Supported Tasks and Leaderboards\n\n\n'PROTEINS' should be used for molecular property prediction (aiming to predict whether molecules are enzymes or not), a binary classification task. The score used is accuracy, using a 10-fold cross-validation.\n\n\nExternal Use\n------------### PyGeometric\n\n\nTo load in PyGeometric, do the following:\n\n\nDataset Structure\n-----------------### Data Properties### Data Fields\n\n\nEach row of a given file is a graph, with:\n\n\n* 'node\\_feat' (list: #nodes x #node-features): nodes\n* 'edge\\_index' (list: 2 x #edges): pairs of nodes constituting edges\n* 'edge\\_attr' (list: #edges x #edge-features): for the aforementioned edges, contains their features\n* 'y' (list: 1 x #labels): contains the number of labels available to predict (here 1, equal to zero or one)\n* 'num\\_nodes' (int): number of nodes of the graph### Data Splits\n\n\nThis data comes from the PyGeometric version of the dataset provided by TUDataset.\nThis information can be found back using\n\n\nAdditional Information\n----------------------### Licensing Information\n\n\nThe dataset has been released under unknown license, please open an issue if you have info about it.### Contributions\n\n\nThanks to @clefourrier for adding this dataset." ]

[ "task_categories:graph-ml", "license:unknown", "region:us" ]

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[ "### Dataset Summary\n\n\nThe 'MUTAG' dataset is 'a collection of nitroaromatic compounds and the goal is to predict their mutagenicity on Salmonella typhimurium'.", "### Supported Tasks and Leaderboards\n\n\n'MUTAG' should be used for molecular property prediction (aiming to predict whether molecules have a mutagenic effect on a given bacterium or not), a binary classification task. The score used is accuracy, using a 10-fold cross-validation.\n\n\nExternal Use\n------------", "### PyGeometric\n\n\nTo load in PyGeometric, do the following:\n\n\nDataset Structure\n-----------------", "### Data Properties", "### Data Fields\n\n\nEach row of a given file is a graph, with:\n\n\n* 'node\\_feat' (list: #nodes x #node-features): nodes\n* 'edge\\_index' (list: 2 x #edges): pairs of nodes constituting edges\n* 'edge\\_attr' (list: #edges x #edge-features): for the aforementioned edges, contains their features\n* 'y' (list: 1 x #labels): contains the number of labels available to predict (here 1, equal to zero or one)\n* 'num\\_nodes' (int): number of nodes of the graph", "### Data Splits\n\n\nThis data comes from the PyGeometric version of the dataset provided by OGB, and follows the provided data splits.\nThis information can be found back using\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nThe dataset has been released under unknown license, please open an issue if you have information.", "### Contributions\n\n\nThanks to @clefourrier for adding this dataset." ]

[ "TAGS\n#task_categories-graph-ml #license-unknown #region-us \n", "### Dataset Summary\n\n\nThe 'MUTAG' dataset is 'a collection of nitroaromatic compounds and the goal is to predict their mutagenicity on Salmonella typhimurium'.", "### Supported Tasks and Leaderboards\n\n\n'MUTAG' should be used for molecular property prediction (aiming to predict whether molecules have a mutagenic effect on a given bacterium or not), a binary classification task. The score used is accuracy, using a 10-fold cross-validation.\n\n\nExternal Use\n------------", "### PyGeometric\n\n\nTo load in PyGeometric, do the following:\n\n\nDataset Structure\n-----------------", "### Data Properties", "### Data Fields\n\n\nEach row of a given file is a graph, with:\n\n\n* 'node\\_feat' (list: #nodes x #node-features): nodes\n* 'edge\\_index' (list: 2 x #edges): pairs of nodes constituting edges\n* 'edge\\_attr' (list: #edges x #edge-features): for the aforementioned edges, contains their features\n* 'y' (list: 1 x #labels): contains the number of labels available to predict (here 1, equal to zero or one)\n* 'num\\_nodes' (int): number of nodes of the graph", "### Data Splits\n\n\nThis data comes from the PyGeometric version of the dataset provided by OGB, and follows the provided data splits.\nThis information can be found back using\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nThe dataset has been released under unknown license, please open an issue if you have information.", "### Contributions\n\n\nThanks to @clefourrier for adding this dataset." ]

[ 23, 44, 74, 25, 4, 158, 47, 27, 17 ]

[ "passage: TAGS\n#task_categories-graph-ml #license-unknown #region-us \n### Dataset Summary\n\n\nThe 'MUTAG' dataset is 'a collection of nitroaromatic compounds and the goal is to predict their mutagenicity on Salmonella typhimurium'.### Supported Tasks and Leaderboards\n\n\n'MUTAG' should be used for molecular property prediction (aiming to predict whether molecules have a mutagenic effect on a given bacterium or not), a binary classification task. The score used is accuracy, using a 10-fold cross-validation.\n\n\nExternal Use\n------------### PyGeometric\n\n\nTo load in PyGeometric, do the following:\n\n\nDataset Structure\n-----------------### Data Properties### Data Fields\n\n\nEach row of a given file is a graph, with:\n\n\n* 'node\\_feat' (list: #nodes x #node-features): nodes\n* 'edge\\_index' (list: 2 x #edges): pairs of nodes constituting edges\n* 'edge\\_attr' (list: #edges x #edge-features): for the aforementioned edges, contains their features\n* 'y' (list: 1 x #labels): contains the number of labels available to predict (here 1, equal to zero or one)\n* 'num\\_nodes' (int): number of nodes of the graph### Data Splits\n\n\nThis data comes from the PyGeometric version of the dataset provided by OGB, and follows the provided data splits.\nThis information can be found back using\n\n\nAdditional Information\n----------------------### Licensing Information\n\n\nThe dataset has been released under unknown license, please open an issue if you have information.### Contributions\n\n\nThanks to @clefourrier for adding this dataset." ]

[ "task_categories:graph-ml", "license:unknown", "region:us" ]

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[ "### Dataset Summary\n\n\nThe 'IMDb-B' dataset is \"a movie collaboration dataset that consists of the ego-networks of 1,000 actors/actresses who played roles in movies in IMDB. In each graph, nodes represent actors/actress, and there is an edge between them if they appear in the same movie. These graphs are derived from the Action and Romance genres\".", "### Supported Tasks and Leaderboards\n\n\n'IMDb-B' should be used for graph classification (aiming to predict whether a movie graph is an action or romance movie), a binary classification task. The score used is accuracy, using a 10-fold cross-validation.\n\n\nExternal Use\n------------", "### PyGeometric\n\n\nTo load in PyGeometric, do the following:\n\n\nDataset Structure\n-----------------", "### Data Properties", "### Data Fields\n\n\nEach row of a given file is a graph, with:\n\n\n* 'edge\\_index' (list: 2 x #edges): pairs of nodes constituting edges\n* 'y' (list: 1 x #labels): contains the number of labels available to predict (here 1, equal to zero or one)\n* 'num\\_nodes' (int): number of nodes of the graph", "### Data Splits\n\n\nThis data comes from the PyGeometric version of the dataset.\nThis information can be found back using\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nThe dataset has been released under unknown license, please open an issue if you have this information.", "### Contributions\n\n\nThanks to @clefourrier for adding this dataset." ]

[ "TAGS\n#task_categories-graph-ml #license-unknown #region-us \n", "### Dataset Summary\n\n\nThe 'IMDb-B' dataset is \"a movie collaboration dataset that consists of the ego-networks of 1,000 actors/actresses who played roles in movies in IMDB. In each graph, nodes represent actors/actress, and there is an edge between them if they appear in the same movie. These graphs are derived from the Action and Romance genres\".", "### Supported Tasks and Leaderboards\n\n\n'IMDb-B' should be used for graph classification (aiming to predict whether a movie graph is an action or romance movie), a binary classification task. The score used is accuracy, using a 10-fold cross-validation.\n\n\nExternal Use\n------------", "### PyGeometric\n\n\nTo load in PyGeometric, do the following:\n\n\nDataset Structure\n-----------------", "### Data Properties", "### Data Fields\n\n\nEach row of a given file is a graph, with:\n\n\n* 'edge\\_index' (list: 2 x #edges): pairs of nodes constituting edges\n* 'y' (list: 1 x #labels): contains the number of labels available to predict (here 1, equal to zero or one)\n* 'num\\_nodes' (int): number of nodes of the graph", "### Data Splits\n\n\nThis data comes from the PyGeometric version of the dataset.\nThis information can be found back using\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nThe dataset has been released under unknown license, please open an issue if you have this information.", "### Contributions\n\n\nThanks to @clefourrier for adding this dataset." ]

[ 23, 96, 72, 25, 4, 97, 34, 28, 17 ]

[ "passage: TAGS\n#task_categories-graph-ml #license-unknown #region-us \n### Dataset Summary\n\n\nThe 'IMDb-B' dataset is \"a movie collaboration dataset that consists of the ego-networks of 1,000 actors/actresses who played roles in movies in IMDB. In each graph, nodes represent actors/actress, and there is an edge between them if they appear in the same movie. These graphs are derived from the Action and Romance genres\".### Supported Tasks and Leaderboards\n\n\n'IMDb-B' should be used for graph classification (aiming to predict whether a movie graph is an action or romance movie), a binary classification task. The score used is accuracy, using a 10-fold cross-validation.\n\n\nExternal Use\n------------### PyGeometric\n\n\nTo load in PyGeometric, do the following:\n\n\nDataset Structure\n-----------------### Data Properties### Data Fields\n\n\nEach row of a given file is a graph, with:\n\n\n* 'edge\\_index' (list: 2 x #edges): pairs of nodes constituting edges\n* 'y' (list: 1 x #labels): contains the number of labels available to predict (here 1, equal to zero or one)\n* 'num\\_nodes' (int): number of nodes of the graph### Data Splits\n\n\nThis data comes from the PyGeometric version of the dataset.\nThis information can be found back using\n\n\nAdditional Information\n----------------------### Licensing Information\n\n\nThe dataset has been released under unknown license, please open an issue if you have this information.### Contributions\n\n\nThanks to @clefourrier for adding this dataset." ]

[ "task_categories:other", "size_categories:100M<n<1B", "region:us" ]

[]

[]

[ "# BigScience BLOOM Evaluation Results\n\n\nThis repository contains evaluation results & original predictions of BLOOM & friends.", "## Usage\n\nYou can load numeric results via:\n\n\nIf it takes too long, it may be faster to clone the repository and load the data from disk:\n\n\nFor example generations (.jsonl files), you need to manually browse the repository.", "## Structure\n\nFor 'bigsciencelmevalharness', 'lmevalharness' & 'codeeval' evaluation_frameworks the structure is:\n'model_name > evaluation_framework > checkpoint_type > dataset_name > data'", "## Evaluation Procedure\n\n- 'bigsciencelmevalharness' files were created using the below:\n - URL\n - URL\n- 'lmevalharness' files were created using the below:\n - URL\n - URL\n- 'codeeval' files were created using the HumanEval code dataset with the below:\n - URL" ]

[ "TAGS\n#task_categories-other #size_categories-100M<n<1B #region-us \n", "# BigScience BLOOM Evaluation Results\n\n\nThis repository contains evaluation results & original predictions of BLOOM & friends.", "## Usage\n\nYou can load numeric results via:\n\n\nIf it takes too long, it may be faster to clone the repository and load the data from disk:\n\n\nFor example generations (.jsonl files), you need to manually browse the repository.", "## Structure\n\nFor 'bigsciencelmevalharness', 'lmevalharness' & 'codeeval' evaluation_frameworks the structure is:\n'model_name > evaluation_framework > checkpoint_type > dataset_name > data'", "## Evaluation Procedure\n\n- 'bigsciencelmevalharness' files were created using the below:\n - URL\n - URL\n- 'lmevalharness' files were created using the below:\n - URL\n - URL\n- 'codeeval' files were created using the HumanEval code dataset with the below:\n - URL" ]

[ 26, 26, 60, 59, 68 ]

[ "passage: TAGS\n#task_categories-other #size_categories-100M<n<1B #region-us \n# BigScience BLOOM Evaluation Results\n\n\nThis repository contains evaluation results & original predictions of BLOOM & friends.## Usage\n\nYou can load numeric results via:\n\n\nIf it takes too long, it may be faster to clone the repository and load the data from disk:\n\n\nFor example generations (.jsonl files), you need to manually browse the repository.## Structure\n\nFor 'bigsciencelmevalharness', 'lmevalharness' & 'codeeval' evaluation_frameworks the structure is:\n'model_name > evaluation_framework > checkpoint_type > dataset_name > data'## Evaluation Procedure\n\n- 'bigsciencelmevalharness' files were created using the below:\n - URL\n - URL\n- 'lmevalharness' files were created using the below:\n - URL\n - URL\n- 'codeeval' files were created using the HumanEval code dataset with the below:\n - URL" ]

[ "task_categories:image-classification", "task_categories:unconditional-image-generation", "size_categories:1K<n<10K", "license:other", "region:us" ]

[]

[]

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[ "TAGS\n#task_categories-image-classification #task_categories-unconditional-image-generation #size_categories-1K<n<10K #license-other #region-us \n" ]

[ 49 ]

[ "passage: TAGS\n#task_categories-image-classification #task_categories-unconditional-image-generation #size_categories-1K<n<10K #license-other #region-us \n" ]

[ "region:us" ]

[]

[]

[]

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

[ 6 ]

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

[ "language:unk", "autotrain", "summarization", "region:us" ]

[]

[ "unk" ]

[ "# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1228646724\n- CO2 Emissions (in grams): 1368.8941", "## Validation Metrics\n\n- Loss: 2.319\n- Rouge1: 43.703\n- Rouge2: 16.106\n- RougeL: 23.715\n- RougeLsum: 38.984\n- Gen Len: 141.091", "## Usage\n\nYou can use cURL to access this model:" ]

[ "TAGS\n#language-Enawené-Nawé #autotrain #summarization #region-us \n", "# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1228646724\n- CO2 Emissions (in grams): 1368.8941", "## Validation Metrics\n\n- Loss: 2.319\n- Rouge1: 43.703\n- Rouge2: 16.106\n- RougeL: 23.715\n- RougeLsum: 38.984\n- Gen Len: 141.091", "## Usage\n\nYou can use cURL to access this model:" ]

[ 23, 38, 47, 13 ]

[ "passage: TAGS\n#language-Enawené-Nawé #autotrain #summarization #region-us \n# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1228646724\n- CO2 Emissions (in grams): 1368.8941## Validation Metrics\n\n- Loss: 2.319\n- Rouge1: 43.703\n- Rouge2: 16.106\n- RougeL: 23.715\n- RougeLsum: 38.984\n- Gen Len: 141.091## Usage\n\nYou can use cURL to access 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: yhavinga/mt5-base-cnn-nl\n* Dataset: ml6team/cnn_dailymail_nl\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @yhavinga 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: yhavinga/mt5-base-cnn-nl\n* Dataset: ml6team/cnn_dailymail_nl\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @yhavinga for evaluating this model." ]

[ 13, 97, 16 ]

[ "passage: 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: yhavinga/mt5-base-cnn-nl\n* Dataset: ml6team/cnn_dailymail_nl\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @yhavinga 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: yhavinga/mt5-base-mixednews-nl\n* Dataset: ml6team/cnn_dailymail_nl\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @yhavinga 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: yhavinga/mt5-base-mixednews-nl\n* Dataset: ml6team/cnn_dailymail_nl\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @yhavinga for evaluating this model." ]

[ 13, 98, 16 ]

[ "passage: 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: yhavinga/mt5-base-mixednews-nl\n* Dataset: ml6team/cnn_dailymail_nl\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @yhavinga 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: Question Answering\n* Model: deepset/roberta-large-squad2\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @ceyda for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/roberta-large-squad2\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @ceyda for evaluating this model." ]

[ 13, 92, 15 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/roberta-large-squad2\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @ceyda 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: Question Answering\n* Model: deepset/xlm-roberta-large-squad2\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @ceyda for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/xlm-roberta-large-squad2\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @ceyda for evaluating this model." ]

[ 13, 95, 15 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/xlm-roberta-large-squad2\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @ceyda 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: Question Answering\n* Model: deepset/roberta-base-squad2-distilled\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @ceyda for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/roberta-base-squad2-distilled\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @ceyda for evaluating this model." ]

[ 13, 94, 15 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/roberta-base-squad2-distilled\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @ceyda 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: Question Answering\n* Model: deepset/xlm-roberta-base-squad2-distilled\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @ceyda for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/xlm-roberta-base-squad2-distilled\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @ceyda for evaluating this model." ]

[ 13, 97, 15 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/xlm-roberta-base-squad2-distilled\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @ceyda 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: Question Answering\n* Model: deepset/tinybert-6l-768d-squad2\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @ceyda for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/tinybert-6l-768d-squad2\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @ceyda for evaluating this model." ]

[ 13, 94, 15 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/tinybert-6l-768d-squad2\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @ceyda 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: Question Answering\n* Model: deepset/bert-base-uncased-squad2\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @ceyda for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/bert-base-uncased-squad2\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @ceyda for evaluating this model." ]

[ 13, 94, 15 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/bert-base-uncased-squad2\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @ceyda for evaluating this model." ]

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

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Binary Text Classification\n* Model: Rhuax/MiniLMv2-L12-H384-distilled-finetuned-spam-detection\n* Dataset: sms_spam\n* Config: plain_text\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Al-Ip for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Binary Text Classification\n* Model: Rhuax/MiniLMv2-L12-H384-distilled-finetuned-spam-detection\n* Dataset: sms_spam\n* Config: plain_text\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Al-Ip for evaluating this model." ]

[ 13, 108, 17 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Binary Text Classification\n* Model: Rhuax/MiniLMv2-L12-H384-distilled-finetuned-spam-detection\n* Dataset: sms_spam\n* Config: plain_text\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @Al-Ip 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: Blaise-g/led_pubmed_sumpubmed_1\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Blaise-g 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: Blaise-g/led_pubmed_sumpubmed_1\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Blaise-g for evaluating this model." ]

[ 13, 105, 18 ]

[ "passage: 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: Blaise-g/led_pubmed_sumpubmed_1\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @Blaise-g 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: Blaise-g/led_pubmed_sumpubmed_2\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Blaise-g 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: Blaise-g/led_pubmed_sumpubmed_2\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Blaise-g for evaluating this model." ]

[ 13, 106, 18 ]

[ "passage: 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: Blaise-g/led_pubmed_sumpubmed_2\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @Blaise-g 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: Blaise-g/long_t5_global_large_pubmed_explanatory\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Blaise-g 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: Blaise-g/long_t5_global_large_pubmed_explanatory\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Blaise-g for evaluating this model." ]

[ 13, 112, 18 ]

[ "passage: 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: Blaise-g/long_t5_global_large_pubmed_explanatory\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @Blaise-g 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: Blaise-g/led_large_baseline_pubmed\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Blaise-g 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: Blaise-g/led_large_baseline_pubmed\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Blaise-g for evaluating this model." ]

[ 13, 106, 18 ]

[ "passage: 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: Blaise-g/led_large_baseline_pubmed\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @Blaise-g 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: Blaise-g/long_t5_global_large_baseline_pubmed\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Blaise-g 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: Blaise-g/long_t5_global_large_baseline_pubmed\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Blaise-g for evaluating this model." ]

[ 13, 111, 18 ]

[ "passage: 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: Blaise-g/long_t5_global_large_baseline_pubmed\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @Blaise-g 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: Blaise-g/long_t5_global_large_baseline_pubmed\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Blaise-g 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: Blaise-g/long_t5_global_large_baseline_pubmed\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Blaise-g for evaluating this model." ]

[ 13, 111, 18 ]

[ "passage: 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: Blaise-g/long_t5_global_large_baseline_pubmed\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @Blaise-g 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: Blaise-g/led_large_baseline_pubmed\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Blaise-g 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: Blaise-g/led_large_baseline_pubmed\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Blaise-g for evaluating this model." ]

[ 13, 106, 18 ]

[ "passage: 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: Blaise-g/led_large_baseline_pubmed\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @Blaise-g 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: Question Answering\n* Model: 21iridescent/RoBERTa-base-finetuned-squad2-lwt\n* Dataset: cuad\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @halima for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: 21iridescent/RoBERTa-base-finetuned-squad2-lwt\n* Dataset: cuad\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @halima for evaluating this model." ]

[ 13, 96, 15 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: 21iridescent/RoBERTa-base-finetuned-squad2-lwt\n* Dataset: cuad\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @halima 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: Blaise-g/led_pubmed_sumpubmed_3\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Blaise-g 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: Blaise-g/led_pubmed_sumpubmed_3\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Blaise-g for evaluating this model." ]

[ 13, 106, 18 ]

[ "passage: 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: Blaise-g/led_pubmed_sumpubmed_3\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @Blaise-g 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: Blaise-g/led_pubmed_sumpubmed_4\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Blaise-g 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: Blaise-g/led_pubmed_sumpubmed_4\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Blaise-g for evaluating this model." ]

[ 13, 106, 18 ]

[ "passage: 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: Blaise-g/led_pubmed_sumpubmed_4\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @Blaise-g 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: Blaise-g/led_pubmed_sumpubmed_5\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Blaise-g 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: Blaise-g/led_pubmed_sumpubmed_5\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Blaise-g for evaluating this model." ]

[ 13, 106, 18 ]

[ "passage: 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: Blaise-g/led_pubmed_sumpubmed_5\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @Blaise-g 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: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP10\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP10\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ 13, 112, 16 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP10\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP10\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 @pszemraj for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP10\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 @pszemraj for evaluating this model." ]

[ 13, 102, 16 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP10\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 @pszemraj for evaluating this model." ]

[ "task_categories:text-classification", "task_ids:multi-label-classification", "size_categories:10K<n<100K", "source_datasets:BioASQ Task A", "language:en", "license:afl-3.0", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-text-classification #task_ids-multi-label-classification #size_categories-10K<n<100K #source_datasets-BioASQ Task A #language-English #license-afl-3.0 #region-us \n" ]

[ 66 ]

[ "passage: TAGS\n#task_categories-text-classification #task_ids-multi-label-classification #size_categories-10K<n<100K #source_datasets-BioASQ Task A #language-English #license-afl-3.0 #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: Blaise-g/led-large-sumpubmed\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Blaise-g 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: Blaise-g/led-large-sumpubmed\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Blaise-g for evaluating this model." ]

[ 13, 104, 18 ]

[ "passage: 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: Blaise-g/led-large-sumpubmed\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @Blaise-g for evaluating this model." ]

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

[]

[ "ko" ]

[ "### Lisence\n\n\nCC-BY-4.0\n\n\nData Structure\n--------------", "### Data Instance", "### Data Size\n\n\ndownload: 1.56 GiB \n\ngenerated: 2.37 GiB \n\ntotal: 3.93 GiB", "### Data Field\n\n\n* 'SAMPLE\\_ID': 'int'\n* 'URL': 'string'\n* 'TEXT': 'string'\n* 'HEIGHT': 'int'\n* 'WIDTH': 'int'\n* 'LICENSE': 'string'\n* 'LANGUAGE': 'string'\n* 'NSFW': 'string'\n* 'similarity': 'float'", "### Data Splits\n\n\n\nNote\n----", "### Height, Width\n\n\n이미지의 가로가 'HEIGHT'로, 세로가 'WIDTH'로 되어있는 것 같습니다.\n\n\n!image", "### csv file, pandas\n\n\n<URL\n\n\n778 MB", "### Code used to generate\n\n\n실행에 약 8시간이 소요되었습니다. 이후에 'HEIGHT'나 'WIDTH'가 None인 데이터를 제거하고 업로드하였습니다.", "### img2dataset\n\n\nimg2dataset을 사용하여 URL로된 이미지들을 데이터셋 형태로 만들 수 있습니다." ]

[ "TAGS\n#task_categories-feature-extraction #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10M<n<100M #language-Korean #license-cc-by-4.0 #region-us \n", "### Lisence\n\n\nCC-BY-4.0\n\n\nData Structure\n--------------", "### Data Instance", "### Data Size\n\n\ndownload: 1.56 GiB \n\ngenerated: 2.37 GiB \n\ntotal: 3.93 GiB", "### Data Field\n\n\n* 'SAMPLE\\_ID': 'int'\n* 'URL': 'string'\n* 'TEXT': 'string'\n* 'HEIGHT': 'int'\n* 'WIDTH': 'int'\n* 'LICENSE': 'string'\n* 'LANGUAGE': 'string'\n* 'NSFW': 'string'\n* 'similarity': 'float'", "### Data Splits\n\n\n\nNote\n----", "### Height, Width\n\n\n이미지의 가로가 'HEIGHT'로, 세로가 'WIDTH'로 되어있는 것 같습니다.\n\n\n!image", "### csv file, pandas\n\n\n<URL\n\n\n778 MB", "### Code used to generate\n\n\n실행에 약 8시간이 소요되었습니다. 이후에 'HEIGHT'나 'WIDTH'가 None인 데이터를 제거하고 업로드하였습니다.", "### img2dataset\n\n\nimg2dataset을 사용하여 URL로된 이미지들을 데이터셋 형태로 만들 수 있습니다." ]

[ 76, 15, 5, 23, 92, 7, 35, 13, 39, 27 ]

[ "passage: TAGS\n#task_categories-feature-extraction #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10M<n<100M #language-Korean #license-cc-by-4.0 #region-us \n### Lisence\n\n\nCC-BY-4.0\n\n\nData Structure\n--------------### Data Instance### Data Size\n\n\ndownload: 1.56 GiB \n\ngenerated: 2.37 GiB \n\ntotal: 3.93 GiB### Data Field\n\n\n* 'SAMPLE\\_ID': 'int'\n* 'URL': 'string'\n* 'TEXT': 'string'\n* 'HEIGHT': 'int'\n* 'WIDTH': 'int'\n* 'LICENSE': 'string'\n* 'LANGUAGE': 'string'\n* 'NSFW': 'string'\n* 'similarity': 'float'### Data Splits\n\n\n\nNote\n----### Height, Width\n\n\n이미지의 가로가 'HEIGHT'로, 세로가 'WIDTH'로 되어있는 것 같습니다.\n\n\n!image### csv file, pandas\n\n\n<URL\n\n\n778 MB### Code used to generate\n\n\n실행에 약 8시간이 소요되었습니다. 이후에 'HEIGHT'나 'WIDTH'가 None인 데이터를 제거하고 업로드하였습니다.### img2dataset\n\n\nimg2dataset을 사용하여 URL로된 이미지들을 데이터셋 형태로 만들 수 있습니다." ]

[ "region:us" ]

[]

[]

[]

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

[ 6 ]

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

[ "region:us" ]

[]

[]

[]

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

[ 6 ]

[ "passage: 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: Binary Text Classification\n* Model: lvwerra/distilbert-imdb\n* Dataset: imdb\n* Config: plain_text\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lvwerra for evaluating this model." ]

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

[ 13, 90, 16 ]

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

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

[]

[]

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

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

[ 13, 90, 16 ]

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

[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:other", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:ru", "license:unknown", "news", "articles", "newspapers", "panorama", "region:us" ]

[]

[ "ru" ]

[ "### Dataset Summary\nDataset of satirical news from \"Panorama\", Russian \"The Onion\".", "### Dataset Format\nDataset is in JSONLines format, where \"title\" is the article title, and \"body\" are contents of the article." ]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #annotations_creators-no-annotation #language_creators-other #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Russian #license-unknown #news #articles #newspapers #panorama #region-us \n", "### Dataset Summary\nDataset of satirical news from \"Panorama\", Russian \"The Onion\".", "### Dataset Format\nDataset is in JSONLines format, where \"title\" is the article title, and \"body\" are contents of the article." ]

[ 100, 25, 35 ]

[ "passage: TAGS\n#task_categories-text-generation #task_ids-language-modeling #annotations_creators-no-annotation #language_creators-other #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Russian #license-unknown #news #articles #newspapers #panorama #region-us \n### Dataset Summary\nDataset of satirical news from \"Panorama\", Russian \"The Onion\".### Dataset Format\nDataset is in JSONLines format, where \"title\" is the article title, and \"body\" are contents of the article." ]

[ "region:us" ]

[]

[]

[]

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

[ 6 ]

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

[ "region:us" ]

[]

[]

[]

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

[ 6 ]

[ "passage: 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: Question Answering\n* Model: deepset/deberta-v3-large-squad2\n* Dataset: squad\n* Config: plain_text\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sjrlee for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/deberta-v3-large-squad2\n* Dataset: squad\n* Config: plain_text\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sjrlee for evaluating this model." ]

[ 13, 93, 17 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/deberta-v3-large-squad2\n* Dataset: squad\n* Config: plain_text\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @sjrlee for evaluating this model." ]

[ "region:us" ]

[]

[]

[]

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

[ 6 ]

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

[ "task_categories:summarization", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:odc-by", "arxiv:2206.10883", "region:us" ]

[ "2206.10883" ]

[ "en" ]

[ "### Talk @ NeurIPS 2022\n\n\n![Watch the video](URL", "### Dataset Summary\n\n\nThe Multi-LexSum dataset is a collection of 9,280 such legal case summaries. Multi-LexSum is distinct from other datasets in its multiple target summaries, each at a different granularity (ranging from one-sentence “extreme” summaries to multi-paragraph narrations of over five hundred words). It presents a challenging multi-document summarization task given the long length of the source documents, often exceeding two hundred pages per case. Unlike other summarization datasets that are (semi-)automatically curated, Multi-LexSum consists of expert-authored summaries: the experts—lawyers and law students—are trained to follow carefully created guidelines, and their work is reviewed by an additional expert to ensure quality.", "### Languages\n\n\nEnglish\n\n\nDataset\n-------", "### Data Fields\n\n\nThe dataset contains a list of instances (cases); each instance contains the following data:\n\n\n\nPlease check the exemplar usage below for loading the data:", "### Data Splits\n\n\n\nDataset Sheet (Datasheet)\n-------------------------\n\n\nPlease check our dataset sheet for details regarding dataset creation, source data, annotation, and considerations for the usage.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe dataset is created by the collaboration between Civil Rights Litigation Clearinghouse (CRLC, from University of Michigan) and Allen Institute for AI. Multi-LexSum builds on the dataset used and posted by the Clearinghouse to inform the public about civil rights litigation.", "### Licensing Information\n\n\nThe Multi-LexSum dataset is distributed under the Open Data Commons Attribution License (ODC-By).\nThe case summaries and metadata are licensed under the Creative Commons Attribution License (CC BY-NC), and the source documents are already in the public domain.\nCommercial users who desire a license for summaries and metadata can contact info@URL, which will allow free use but limit summary re-posting.\nThe corresponding code for downloading and loading the dataset is licensed under the Apache License 2.0.\n\n\nRelease History\n---------------" ]

[ "TAGS\n#task_categories-summarization #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #size_categories-10K<n<100K #source_datasets-original #language-English #license-odc-by #arxiv-2206.10883 #region-us \n", "### Talk @ NeurIPS 2022\n\n\n![Watch the video](URL", "### Dataset Summary\n\n\nThe Multi-LexSum dataset is a collection of 9,280 such legal case summaries. Multi-LexSum is distinct from other datasets in its multiple target summaries, each at a different granularity (ranging from one-sentence “extreme” summaries to multi-paragraph narrations of over five hundred words). It presents a challenging multi-document summarization task given the long length of the source documents, often exceeding two hundred pages per case. Unlike other summarization datasets that are (semi-)automatically curated, Multi-LexSum consists of expert-authored summaries: the experts—lawyers and law students—are trained to follow carefully created guidelines, and their work is reviewed by an additional expert to ensure quality.", "### Languages\n\n\nEnglish\n\n\nDataset\n-------", "### Data Fields\n\n\nThe dataset contains a list of instances (cases); each instance contains the following data:\n\n\n\nPlease check the exemplar usage below for loading the data:", "### Data Splits\n\n\n\nDataset Sheet (Datasheet)\n-------------------------\n\n\nPlease check our dataset sheet for details regarding dataset creation, source data, annotation, and considerations for the usage.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe dataset is created by the collaboration between Civil Rights Litigation Clearinghouse (CRLC, from University of Michigan) and Allen Institute for AI. Multi-LexSum builds on the dataset used and posted by the Clearinghouse to inform the public about civil rights litigation.", "### Licensing Information\n\n\nThe Multi-LexSum dataset is distributed under the Open Data Commons Attribution License (ODC-By).\nThe case summaries and metadata are licensed under the Creative Commons Attribution License (CC BY-NC), and the source documents are already in the public domain.\nCommercial users who desire a license for summaries and metadata can contact info@URL, which will allow free use but limit summary re-posting.\nThe corresponding code for downloading and loading the dataset is licensed under the Apache License 2.0.\n\n\nRelease History\n---------------" ]

[ 97, 17, 182, 9, 39, 50, 69, 125 ]

[ "passage: TAGS\n#task_categories-summarization #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #size_categories-10K<n<100K #source_datasets-original #language-English #license-odc-by #arxiv-2206.10883 #region-us \n### Talk @ NeurIPS 2022\n\n\n. It presents a challenging multi-document summarization task given the long length of the source documents, often exceeding two hundred pages per case. Unlike other summarization datasets that are (semi-)automatically curated, Multi-LexSum consists of expert-authored summaries: the experts—lawyers and law students—are trained to follow carefully created guidelines, and their work is reviewed by an additional expert to ensure quality.### Languages\n\n\nEnglish\n\n\nDataset\n-------### Data Fields\n\n\nThe dataset contains a list of instances (cases); each instance contains the following data:\n\n\n\nPlease check the exemplar usage below for loading the data:### Data Splits\n\n\n\nDataset Sheet (Datasheet)\n-------------------------\n\n\nPlease check our dataset sheet for details regarding dataset creation, source data, annotation, and considerations for the usage.\n\n\nAdditional Information\n----------------------### Dataset Curators\n\n\nThe dataset is created by the collaboration between Civil Rights Litigation Clearinghouse (CRLC, from University of Michigan) and Allen Institute for AI. Multi-LexSum builds on the dataset used and posted by the Clearinghouse to inform the public about civil rights litigation." ]

[ "task_categories:question-answering", "task_ids:closed-domain-qa", "task_ids:extractive-qa", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:cuad", "language:en", "license:cc-by-4.0", "arxiv:2103.06268", "region:us" ]

[ "2103.06268" ]

[ "en" ]

[ "### Dataset Summary\n\n\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510 commercial legal contracts that have been manually labeled to identify 41 categories of important clauses that lawyers look for when reviewing contracts in connection with corporate transactions. This dataset is a filtered version of CUAD. It excludes legal contracts with an Agreement date prior to 2002 and contracts which are not Business to Business. From the 41 categories we filtered them down to 12 which we considered the most crucial.\n\n\nWe wanted a small dataset to quickly fine-tune different models without sacrificing the categories which we deemed as important. The need to remove most questions was due to them not having an answer which is problematic since it can scue the resulting metrics such as the F1 score and the AUPR curve.\n\n\nCUAD is curated and maintained by The Atticus Project, Inc. to support NLP research and development in legal contract review. Analysis of CUAD can be found at URL Code for replicating the results and the trained model can be found at URL", "### Supported Tasks and Leaderboards", "### Languages\n\n\nThe dataset contains samples in English only.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'train' looks as follows.", "### Data Fields\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\nThis dataset is split into train/test set. Number of samples in each set is given below:\n\n\nTrain: CUAD, Test: 5442\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nA highly valuable specialized task without a public large-scale dataset is contract review, which costs humans substantial time, money, and attention. Many law firms spend approximately 50% of their time reviewing contracts (CEB, 2017). Due to the specialized training necessary to understand and interpret contracts, the billing rates for lawyers at large law firms are typically around $500-$900 per hour in the US. As a result, many transactions cost companies hundreds of thousands of dollars just so that lawyers can verify that there are no problematic obligations or requirements included in the contracts. Contract review can be a source of drudgery and, in comparison to other legal tasks, is widely considered to be especially boring.\n\n\nContract review costs also affect consumers. Since contract review costs are so prohibitive, contract review is not often performed outside corporate transactions. Small companies and individuals consequently often sign contracts without even reading them, which can result in predatory behavior that harms consumers. Automating contract review by openly releasing high-quality data and fine-tuned models can increase access to legal support for small businesses and individuals, so that legal support is not exclusively available to wealthy companies.\n\n\nTo reduce the disparate societal costs of contract review, and to study how well NLP models generalize to specialized domains, the authors introduced a new large-scale dataset for contract review. As part of The Atticus Project, a non-profit organization of legal experts, CUAD is introduced, the Contract Understanding Atticus Dataset. This dataset was created with a year-long effort pushed forward by dozens of law student annotators, lawyers, and machine learning researchers. The dataset includes more than 500 contracts and more than 13,000 expert annotations that span 41 label categories. For each of 41 different labels, models must learn to highlight the portions of a contract most salient to that label. This makes the task a matter of finding needles in a haystack.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe CUAD includes commercial contracts selected from 25 different types of contracts based on the contract names as shown below. Within each type, the creators randomly selected contracts based on the names of the filing companies across the alphabet.\n\n\nType of Contracts: # of Docs\n\n\n\n```\nAffiliate Agreement:\t\t8\nAgency Agreement:\t\t 8 \nCollaboration/Cooperation Agreement: 26\nCo-Branding Agreement:\t\t6\nConsulting Agreement:\t\t11\nDevelopment Agreement:\t\t28\nDistributor Agreement:\t\t23\nEndorsement Agreement:\t\t10\nFranchise Agreement:\t\t14\nHosting Agreement:\t\t12\nIP Agreement:\t\t\t16\nJoint Venture Agreemen:\t\t22\nLicense Agreement:\t\t32\nMaintenance Agreement:\t\t24\nManufacturing Agreement:\t6\nMarketing Agreement:\t\t16\nNon-Compete/No-Solicit/Non-Disparagement Agreement: 3\nOutsourcing Agreement:\t\t12\nPromotion Agreement:\t\t9\nReseller Agreement:\t\t12\nService Agreement:\t\t24\nSponsorship Agreement:\t17\nSupply Agreement:\t\t13\nStrategic Alliance Agreement:\t32\nTransportation Agreement:\t1\nTOTAL:\t\t\t\t385\n\n```\n\nCategories\n\n\n\n```\nDocument Name\nParties\nAgreement Date\nEffective Date\nExpiration Date\nRenewal Term\nNotice Period To Terminate Renewal\nGoverning Law\nNon-Compete\nExclusivity\nChange Of Control\nAnti-Assignment\n\n```", "#### Who are the source language producers?\n\n\nThe contracts were sourced from EDGAR, the Electronic Data Gathering, Analysis, and Retrieval system used at the U.S. Securities and Exchange Commission (SEC). Publicly traded companies in the United States are required to file certain contracts under the SEC rules. Access to these contracts is available to the public for free at URL Please read the Datasheet at URL for information on the intended use and limitations of the CUAD.", "### Annotations", "#### Annotation process\n\n\nThe labeling process included multiple steps to ensure accuracy:\n\n\n1. Law Student Training: law students attended training sessions on each of the categories that included a summary, video instructions by experienced attorneys, multiple quizzes and workshops. Students were then required to label sample contracts in eBrevia, an online contract review tool. The initial training took approximately 70-100 hours.\n2. Law Student Label: law students conducted manual contract review and labeling in eBrevia.\n3. Key Word Search: law students conducted keyword search in eBrevia to capture additional categories that have been missed during the “Student Label” step.\n4. Category-by-Category Report Review: law students exported the labeled clauses into reports, review each clause category-by-category and highlight clauses that they believe are mislabeled.\n5. Attorney Review: experienced attorneys reviewed the category-by-category report with students comments, provided comments and addressed student questions. When applicable, attorneys discussed such results with the students and reached consensus. Students made changes in eBrevia accordingly.\n6. eBrevia Extras Review. Attorneys and students used eBrevia to generate a list of “extras”, which are clauses that eBrevia AI tool identified as responsive to a category but not labeled by human annotators. Attorneys and students reviewed all of the “extras” and added the correct ones. The process is repeated until all or substantially all of the “extras” are incorrect labels.\n7. Final Report: The final report was exported into a CSV file. Volunteers manually added the “Yes/No” answer column to categories that do not contain an answer.", "#### Who are the annotators?\n\n\nAnswered in above section.", "### Personal and Sensitive Information\n\n\nSome clauses in the files are redacted because the party submitting these contracts redacted them to protect confidentiality. Such redaction may show up as asterisks (\\*\\*\\*) or underscores (\\_\\_\\_) or blank spaces. The dataset and the answers reflect such redactions. For example, the answer for “January \\_\\_ 2020” would be “1/[]/2020”).\n\n\nFor any categories that require an answer of “Yes/No”, annotators include full sentences as text context in a contract. To maintain consistency and minimize inter-annotator disagreement, annotators select text for the full sentence, under the instruction of “from period to period”.\n\n\nFor the other categories, annotators selected segments of the text in the contract that are responsive to each such category. One category in a contract may include multiple labels. For example, “Parties” may include 4-10 separate text strings that are not continuous in a contract. The answer is presented in the unified format separated by semicolons of “Party A Inc. (“Party A”); Party B Corp. (“Party B”)”.\n\n\nSome sentences in the files include confidential legends that are not part of the contracts. An example of such confidential legend is as follows:\n\n\nTHIS EXHIBIT HAS BEEN REDACTED AND IS THE SUBJECT OF A CONFIDENTIAL TREATMENT REQUEST. REDACTED MATERIAL IS MARKED WITH [\\* \\* \\*] AND HAS BEEN FILED SEPARATELY WITH THE SECURITIES AND EXCHANGE COMMISSION.\n\n\nSome sentences in the files contain irrelevant information such as footers or page numbers. Some sentences may not be relevant to the corresponding category. Some sentences may correspond to a different category. Because many legal clauses are very long and contain various sub-parts, sometimes only a sub-part of a sentence is responsive to a category.\n\n\nTo address the foregoing limitations, annotators manually deleted the portion that is not responsive, replacing it with the symbol \"\" to indicate that the two text segments do not appear immediately next to each other in the contracts. For example, if a “Termination for Convenience” clause starts with “Each Party may terminate this Agreement if” followed by three subparts “(a), (b) and (c)”, but only subpart (c) is responsive to this category, the authors manually deleted subparts (a) and (b) and replaced them with the symbol \"”. Another example is for “Effective Date”, the contract includes a sentence “This Agreement is effective as of the date written above” that appears after the date “January 1, 2010”. The annotation is as follows: “January 1, 2010 This Agreement is effective as of the date written above.”\n\n\nBecause the contracts were converted from PDF into TXT files, the converted TXT files may not stay true to the format of the original PDF files. For example, some contracts contain inconsistent spacing between words, sentences and paragraphs. Table format is not maintained in the TXT files.\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\nAttorney Advisors\nWei Chen, John Brockland, Kevin Chen, Jacky Fink, Spencer P. Goodson, Justin Haan, Alex Haskell, Kari Krusmark, Jenny Lin, Jonas Marson, Benjamin Petersen, Alexander Kwonji Rosenberg, William R. Sawyers, Brittany Schmeltz, Max Scott, Zhu Zhu\n\n\nLaw Student Leaders\nJohn Batoha, Daisy Beckner, Lovina Consunji, Gina Diaz, Chris Gronseth, Calvin Hannagan, Joseph Kroon, Sheetal Sharma Saran\n\n\nLaw Student Contributors\nScott Aronin, Bryan Burgoon, Jigar Desai, Imani Haynes, Jeongsoo Kim, Margaret Lynch, Allison Melville, Felix Mendez-Burgos, Nicole Mirkazemi, David Myers, Emily Rissberger, Behrang Seraj, Sarahginy Valcin\n\n\nTechnical Advisors & Contributors\nDan Hendrycks, Collin Burns, Spencer Ball, Anya Chen", "### Licensing Information\n\n\nCUAD is licensed under the Creative Commons Attribution 4.0 (CC BY 4.0) license and free to the public for commercial and non-commercial use.\n\n\nThe creators make no representations or warranties regarding the license status of the underlying contracts, which are publicly available and downloadable from EDGAR.\nPrivacy Policy & Disclaimers\n\n\nThe categories or the contracts included in the dataset are not comprehensive or representative. The authors encourage the public to help improve them by sending them your comments and suggestions to info@URL. Comments and suggestions will be reviewed by The Atticus Project at its discretion and will be included in future versions of Atticus categories once approved.\n\n\nThe use of CUAD is subject to their privacy policy URL and disclaimer URL", "### Contributions\n\n\nThanks to @bhavitvyamalik for adding this dataset." ]

[ "TAGS\n#task_categories-question-answering #task_ids-closed-domain-qa #task_ids-extractive-qa #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-cuad #language-English #license-cc-by-4.0 #arxiv-2103.06268 #region-us \n", "### Dataset Summary\n\n\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510 commercial legal contracts that have been manually labeled to identify 41 categories of important clauses that lawyers look for when reviewing contracts in connection with corporate transactions. This dataset is a filtered version of CUAD. It excludes legal contracts with an Agreement date prior to 2002 and contracts which are not Business to Business. From the 41 categories we filtered them down to 12 which we considered the most crucial.\n\n\nWe wanted a small dataset to quickly fine-tune different models without sacrificing the categories which we deemed as important. The need to remove most questions was due to them not having an answer which is problematic since it can scue the resulting metrics such as the F1 score and the AUPR curve.\n\n\nCUAD is curated and maintained by The Atticus Project, Inc. to support NLP research and development in legal contract review. Analysis of CUAD can be found at URL Code for replicating the results and the trained model can be found at URL", "### Supported Tasks and Leaderboards", "### Languages\n\n\nThe dataset contains samples in English only.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'train' looks as follows.", "### Data Fields\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\nThis dataset is split into train/test set. Number of samples in each set is given below:\n\n\nTrain: CUAD, Test: 5442\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nA highly valuable specialized task without a public large-scale dataset is contract review, which costs humans substantial time, money, and attention. Many law firms spend approximately 50% of their time reviewing contracts (CEB, 2017). Due to the specialized training necessary to understand and interpret contracts, the billing rates for lawyers at large law firms are typically around $500-$900 per hour in the US. As a result, many transactions cost companies hundreds of thousands of dollars just so that lawyers can verify that there are no problematic obligations or requirements included in the contracts. Contract review can be a source of drudgery and, in comparison to other legal tasks, is widely considered to be especially boring.\n\n\nContract review costs also affect consumers. Since contract review costs are so prohibitive, contract review is not often performed outside corporate transactions. Small companies and individuals consequently often sign contracts without even reading them, which can result in predatory behavior that harms consumers. Automating contract review by openly releasing high-quality data and fine-tuned models can increase access to legal support for small businesses and individuals, so that legal support is not exclusively available to wealthy companies.\n\n\nTo reduce the disparate societal costs of contract review, and to study how well NLP models generalize to specialized domains, the authors introduced a new large-scale dataset for contract review. As part of The Atticus Project, a non-profit organization of legal experts, CUAD is introduced, the Contract Understanding Atticus Dataset. This dataset was created with a year-long effort pushed forward by dozens of law student annotators, lawyers, and machine learning researchers. The dataset includes more than 500 contracts and more than 13,000 expert annotations that span 41 label categories. For each of 41 different labels, models must learn to highlight the portions of a contract most salient to that label. This makes the task a matter of finding needles in a haystack.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe CUAD includes commercial contracts selected from 25 different types of contracts based on the contract names as shown below. Within each type, the creators randomly selected contracts based on the names of the filing companies across the alphabet.\n\n\nType of Contracts: # of Docs\n\n\n\n```\nAffiliate Agreement:\t\t8\nAgency Agreement:\t\t 8 \nCollaboration/Cooperation Agreement: 26\nCo-Branding Agreement:\t\t6\nConsulting Agreement:\t\t11\nDevelopment Agreement:\t\t28\nDistributor Agreement:\t\t23\nEndorsement Agreement:\t\t10\nFranchise Agreement:\t\t14\nHosting Agreement:\t\t12\nIP Agreement:\t\t\t16\nJoint Venture Agreemen:\t\t22\nLicense Agreement:\t\t32\nMaintenance Agreement:\t\t24\nManufacturing Agreement:\t6\nMarketing Agreement:\t\t16\nNon-Compete/No-Solicit/Non-Disparagement Agreement: 3\nOutsourcing Agreement:\t\t12\nPromotion Agreement:\t\t9\nReseller Agreement:\t\t12\nService Agreement:\t\t24\nSponsorship Agreement:\t17\nSupply Agreement:\t\t13\nStrategic Alliance Agreement:\t32\nTransportation Agreement:\t1\nTOTAL:\t\t\t\t385\n\n```\n\nCategories\n\n\n\n```\nDocument Name\nParties\nAgreement Date\nEffective Date\nExpiration Date\nRenewal Term\nNotice Period To Terminate Renewal\nGoverning Law\nNon-Compete\nExclusivity\nChange Of Control\nAnti-Assignment\n\n```", "#### Who are the source language producers?\n\n\nThe contracts were sourced from EDGAR, the Electronic Data Gathering, Analysis, and Retrieval system used at the U.S. Securities and Exchange Commission (SEC). Publicly traded companies in the United States are required to file certain contracts under the SEC rules. Access to these contracts is available to the public for free at URL Please read the Datasheet at URL for information on the intended use and limitations of the CUAD.", "### Annotations", "#### Annotation process\n\n\nThe labeling process included multiple steps to ensure accuracy:\n\n\n1. Law Student Training: law students attended training sessions on each of the categories that included a summary, video instructions by experienced attorneys, multiple quizzes and workshops. Students were then required to label sample contracts in eBrevia, an online contract review tool. The initial training took approximately 70-100 hours.\n2. Law Student Label: law students conducted manual contract review and labeling in eBrevia.\n3. Key Word Search: law students conducted keyword search in eBrevia to capture additional categories that have been missed during the “Student Label” step.\n4. Category-by-Category Report Review: law students exported the labeled clauses into reports, review each clause category-by-category and highlight clauses that they believe are mislabeled.\n5. Attorney Review: experienced attorneys reviewed the category-by-category report with students comments, provided comments and addressed student questions. When applicable, attorneys discussed such results with the students and reached consensus. Students made changes in eBrevia accordingly.\n6. eBrevia Extras Review. Attorneys and students used eBrevia to generate a list of “extras”, which are clauses that eBrevia AI tool identified as responsive to a category but not labeled by human annotators. Attorneys and students reviewed all of the “extras” and added the correct ones. The process is repeated until all or substantially all of the “extras” are incorrect labels.\n7. Final Report: The final report was exported into a CSV file. Volunteers manually added the “Yes/No” answer column to categories that do not contain an answer.", "#### Who are the annotators?\n\n\nAnswered in above section.", "### Personal and Sensitive Information\n\n\nSome clauses in the files are redacted because the party submitting these contracts redacted them to protect confidentiality. Such redaction may show up as asterisks (\\*\\*\\*) or underscores (\\_\\_\\_) or blank spaces. The dataset and the answers reflect such redactions. For example, the answer for “January \\_\\_ 2020” would be “1/[]/2020”).\n\n\nFor any categories that require an answer of “Yes/No”, annotators include full sentences as text context in a contract. To maintain consistency and minimize inter-annotator disagreement, annotators select text for the full sentence, under the instruction of “from period to period”.\n\n\nFor the other categories, annotators selected segments of the text in the contract that are responsive to each such category. One category in a contract may include multiple labels. For example, “Parties” may include 4-10 separate text strings that are not continuous in a contract. The answer is presented in the unified format separated by semicolons of “Party A Inc. (“Party A”); Party B Corp. (“Party B”)”.\n\n\nSome sentences in the files include confidential legends that are not part of the contracts. An example of such confidential legend is as follows:\n\n\nTHIS EXHIBIT HAS BEEN REDACTED AND IS THE SUBJECT OF A CONFIDENTIAL TREATMENT REQUEST. REDACTED MATERIAL IS MARKED WITH [\\* \\* \\*] AND HAS BEEN FILED SEPARATELY WITH THE SECURITIES AND EXCHANGE COMMISSION.\n\n\nSome sentences in the files contain irrelevant information such as footers or page numbers. Some sentences may not be relevant to the corresponding category. Some sentences may correspond to a different category. Because many legal clauses are very long and contain various sub-parts, sometimes only a sub-part of a sentence is responsive to a category.\n\n\nTo address the foregoing limitations, annotators manually deleted the portion that is not responsive, replacing it with the symbol \"\" to indicate that the two text segments do not appear immediately next to each other in the contracts. For example, if a “Termination for Convenience” clause starts with “Each Party may terminate this Agreement if” followed by three subparts “(a), (b) and (c)”, but only subpart (c) is responsive to this category, the authors manually deleted subparts (a) and (b) and replaced them with the symbol \"”. Another example is for “Effective Date”, the contract includes a sentence “This Agreement is effective as of the date written above” that appears after the date “January 1, 2010”. The annotation is as follows: “January 1, 2010 This Agreement is effective as of the date written above.”\n\n\nBecause the contracts were converted from PDF into TXT files, the converted TXT files may not stay true to the format of the original PDF files. For example, some contracts contain inconsistent spacing between words, sentences and paragraphs. Table format is not maintained in the TXT files.\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\nAttorney Advisors\nWei Chen, John Brockland, Kevin Chen, Jacky Fink, Spencer P. Goodson, Justin Haan, Alex Haskell, Kari Krusmark, Jenny Lin, Jonas Marson, Benjamin Petersen, Alexander Kwonji Rosenberg, William R. Sawyers, Brittany Schmeltz, Max Scott, Zhu Zhu\n\n\nLaw Student Leaders\nJohn Batoha, Daisy Beckner, Lovina Consunji, Gina Diaz, Chris Gronseth, Calvin Hannagan, Joseph Kroon, Sheetal Sharma Saran\n\n\nLaw Student Contributors\nScott Aronin, Bryan Burgoon, Jigar Desai, Imani Haynes, Jeongsoo Kim, Margaret Lynch, Allison Melville, Felix Mendez-Burgos, Nicole Mirkazemi, David Myers, Emily Rissberger, Behrang Seraj, Sarahginy Valcin\n\n\nTechnical Advisors & Contributors\nDan Hendrycks, Collin Burns, Spencer Ball, Anya Chen", "### Licensing Information\n\n\nCUAD is licensed under the Creative Commons Attribution 4.0 (CC BY 4.0) license and free to the public for commercial and non-commercial use.\n\n\nThe creators make no representations or warranties regarding the license status of the underlying contracts, which are publicly available and downloadable from EDGAR.\nPrivacy Policy & Disclaimers\n\n\nThe categories or the contracts included in the dataset are not comprehensive or representative. The authors encourage the public to help improve them by sending them your comments and suggestions to info@URL. Comments and suggestions will be reviewed by The Atticus Project at its discretion and will be included in future versions of Atticus categories once approved.\n\n\nThe use of CUAD is subject to their privacy policy URL and disclaimer URL", "### Contributions\n\n\nThanks to @bhavitvyamalik for adding this dataset." ]

[ 114, 244, 10, 22, 18, 92, 42, 448, 4, 273, 109, 5, 387, 15, 733, 7, 8, 14, 218, 167, 19 ]

[ "passage: TAGS\n#task_categories-question-answering #task_ids-closed-domain-qa #task_ids-extractive-qa #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-cuad #language-English #license-cc-by-4.0 #arxiv-2103.06268 #region-us \n### Dataset Summary\n\n\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510 commercial legal contracts that have been manually labeled to identify 41 categories of important clauses that lawyers look for when reviewing contracts in connection with corporate transactions. This dataset is a filtered version of CUAD. It excludes legal contracts with an Agreement date prior to 2002 and contracts which are not Business to Business. From the 41 categories we filtered them down to 12 which we considered the most crucial.\n\n\nWe wanted a small dataset to quickly fine-tune different models without sacrificing the categories which we deemed as important. The need to remove most questions was due to them not having an answer which is problematic since it can scue the resulting metrics such as the F1 score and the AUPR curve.\n\n\nCUAD is curated and maintained by The Atticus Project, Inc. to support NLP research and development in legal contract review. Analysis of CUAD can be found at URL Code for replicating the results and the trained model can be found at URL### Supported Tasks and Leaderboards### Languages\n\n\nThe dataset contains samples in English only.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nAn example of 'train' looks as follows.### Data Fields\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.", "passage: ### Data Splits\n\n\nThis dataset is split into train/test set. Number of samples in each set is given below:\n\n\nTrain: CUAD, Test: 5442\n\n\nDataset Creation\n----------------### Curation Rationale\n\n\nA highly valuable specialized task without a public large-scale dataset is contract review, which costs humans substantial time, money, and attention. Many law firms spend approximately 50% of their time reviewing contracts (CEB, 2017). Due to the specialized training necessary to understand and interpret contracts, the billing rates for lawyers at large law firms are typically around $500-$900 per hour in the US. As a result, many transactions cost companies hundreds of thousands of dollars just so that lawyers can verify that there are no problematic obligations or requirements included in the contracts. Contract review can be a source of drudgery and, in comparison to other legal tasks, is widely considered to be especially boring.\n\n\nContract review costs also affect consumers. Since contract review costs are so prohibitive, contract review is not often performed outside corporate transactions. Small companies and individuals consequently often sign contracts without even reading them, which can result in predatory behavior that harms consumers. Automating contract review by openly releasing high-quality data and fine-tuned models can increase access to legal support for small businesses and individuals, so that legal support is not exclusively available to wealthy companies.\n\n\nTo reduce the disparate societal costs of contract review, and to study how well NLP models generalize to specialized domains, the authors introduced a new large-scale dataset for contract review. As part of The Atticus Project, a non-profit organization of legal experts, CUAD is introduced, the Contract Understanding Atticus Dataset. This dataset was created with a year-long effort pushed forward by dozens of law student annotators, lawyers, and machine learning researchers. The dataset includes more than 500 contracts and more than 13,000 expert annotations that span 41 label categories. For each of 41 different labels, models must learn to highlight the portions of a contract most salient to that label. This makes the task a matter of finding needles in a haystack.### Source Data", "passage: #### Initial Data Collection and Normalization\n\n\nThe CUAD includes commercial contracts selected from 25 different types of contracts based on the contract names as shown below. Within each type, the creators randomly selected contracts based on the names of the filing companies across the alphabet.\n\n\nType of Contracts: # of Docs\n\n\n\n```\nAffiliate Agreement:\t\t8\nAgency Agreement:\t\t 8 \nCollaboration/Cooperation Agreement: 26\nCo-Branding Agreement:\t\t6\nConsulting Agreement:\t\t11\nDevelopment Agreement:\t\t28\nDistributor Agreement:\t\t23\nEndorsement Agreement:\t\t10\nFranchise Agreement:\t\t14\nHosting Agreement:\t\t12\nIP Agreement:\t\t\t16\nJoint Venture Agreemen:\t\t22\nLicense Agreement:\t\t32\nMaintenance Agreement:\t\t24\nManufacturing Agreement:\t6\nMarketing Agreement:\t\t16\nNon-Compete/No-Solicit/Non-Disparagement Agreement: 3\nOutsourcing Agreement:\t\t12\nPromotion Agreement:\t\t9\nReseller Agreement:\t\t12\nService Agreement:\t\t24\nSponsorship Agreement:\t17\nSupply Agreement:\t\t13\nStrategic Alliance Agreement:\t32\nTransportation Agreement:\t1\nTOTAL:\t\t\t\t385\n\n```\n\nCategories\n\n\n\n```\nDocument Name\nParties\nAgreement Date\nEffective Date\nExpiration Date\nRenewal Term\nNotice Period To Terminate Renewal\nGoverning Law\nNon-Compete\nExclusivity\nChange Of Control\nAnti-Assignment\n\n```#### Who are the source language producers?\n\n\nThe contracts were sourced from EDGAR, the Electronic Data Gathering, Analysis, and Retrieval system used at the U.S. Securities and Exchange Commission (SEC). Publicly traded companies in the United States are required to file certain contracts under the SEC rules. Access to these contracts is available to the public for free at URL Please read the Datasheet at URL for information on the intended use and limitations of the CUAD.### Annotations#### Annotation process\n\n\nThe labeling process included multiple steps to ensure accuracy:\n\n\n1. Law Student Training: law students attended training sessions on each of the categories that included a summary, video instructions by experienced attorneys, multiple quizzes and workshops. Students were then required to label sample contracts in eBrevia, an online contract review tool. The initial training took approximately 70-100 hours.\n2. Law Student Label: law students conducted manual contract review and labeling in eBrevia.\n3. Key Word Search: law students conducted keyword search in eBrevia to capture additional categories that have been missed during the “Student Label” step.\n4. Category-by-Category Report Review: law students exported the labeled clauses into reports, review each clause category-by-category and highlight clauses that they believe are mislabeled.\n5. Attorney Review: experienced attorneys reviewed the category-by-category report with students comments, provided comments and addressed student questions. When applicable, attorneys discussed such results with the students and reached consensus. Students made changes in eBrevia accordingly.\n6. eBrevia Extras Review. Attorneys and students used eBrevia to generate a list of “extras”, which are clauses that eBrevia AI tool identified as responsive to a category but not labeled by human annotators. Attorneys and students reviewed all of the “extras” and added the correct ones. The process is repeated until all or substantially all of the “extras” are incorrect labels.\n7. Final Report: The final report was exported into a CSV file. Volunteers manually added the “Yes/No” answer column to categories that do not contain an answer.", "passage: #### Who are the annotators?\n\n\nAnswered in above section." ]

[ "region:us" ]

[]

[]

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

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

[ 6, 27, 17, 23, 27 ]

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

[ "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: yhavinga/long-t5-tglobal-small-dutch-cnn\n* Dataset: ml6team/cnn_dailymail_nl\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @yhavinga 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: yhavinga/long-t5-tglobal-small-dutch-cnn\n* Dataset: ml6team/cnn_dailymail_nl\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @yhavinga for evaluating this model." ]

[ 13, 103, 16 ]

[ "passage: 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: yhavinga/long-t5-tglobal-small-dutch-cnn\n* Dataset: ml6team/cnn_dailymail_nl\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @yhavinga 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: Blaise-g/long_t5_global_large_pubmed_explanatory\n* Dataset: ben-yu/ms2_combined\n* Config: ben-yu--ms2_combined\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @ben-yu 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: Blaise-g/long_t5_global_large_pubmed_explanatory\n* Dataset: ben-yu/ms2_combined\n* Config: ben-yu--ms2_combined\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @ben-yu for evaluating this model." ]

[ 13, 114, 16 ]

[ "passage: 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: Blaise-g/long_t5_global_large_pubmed_explanatory\n* Dataset: ben-yu/ms2_combined\n* Config: ben-yu--ms2_combined\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @ben-yu 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: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP10\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP10\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ 13, 103, 16 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP10\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP\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 @pszemraj for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP\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 @pszemraj for evaluating this model." ]

[ 13, 101, 16 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP\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 @pszemraj 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: Jacobsith/autotrain-Hello_there-1209845735\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Jacobsith 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: Jacobsith/autotrain-Hello_there-1209845735\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Jacobsith for evaluating this model." ]

[ 13, 109, 17 ]

[ "passage: 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: Jacobsith/autotrain-Hello_there-1209845735\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @Jacobsith for evaluating this model." ]

[ "region:us" ]

[]

[]

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

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

[ 6, 125, 24, 6, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 19 ]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for WITS", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Languages\n- Additional Information\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Repository: URL\n- Paper: URL", "### Dataset Summary\n\nWITS (Wikipedia for Italian Text Summarization) is a large-scale dataset for abstractive summarization in Italian, built exploiting Wikipedia articles' structure. WITS contains almost 700,000 Wikipedia articles, together with their human-written summaries.\n\nCompared to existing data for text summarization in Italian, WITS is more than an order of magnitude larger and more challenging, given its lengthy sources.", "### Languages\n\nThe dataset is in Italian.", "### Licensing Information\n\nThe dataset uses text from Wikipedia. Please refer to Wikipedia's license.\n\n\n\nIf you use the dataset, please cite:" ]

[ "TAGS\n#region-us \n", "# Dataset Card for WITS", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Languages\n- Additional Information\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Repository: URL\n- Paper: URL", "### Dataset Summary\n\nWITS (Wikipedia for Italian Text Summarization) is a large-scale dataset for abstractive summarization in Italian, built exploiting Wikipedia articles' structure. WITS contains almost 700,000 Wikipedia articles, together with their human-written summaries.\n\nCompared to existing data for text summarization in Italian, WITS is more than an order of magnitude larger and more challenging, given its lengthy sources.", "### Languages\n\nThe dataset is in Italian.", "### Licensing Information\n\nThe dataset uses text from Wikipedia. Please refer to Wikipedia's license.\n\n\n\nIf you use the dataset, please cite:" ]

[ 6, 7, 31, 14, 97, 11, 33 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for WITS## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Languages\n- Additional Information\n - Licensing Information\n - Citation Information## Dataset Description\n\n- Repository: URL\n- Paper: URL### Dataset Summary\n\nWITS (Wikipedia for Italian Text Summarization) is a large-scale dataset for abstractive summarization in Italian, built exploiting Wikipedia articles' structure. WITS contains almost 700,000 Wikipedia articles, together with their human-written summaries.\n\nCompared to existing data for text summarization in Italian, WITS is more than an order of magnitude larger and more challenging, given its lengthy sources.### Languages\n\nThe dataset is in Italian.### Licensing Information\n\nThe dataset uses text from Wikipedia. Please refer to Wikipedia's license.\n\n\n\nIf you use the dataset, please cite:" ]

[ "annotations_creators:no-annotation", "size_categories:n<1K", "source_datasets:original", "license:cc0-1.0", "region:us" ]

[]

[]

[ "# Dataset Card for severo/embellishments\n\nTest: link to a space:\n\nURL\n\nURL", "## Dataset Description\n\n- Homepage: Digitised Books - Images identified as Embellishments - Homepage\n- Point of Contact: Sylvain Lesage", "### Dataset Summary\n\nThis small dataset contains the thumbnails of the first 100 entries of Digitised Books - Images identified as Embellishments. c. 1510 - c. 1900. JPG. It has been uploaded to the Hub to reproduce the tutorial by Daniel van Strien: Using datasets for image search.", "## Dataset Structure", "### Data Instances\n\nA typical row contains an image thumbnail, its filename, and the year of publication of the book it was extracted from.\n\nAn example looks as follows:", "### Data Fields\n\n- 'fname': the image filename.\n- 'year': a string with the year of publication of the book from which the image has been extracted\n- 'path': local path to the image\n- 'img': a thumbnail of the image with a max height and width of 224 pixels", "### Data Splits\n\nThe dataset only contains 100 rows, in a single 'train' split.", "## Dataset Creation", "### Curation Rationale\n\nThis dataset was chosen by Daniel van Strien for his tutorial Using datasets for image search, which includes the code in Python to do it.", "### Source Data", "#### Initial Data Collection and Normalization\n\nAs stated on the British Library webpage:\n> The images were algorithmically gathered from 49,455 digitised books, equating to 65,227 volumes (25+ million pages), published between c. 1510 - c. 1900. The books cover a wide range of subject areas including philosophy, history, poetry and literature. The images are in .JPEG format.d BCP-47 code is 'en'.", "#### Who are the source data producers?\n\nBritish Library, British Library Labs, Adrian Edwards (Curator), Neil Fitzgerald (Contributor ORCID)", "### Annotations\n\nThe dataset does not contain any additional annotations.", "#### Annotation process\n\n[N/A]", "#### Who are the annotators?\n\n[N/A]", "### Personal and Sensitive Information\n\n[N/A]", "## Considerations for Using the Data", "### Social Impact of Dataset\n\n[N/A]", "### Discussion of Biases\n\n[N/A]", "### Other Known Limitations\n\nThis is a toy dataset that aims at:\n- validating the process described in the tutorial Using datasets for image search by Daniel van Strien,\n- showing the dataset viewer on an image dataset.", "## Additional Information", "### Dataset Curators\n\nThe dataset was created by Sylvain Lesage at Hugging Face, to replicate the tutorial Using datasets for image search by Daniel van Strien.", "### Licensing Information\n\nCC0 1.0 Universal Public Domain" ]

[ "TAGS\n#annotations_creators-no-annotation #size_categories-n<1K #source_datasets-original #license-cc0-1.0 #region-us \n", "# Dataset Card for severo/embellishments\n\nTest: link to a space:\n\nURL\n\nURL", "## Dataset Description\n\n- Homepage: Digitised Books - Images identified as Embellishments - Homepage\n- Point of Contact: Sylvain Lesage", "### Dataset Summary\n\nThis small dataset contains the thumbnails of the first 100 entries of Digitised Books - Images identified as Embellishments. c. 1510 - c. 1900. JPG. It has been uploaded to the Hub to reproduce the tutorial by Daniel van Strien: Using datasets for image search.", "## Dataset Structure", "### Data Instances\n\nA typical row contains an image thumbnail, its filename, and the year of publication of the book it was extracted from.\n\nAn example looks as follows:", "### Data Fields\n\n- 'fname': the image filename.\n- 'year': a string with the year of publication of the book from which the image has been extracted\n- 'path': local path to the image\n- 'img': a thumbnail of the image with a max height and width of 224 pixels", "### Data Splits\n\nThe dataset only contains 100 rows, in a single 'train' split.", "## Dataset Creation", "### Curation Rationale\n\nThis dataset was chosen by Daniel van Strien for his tutorial Using datasets for image search, which includes the code in Python to do it.", "### Source Data", "#### Initial Data Collection and Normalization\n\nAs stated on the British Library webpage:\n> The images were algorithmically gathered from 49,455 digitised books, equating to 65,227 volumes (25+ million pages), published between c. 1510 - c. 1900. The books cover a wide range of subject areas including philosophy, history, poetry and literature. The images are in .JPEG format.d BCP-47 code is 'en'.", "#### Who are the source data producers?\n\nBritish Library, British Library Labs, Adrian Edwards (Curator), Neil Fitzgerald (Contributor ORCID)", "### Annotations\n\nThe dataset does not contain any additional annotations.", "#### Annotation process\n\n[N/A]", "#### Who are the annotators?\n\n[N/A]", "### Personal and Sensitive Information\n\n[N/A]", "## Considerations for Using the Data", "### Social Impact of Dataset\n\n[N/A]", "### Discussion of Biases\n\n[N/A]", "### Other Known Limitations\n\nThis is a toy dataset that aims at:\n- validating the process described in the tutorial Using datasets for image search by Daniel van Strien,\n- showing the dataset viewer on an image dataset.", "## Additional Information", "### Dataset Curators\n\nThe dataset was created by Sylvain Lesage at Hugging Face, to replicate the tutorial Using datasets for image search by Daniel van Strien.", "### Licensing Information\n\nCC0 1.0 Universal Public Domain" ]

[ 45, 21, 30, 77, 6, 44, 74, 25, 5, 40, 4, 105, 39, 17, 10, 14, 13, 8, 12, 13, 53, 5, 40, 12 ]

[ "passage: TAGS\n#annotations_creators-no-annotation #size_categories-n<1K #source_datasets-original #license-cc0-1.0 #region-us \n# Dataset Card for severo/embellishments\n\nTest: link to a space:\n\nURL\n\nURL## Dataset Description\n\n- Homepage: Digitised Books - Images identified as Embellishments - Homepage\n- Point of Contact: Sylvain Lesage### Dataset Summary\n\nThis small dataset contains the thumbnails of the first 100 entries of Digitised Books - Images identified as Embellishments. c. 1510 - c. 1900. JPG. It has been uploaded to the Hub to reproduce the tutorial by Daniel van Strien: Using datasets for image search.## Dataset Structure### Data Instances\n\nA typical row contains an image thumbnail, its filename, and the year of publication of the book it was extracted from.\n\nAn example looks as follows:### Data Fields\n\n- 'fname': the image filename.\n- 'year': a string with the year of publication of the book from which the image has been extracted\n- 'path': local path to the image\n- 'img': a thumbnail of the image with a max height and width of 224 pixels### Data Splits\n\nThe dataset only contains 100 rows, in a single 'train' split.## Dataset Creation### Curation Rationale\n\nThis dataset was chosen by Daniel van Strien for his tutorial Using datasets for image search, which includes the code in Python to do it.### Source Data#### Initial Data Collection and Normalization\n\nAs stated on the British Library webpage:\n> The images were algorithmically gathered from 49,455 digitised books, equating to 65,227 volumes (25+ million pages), published between c. 1510 - c. 1900. The books cover a wide range of subject areas including philosophy, history, poetry and literature. The images are in .JPEG format.d BCP-47 code is 'en'." ]

[ "task_categories:translation", "multilinguality:translation", "size_categories:10K<n<100K", "language:en", "language:me", "license:afl-3.0", "middle english", "region:us" ]

[]

[ "en", "me" ]

[]

[ "TAGS\n#task_categories-translation #multilinguality-translation #size_categories-10K<n<100K #language-English #language-me #license-afl-3.0 #middle english #region-us \n" ]

[ 54 ]

[ "passage: TAGS\n#task_categories-translation #multilinguality-translation #size_categories-10K<n<100K #language-English #language-me #license-afl-3.0 #middle english #region-us \n" ]

[ "region:us" ]

[]

[]

[]

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

[ 6 ]

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

[ "region:us" ]

[]

[]

[ "## Labels\n- biological --- 1\n- earthquake --- 2\n- flood --- 3\n- hurricane & tornado --- 4\n- wildfire --- 5\n- industrial --- 6\n- societal --- 7\n- transportation --- 8\n- meteor --- 9\n- haze --- 10" ]

[ "TAGS\n#region-us \n", "## Labels\n- biological --- 1\n- earthquake --- 2\n- flood --- 3\n- hurricane & tornado --- 4\n- wildfire --- 5\n- industrial --- 6\n- societal --- 7\n- transportation --- 8\n- meteor --- 9\n- haze --- 10" ]

[ 6, 55 ]

[ "passage: TAGS\n#region-us \n## Labels\n- biological --- 1\n- earthquake --- 2\n- flood --- 3\n- hurricane & tornado --- 4\n- wildfire --- 5\n- industrial --- 6\n- societal --- 7\n- transportation --- 8\n- meteor --- 9\n- haze --- 10" ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Translation\n* Model: Lvxue/finetuned-mt5-small-10epoch\n* Dataset: wmt16\n* Config: ro-en\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Lvxue for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Translation\n* Model: Lvxue/finetuned-mt5-small-10epoch\n* Dataset: wmt16\n* Config: ro-en\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Lvxue for evaluating this model." ]

[ 13, 94, 17 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Translation\n* Model: Lvxue/finetuned-mt5-small-10epoch\n* Dataset: wmt16\n* Config: ro-en\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @Lvxue for evaluating this model." ]

[ "language_creators:found", "language:cn", "region:us" ]

[]

[ "cn" ]

[ "### Dataset Summary \nPlaceholder\nYou can load the dataset via:\n\nThe data loader can be found here.", "#### website\nNone (See Repository)", "#### paper\nURL", "#### authors\nFaisal Ladhak (Columbia University), Esin Durmus (Stanford University), Claire Cardie (Cornell University), Kathleen McKeown (Columbia University)", "## Dataset Overview", "### Where to find the Data and its Documentation", "#### Webpage\n\n\nNone (See Repository)", "#### Download\n\n\nURL", "#### Paper\n\n\nURL", "#### BibTex\n\n\n@inproceedings{ladhak-etal-2020-wikilingua,\n title = \"{W}iki{L}ingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization\",\n author = \"Ladhak, Faisal and\n Durmus, Esin and\n Cardie, Claire and\n McKeown, Kathleen\",\n booktitle = \"Findings of the Association for Computational Linguistics: EMNLP 2020\",\n month = nov,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"URL\n doi = \"10.18653/v1/2020.findings-emnlp.360\",\n pages = \"4034--4048\",\n abstract = \"We introduce WikiLingua, a large-scale, multilingual dataset for the evaluation of cross-lingual abstractive summarization systems. We extract article and summary pairs in 18 languages from WikiHow, a high quality, collaborative resource of how-to guides on a diverse set of topics written by human authors. We create gold-standard article-summary alignments across languages by aligning the images that are used to describe each how-to step in an article. As a set of baselines for further studies, we evaluate the performance of existing cross-lingual abstractive summarization methods on our dataset. We further propose a method for direct cross-lingual summarization (i.e., without requiring translation at inference time) by leveraging synthetic data and Neural Machine Translation as a pre-training step. Our method significantly outperforms the baseline approaches, while being more cost efficient during inference.\",\n}", "#### Contact Name\n\n\n\nFaisal Ladhak, Esin Durmus", "#### Contact Email\n\n\nfaisal@URL, esdurmus@URL", "#### Has a Leaderboard?\n\n\nno", "### Languages and Intended Use", "#### Multilingual?\n\n\n\nyes", "#### Covered Dialects\n\n\nDataset does not have multiple dialects per language.", "#### Covered Languages\n\n\n\n'English', 'Spanish, Castilian', 'Portuguese', 'French', 'German', 'Russian', 'Italian', 'Indonesian', 'Dutch, Flemish', 'Arabic', 'Chinese', 'Vietnamese', 'Thai', 'Japanese', 'Korean', 'Hindi', 'Czech', 'Turkish'", "#### Whose Language?\n\n\nNo information about the user demographic is available.", "#### License\n\n\n\ncc-by-3.0: Creative Commons Attribution 3.0 Unported", "#### Intended Use\n\n\nThe dataset was intended to serve as a large-scale, high-quality benchmark dataset for cross-lingual summarization.", "#### Primary Task\n\n\nSummarization", "#### Communicative Goal\n\n\n\nProduce a high quality summary for the given input article.", "### Credit", "#### Curation Organization Type(s)\n\n\n'academic'", "#### Curation Organization(s)\n\n\nColumbia University", "#### Dataset Creators\n\n\nFaisal Ladhak (Columbia University), Esin Durmus (Stanford University), Claire Cardie (Cornell University), Kathleen McKeown (Columbia University)", "#### Who added the Dataset to GEM?\n\n\nJenny Chim (Queen Mary University of London), Faisal Ladhak (Columbia University)", "### Dataset Structure", "#### Data Fields\n\n\ngem_id -- The id for the data instance.\nsource_language -- The language of the source article.\ntarget_language -- The language of the target summary.\nsource -- The source document.", "#### Example Instance\n\n\n\n{\n \"gem_id\": \"wikilingua_crosslingual-train-12345\",\n \"gem_parent_id\": \"wikilingua_crosslingual-train-12345\",\n \"source_language\": \"fr\",\n \"target_language\": \"de\",\n \"source\": \"Document in fr\",\n \"target\": \"Summary in de\",\n}", "#### Data Splits\n\n\n\nThe data is split into train/dev/test. In addition to the full test set, there's also a sampled version of the test set.", "#### Splitting Criteria\n\n\n\nThe data was split to ensure the same document would appear in the same split across languages so as to ensure there's no leakage into the test set.", "## Dataset in GEM", "### Rationale for Inclusion in GEM", "#### Why is the Dataset in GEM?\n\n\n\nThis dataset provides a large-scale, high-quality resource for cross-lingual summarization in 18 languages, increasing the coverage of languages for the GEM summarization task.", "#### Similar Datasets\n\n\n\nyes", "#### Unique Language Coverage\n\n\n\nyes", "#### Difference from other GEM datasets\n\n\n\nXSum covers English news articles, and MLSum covers news articles in German and Spanish. \nIn contrast, this dataset has how-to articles in 18 languages, substantially increasing the languages covered. Moreover, it also provides a a different domain than the other two datasets.", "#### Ability that the Dataset measures\n\n\n\nThe ability to generate quality summaries across multiple languages.", "### GEM-Specific Curation", "#### Modificatied for GEM?\n\n\n\nyes", "#### GEM Modifications\n\n\n\n'other'", "#### Modification Details\n\n\n\nPrevious version had separate data loaders for each language. In this version, we've created a single monolingual data loader, which contains monolingual data in each of the 18 languages. In addition, we've also created a single cross-lingual data loader across all the language pairs in the dataset.", "#### Additional Splits?\n\n\n\nno", "### Getting Started with the Task", "## Previous Results", "### Previous Results", "#### Measured Model Abilities\n\n\n\nAbility to summarize content across different languages.", "#### Metrics\n\n\n\n'ROUGE'", "#### Proposed Evaluation\n\n\n\nROUGE is used to measure content selection by comparing word overlap with reference summaries. In addition, the authors of the dataset also used human evaluation to evaluate content selection and fluency of the systems.", "#### Previous results available?\n\n\n\nno", "## Dataset Curation", "### Original Curation", "#### Original Curation Rationale\n\n\n\nThe dataset was created in order to enable new approaches for cross-lingual and multilingual summarization, which are currently understudied as well as open up inetersting new directions for research in summarization. E.g., exploration of multi-source cross-lingual architectures, i.e. models that can summarize from multiple source languages into a target language, building models that can summarize articles from any language to any other language for a given set of languages.", "#### Communicative Goal\n\n\n\nGiven an input article, produce a high quality summary of the article in the target language.", "#### Sourced from Different Sources\n\n\n\nno", "### Language Data", "#### How was Language Data Obtained?\n\n\n\n'Found'", "#### Where was it found?\n\n\n\n'Single website'", "#### Language Producers\n\n\n\nWikiHow, which is an online resource of how-to guides (written and reviewed by human authors) is used as the data source.", "#### Topics Covered\n\n\n\nThe articles cover 19 broad categories including health, arts and entertainment, personal care and style, travel, education and communications, etc. The categories cover a broad set of genres and topics.", "#### Data Validation\n\n\n\nnot validated", "#### Was Data Filtered?\n\n\n\nnot filtered", "### Structured Annotations", "#### Additional Annotations?\n\n\n\n\nnone", "#### Annotation Service?\n\n\n\nno", "### Consent", "#### Any Consent Policy?\n\n\n\nyes", "#### Consent Policy Details\n\n\n\n(1) Text Content. All text posted by Users to the Service is sub-licensed by wikiHow to other Users under a Creative Commons license as provided herein. The Creative Commons license allows such text content be used freely for non-commercial purposes, so long as it is used and attributed to the original author as specified under the terms of the license. Allowing free republication of our articles helps wikiHow achieve its mission by providing instruction on solving the problems of everyday life to more people for free. In order to support this goal, wikiHow hereby grants each User of the Service a license to all text content that Users contribute to the Service under the terms and conditions of a Creative Commons CC BY-NC-SA 3.0 License. Please be sure to read the terms of the license carefully. You continue to own all right, title, and interest in and to your User Content, and you are free to distribute it as you wish, whether for commercial or non-commercial purposes.", "#### Other Consented Downstream Use\n\n\n\nThe data is made freely available under the Creative Commons license, therefore there are no restrictions about downstream uses as long is it's for non-commercial purposes.", "### Private Identifying Information (PII)", "#### Contains PII?\n\n\n\n\nno PII", "#### Justification for no PII\n\n\n\nOnly the article text and summaries were collected. No user information was retained in the dataset.", "### Maintenance", "#### Any Maintenance Plan?\n\n\n\nno", "## Broader Social Context", "### Previous Work on the Social Impact of the Dataset", "#### Usage of Models based on the Data\n\n\n\nyes - other datasets featuring the same task", "### Impact on Under-Served Communities", "#### Addresses needs of underserved Communities?\n\n\n\nno", "### Discussion of Biases", "#### Any Documented Social Biases?\n\n\n\nyes", "## Considerations for Using the Data", "### PII Risks and Liability", "### Licenses", "#### Copyright Restrictions on the Dataset\n\n\n\n'non-commercial use only'", "#### Copyright Restrictions on the Language Data\n\n\n\n'non-commercial use only'", "### Known Technical Limitations" ]

[ "TAGS\n#language_creators-found #language-cn #region-us \n", "### Dataset Summary \nPlaceholder\nYou can load the dataset via:\n\nThe data loader can be found here.", "#### website\nNone (See Repository)", "#### paper\nURL", "#### authors\nFaisal Ladhak (Columbia University), Esin Durmus (Stanford University), Claire Cardie (Cornell University), Kathleen McKeown (Columbia University)", "## Dataset Overview", "### Where to find the Data and its Documentation", "#### Webpage\n\n\nNone (See Repository)", "#### Download\n\n\nURL", "#### Paper\n\n\nURL", "#### BibTex\n\n\n@inproceedings{ladhak-etal-2020-wikilingua,\n title = \"{W}iki{L}ingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization\",\n author = \"Ladhak, Faisal and\n Durmus, Esin and\n Cardie, Claire and\n McKeown, Kathleen\",\n booktitle = \"Findings of the Association for Computational Linguistics: EMNLP 2020\",\n month = nov,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"URL\n doi = \"10.18653/v1/2020.findings-emnlp.360\",\n pages = \"4034--4048\",\n abstract = \"We introduce WikiLingua, a large-scale, multilingual dataset for the evaluation of cross-lingual abstractive summarization systems. We extract article and summary pairs in 18 languages from WikiHow, a high quality, collaborative resource of how-to guides on a diverse set of topics written by human authors. We create gold-standard article-summary alignments across languages by aligning the images that are used to describe each how-to step in an article. As a set of baselines for further studies, we evaluate the performance of existing cross-lingual abstractive summarization methods on our dataset. We further propose a method for direct cross-lingual summarization (i.e., without requiring translation at inference time) by leveraging synthetic data and Neural Machine Translation as a pre-training step. Our method significantly outperforms the baseline approaches, while being more cost efficient during inference.\",\n}", "#### Contact Name\n\n\n\nFaisal Ladhak, Esin Durmus", "#### Contact Email\n\n\nfaisal@URL, esdurmus@URL", "#### Has a Leaderboard?\n\n\nno", "### Languages and Intended Use", "#### Multilingual?\n\n\n\nyes", "#### Covered Dialects\n\n\nDataset does not have multiple dialects per language.", "#### Covered Languages\n\n\n\n'English', 'Spanish, Castilian', 'Portuguese', 'French', 'German', 'Russian', 'Italian', 'Indonesian', 'Dutch, Flemish', 'Arabic', 'Chinese', 'Vietnamese', 'Thai', 'Japanese', 'Korean', 'Hindi', 'Czech', 'Turkish'", "#### Whose Language?\n\n\nNo information about the user demographic is available.", "#### License\n\n\n\ncc-by-3.0: Creative Commons Attribution 3.0 Unported", "#### Intended Use\n\n\nThe dataset was intended to serve as a large-scale, high-quality benchmark dataset for cross-lingual summarization.", "#### Primary Task\n\n\nSummarization", "#### Communicative Goal\n\n\n\nProduce a high quality summary for the given input article.", "### Credit", "#### Curation Organization Type(s)\n\n\n'academic'", "#### Curation Organization(s)\n\n\nColumbia University", "#### Dataset Creators\n\n\nFaisal Ladhak (Columbia University), Esin Durmus (Stanford University), Claire Cardie (Cornell University), Kathleen McKeown (Columbia University)", "#### Who added the Dataset to GEM?\n\n\nJenny Chim (Queen Mary University of London), Faisal Ladhak (Columbia University)", "### Dataset Structure", "#### Data Fields\n\n\ngem_id -- The id for the data instance.\nsource_language -- The language of the source article.\ntarget_language -- The language of the target summary.\nsource -- The source document.", "#### Example Instance\n\n\n\n{\n \"gem_id\": \"wikilingua_crosslingual-train-12345\",\n \"gem_parent_id\": \"wikilingua_crosslingual-train-12345\",\n \"source_language\": \"fr\",\n \"target_language\": \"de\",\n \"source\": \"Document in fr\",\n \"target\": \"Summary in de\",\n}", "#### Data Splits\n\n\n\nThe data is split into train/dev/test. In addition to the full test set, there's also a sampled version of the test set.", "#### Splitting Criteria\n\n\n\nThe data was split to ensure the same document would appear in the same split across languages so as to ensure there's no leakage into the test set.", "## Dataset in GEM", "### Rationale for Inclusion in GEM", "#### Why is the Dataset in GEM?\n\n\n\nThis dataset provides a large-scale, high-quality resource for cross-lingual summarization in 18 languages, increasing the coverage of languages for the GEM summarization task.", "#### Similar Datasets\n\n\n\nyes", "#### Unique Language Coverage\n\n\n\nyes", "#### Difference from other GEM datasets\n\n\n\nXSum covers English news articles, and MLSum covers news articles in German and Spanish. \nIn contrast, this dataset has how-to articles in 18 languages, substantially increasing the languages covered. Moreover, it also provides a a different domain than the other two datasets.", "#### Ability that the Dataset measures\n\n\n\nThe ability to generate quality summaries across multiple languages.", "### GEM-Specific Curation", "#### Modificatied for GEM?\n\n\n\nyes", "#### GEM Modifications\n\n\n\n'other'", "#### Modification Details\n\n\n\nPrevious version had separate data loaders for each language. In this version, we've created a single monolingual data loader, which contains monolingual data in each of the 18 languages. In addition, we've also created a single cross-lingual data loader across all the language pairs in the dataset.", "#### Additional Splits?\n\n\n\nno", "### Getting Started with the Task", "## Previous Results", "### Previous Results", "#### Measured Model Abilities\n\n\n\nAbility to summarize content across different languages.", "#### Metrics\n\n\n\n'ROUGE'", "#### Proposed Evaluation\n\n\n\nROUGE is used to measure content selection by comparing word overlap with reference summaries. In addition, the authors of the dataset also used human evaluation to evaluate content selection and fluency of the systems.", "#### Previous results available?\n\n\n\nno", "## Dataset Curation", "### Original Curation", "#### Original Curation Rationale\n\n\n\nThe dataset was created in order to enable new approaches for cross-lingual and multilingual summarization, which are currently understudied as well as open up inetersting new directions for research in summarization. E.g., exploration of multi-source cross-lingual architectures, i.e. models that can summarize from multiple source languages into a target language, building models that can summarize articles from any language to any other language for a given set of languages.", "#### Communicative Goal\n\n\n\nGiven an input article, produce a high quality summary of the article in the target language.", "#### Sourced from Different Sources\n\n\n\nno", "### Language Data", "#### How was Language Data Obtained?\n\n\n\n'Found'", "#### Where was it found?\n\n\n\n'Single website'", "#### Language Producers\n\n\n\nWikiHow, which is an online resource of how-to guides (written and reviewed by human authors) is used as the data source.", "#### Topics Covered\n\n\n\nThe articles cover 19 broad categories including health, arts and entertainment, personal care and style, travel, education and communications, etc. The categories cover a broad set of genres and topics.", "#### Data Validation\n\n\n\nnot validated", "#### Was Data Filtered?\n\n\n\nnot filtered", "### Structured Annotations", "#### Additional Annotations?\n\n\n\n\nnone", "#### Annotation Service?\n\n\n\nno", "### Consent", "#### Any Consent Policy?\n\n\n\nyes", "#### Consent Policy Details\n\n\n\n(1) Text Content. All text posted by Users to the Service is sub-licensed by wikiHow to other Users under a Creative Commons license as provided herein. The Creative Commons license allows such text content be used freely for non-commercial purposes, so long as it is used and attributed to the original author as specified under the terms of the license. Allowing free republication of our articles helps wikiHow achieve its mission by providing instruction on solving the problems of everyday life to more people for free. In order to support this goal, wikiHow hereby grants each User of the Service a license to all text content that Users contribute to the Service under the terms and conditions of a Creative Commons CC BY-NC-SA 3.0 License. Please be sure to read the terms of the license carefully. You continue to own all right, title, and interest in and to your User Content, and you are free to distribute it as you wish, whether for commercial or non-commercial purposes.", "#### Other Consented Downstream Use\n\n\n\nThe data is made freely available under the Creative Commons license, therefore there are no restrictions about downstream uses as long is it's for non-commercial purposes.", "### Private Identifying Information (PII)", "#### Contains PII?\n\n\n\n\nno PII", "#### Justification for no PII\n\n\n\nOnly the article text and summaries were collected. No user information was retained in the dataset.", "### Maintenance", "#### Any Maintenance Plan?\n\n\n\nno", "## Broader Social Context", "### Previous Work on the Social Impact of the Dataset", "#### Usage of Models based on the Data\n\n\n\nyes - other datasets featuring the same task", "### Impact on Under-Served Communities", "#### Addresses needs of underserved Communities?\n\n\n\nno", "### Discussion of Biases", "#### Any Documented Social Biases?\n\n\n\nyes", "## Considerations for Using the Data", "### PII Risks and Liability", "### Licenses", "#### Copyright Restrictions on the Dataset\n\n\n\n'non-commercial use only'", "#### Copyright Restrictions on the Language Data\n\n\n\n'non-commercial use only'", "### Known Technical Limitations" ]

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[ "passage: TAGS\n#language_creators-found #language-cn #region-us \n### Dataset Summary \nPlaceholder\nYou can load the dataset via:\n\nThe data loader can be found here.#### website\nNone (See Repository)#### paper\nURL#### authors\nFaisal Ladhak (Columbia University), Esin Durmus (Stanford University), Claire Cardie (Cornell University), Kathleen McKeown (Columbia University)## Dataset Overview### Where to find the Data and its Documentation#### Webpage\n\n\nNone (See Repository)#### Download\n\n\nURL#### Paper\n\n\nURL", "passage: #### BibTex\n\n\n@inproceedings{ladhak-etal-2020-wikilingua,\n title = \"{W}iki{L}ingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization\",\n author = \"Ladhak, Faisal and\n Durmus, Esin and\n Cardie, Claire and\n McKeown, Kathleen\",\n booktitle = \"Findings of the Association for Computational Linguistics: EMNLP 2020\",\n month = nov,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"URL\n doi = \"10.18653/v1/2020.findings-emnlp.360\",\n pages = \"4034--4048\",\n abstract = \"We introduce WikiLingua, a large-scale, multilingual dataset for the evaluation of cross-lingual abstractive summarization systems. We extract article and summary pairs in 18 languages from WikiHow, a high quality, collaborative resource of how-to guides on a diverse set of topics written by human authors. We create gold-standard article-summary alignments across languages by aligning the images that are used to describe each how-to step in an article. As a set of baselines for further studies, we evaluate the performance of existing cross-lingual abstractive summarization methods on our dataset. We further propose a method for direct cross-lingual summarization (i.e., without requiring translation at inference time) by leveraging synthetic data and Neural Machine Translation as a pre-training step. Our method significantly outperforms the baseline approaches, while being more cost efficient during inference.\",\n}#### Contact Name\n\n\n\nFaisal Ladhak, Esin Durmus#### Contact Email\n\n\nfaisal@URL, esdurmus@URL#### Has a Leaderboard?\n\n\nno### Languages and Intended Use#### Multilingual?\n\n\n\nyes#### Covered Dialects\n\n\nDataset does not have multiple dialects per language.#### Covered Languages\n\n\n\n'English', 'Spanish, Castilian', 'Portuguese', 'French', 'German', 'Russian', 'Italian', 'Indonesian', 'Dutch, Flemish', 'Arabic', 'Chinese', 'Vietnamese', 'Thai', 'Japanese', 'Korean', 'Hindi', 'Czech', 'Turkish'#### Whose Language?\n\n\nNo information about the user demographic is available.#### License\n\n\n\ncc-by-3.0: Creative Commons Attribution 3.0 Unported#### Intended Use\n\n\nThe dataset was intended to serve as a large-scale, high-quality benchmark dataset for cross-lingual summarization.#### Primary Task\n\n\nSummarization#### Communicative Goal\n\n\n\nProduce a high quality summary for the given input article.### Credit#### Curation Organization Type(s)\n\n\n'academic'#### Curation Organization(s)\n\n\nColumbia University#### Dataset Creators\n\n\nFaisal Ladhak (Columbia University), Esin Durmus (Stanford University), Claire Cardie (Cornell University), Kathleen McKeown (Columbia University)#### Who added the Dataset to GEM?\n\n\nJenny Chim (Queen Mary University of London), Faisal Ladhak (Columbia University)### Dataset Structure#### Data Fields\n\n\ngem_id -- The id for the data instance.\nsource_language -- The language of the source article.\ntarget_language -- The language of the target summary.\nsource -- The source document.", "passage: #### Example Instance\n\n\n\n{\n \"gem_id\": \"wikilingua_crosslingual-train-12345\",\n \"gem_parent_id\": \"wikilingua_crosslingual-train-12345\",\n \"source_language\": \"fr\",\n \"target_language\": \"de\",\n \"source\": \"Document in fr\",\n \"target\": \"Summary in de\",\n}#### Data Splits\n\n\n\nThe data is split into train/dev/test. In addition to the full test set, there's also a sampled version of the test set.#### Splitting Criteria\n\n\n\nThe data was split to ensure the same document would appear in the same split across languages so as to ensure there's no leakage into the test set.## Dataset in GEM### Rationale for Inclusion in GEM#### Why is the Dataset in GEM?\n\n\n\nThis dataset provides a large-scale, high-quality resource for cross-lingual summarization in 18 languages, increasing the coverage of languages for the GEM summarization task.#### Similar Datasets\n\n\n\nyes#### Unique Language Coverage\n\n\n\nyes#### Difference from other GEM datasets\n\n\n\nXSum covers English news articles, and MLSum covers news articles in German and Spanish. \nIn contrast, this dataset has how-to articles in 18 languages, substantially increasing the languages covered. Moreover, it also provides a a different domain than the other two datasets.#### Ability that the Dataset measures\n\n\n\nThe ability to generate quality summaries across multiple languages.### GEM-Specific Curation#### Modificatied for GEM?\n\n\n\nyes#### GEM Modifications\n\n\n\n'other'#### Modification Details\n\n\n\nPrevious version had separate data loaders for each language. In this version, we've created a single monolingual data loader, which contains monolingual data in each of the 18 languages. In addition, we've also created a single cross-lingual data loader across all the language pairs in the dataset.#### Additional Splits?\n\n\n\nno### Getting Started with the Task## Previous Results### Previous Results#### Measured Model Abilities\n\n\n\nAbility to summarize content across different languages.#### Metrics\n\n\n\n'ROUGE'#### Proposed Evaluation\n\n\n\nROUGE is used to measure content selection by comparing word overlap with reference summaries. In addition, the authors of the dataset also used human evaluation to evaluate content selection and fluency of the systems.#### Previous results available?\n\n\n\nno## Dataset Curation### Original Curation", "passage: #### Original Curation Rationale\n\n\n\nThe dataset was created in order to enable new approaches for cross-lingual and multilingual summarization, which are currently understudied as well as open up inetersting new directions for research in summarization. E.g., exploration of multi-source cross-lingual architectures, i.e. models that can summarize from multiple source languages into a target language, building models that can summarize articles from any language to any other language for a given set of languages.#### Communicative Goal\n\n\n\nGiven an input article, produce a high quality summary of the article in the target language.#### Sourced from Different Sources\n\n\n\nno### Language Data#### How was Language Data Obtained?\n\n\n\n'Found'#### Where was it found?\n\n\n\n'Single website'#### Language Producers\n\n\n\nWikiHow, which is an online resource of how-to guides (written and reviewed by human authors) is used as the data source.#### Topics Covered\n\n\n\nThe articles cover 19 broad categories including health, arts and entertainment, personal care and style, travel, education and communications, etc. The categories cover a broad set of genres and topics.#### Data Validation\n\n\n\nnot validated#### Was Data Filtered?\n\n\n\nnot filtered### Structured Annotations#### Additional Annotations?\n\n\n\n\nnone#### Annotation Service?\n\n\n\nno### Consent#### Any Consent Policy?\n\n\n\nyes#### Consent Policy Details\n\n\n\n(1) Text Content. All text posted by Users to the Service is sub-licensed by wikiHow to other Users under a Creative Commons license as provided herein. The Creative Commons license allows such text content be used freely for non-commercial purposes, so long as it is used and attributed to the original author as specified under the terms of the license. Allowing free republication of our articles helps wikiHow achieve its mission by providing instruction on solving the problems of everyday life to more people for free. In order to support this goal, wikiHow hereby grants each User of the Service a license to all text content that Users contribute to the Service under the terms and conditions of a Creative Commons CC BY-NC-SA 3.0 License. Please be sure to read the terms of the license carefully. You continue to own all right, title, and interest in and to your User Content, and you are free to distribute it as you wish, whether for commercial or non-commercial purposes.#### Other Consented Downstream Use\n\n\n\nThe data is made freely available under the Creative Commons license, therefore there are no restrictions about downstream uses as long is it's for non-commercial purposes.### Private Identifying Information (PII)#### Contains PII?\n\n\n\n\nno PII" ]

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

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

[ 6, 10, 125, 24, 6, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 19 ]

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

[ "task_categories:image-classification", "task_ids:multi-class-classification", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended|imdb", "language:en", "license:mit", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for 'reviews_with_drift'", "## 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", "### Dataset Summary\n\nThis dataset was crafted to be used in our tutorial [Link to the tutorial when ready]. It consists on a large Movie Review Dataset mixed with some reviews from a Hotel Review Dataset. The training/validation set are purely obtained from the Movie Review Dataset while the production set is mixed. Some other features have been added ('age', 'gender', 'context') as well as a made up timestamp 'prediction_ts' of when the inference took place.", "### Supported Tasks and Leaderboards\n\n'text-classification', 'sentiment-classification': The dataset is mainly used for text classification: given the text, predict the sentiment (positive or negative).", "### Languages\n\nText is mainly written in english.", "## 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 @fjcasti1 for adding this dataset." ]

[ "TAGS\n#task_categories-image-classification #task_ids-multi-class-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|imdb #language-English #license-mit #region-us \n", "# Dataset Card for 'reviews_with_drift'", "## 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", "### Dataset Summary\n\nThis dataset was crafted to be used in our tutorial [Link to the tutorial when ready]. It consists on a large Movie Review Dataset mixed with some reviews from a Hotel Review Dataset. The training/validation set are purely obtained from the Movie Review Dataset while the production set is mixed. Some other features have been added ('age', 'gender', 'context') as well as a made up timestamp 'prediction_ts' of when the inference took place.", "### Supported Tasks and Leaderboards\n\n'text-classification', 'sentiment-classification': The dataset is mainly used for text classification: given the text, predict the sentiment (positive or negative).", "### Languages\n\nText is mainly written in english.", "## 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 @fjcasti1 for adding this dataset." ]

[ 95, 13, 125, 4, 120, 50, 12, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 19 ]

[ "passage: TAGS\n#task_categories-image-classification #task_ids-multi-class-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|imdb #language-English #license-mit #region-us \n# Dataset Card for 'reviews_with_drift'## 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### Dataset Summary\n\nThis dataset was crafted to be used in our tutorial [Link to the tutorial when ready]. It consists on a large Movie Review Dataset mixed with some reviews from a Hotel Review Dataset. The training/validation set are purely obtained from the Movie Review Dataset while the production set is mixed. Some other features have been added ('age', 'gender', 'context') as well as a made up timestamp 'prediction_ts' of when the inference took place.### Supported Tasks and Leaderboards\n\n'text-classification', 'sentiment-classification': The dataset is mainly used for text classification: given the text, predict the sentiment (positive or negative).### Languages\n\nText is mainly written in english.## 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" ]

[ "task_categories:question-answering", "task_categories:summarization", "task_categories:text-generation", "task_ids:multiple-choice-qa", "task_ids:natural-language-inference", "language:en", "license:mit", "multi-hop-question-answering", "query-based-summarization", "long-texts", "arxiv:2208.00748", "arxiv:2201.03533", "arxiv:2104.02112", "arxiv:2104.07091", "arxiv:2104.05938", "arxiv:1712.07040", "arxiv:2105.03011", "arxiv:2112.08608", "arxiv:2110.01799", "arxiv:1606.05250", "arxiv:1809.09600", "region:us" ]

[ "2208.00748", "2201.03533", "2104.02112", "2104.07091", "2104.05938", "1712.07040", "2105.03011", "2112.08608", "2110.01799", "1606.05250", "1809.09600" ]

[ "en" ]

[ "## Dataset Description\n- Repository: SLED Github repository\n- Paper: Efficient Long-Text Understanding with Short-Text Models", "# Dataset Card for SCROLLS", "## Overview\nThis dataset is based on the SCROLLS dataset (paper), the SQuAD 1.1 dataset and the HotpotQA dataset.\nIt doesn't contain any unpblished data, but includes the configuration needed for the Efficient Long-Text Understanding with Short-Text Models\n paper.", "## Tasks\nThe tasks included are:", "#### GovReport (Huang et al., 2021)\nGovReport is a summarization dataset of reports addressing various national policy issues published by the \nCongressional Research Service and the U.S. Government Accountability Office, where each document is paired with a hand-written executive summary.\nThe reports and their summaries are longer than their equivalents in other popular long-document summarization datasets; \nfor example, GovReport's documents are approximately 1.5 and 2.5 times longer than the documents in Arxiv and PubMed, respectively.", "#### SummScreenFD (Chen et al., 2021)\nSummScreenFD is a summarization dataset in the domain of TV shows (e.g. Friends, Game of Thrones).\nGiven a transcript of a specific episode, the goal is to produce the episode's recap.\nThe original dataset is divided into two complementary subsets, based on the source of its community contributed transcripts. \nFor SCROLLS, we use the ForeverDreaming (FD) subset, as it incorporates 88 different shows, \nmaking it a more diverse alternative to the TV MegaSite (TMS) subset, which has only 10 shows. \nCommunity-authored recaps for the ForeverDreaming transcripts were collected from English Wikipedia and TVMaze.", "#### QMSum (Zhong et al., 2021)\nQMSum is a query-based summarization dataset, consisting of 232 meetings transcripts from multiple domains. \nThe corpus covers academic group meetings at the International Computer Science Institute and their summaries, industrial product meetings for designing a remote control, \nand committee meetings of the Welsh and Canadian Parliaments, dealing with a variety of public policy issues.\nAnnotators were tasked with writing queries about the broad contents of the meetings, as well as specific questions about certain topics or decisions, \nwhile ensuring that the relevant text for answering each query spans at least 200 words or 10 turns.", "#### NarrativeQA (Kočiský et al., 2021)\nNarrativeQA (Kočiský et al., 2021) is an established question answering dataset over entire books from Project Gutenberg and movie scripts from different websites.\nAnnotators were given summaries of the books and scripts obtained from Wikipedia, and asked to generate question-answer pairs, \nresulting in about 30 questions and answers for each of the 1,567 books and scripts.\nThey were encouraged to use their own words rather then copying, and avoid asking yes/no questions or ones about the cast.\nEach question was then answered by an additional annotator, providing each question with two reference answers (unless both answers are identical).", "#### Qasper (Dasigi et al., 2021)\nQasper is a question answering dataset over NLP papers filtered from the Semantic Scholar Open Research Corpus (S2ORC).\nQuestions were written by NLP practitioners after reading only the title and abstract of the papers, \nwhile another set of NLP practitioners annotated the answers given the entire document.\nQasper contains abstractive, extractive, and yes/no questions, as well as unanswerable ones.", "#### QuALITY (Pang et al., 2021)\nQuALITY is a multiple-choice question answering dataset over articles and stories sourced from Project Gutenberg, \nthe Open American National Corpus, and more.\nExperienced writers wrote questions and distractors, and were incentivized to write answerable, unambiguous questions such that in order to correctly answer them, \nhuman annotators must read large portions of the given document. \nReference answers were then calculated using the majority vote between of the annotators and writer's answers.\nTo measure the difficulty of their questions, Pang et al. conducted a speed validation process, \nwhere another set of annotators were asked to answer questions given only a short period of time to skim through the document.\nAs a result, 50% of the questions in QuALITY are labeled as hard, i.e. the majority of the annotators in the speed validation setting chose the wrong answer.", "#### ContractNLI (Koreeda and Manning, 2021)\nContract NLI is a natural language inference dataset in the legal domain.\nGiven a non-disclosure agreement (the premise), the task is to predict whether a particular legal statement (the hypothesis) is entailed, not entailed (neutral), or cannot be entailed (contradiction) from the contract.\nThe NDAs were manually picked after simple filtering from the Electronic Data Gathering, Analysis, and Retrieval system (EDGAR) and Google.\nThe dataset contains a total of 607 contracts and 17 unique hypotheses, which were combined to produce the dataset's 10,319 examples.", "#### SQuAD 1.1 (Rajpurkar et al., 2016) \nStanford Question Answering Dataset (SQuAD) is a reading comprehension \\\ndataset, consisting of questions posed by crowdworkers on a set of Wikipedia \\\narticles, where the answer to every question is a segment of text, or span, \\\nfrom the corresponding reading passage, or the question might be unanswerable.", "#### HotpotQA (Yang et al., 2018) \nHotpotQA is a new dataset with 113k Wikipedia-based question-answer pairs with four key features:\n(1) the questions require finding and reasoning over multiple supporting documents to answer;\n(2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas;\n(3) we provide sentence-level supporting facts required for reasoning, allowingQA systems to reason with strong supervisionand explain the predictions;\n(4) we offer a new type of factoid comparison questions to testQA systems’ ability to extract relevant facts and perform necessary comparison.", "## Data Fields\n\nAll the datasets in the benchmark are in the same input-output format\n\n- 'input': a 'string' feature. The input document.\n- 'input_prefix': an optional 'string' feature, for the datasets containing prefix (e.g. question)\n- 'output': a 'string' feature. The target.\n- 'id': a 'string' feature. Unique per input.\n- 'pid': a 'string' feature. Unique per input-output pair (can differ from 'id' in NarrativeQA and Qasper, where there is more then one valid target).\n\nThe dataset that contain 'input_prefix' are:\n- SQuAD - the question\n- HotpotQA - the question\n- qmsum - the query\n- qasper - the question\n- narrative_qa - the question\n- quality - the question + the four choices\n- contract_nli - the hypothesis", "## Controlled experiments\nTo test multiple properties of SLED, we modify SQuAD 1.1 Rajpurkar et al., 2016 \nand HotpotQA Yang et al., 2018 to create a few controlled experiments settings.\nThose are accessible via the following configurations:\n- squad - Contains the original version of SQuAD 1.1 (question + passage)\n- squad_ordered_distractors - For each example, 9 random distrctor passages are concatenated (separated by '\\n')\n- squad_shuffled_distractors - For each example, 9 random distrctor passages are added (separated by '\\n'), and jointly the 10 passages are randomly shuffled\n- hotpotqa - A clean version of HotpotQA, where each input contains only the two gold passages (separated by '\\n')\n- hotpotqa_second_only - In each example, the input contains only the second gold passage\n\nIf you use this dataset, please make sure to cite all the original dataset papers as well SCROLLS. [bibtex]" ]

[ "TAGS\n#task_categories-question-answering #task_categories-summarization #task_categories-text-generation #task_ids-multiple-choice-qa #task_ids-natural-language-inference #language-English #license-mit #multi-hop-question-answering #query-based-summarization #long-texts #arxiv-2208.00748 #arxiv-2201.03533 #arxiv-2104.02112 #arxiv-2104.07091 #arxiv-2104.05938 #arxiv-1712.07040 #arxiv-2105.03011 #arxiv-2112.08608 #arxiv-2110.01799 #arxiv-1606.05250 #arxiv-1809.09600 #region-us \n", "## Dataset Description\n- Repository: SLED Github repository\n- Paper: Efficient Long-Text Understanding with Short-Text Models", "# Dataset Card for SCROLLS", "## Overview\nThis dataset is based on the SCROLLS dataset (paper), the SQuAD 1.1 dataset and the HotpotQA dataset.\nIt doesn't contain any unpblished data, but includes the configuration needed for the Efficient Long-Text Understanding with Short-Text Models\n paper.", "## Tasks\nThe tasks included are:", "#### GovReport (Huang et al., 2021)\nGovReport is a summarization dataset of reports addressing various national policy issues published by the \nCongressional Research Service and the U.S. Government Accountability Office, where each document is paired with a hand-written executive summary.\nThe reports and their summaries are longer than their equivalents in other popular long-document summarization datasets; \nfor example, GovReport's documents are approximately 1.5 and 2.5 times longer than the documents in Arxiv and PubMed, respectively.", "#### SummScreenFD (Chen et al., 2021)\nSummScreenFD is a summarization dataset in the domain of TV shows (e.g. Friends, Game of Thrones).\nGiven a transcript of a specific episode, the goal is to produce the episode's recap.\nThe original dataset is divided into two complementary subsets, based on the source of its community contributed transcripts. \nFor SCROLLS, we use the ForeverDreaming (FD) subset, as it incorporates 88 different shows, \nmaking it a more diverse alternative to the TV MegaSite (TMS) subset, which has only 10 shows. \nCommunity-authored recaps for the ForeverDreaming transcripts were collected from English Wikipedia and TVMaze.", "#### QMSum (Zhong et al., 2021)\nQMSum is a query-based summarization dataset, consisting of 232 meetings transcripts from multiple domains. \nThe corpus covers academic group meetings at the International Computer Science Institute and their summaries, industrial product meetings for designing a remote control, \nand committee meetings of the Welsh and Canadian Parliaments, dealing with a variety of public policy issues.\nAnnotators were tasked with writing queries about the broad contents of the meetings, as well as specific questions about certain topics or decisions, \nwhile ensuring that the relevant text for answering each query spans at least 200 words or 10 turns.", "#### NarrativeQA (Kočiský et al., 2021)\nNarrativeQA (Kočiský et al., 2021) is an established question answering dataset over entire books from Project Gutenberg and movie scripts from different websites.\nAnnotators were given summaries of the books and scripts obtained from Wikipedia, and asked to generate question-answer pairs, \nresulting in about 30 questions and answers for each of the 1,567 books and scripts.\nThey were encouraged to use their own words rather then copying, and avoid asking yes/no questions or ones about the cast.\nEach question was then answered by an additional annotator, providing each question with two reference answers (unless both answers are identical).", "#### Qasper (Dasigi et al., 2021)\nQasper is a question answering dataset over NLP papers filtered from the Semantic Scholar Open Research Corpus (S2ORC).\nQuestions were written by NLP practitioners after reading only the title and abstract of the papers, \nwhile another set of NLP practitioners annotated the answers given the entire document.\nQasper contains abstractive, extractive, and yes/no questions, as well as unanswerable ones.", "#### QuALITY (Pang et al., 2021)\nQuALITY is a multiple-choice question answering dataset over articles and stories sourced from Project Gutenberg, \nthe Open American National Corpus, and more.\nExperienced writers wrote questions and distractors, and were incentivized to write answerable, unambiguous questions such that in order to correctly answer them, \nhuman annotators must read large portions of the given document. \nReference answers were then calculated using the majority vote between of the annotators and writer's answers.\nTo measure the difficulty of their questions, Pang et al. conducted a speed validation process, \nwhere another set of annotators were asked to answer questions given only a short period of time to skim through the document.\nAs a result, 50% of the questions in QuALITY are labeled as hard, i.e. the majority of the annotators in the speed validation setting chose the wrong answer.", "#### ContractNLI (Koreeda and Manning, 2021)\nContract NLI is a natural language inference dataset in the legal domain.\nGiven a non-disclosure agreement (the premise), the task is to predict whether a particular legal statement (the hypothesis) is entailed, not entailed (neutral), or cannot be entailed (contradiction) from the contract.\nThe NDAs were manually picked after simple filtering from the Electronic Data Gathering, Analysis, and Retrieval system (EDGAR) and Google.\nThe dataset contains a total of 607 contracts and 17 unique hypotheses, which were combined to produce the dataset's 10,319 examples.", "#### SQuAD 1.1 (Rajpurkar et al., 2016) \nStanford Question Answering Dataset (SQuAD) is a reading comprehension \\\ndataset, consisting of questions posed by crowdworkers on a set of Wikipedia \\\narticles, where the answer to every question is a segment of text, or span, \\\nfrom the corresponding reading passage, or the question might be unanswerable.", "#### HotpotQA (Yang et al., 2018) \nHotpotQA is a new dataset with 113k Wikipedia-based question-answer pairs with four key features:\n(1) the questions require finding and reasoning over multiple supporting documents to answer;\n(2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas;\n(3) we provide sentence-level supporting facts required for reasoning, allowingQA systems to reason with strong supervisionand explain the predictions;\n(4) we offer a new type of factoid comparison questions to testQA systems’ ability to extract relevant facts and perform necessary comparison.", "## Data Fields\n\nAll the datasets in the benchmark are in the same input-output format\n\n- 'input': a 'string' feature. The input document.\n- 'input_prefix': an optional 'string' feature, for the datasets containing prefix (e.g. question)\n- 'output': a 'string' feature. The target.\n- 'id': a 'string' feature. Unique per input.\n- 'pid': a 'string' feature. Unique per input-output pair (can differ from 'id' in NarrativeQA and Qasper, where there is more then one valid target).\n\nThe dataset that contain 'input_prefix' are:\n- SQuAD - the question\n- HotpotQA - the question\n- qmsum - the query\n- qasper - the question\n- narrative_qa - the question\n- quality - the question + the four choices\n- contract_nli - the hypothesis", "## Controlled experiments\nTo test multiple properties of SLED, we modify SQuAD 1.1 Rajpurkar et al., 2016 \nand HotpotQA Yang et al., 2018 to create a few controlled experiments settings.\nThose are accessible via the following configurations:\n- squad - Contains the original version of SQuAD 1.1 (question + passage)\n- squad_ordered_distractors - For each example, 9 random distrctor passages are concatenated (separated by '\\n')\n- squad_shuffled_distractors - For each example, 9 random distrctor passages are added (separated by '\\n'), and jointly the 10 passages are randomly shuffled\n- hotpotqa - A clean version of HotpotQA, where each input contains only the two gold passages (separated by '\\n')\n- hotpotqa_second_only - In each example, the input contains only the second gold passage\n\nIf you use this dataset, please make sure to cite all the original dataset papers as well SCROLLS. [bibtex]" ]

[ 191, 34, 8, 68, 9, 120, 172, 153, 159, 113, 211, 155, 90, 134, 213, 246 ]

[ "passage: TAGS\n#task_categories-question-answering #task_categories-summarization #task_categories-text-generation #task_ids-multiple-choice-qa #task_ids-natural-language-inference #language-English #license-mit #multi-hop-question-answering #query-based-summarization #long-texts #arxiv-2208.00748 #arxiv-2201.03533 #arxiv-2104.02112 #arxiv-2104.07091 #arxiv-2104.05938 #arxiv-1712.07040 #arxiv-2105.03011 #arxiv-2112.08608 #arxiv-2110.01799 #arxiv-1606.05250 #arxiv-1809.09600 #region-us \n## Dataset Description\n- Repository: SLED Github repository\n- Paper: Efficient Long-Text Understanding with Short-Text Models# Dataset Card for SCROLLS## Overview\nThis dataset is based on the SCROLLS dataset (paper), the SQuAD 1.1 dataset and the HotpotQA dataset.\nIt doesn't contain any unpblished data, but includes the configuration needed for the Efficient Long-Text Understanding with Short-Text Models\n paper.## Tasks\nThe tasks included are:#### GovReport (Huang et al., 2021)\nGovReport is a summarization dataset of reports addressing various national policy issues published by the \nCongressional Research Service and the U.S. Government Accountability Office, where each document is paired with a hand-written executive summary.\nThe reports and their summaries are longer than their equivalents in other popular long-document summarization datasets; \nfor example, GovReport's documents are approximately 1.5 and 2.5 times longer than the documents in Arxiv and PubMed, respectively.", "passage: #### SummScreenFD (Chen et al., 2021)\nSummScreenFD is a summarization dataset in the domain of TV shows (e.g. Friends, Game of Thrones).\nGiven a transcript of a specific episode, the goal is to produce the episode's recap.\nThe original dataset is divided into two complementary subsets, based on the source of its community contributed transcripts. \nFor SCROLLS, we use the ForeverDreaming (FD) subset, as it incorporates 88 different shows, \nmaking it a more diverse alternative to the TV MegaSite (TMS) subset, which has only 10 shows. \nCommunity-authored recaps for the ForeverDreaming transcripts were collected from English Wikipedia and TVMaze.#### QMSum (Zhong et al., 2021)\nQMSum is a query-based summarization dataset, consisting of 232 meetings transcripts from multiple domains. \nThe corpus covers academic group meetings at the International Computer Science Institute and their summaries, industrial product meetings for designing a remote control, \nand committee meetings of the Welsh and Canadian Parliaments, dealing with a variety of public policy issues.\nAnnotators were tasked with writing queries about the broad contents of the meetings, as well as specific questions about certain topics or decisions, \nwhile ensuring that the relevant text for answering each query spans at least 200 words or 10 turns.#### NarrativeQA (Kočiský et al., 2021)\nNarrativeQA (Kočiský et al., 2021) is an established question answering dataset over entire books from Project Gutenberg and movie scripts from different websites.\nAnnotators were given summaries of the books and scripts obtained from Wikipedia, and asked to generate question-answer pairs, \nresulting in about 30 questions and answers for each of the 1,567 books and scripts.\nThey were encouraged to use their own words rather then copying, and avoid asking yes/no questions or ones about the cast.\nEach question was then answered by an additional annotator, providing each question with two reference answers (unless both answers are identical).#### Qasper (Dasigi et al., 2021)\nQasper is a question answering dataset over NLP papers filtered from the Semantic Scholar Open Research Corpus (S2ORC).\nQuestions were written by NLP practitioners after reading only the title and abstract of the papers, \nwhile another set of NLP practitioners annotated the answers given the entire document.\nQasper contains abstractive, extractive, and yes/no questions, as well as unanswerable ones.", "passage: #### QuALITY (Pang et al., 2021)\nQuALITY is a multiple-choice question answering dataset over articles and stories sourced from Project Gutenberg, \nthe Open American National Corpus, and more.\nExperienced writers wrote questions and distractors, and were incentivized to write answerable, unambiguous questions such that in order to correctly answer them, \nhuman annotators must read large portions of the given document. \nReference answers were then calculated using the majority vote between of the annotators and writer's answers.\nTo measure the difficulty of their questions, Pang et al. conducted a speed validation process, \nwhere another set of annotators were asked to answer questions given only a short period of time to skim through the document.\nAs a result, 50% of the questions in QuALITY are labeled as hard, i.e. the majority of the annotators in the speed validation setting chose the wrong answer.#### ContractNLI (Koreeda and Manning, 2021)\nContract NLI is a natural language inference dataset in the legal domain.\nGiven a non-disclosure agreement (the premise), the task is to predict whether a particular legal statement (the hypothesis) is entailed, not entailed (neutral), or cannot be entailed (contradiction) from the contract.\nThe NDAs were manually picked after simple filtering from the Electronic Data Gathering, Analysis, and Retrieval system (EDGAR) and Google.\nThe dataset contains a total of 607 contracts and 17 unique hypotheses, which were combined to produce the dataset's 10,319 examples.#### SQuAD 1.1 (Rajpurkar et al., 2016) \nStanford Question Answering Dataset (SQuAD) is a reading comprehension \\\ndataset, consisting of questions posed by crowdworkers on a set of Wikipedia \\\narticles, where the answer to every question is a segment of text, or span, \\\nfrom the corresponding reading passage, or the question might be unanswerable.#### HotpotQA (Yang et al., 2018) \nHotpotQA is a new dataset with 113k Wikipedia-based question-answer pairs with four key features:\n(1) the questions require finding and reasoning over multiple supporting documents to answer;\n(2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas;\n(3) we provide sentence-level supporting facts required for reasoning, allowingQA systems to reason with strong supervisionand explain the predictions;\n(4) we offer a new type of factoid comparison questions to testQA systems’ ability to extract relevant facts and perform necessary comparison." ]

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

[]

[]

[]

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

[ 11 ]

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

[ "task_categories:text-classification", "task_ids:semantic-similarity-classification", "task_ids:semantic-similarity-scoring", "task_ids:text-scoring", "task_ids:multi-input-text-classification", "annotations_creators:expert-generated", "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:multilingual", "size_categories:10K<n<100K", "source_datasets:extended|other-paws", "language:nb", "language:nn", "license:cc-by-4.0", "region:us" ]

[]

[ "nb", "nn" ]

[ "# Dataset Card for Norwegian PAWS-X", "## Table of Contents\n- Dataset Description\n - Dataset Summary\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: NB AiLab\n- Repository: Norwegian PAWS-X Repository\n- Point of Contact: ai-lab@URL", "### Dataset Summary\n\nNorwegian PAWS-X is an extension of the PAWS-X dataset. PAWS-X is a multilingual version of PAWS (Paraphrase Adversaries from Word Scrambling) for six languages. The Norwegian PAWS-X dataset has machine-translated versions of the original PAWS-X dataset into Norwegian Bokmål and Nynorsk.", "### Languages\n\n- Norwegian Bokmål ('nb')\n- Norwegian Nynorsk ('nn')", "## Dataset Structure", "### Data Instances\n\nEach instance includes a pair of sentences in Norwegian along with a binary label indicating whether the sentences are paraphrases of each other.", "### Data Fields\n\n- 'id': An identifier for each example (int32)\n- 'sentence1': The first sentence in Norwegian (string)\n- 'sentence2': The second sentence in Norwegian (string)\n- 'label': Binary label, where '1' indicates the sentences are paraphrases and '0' indicates they are not (class_label: '0', '1')", "### Data Splits\n\nThe dataset is divided into training, validation, and test sets. The exact numbers of instances in each split will be as per the original PAWS-X dataset.", "## Dataset Creation", "### Curation Rationale\n\nNorwegian PAWS-X was created to extend the PAWS paraphrase identification task to the Norwegian language, including both Bokmål and Nynorsk standards. This promotes multilingual and cross-lingual research in paraphrase identification.", "### Source Data\n\nThe source data consists of human-translated PAWS pairs in six languages. For the Norwegian PAWS-X dataset, these pairs were translated into Norwegian Bokmål and Nynorsk using FAIR’s No Language Left Behind 3.3B parameters model.", "### Annotations\n\nThe dataset retains the original PAWS labels, which were created through a combination of expert and machine-generated annotations.", "### Personal and Sensitive Information\n\nThere is no known personal or sensitive information in this dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThis dataset helps in promoting the development of NLP technologies in Norwegian.", "### Other Known Limitations\n\nThere may be potential issues related to the translation quality, as the translations were generated using a machine translation model.", "## Additional Information", "### Dataset Curators\n\nThe dataset was curated by researcher Javier de la Rosa.", "### Licensing Information\n\nOriginal PAWS-X License:\n- The dataset may be freely used for any purpose, with acknowledgment of Google LLC as the data source being appreciated. The dataset is provided \"AS IS\" without any warranty, express or implied. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.\n\nNorwegian PAWS-X License:\n- CC BY 4.0" ]

[ "TAGS\n#task_categories-text-classification #task_ids-semantic-similarity-classification #task_ids-semantic-similarity-scoring #task_ids-text-scoring #task_ids-multi-input-text-classification #annotations_creators-expert-generated #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-extended|other-paws #language-Norwegian Bokmål #language-Norwegian Nynorsk #license-cc-by-4.0 #region-us \n", "# Dataset Card for Norwegian PAWS-X", "## Table of Contents\n- Dataset Description\n - Dataset Summary\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: NB AiLab\n- Repository: Norwegian PAWS-X Repository\n- Point of Contact: ai-lab@URL", "### Dataset Summary\n\nNorwegian PAWS-X is an extension of the PAWS-X dataset. PAWS-X is a multilingual version of PAWS (Paraphrase Adversaries from Word Scrambling) for six languages. The Norwegian PAWS-X dataset has machine-translated versions of the original PAWS-X dataset into Norwegian Bokmål and Nynorsk.", "### Languages\n\n- Norwegian Bokmål ('nb')\n- Norwegian Nynorsk ('nn')", "## Dataset Structure", "### Data Instances\n\nEach instance includes a pair of sentences in Norwegian along with a binary label indicating whether the sentences are paraphrases of each other.", "### Data Fields\n\n- 'id': An identifier for each example (int32)\n- 'sentence1': The first sentence in Norwegian (string)\n- 'sentence2': The second sentence in Norwegian (string)\n- 'label': Binary label, where '1' indicates the sentences are paraphrases and '0' indicates they are not (class_label: '0', '1')", "### Data Splits\n\nThe dataset is divided into training, validation, and test sets. The exact numbers of instances in each split will be as per the original PAWS-X dataset.", "## Dataset Creation", "### Curation Rationale\n\nNorwegian PAWS-X was created to extend the PAWS paraphrase identification task to the Norwegian language, including both Bokmål and Nynorsk standards. This promotes multilingual and cross-lingual research in paraphrase identification.", "### Source Data\n\nThe source data consists of human-translated PAWS pairs in six languages. For the Norwegian PAWS-X dataset, these pairs were translated into Norwegian Bokmål and Nynorsk using FAIR’s No Language Left Behind 3.3B parameters model.", "### Annotations\n\nThe dataset retains the original PAWS labels, which were created through a combination of expert and machine-generated annotations.", "### Personal and Sensitive Information\n\nThere is no known personal or sensitive information in this dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThis dataset helps in promoting the development of NLP technologies in Norwegian.", "### Other Known Limitations\n\nThere may be potential issues related to the translation quality, as the translations were generated using a machine translation model.", "## Additional Information", "### Dataset Curators\n\nThe dataset was curated by researcher Javier de la Rosa.", "### Licensing Information\n\nOriginal PAWS-X License:\n- The dataset may be freely used for any purpose, with acknowledgment of Google LLC as the data source being appreciated. The dataset is provided \"AS IS\" without any warranty, express or implied. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.\n\nNorwegian PAWS-X License:\n- CC BY 4.0" ]

[ 169, 10, 111, 33, 87, 23, 6, 36, 94, 44, 5, 59, 65, 34, 21, 8, 23, 31, 5, 20, 99 ]

[ "passage: TAGS\n#task_categories-text-classification #task_ids-semantic-similarity-classification #task_ids-semantic-similarity-scoring #task_ids-text-scoring #task_ids-multi-input-text-classification #annotations_creators-expert-generated #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-extended|other-paws #language-Norwegian Bokmål #language-Norwegian Nynorsk #license-cc-by-4.0 #region-us \n# Dataset Card for Norwegian PAWS-X## Table of Contents\n- Dataset Description\n - Dataset Summary\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: NB AiLab\n- Repository: Norwegian PAWS-X Repository\n- Point of Contact: ai-lab@URL### Dataset Summary\n\nNorwegian PAWS-X is an extension of the PAWS-X dataset. PAWS-X is a multilingual version of PAWS (Paraphrase Adversaries from Word Scrambling) for six languages. The Norwegian PAWS-X dataset has machine-translated versions of the original PAWS-X dataset into Norwegian Bokmål and Nynorsk.### Languages\n\n- Norwegian Bokmål ('nb')\n- Norwegian Nynorsk ('nn')## Dataset Structure### Data Instances\n\nEach instance includes a pair of sentences in Norwegian along with a binary label indicating whether the sentences are paraphrases of each other." ]

[ "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:id", "license:cc-by-4.0", "sentiment", "google-play", "indonesian", "region:us" ]

[]

[ "id" ]

[ "# Dataset Card for Indonesian Google Play Review", "## 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\nScrapped from e-commerce app on Google Play.", "### Supported Tasks and Leaderboards\n\nSentiment Analysis", "### 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-text-classification #task_ids-sentiment-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Indonesian #license-cc-by-4.0 #sentiment #google-play #indonesian #region-us \n", "# Dataset Card for Indonesian Google Play Review", "## 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\nScrapped from e-commerce app on Google Play.", "### Supported Tasks and Leaderboards\n\nSentiment Analysis", "### 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." ]

[ 99, 10, 125, 24, 17, 14, 6, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 17 ]

[ "passage: 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-Indonesian #license-cc-by-4.0 #sentiment #google-play #indonesian #region-us \n# Dataset Card for Indonesian Google Play Review## 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\nScrapped from e-commerce app on Google Play.### Supported Tasks and Leaderboards\n\nSentiment Analysis### 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." ]

[ "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: L-macc/autotrain-Biomedical_sc_summ-1217846148\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @L-macc 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: L-macc/autotrain-Biomedical_sc_summ-1217846148\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @L-macc for evaluating this model." ]

[ 13, 112, 17 ]

[ "passage: 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: L-macc/autotrain-Biomedical_sc_summ-1217846148\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @L-macc 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: L-macc/autotrain-Biomedical_sc_summ-1217846144\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @L-macc 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: L-macc/autotrain-Biomedical_sc_summ-1217846144\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @L-macc for evaluating this model." ]

[ 13, 112, 17 ]

[ "passage: 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: L-macc/autotrain-Biomedical_sc_summ-1217846144\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @L-macc 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: L-macc/autotrain-Biomedical_sc_summ-1217846142\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @L-macc 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: L-macc/autotrain-Biomedical_sc_summ-1217846142\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @L-macc for evaluating this model." ]

[ 13, 112, 17 ]

[ "passage: 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: L-macc/autotrain-Biomedical_sc_summ-1217846142\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @L-macc 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: Blaise-g/long_t5_global_large_pubmed_wip2\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Blaise-g 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: Blaise-g/long_t5_global_large_pubmed_wip2\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Blaise-g for evaluating this model." ]

[ 13, 112, 18 ]

[ "passage: 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: Blaise-g/long_t5_global_large_pubmed_wip2\n* Dataset: Blaise-g/SumPubmed\n* Config: Blaise-g--SumPubmed\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @Blaise-g for evaluating this model." ]

[ "task_categories:visual-question-answering", "task_ids:visual-question-answering", "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:monolingual", "source_datasets:clevr", "language:en", "license:cc-by-4.0", "reasoning", "neuro-symbolic", "multimodal", "arxiv:2208.05358", "region:us" ]

[ "2208.05358" ]

[ "en" ]

[ "# Dataset Card for CLEVR-Math", "## 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- Considerations for Using the Data\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:*URL\n- Paper:*URL\n- Leaderboard:\n- Point of Contact:*dali@URL*", "### Dataset Summary\n\nDataset for compositional multimodal mathematical reasoning based on CLEVR.", "#### Loading the data, preprocessing text with CLIP", "### Supported Tasks and Leaderboards\n\nLeaderboard will be announced at a later date.", "### Languages\n\nThe dataset is currently only available in English. To extend the dataset to other languages, the CLEVR templates must be rewritten in the target language.", "## Dataset Structure", "### Data Instances\n\n* 'general' containing the default version with multihop questions in train and test\n* 'multihop' containing multihop questions only in test data to test generalisation of reasoning", "### Data Fields", "### Data Splits\n\ntrain/val/test", "## Dataset Creation\n\nData is generated using code provided with the CLEVR-dataset, using blender and templates constructed by the dataset curators.", "## Considerations for Using the Data", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nAdam Dahlgren Lindström - dali@URL", "### Licensing Information\n\nLicensed under Creative Commons Attribution Share Alike 4.0 International (CC-by 4.0).", "### Contributions\n\nThanks to @dali-does for adding this dataset." ]

[ "TAGS\n#task_categories-visual-question-answering #task_ids-visual-question-answering #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #source_datasets-clevr #language-English #license-cc-by-4.0 #reasoning #neuro-symbolic #multimodal #arxiv-2208.05358 #region-us \n", "# Dataset Card for CLEVR-Math", "## 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- Considerations for Using the Data\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:*URL\n- Paper:*URL\n- Leaderboard:\n- Point of Contact:*dali@URL*", "### Dataset Summary\n\nDataset for compositional multimodal mathematical reasoning based on CLEVR.", "#### Loading the data, preprocessing text with CLIP", "### Supported Tasks and Leaderboards\n\nLeaderboard will be announced at a later date.", "### Languages\n\nThe dataset is currently only available in English. To extend the dataset to other languages, the CLEVR templates must be rewritten in the target language.", "## Dataset Structure", "### Data Instances\n\n* 'general' containing the default version with multihop questions in train and test\n* 'multihop' containing multihop questions only in test data to test generalisation of reasoning", "### Data Fields", "### Data Splits\n\ntrain/val/test", "## Dataset Creation\n\nData is generated using code provided with the CLEVR-dataset, using blender and templates constructed by the dataset curators.", "## Considerations for Using the Data", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nAdam Dahlgren Lindström - dali@URL", "### Licensing Information\n\nLicensed under Creative Commons Attribution Share Alike 4.0 International (CC-by 4.0).", "### Contributions\n\nThanks to @dali-does for adding this dataset." ]

[ 111, 10, 92, 33, 25, 13, 20, 40, 6, 44, 5, 10, 35, 8, 7, 5, 15, 23, 18 ]

[ "passage: TAGS\n#task_categories-visual-question-answering #task_ids-visual-question-answering #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #source_datasets-clevr #language-English #license-cc-by-4.0 #reasoning #neuro-symbolic #multimodal #arxiv-2208.05358 #region-us \n# Dataset Card for CLEVR-Math## 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- Considerations for Using the Data\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions## Dataset Description\n\n- Homepage:\n- Repository:*URL\n- Paper:*URL\n- Leaderboard:\n- Point of Contact:*dali@URL*### Dataset Summary\n\nDataset for compositional multimodal mathematical reasoning based on CLEVR.#### Loading the data, preprocessing text with CLIP### Supported Tasks and Leaderboards\n\nLeaderboard will be announced at a later date.### Languages\n\nThe dataset is currently only available in English. To extend the dataset to other languages, the CLEVR templates must be rewritten in the target language.## Dataset Structure### Data Instances\n\n* 'general' containing the default version with multihop questions in train and test\n* 'multihop' containing multihop questions only in test data to test generalisation of reasoning### Data Fields### Data Splits\n\ntrain/val/test## Dataset Creation\n\nData is generated using code provided with the CLEVR-dataset, using blender and templates constructed by the dataset curators.## Considerations for Using the Data### Other Known Limitations## Additional Information### Dataset Curators\n\nAdam Dahlgren Lindström - dali@URL### Licensing Information\n\nLicensed under Creative Commons Attribution Share Alike 4.0 International (CC-by 4.0)." ]

[ "task_categories:image-classification", "source_datasets:https://www.robots.ox.ac.uk/~vgg/data/pets/", "license:cc-by-sa-4.0", "pets", "oxford", "region:us" ]

[]

[]

[ "# Oxford-IIIT Pet Dataset\n\nImages from The Oxford-IIIT Pet Dataset. Only images and labels have been pushed, segmentation annotations were ignored.\n\n- Homepage: URL\n\nLicense:\nSame as the original dataset." ]

[ "TAGS\n#task_categories-image-classification #source_datasets-https-//www.robots.ox.ac.uk/~vgg/data/pets/ #license-cc-by-sa-4.0 #pets #oxford #region-us \n", "# Oxford-IIIT Pet Dataset\n\nImages from The Oxford-IIIT Pet Dataset. Only images and labels have been pushed, segmentation annotations were ignored.\n\n- Homepage: URL\n\nLicense:\nSame as the original dataset." ]

[ 64, 51 ]

[ "passage: TAGS\n#task_categories-image-classification #source_datasets-https-//www.robots.ox.ac.uk/~vgg/data/pets/ #license-cc-by-sa-4.0 #pets #oxford #region-us \n# Oxford-IIIT Pet Dataset\n\nImages from The Oxford-IIIT Pet Dataset. Only images and labels have been pushed, segmentation annotations were ignored.\n\n- Homepage: URL\n\nLicense:\nSame as the original dataset." ]

[ "task_categories:translation", "multilinguality:translation", "size_categories:10K<n<100K", "language:en", "language:me", "license:afl-3.0", "middle english", "region:us" ]

[]

[ "en", "me" ]

[]

[ "TAGS\n#task_categories-translation #multilinguality-translation #size_categories-10K<n<100K #language-English #language-me #license-afl-3.0 #middle english #region-us \n" ]

[ 54 ]

[ "passage: TAGS\n#task_categories-translation #multilinguality-translation #size_categories-10K<n<100K #language-English #language-me #license-afl-3.0 #middle english #region-us \n" ]

[ "task_categories:translation", "annotations_creators:found", "language_creators:found", "multilinguality:translation", "size_categories:n<1K", "source_datasets:original", "language:en", "language:pt", "license:afl-3.0", "region:us" ]

[]

[ "en", "pt" ]

[]

[ "TAGS\n#task_categories-translation #annotations_creators-found #language_creators-found #multilinguality-translation #size_categories-n<1K #source_datasets-original #language-English #language-Portuguese #license-afl-3.0 #region-us \n" ]

[ 76 ]

[ "passage: TAGS\n#task_categories-translation #annotations_creators-found #language_creators-found #multilinguality-translation #size_categories-n<1K #source_datasets-original #language-English #language-Portuguese #license-afl-3.0 #region-us \n" ]

[ "task_categories:translation", "annotations_creators:found", "language_creators:found", "multilinguality:translation", "size_categories:1K<n<10K", "source_datasets:extended|opus_books", "language:en", "language:pt", "license:afl-3.0", "region:us" ]

[]

[ "en", "pt" ]

[]

[ "TAGS\n#task_categories-translation #annotations_creators-found #language_creators-found #multilinguality-translation #size_categories-1K<n<10K #source_datasets-extended|opus_books #language-English #language-Portuguese #license-afl-3.0 #region-us \n" ]

[ 85 ]

[ "passage: TAGS\n#task_categories-translation #annotations_creators-found #language_creators-found #multilinguality-translation #size_categories-1K<n<10K #source_datasets-extended|opus_books #language-English #language-Portuguese #license-afl-3.0 #region-us \n" ]

[ "task_categories:text-classification", "task_ids:multi-class-classification", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:id", "license:cc-by-4.0", "news", "news-classifcation", "indonesia", "region:us" ]

[]

[ "id" ]

[ "# Indonesian News Categorization", "## 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\nIndonews: Multiclass News Categorization scrapped popular news portals in Indonesia.", "### 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-multi-class-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Indonesian #license-cc-by-4.0 #news #news-classifcation #indonesia #region-us \n", "# Indonesian News Categorization", "## 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\nIndonews: Multiclass News Categorization scrapped popular news portals in Indonesia.", "### 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." ]

[ 101, 8, 125, 24, 25, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 17 ]

[ "passage: TAGS\n#task_categories-text-classification #task_ids-multi-class-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Indonesian #license-cc-by-4.0 #news #news-classifcation #indonesia #region-us \n# Indonesian News Categorization## 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\nIndonews: Multiclass News Categorization scrapped popular news portals in Indonesia.### 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", "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", "poem", "tweets", "twitter", "indonesian", "region:us" ]

[]

[ "id" ]

[ "# Dataset Card for Poem Tweets", "## 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 data are from Twitter. The purpose of this data is to create text generation model for short text and make sure they are all coherence and rhythmic", "### Supported Tasks and Leaderboards\n\n- Text Generation\n- Language Model", "### 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-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 #poem #tweets #twitter #indonesian #region-us \n", "# Dataset Card for Poem Tweets", "## 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 data are from Twitter. The purpose of this data is to create text generation model for short text and make sure they are all coherence and rhythmic", "### Supported Tasks and Leaderboards\n\n- Text Generation\n- Language Model", "### 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." ]

[ 102, 9, 125, 24, 39, 16, 6, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 17 ]

[ "passage: 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 #poem #tweets #twitter #indonesian #region-us \n# Dataset Card for Poem Tweets## 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 data are from Twitter. The purpose of this data is to create text generation model for short text and make sure they are all coherence and rhythmic### Supported Tasks and Leaderboards\n\n- Text Generation\n- Language Model### 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." ]

[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:id", "license:cc", "arxiv:2201.06642", "region:us" ]

[ "2201.06642" ]

[ "id" ]

[ "### Dataset Summary\n\nKoPI-CC (Korpus Perayapan Indonesia)-CC is Indonesian only extract from Common Crawl snapshots using ungoliant, each snapshot also filtered using some some deduplicate technique such as exact hash(md5) dedup technique and minhash LSH neardup", "### Preprocessing\n\nEach folder name inside snapshots folder denoted preprocessing technique that has been applied .\n\n - Raw\n\t - this processed directly from cc snapshot using ungoliant without any addition filter ,you can read it in their paper (citation below)\n\t - use same \"raw cc snapshot\" for '2021_10' and '2021_49' which can be found in oscar dataset (2109 and 2201)\n - Dedup\n\t - use data from raw folder\n\t - apply cleaning techniques for every text in documents such as \n\t\t - fix html\n\t\t - remove noisy unicode\n\t\t - fix news tag\n\t\t - remove control char\n\t - filter by removing short text (20 words)\n\t - filter by character ratio occurred inside text such as\n\t\t - min_alphabet_ratio (0.75)\n\t\t - max_upper_ratio (0.10)\n\t\t - max_number_ratio(0.05)\n\t\n\t - filter by exact dedup technique\n\t\t - hash all text with md5 hashlib\n\t\t - remove non-unique hash\n\t - full code about dedup step adapted from here\n - Neardup\n\t - use data from dedup folder\n\t\n\t - create index cluster using neardup Minhash and LSH with following config :\n\t\t - use 128 permuation\n\t\t - 6 n-grams size\n\t\t - use word tokenization (split sentence by space)\n\t\t - use 0.8 as similarity score\n\t\n\t - fillter by removing all index from cluster\n\t - full code about neardup step adapted from here\n - Neardup_clean\n\t - use data from neardup folder\n\t - Removing documents containing words from a selection of the Indonesian Bad Words.\n\t\n \n\t- Removing sentences containing:\n\t \n\t - Less than 3 words.\n\t \n\t - A word longer than 1000 characters.\n\t \n\t - An end symbol not matching end-of-sentence punctuation.\n\t \n\t - Strings associated to javascript code (e.g. '{'), lorem ipsum, policy information in indonesia\n \n\t- Removing documents (after sentence filtering):\n\t \n\t - Containing less than 5 sentences.\n\t \n\t - Containing less than 500 or more than 50'000 characters.\n\t - full code about neardup_clean step adapted from here", "## Dataset Structure", "### Data Instances\n\nAn example from the dataset:", "### Data Fields\nThe data contains the following fields:\n- 'url': url of the source as a string\n- 'text': text content as a string\n- 'timestamp': timestamp of extraction as a string\n- 'meta' : json representation of the original from ungoliant tools,can be found here (warc_heder)", "## Additional Information", "### Dataset Curators\nFor inquiries or requests regarding the KoPI-CC contained in this repository, please contact me at samsulrahmadani@URL", "### Licensing Information\n These data are released under this licensing scheme\n I do not own any of the text from which these data has been extracted.\n \tthe license actual packaging of these data under the Creative Commons CC0 license (\"no rights reserved\") URL\n Should you consider that data contains material that is owned by you and should therefore not be reproduced here, please:\n * Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted.\n * Clearly identify the copyrighted work claimed to be infringed.\n * Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material.\n I will comply to legitimate requests by removing the affected sources from the next release of the corpus." ]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #source_datasets-original #language-Indonesian #license-cc #arxiv-2201.06642 #region-us \n", "### Dataset Summary\n\nKoPI-CC (Korpus Perayapan Indonesia)-CC is Indonesian only extract from Common Crawl snapshots using ungoliant, each snapshot also filtered using some some deduplicate technique such as exact hash(md5) dedup technique and minhash LSH neardup", "### Preprocessing\n\nEach folder name inside snapshots folder denoted preprocessing technique that has been applied .\n\n - Raw\n\t - this processed directly from cc snapshot using ungoliant without any addition filter ,you can read it in their paper (citation below)\n\t - use same \"raw cc snapshot\" for '2021_10' and '2021_49' which can be found in oscar dataset (2109 and 2201)\n - Dedup\n\t - use data from raw folder\n\t - apply cleaning techniques for every text in documents such as \n\t\t - fix html\n\t\t - remove noisy unicode\n\t\t - fix news tag\n\t\t - remove control char\n\t - filter by removing short text (20 words)\n\t - filter by character ratio occurred inside text such as\n\t\t - min_alphabet_ratio (0.75)\n\t\t - max_upper_ratio (0.10)\n\t\t - max_number_ratio(0.05)\n\t\n\t - filter by exact dedup technique\n\t\t - hash all text with md5 hashlib\n\t\t - remove non-unique hash\n\t - full code about dedup step adapted from here\n - Neardup\n\t - use data from dedup folder\n\t\n\t - create index cluster using neardup Minhash and LSH with following config :\n\t\t - use 128 permuation\n\t\t - 6 n-grams size\n\t\t - use word tokenization (split sentence by space)\n\t\t - use 0.8 as similarity score\n\t\n\t - fillter by removing all index from cluster\n\t - full code about neardup step adapted from here\n - Neardup_clean\n\t - use data from neardup folder\n\t - Removing documents containing words from a selection of the Indonesian Bad Words.\n\t\n \n\t- Removing sentences containing:\n\t \n\t - Less than 3 words.\n\t \n\t - A word longer than 1000 characters.\n\t \n\t - An end symbol not matching end-of-sentence punctuation.\n\t \n\t - Strings associated to javascript code (e.g. '{'), lorem ipsum, policy information in indonesia\n \n\t- Removing documents (after sentence filtering):\n\t \n\t - Containing less than 5 sentences.\n\t \n\t - Containing less than 500 or more than 50'000 characters.\n\t - full code about neardup_clean step adapted from here", "## Dataset Structure", "### Data Instances\n\nAn example from the dataset:", "### Data Fields\nThe data contains the following fields:\n- 'url': url of the source as a string\n- 'text': text content as a string\n- 'timestamp': timestamp of extraction as a string\n- 'meta' : json representation of the original from ungoliant tools,can be found here (warc_heder)", "## Additional Information", "### Dataset Curators\nFor inquiries or requests regarding the KoPI-CC contained in this repository, please contact me at samsulrahmadani@URL", "### Licensing Information\n These data are released under this licensing scheme\n I do not own any of the text from which these data has been extracted.\n \tthe license actual packaging of these data under the Creative Commons CC0 license (\"no rights reserved\") URL\n Should you consider that data contains material that is owned by you and should therefore not be reproduced here, please:\n * Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted.\n * Clearly identify the copyrighted work claimed to be infringed.\n * Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material.\n I will comply to legitimate requests by removing the affected sources from the next release of the corpus." ]

[ 83, 71, 462, 6, 13, 81, 5, 38, 175 ]

[ "passage: TAGS\n#task_categories-text-generation #task_ids-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #source_datasets-original #language-Indonesian #license-cc #arxiv-2201.06642 #region-us \n### Dataset Summary\n\nKoPI-CC (Korpus Perayapan Indonesia)-CC is Indonesian only extract from Common Crawl snapshots using ungoliant, each snapshot also filtered using some some deduplicate technique such as exact hash(md5) dedup technique and minhash LSH neardup" ]

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

[]

[]

[ "### Dataset description\n\n33.000 transcribed text lines from historical newspapers (before 1878) along with the cropped images of the original scans\n\nText line based OCR\n19.000 text lines in Antiqua\n14.000 text lines in Fraktur\nTranscribed using double-keying (99.95% accuracy)\nPublic Domain, CC0 (See copyright notice)\nBest for training an OCR engine\n\nThe newspapers used are:\n- Le Gratis luxembourgeois (1857-1858)\n- Luxemburger Volks-Freund (1869-1876)\n- L'Arlequin (1848-1848)\n- Courrier du Grand-Duché de Luxembourg (1844-1868)\n- L'Avenir (1868-1871)\n- Der Wächter an der Sauer (1849-1869)\n- Luxemburger Zeitung (1844-1845)\n- Luxemburger Zeitung = Journal de Luxembourg (1858-1859)\n- Der Volksfreund (1848-1849)\n- Cäcilia (1862-1871)\n- Kirchlicher Anzeiger für die Diözese Luxemburg (1871-1878)\n- L'Indépendance luxembourgeoise (1871-1878)\n- Luxemburger Anzeiger (1856)\n- L'Union (1860-1871)\n- Diekircher Wochenblatt (1837-1848)\n- Das Vaterland (1869-1870)\n- D'Wäschfra (1868-1878)\n- Luxemburger Bauernzeitung (1857)\n- Luxemburger Wort (1848-1878)", "### URL for this dataset\n\nURL", "### Dataset format\n\nTwo JSONL files (URL and URL) with the follwing fields:\n- 'font' is either antiqua or fraktur\n- 'img' is the filename of the associated image for the text\n- 'text' is the handcorrected double-keyed text transcribed from the image\n\nSample:\n\n\nIn addition there are two '.zip' files with the associated images", "### Dataset modality\n\nText and associated Images from Scans", "### Dataset licence\n\nCreative Commons Public Domain Dedication and Certification", "### size of dataset\n\n500MB-2GB", "### Contact details for data custodian\n\nopendata@URL" ]

[ "TAGS\n#license-cc0-1.0 #region-us \n", "### Dataset description\n\n33.000 transcribed text lines from historical newspapers (before 1878) along with the cropped images of the original scans\n\nText line based OCR\n19.000 text lines in Antiqua\n14.000 text lines in Fraktur\nTranscribed using double-keying (99.95% accuracy)\nPublic Domain, CC0 (See copyright notice)\nBest for training an OCR engine\n\nThe newspapers used are:\n- Le Gratis luxembourgeois (1857-1858)\n- Luxemburger Volks-Freund (1869-1876)\n- L'Arlequin (1848-1848)\n- Courrier du Grand-Duché de Luxembourg (1844-1868)\n- L'Avenir (1868-1871)\n- Der Wächter an der Sauer (1849-1869)\n- Luxemburger Zeitung (1844-1845)\n- Luxemburger Zeitung = Journal de Luxembourg (1858-1859)\n- Der Volksfreund (1848-1849)\n- Cäcilia (1862-1871)\n- Kirchlicher Anzeiger für die Diözese Luxemburg (1871-1878)\n- L'Indépendance luxembourgeoise (1871-1878)\n- Luxemburger Anzeiger (1856)\n- L'Union (1860-1871)\n- Diekircher Wochenblatt (1837-1848)\n- Das Vaterland (1869-1870)\n- D'Wäschfra (1868-1878)\n- Luxemburger Bauernzeitung (1857)\n- Luxemburger Wort (1848-1878)", "### URL for this dataset\n\nURL", "### Dataset format\n\nTwo JSONL files (URL and URL) with the follwing fields:\n- 'font' is either antiqua or fraktur\n- 'img' is the filename of the associated image for the text\n- 'text' is the handcorrected double-keyed text transcribed from the image\n\nSample:\n\n\nIn addition there are two '.zip' files with the associated images", "### Dataset modality\n\nText and associated Images from Scans", "### Dataset licence\n\nCreative Commons Public Domain Dedication and Certification", "### size of dataset\n\n500MB-2GB", "### Contact details for data custodian\n\nopendata@URL" ]

[ 14, 292, 8, 87, 13, 15, 10, 12 ]

[ "passage: TAGS\n#license-cc0-1.0 #region-us \n### Dataset description\n\n33.000 transcribed text lines from historical newspapers (before 1878) along with the cropped images of the original scans\n\nText line based OCR\n19.000 text lines in Antiqua\n14.000 text lines in Fraktur\nTranscribed using double-keying (99.95% accuracy)\nPublic Domain, CC0 (See copyright notice)\nBest for training an OCR engine\n\nThe newspapers used are:\n- Le Gratis luxembourgeois (1857-1858)\n- Luxemburger Volks-Freund (1869-1876)\n- L'Arlequin (1848-1848)\n- Courrier du Grand-Duché de Luxembourg (1844-1868)\n- L'Avenir (1868-1871)\n- Der Wächter an der Sauer (1849-1869)\n- Luxemburger Zeitung (1844-1845)\n- Luxemburger Zeitung = Journal de Luxembourg (1858-1859)\n- Der Volksfreund (1848-1849)\n- Cäcilia (1862-1871)\n- Kirchlicher Anzeiger für die Diözese Luxemburg (1871-1878)\n- L'Indépendance luxembourgeoise (1871-1878)\n- Luxemburger Anzeiger (1856)\n- L'Union (1860-1871)\n- Diekircher Wochenblatt (1837-1848)\n- Das Vaterland (1869-1870)\n- D'Wäschfra (1868-1878)\n- Luxemburger Bauernzeitung (1857)\n- Luxemburger Wort (1848-1878)### URL for this dataset\n\nURL### Dataset format\n\nTwo JSONL files (URL and URL) with the follwing fields:\n- 'font' is either antiqua or fraktur\n- 'img' is the filename of the associated image for the text\n- 'text' is the handcorrected double-keyed text transcribed from the image\n\nSample:\n\n\nIn addition there are two '.zip' files with the associated images### Dataset modality\n\nText and associated Images from Scans### Dataset licence\n\nCreative Commons Public Domain Dedication and Certification### size of dataset\n\n500MB-2GB### Contact details for data custodian\n\nopendata@URL" ]

[ "region:us" ]

[]

[]

[]

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

[ 6 ]

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

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

[]

[]

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

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

[ 17, 27, 17, 23, 27 ]

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

[ "task_categories:text-classification", "task_ids:topic-classification", "multilinguality:monolingual", "size_categories:1M<n<10M", "language:en", "license:cc0-1.0", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-text-classification #task_ids-topic-classification #multilinguality-monolingual #size_categories-1M<n<10M #language-English #license-cc0-1.0 #region-us \n" ]

[ 59 ]

[ "passage: TAGS\n#task_categories-text-classification #task_ids-topic-classification #multilinguality-monolingual #size_categories-1M<n<10M #language-English #license-cc0-1.0 #region-us \n" ]

[ "multilinguality:monolingual", "size_categories:1M<n<10M", "language:en", "license:cc0-1.0", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#multilinguality-monolingual #size_categories-1M<n<10M #language-English #license-cc0-1.0 #region-us \n" ]

[ 38 ]

[ "passage: TAGS\n#multilinguality-monolingual #size_categories-1M<n<10M #language-English #license-cc0-1.0 #region-us \n" ]

[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:n<1K", "size_categories:10K<n<100K", "source_datasets:original", "language:id", "license:cc-by-4.0", "cerpen", "short-story", "region:us" ]

[]

[ "id" ]

[ "# Dataset Card for Cerpen Corpus", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThis is a small size for Indonesian short story gathered from the internet.\nWe keep the large size for internal research. if you are interested, please join to our discord server", "### 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-n<1K #size_categories-10K<n<100K #source_datasets-original #language-Indonesian #license-cc-by-4.0 #cerpen #short-story #region-us \n", "# Dataset Card for Cerpen Corpus", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThis is a small size for Indonesian short story gathered from the internet.\nWe keep the large size for internal research. if you are interested, please join to our discord server", "### 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." ]

[ 107, 8, 125, 24, 43, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 17 ]

[ "passage: TAGS\n#task_categories-text-generation #task_ids-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-n<1K #size_categories-10K<n<100K #source_datasets-original #language-Indonesian #license-cc-by-4.0 #cerpen #short-story #region-us \n# Dataset Card for Cerpen Corpus## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:### Dataset Summary\n\nThis is a small size for Indonesian short story gathered from the internet.\nWe keep the large size for internal research. if you are interested, please join to our discord server### 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." ]

[ "region:us" ]

[]

[]

[]

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

[ 6 ]

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

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

[]

[]

[]

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

[ 15 ]

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

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

[]

[]

[]

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

[ 17 ]

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

[ "license:cc-by-nc-sa-3.0", "region:us" ]

[]

[]

[]

[ "TAGS\n#license-cc-by-nc-sa-3.0 #region-us \n" ]

[ 19 ]

[ "passage: TAGS\n#license-cc-by-nc-sa-3.0 #region-us \n" ]

[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "language:en", "license:apache-2.0", "math", "mathematics", "formal-mathematics", "region:us" ]

[]

[ "en" ]

[ "# Dataset Description\nThe 'proof-pile' is a 13GB pre-training dataset of mathematical text that comprises 8.3 billion tokens (using the 'gpt-neox' tokenizer). Models trained on this dataset are coming soon :) The dataset is composed of diverse sources of both informal and formal mathematics, namely\n- URL (10GB)\n- Open-source math textbooks (50MB)\n- Formal mathematics libraries (500MB)\n - Lean mathlib and other Lean repositories \n - Isabelle AFP\n - Coq mathematical components and other Coq repositories \n - HOL Light\n - URL\n - Mizar Mathematical Library\n- Math Overflow and Math Stack Exchange (2.5GB)\n- Wiki-style sources (50MB)\n - ProofWiki\n - Wikipedia math articles\n- MATH dataset (6MB)\n\nThe construction of the dataset is reproducible using the code and instructions in the proof-pile Github\nrepo.", "# Supported Tasks\nThis dataset is intended to be used for pre-training and fine-tuning language models. We envision models trained on the 'proof-pile' will have many downstream applications, including informal quantitative reasoning, formal theorem proving, semantic search for formal mathematics, and autoformalization.", "# Languages\nAll informal mathematics in the 'proof-pile' is written in English and LaTeX (arXiv articles in other languages are filtered out using languagedetect). Formal theorem proving languages represented in this dataset are Lean 3, Isabelle, Coq, HOL Light, Metamath, and Mizar.", "# Evaluation\nThe version of 'URL' in this dataset has 10% of proofs replaced with the '?' character in order to preserve a validation and test set for Metamath provers pre-trained on the 'proof-pile'. The precise split can be found here: validation and test. \nThe Lean mathlib commit used in this dataset is '6313863'. Theorems created in subsequent commits can be used for evaluating Lean theorem provers. \n\nThis dataset contains only the training set of the MATH dataset. However, because this dataset contains ProofWiki, the Stacks Project, Trench's Analysis, and Stein's Number Theory, models trained on it cannot be evaluated on the NaturalProofs dataset.", "# Data Preprocessing\nThis section describes any significant filtering and transformations made to various subsets of the data. \n\nURL.\nThe URL dataset is large, heterogeneous, and contains a great deal of noise. We used the following heuristics\nwhen choosing which files from URL source folders to include in the dataset:\n- Keep only files with a '.tex' extension.\n- Only include files that use either a 'utf-8/16/32' or 'latin-1' text encoding. \n- Discard files that do not contain a part, chapter, section, sub...section, paragraph, or subparagraph heading. \n- Delete files that contain the keyword 'gnuplot'. Gnuplot-latex is an old command line utility that generates blocks\n of entirely unintelligible source. \n- Include only articles in English, as determined by the langdetect library. \\n\",\n \"\\n\",\n- Exclude files shorter than 280 characters (characters counted after substring removal described below).\n\nIn addition, we apply the following transformations to URL texts: \n\n- Delete everything outside of '\\begin{document}' and '\\end{document}'. \n- Delete everything including or after '\\Refs', '\\begin{thebibliography}', or '\\begin{bibdiv}'\n- Delete comments. \n- Any more than three consecutive newlines are replaced by three consecutive newlines. \nIn this notebook, we provide an analysis of the prevalence of noisy documents in the URL subset of the\nproof-pile. \n\nStack Exchange.\nWe only include questions that have at least 5 upvotes and an answer. We format Stack Exchange posts as follows\n\n\nURL.\nWe converted 'URL' into human-readable form by following the instructions in the mm-extract repo", "## Contributions\nAuthors: Zhangir Azerbayev, Edward Ayers, Bartosz Piotrowski. \n\nWe would like to thank Jeremy Avigad, Albert Jiang, and Wenda Li for their invaluable guidance, and the Hoskinson Center for Formal Mathematics for its support." ]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #language-English #license-apache-2.0 #math #mathematics #formal-mathematics #region-us \n", "# Dataset Description\nThe 'proof-pile' is a 13GB pre-training dataset of mathematical text that comprises 8.3 billion tokens (using the 'gpt-neox' tokenizer). Models trained on this dataset are coming soon :) The dataset is composed of diverse sources of both informal and formal mathematics, namely\n- URL (10GB)\n- Open-source math textbooks (50MB)\n- Formal mathematics libraries (500MB)\n - Lean mathlib and other Lean repositories \n - Isabelle AFP\n - Coq mathematical components and other Coq repositories \n - HOL Light\n - URL\n - Mizar Mathematical Library\n- Math Overflow and Math Stack Exchange (2.5GB)\n- Wiki-style sources (50MB)\n - ProofWiki\n - Wikipedia math articles\n- MATH dataset (6MB)\n\nThe construction of the dataset is reproducible using the code and instructions in the proof-pile Github\nrepo.", "# Supported Tasks\nThis dataset is intended to be used for pre-training and fine-tuning language models. We envision models trained on the 'proof-pile' will have many downstream applications, including informal quantitative reasoning, formal theorem proving, semantic search for formal mathematics, and autoformalization.", "# Languages\nAll informal mathematics in the 'proof-pile' is written in English and LaTeX (arXiv articles in other languages are filtered out using languagedetect). Formal theorem proving languages represented in this dataset are Lean 3, Isabelle, Coq, HOL Light, Metamath, and Mizar.", "# Evaluation\nThe version of 'URL' in this dataset has 10% of proofs replaced with the '?' character in order to preserve a validation and test set for Metamath provers pre-trained on the 'proof-pile'. The precise split can be found here: validation and test. \nThe Lean mathlib commit used in this dataset is '6313863'. Theorems created in subsequent commits can be used for evaluating Lean theorem provers. \n\nThis dataset contains only the training set of the MATH dataset. However, because this dataset contains ProofWiki, the Stacks Project, Trench's Analysis, and Stein's Number Theory, models trained on it cannot be evaluated on the NaturalProofs dataset.", "# Data Preprocessing\nThis section describes any significant filtering and transformations made to various subsets of the data. \n\nURL.\nThe URL dataset is large, heterogeneous, and contains a great deal of noise. We used the following heuristics\nwhen choosing which files from URL source folders to include in the dataset:\n- Keep only files with a '.tex' extension.\n- Only include files that use either a 'utf-8/16/32' or 'latin-1' text encoding. \n- Discard files that do not contain a part, chapter, section, sub...section, paragraph, or subparagraph heading. \n- Delete files that contain the keyword 'gnuplot'. Gnuplot-latex is an old command line utility that generates blocks\n of entirely unintelligible source. \n- Include only articles in English, as determined by the langdetect library. \\n\",\n \"\\n\",\n- Exclude files shorter than 280 characters (characters counted after substring removal described below).\n\nIn addition, we apply the following transformations to URL texts: \n\n- Delete everything outside of '\\begin{document}' and '\\end{document}'. \n- Delete everything including or after '\\Refs', '\\begin{thebibliography}', or '\\begin{bibdiv}'\n- Delete comments. \n- Any more than three consecutive newlines are replaced by three consecutive newlines. \nIn this notebook, we provide an analysis of the prevalence of noisy documents in the URL subset of the\nproof-pile. \n\nStack Exchange.\nWe only include questions that have at least 5 upvotes and an answer. We format Stack Exchange posts as follows\n\n\nURL.\nWe converted 'URL' into human-readable form by following the instructions in the mm-extract repo", "## Contributions\nAuthors: Zhangir Azerbayev, Edward Ayers, Bartosz Piotrowski. \n\nWe would like to thank Jeremy Avigad, Albert Jiang, and Wenda Li for their invaluable guidance, and the Hoskinson Center for Formal Mathematics for its support." ]

[ 81, 212, 74, 77, 174, 411, 64 ]

[ "passage: TAGS\n#task_categories-text-generation #task_ids-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #language-English #license-apache-2.0 #math #mathematics #formal-mathematics #region-us \n# Dataset Description\nThe 'proof-pile' is a 13GB pre-training dataset of mathematical text that comprises 8.3 billion tokens (using the 'gpt-neox' tokenizer). Models trained on this dataset are coming soon :) The dataset is composed of diverse sources of both informal and formal mathematics, namely\n- URL (10GB)\n- Open-source math textbooks (50MB)\n- Formal mathematics libraries (500MB)\n - Lean mathlib and other Lean repositories \n - Isabelle AFP\n - Coq mathematical components and other Coq repositories \n - HOL Light\n - URL\n - Mizar Mathematical Library\n- Math Overflow and Math Stack Exchange (2.5GB)\n- Wiki-style sources (50MB)\n - ProofWiki\n - Wikipedia math articles\n- MATH dataset (6MB)\n\nThe construction of the dataset is reproducible using the code and instructions in the proof-pile Github\nrepo.# Supported Tasks\nThis dataset is intended to be used for pre-training and fine-tuning language models. We envision models trained on the 'proof-pile' will have many downstream applications, including informal quantitative reasoning, formal theorem proving, semantic search for formal mathematics, and autoformalization.# Languages\nAll informal mathematics in the 'proof-pile' is written in English and LaTeX (arXiv articles in other languages are filtered out using languagedetect). Formal theorem proving languages represented in this dataset are Lean 3, Isabelle, Coq, HOL Light, Metamath, and Mizar." ]

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

[]

[]

[ "#### Mantainers:\n- Kondrolla Dinesh Reddy\n- Keshaw Soni\n- Somya Gautam" ]

[ "TAGS\n#license-cc #region-us \n", "#### Mantainers:\n- Kondrolla Dinesh Reddy\n- Keshaw Soni\n- Somya Gautam" ]

[ 11, 24 ]

[ "passage: TAGS\n#license-cc #region-us \n#### Mantainers:\n- Kondrolla Dinesh Reddy\n- Keshaw Soni\n- Somya Gautam" ]

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

[]

[]

[ "#### Mantainers:\n- Keshaw Soni\n- Somya Gautam\n- Kondrolla Dinesh Reddy" ]

[ "TAGS\n#license-cc #region-us \n", "#### Mantainers:\n- Keshaw Soni\n- Somya Gautam\n- Kondrolla Dinesh Reddy" ]

[ 11, 24 ]

[ "passage: TAGS\n#license-cc #region-us \n#### Mantainers:\n- Keshaw Soni\n- Somya Gautam\n- Kondrolla Dinesh Reddy" ]

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

[]

[]

[ "#### Mantainers:\n- Somya Gautam\n- Kondrolla Dinesh Reddy\n- Keshaw Soni" ]

[ "TAGS\n#license-cc #region-us \n", "#### Mantainers:\n- Somya Gautam\n- Kondrolla Dinesh Reddy\n- Keshaw Soni" ]

[ 11, 24 ]

[ "passage: TAGS\n#license-cc #region-us \n#### Mantainers:\n- Somya Gautam\n- Kondrolla Dinesh Reddy\n- Keshaw Soni" ]

[ "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: Blaise-g/longt5_tglobal_large_scitldr\n* Dataset: Blaise-g/scitldr\n* Config: Blaise-g--scitldr\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Blaise-g 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: Blaise-g/longt5_tglobal_large_scitldr\n* Dataset: Blaise-g/scitldr\n* Config: Blaise-g--scitldr\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Blaise-g for evaluating this model." ]

[ 13, 110, 18 ]

[ "passage: 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: Blaise-g/longt5_tglobal_large_scitldr\n* Dataset: Blaise-g/scitldr\n* Config: Blaise-g--scitldr\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @Blaise-g 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: Blaise-g/longt5_tglobal_large_explanatory_baseline_scitldr\n* Dataset: Blaise-g/scitldr\n* Config: Blaise-g--scitldr\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Blaise-g 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: Blaise-g/longt5_tglobal_large_explanatory_baseline_scitldr\n* Dataset: Blaise-g/scitldr\n* Config: Blaise-g--scitldr\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Blaise-g for evaluating this model." ]

[ 13, 117, 18 ]

[ "passage: 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: Blaise-g/longt5_tglobal_large_explanatory_baseline_scitldr\n* Dataset: Blaise-g/scitldr\n* Config: Blaise-g--scitldr\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @Blaise-g 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: 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 @grappler 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: 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 @grappler for evaluating this model." ]

[ 13, 92, 16 ]

[ "passage: 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: 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 @grappler for evaluating this model." ]

[ "task_categories:question-answering", "task_categories:text2text-generation", "task_categories:text-generation", "multilinguality:monolingual", "size_categories:30k<n<50k", "language:fa", "license:mit", "conditional-text-generation", "conversational-question-answering", "region:us" ]

[]

[ "fa" ]

[ "# SynTran-fa \nSyntactic Transformed Version of Farsi QA datasets to make fluent responses from questions and short answers. You can use this dataset by the code below:", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n- Dataset Creation\n - Source Data\n- Considerations for Using the Data\n - Social Impact of Dataset\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n \n- Homepage: Sharif-SLPL\n- Repository: SynTran-fa\n- Point of Contact: Sadra Sabouri", "### Dataset Summary\n\nGenerating fluent responses has always been challenging for the question-answering task, especially in low-resource languages like Farsi. In recent years there were some efforts for enhancing the size of datasets in Farsi. Syntran-fa is a question-answering dataset that accumulates the former Farsi QA dataset's short answers and proposes a complete fluent answer for each pair of (question, short_answer).\n\nThis dataset contains nearly 50,000 indices of questions and answers. The dataset that has been used as our sources are in Source Data section.\n\nThe main idea for this dataset comes from Fluent Response Generation for Conversational Question Answering where they used a \"parser + syntactic rules\" module to make different fluent answers from a pair of question and a short answer using a parser and some syntactic rules. In this project, we used stanza as our parser to parse the question and generate a response according to it using the short (sentences without verbs - up to ~4 words) answers. One can continue this project by generating different permutations of the sentence's parts (and thus providing more than one sentence for an answer) or training a seq2seq model which does what we do with our rule-based system (by defining a new text-to-text task).", "### Supported Tasks and Leaderboards\n\nThis dataset can be used for the question-answering task, especially when you are going to generate fluent responses. You can train a seq2seq model with this dataset to generate fluent responses - as done by Fluent Response Generation for Conversational Question Answering.", "### Languages\n\n+ Persian (fa)", "## Dataset Structure\nEach row of the dataset will look like something like the below:\n\n+ 'id' : the entry id in dataset\n+ 'question' : the question\n+ 'short_answer' : the short answer corresponding to the 'question' (the primary answer)\n+ 'fluent_answer' : fluent (long) answer generated from both 'question' and the 'short_answer' (the secondary answer)\n+ 'bert_loss' : the loss that pars-bert gives when inputting the 'fluent_answer' to it. As it increases the sentence is more likely to be influent.\n\nNote: the dataset is sorted increasingly by the 'bert_loss', so first sentences are more likely to be fluent.", "### Data Splits\n\nCurrently, the dataset just provided the 'train' split. There would be a 'test' split soon.", "## Dataset Creation", "### Source Data\nThe source datasets that we used are as follows:\n\n+ PersianQA\n+ PersianQuAD", "#### Initial Data Collection and Normalization\n\nWe extract all short answer (sentences without verbs - up to ~4 words) entries of all open source QA datasets in Farsi and used some rules featuring the question parse tree to make long (fluent) answers.", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\nThe dataset is completely a subset of open source known datasets so all information in it is already there on the internet as a open-source dataset. By the way, we do not take responsibility for any of that.", "## Additional Information", "### Dataset Curators\n\nThe dataset is gathered together completely in the Asr Gooyesh Pardaz company's summer internship under the supervision of Soroush Gooran, Prof. Hossein Sameti, and the mentorship of Sadra Sabouri. This project was Farhan Farsi's first internship project.", "### Licensing Information\n\nMIT", "### Contributions\n\nThanks to @farhaaaaa and @sadrasabouri for adding this dataset." ]

[ "TAGS\n#task_categories-question-answering #task_categories-text2text-generation #task_categories-text-generation #multilinguality-monolingual #size_categories-30k<n<50k #language-Persian #license-mit #conditional-text-generation #conversational-question-answering #region-us \n", "# SynTran-fa \nSyntactic Transformed Version of Farsi QA datasets to make fluent responses from questions and short answers. You can use this dataset by the code below:", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n- Dataset Creation\n - Source Data\n- Considerations for Using the Data\n - Social Impact of Dataset\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n \n- Homepage: Sharif-SLPL\n- Repository: SynTran-fa\n- Point of Contact: Sadra Sabouri", "### Dataset Summary\n\nGenerating fluent responses has always been challenging for the question-answering task, especially in low-resource languages like Farsi. In recent years there were some efforts for enhancing the size of datasets in Farsi. Syntran-fa is a question-answering dataset that accumulates the former Farsi QA dataset's short answers and proposes a complete fluent answer for each pair of (question, short_answer).\n\nThis dataset contains nearly 50,000 indices of questions and answers. The dataset that has been used as our sources are in Source Data section.\n\nThe main idea for this dataset comes from Fluent Response Generation for Conversational Question Answering where they used a \"parser + syntactic rules\" module to make different fluent answers from a pair of question and a short answer using a parser and some syntactic rules. In this project, we used stanza as our parser to parse the question and generate a response according to it using the short (sentences without verbs - up to ~4 words) answers. One can continue this project by generating different permutations of the sentence's parts (and thus providing more than one sentence for an answer) or training a seq2seq model which does what we do with our rule-based system (by defining a new text-to-text task).", "### Supported Tasks and Leaderboards\n\nThis dataset can be used for the question-answering task, especially when you are going to generate fluent responses. You can train a seq2seq model with this dataset to generate fluent responses - as done by Fluent Response Generation for Conversational Question Answering.", "### Languages\n\n+ Persian (fa)", "## Dataset Structure\nEach row of the dataset will look like something like the below:\n\n+ 'id' : the entry id in dataset\n+ 'question' : the question\n+ 'short_answer' : the short answer corresponding to the 'question' (the primary answer)\n+ 'fluent_answer' : fluent (long) answer generated from both 'question' and the 'short_answer' (the secondary answer)\n+ 'bert_loss' : the loss that pars-bert gives when inputting the 'fluent_answer' to it. As it increases the sentence is more likely to be influent.\n\nNote: the dataset is sorted increasingly by the 'bert_loss', so first sentences are more likely to be fluent.", "### Data Splits\n\nCurrently, the dataset just provided the 'train' split. There would be a 'test' split soon.", "## Dataset Creation", "### Source Data\nThe source datasets that we used are as follows:\n\n+ PersianQA\n+ PersianQuAD", "#### Initial Data Collection and Normalization\n\nWe extract all short answer (sentences without verbs - up to ~4 words) entries of all open source QA datasets in Farsi and used some rules featuring the question parse tree to make long (fluent) answers.", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\nThe dataset is completely a subset of open source known datasets so all information in it is already there on the internet as a open-source dataset. By the way, we do not take responsibility for any of that.", "## Additional Information", "### Dataset Curators\n\nThe dataset is gathered together completely in the Asr Gooyesh Pardaz company's summer internship under the supervision of Soroush Gooran, Prof. Hossein Sameti, and the mentorship of Sadra Sabouri. This project was Farhan Farsi's first internship project.", "### Licensing Information\n\nMIT", "### Contributions\n\nThanks to @farhaaaaa and @sadrasabouri for adding this dataset." ]

[ 90, 45, 82, 31, 305, 72, 10, 174, 30, 5, 26, 61, 5, 5, 9, 56, 5, 74, 7, 23 ]

[ "passage: TAGS\n#task_categories-question-answering #task_categories-text2text-generation #task_categories-text-generation #multilinguality-monolingual #size_categories-30k<n<50k #language-Persian #license-mit #conditional-text-generation #conversational-question-answering #region-us \n# SynTran-fa \nSyntactic Transformed Version of Farsi QA datasets to make fluent responses from questions and short answers. You can use this dataset by the code below:## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n- Dataset Creation\n - Source Data\n- Considerations for Using the Data\n - Social Impact of Dataset\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions## Dataset Description\n \n- Homepage: Sharif-SLPL\n- Repository: SynTran-fa\n- Point of Contact: Sadra Sabouri", "passage: ### Dataset Summary\n\nGenerating fluent responses has always been challenging for the question-answering task, especially in low-resource languages like Farsi. In recent years there were some efforts for enhancing the size of datasets in Farsi. Syntran-fa is a question-answering dataset that accumulates the former Farsi QA dataset's short answers and proposes a complete fluent answer for each pair of (question, short_answer).\n\nThis dataset contains nearly 50,000 indices of questions and answers. The dataset that has been used as our sources are in Source Data section.\n\nThe main idea for this dataset comes from Fluent Response Generation for Conversational Question Answering where they used a \"parser + syntactic rules\" module to make different fluent answers from a pair of question and a short answer using a parser and some syntactic rules. In this project, we used stanza as our parser to parse the question and generate a response according to it using the short (sentences without verbs - up to ~4 words) answers. One can continue this project by generating different permutations of the sentence's parts (and thus providing more than one sentence for an answer) or training a seq2seq model which does what we do with our rule-based system (by defining a new text-to-text task).### Supported Tasks and Leaderboards\n\nThis dataset can be used for the question-answering task, especially when you are going to generate fluent responses. You can train a seq2seq model with this dataset to generate fluent responses - as done by Fluent Response Generation for Conversational Question Answering.### Languages\n\n+ Persian (fa)## Dataset Structure\nEach row of the dataset will look like something like the below:\n\n+ 'id' : the entry id in dataset\n+ 'question' : the question\n+ 'short_answer' : the short answer corresponding to the 'question' (the primary answer)\n+ 'fluent_answer' : fluent (long) answer generated from both 'question' and the 'short_answer' (the secondary answer)\n+ 'bert_loss' : the loss that pars-bert gives when inputting the 'fluent_answer' to it. As it increases the sentence is more likely to be influent.\n\nNote: the dataset is sorted increasingly by the 'bert_loss', so first sentences are more likely to be fluent.### Data Splits\n\nCurrently, the dataset just provided the 'train' split. There would be a 'test' split soon.## Dataset Creation### Source Data\nThe source datasets that we used are as follows:\n\n+ PersianQA\n+ PersianQuAD#### Initial Data Collection and Normalization\n\nWe extract all short answer (sentences without verbs - up to ~4 words) entries of all open source QA datasets in Farsi and used some rules featuring the question parse tree to make long (fluent) answers.### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information\n\nThe dataset is completely a subset of open source known datasets so all information in it is already there on the internet as a open-source dataset. By the way, we do not take responsibility for any of that.## Additional Information" ]

[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:en", "license:cc-by-4.0", "implicature", "pragmatics", "language", "llm", "conversation", "dialogue", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for LUDWIG", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository: URL\n- Paper: TODO\n- Leaderboard: TODO\n- Point of Contact: Laura Ruis", "### Dataset Summary\n\nLUDWIG (Language Understanding With Implied meaninG) is a dataset containing English conversational implicatures.\nImplicature is the act of meaning or implying one thing by saying something else. \nThere's different types of implicatures, from simple ones like \"Some guests came to the party\" \n(implying not all guests came) to more complicated implicatures that depend on context like \n\"A: Are you going to the party this Friday? B: There's a global pandemic.\", implying no. Implicatures serve a wide range of\ngoals in communication: efficiency, style, navigating social interactions, and more. We cannot fully\nunderstand utterances without understanding their implications. \nThe implicatures in this dataset are conversational because they come in utterance-response tuples. \nEach tuple has an implicature associated with it,\nwhich is the implied meaning of the response. For example:\n\nUtterance: Are you going to the party this Friday? \nResponse: There's a global pandemic. \nImplicature: No. \n\nThis dataset can be used to evaluate language models on their pragmatic language understanding.", "### Supported Tasks and Leaderboards\n\n- : The dataset can be used to evaluate a models ability to generate the correct next token, i.e. \"yes\" or \"no\", depending on the implicature. For example, if you pass the model an example wrapped in a template like \"Esther asked 'Are you coming to the party this Friday' and Juan responded 'There's a global pandemic', which means\" the correct completion would be \"no\". Success in this task can be determined by the ability to generate the correct answer or by the ability to give the right token a higher likelihood than the wrong token, e.g. p(\"no\") > p(\"yes\").\n- : The dataset can be used to evaluate a models ability to fill the correct token, i.e. \"yes\" or \"no\", depending on the implicature. For example, if you pass the model an example wrapped in a template like \"Esther asked 'Are you coming to the party this Friday' and Juan responded 'There's a global pandemic', which means [mask]\" the correct mask-fill would be \"no\". Success in this task can be determined by the ability to fill the correct answer or by the ability to give the right token a higher likelihood than the wrong token, e.g. p(\"no\") > p(\"yes\").", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances\n\nFind below an example of a 1-shot example instance (1-shot because there's 1 prompt example).", "### Data Fields", "### Data Splits\n\nValidation: 118 instances that can be used for finetuning or few-shot learning \nTest: 600 instances that can be used for evaluating models.\n\nNB: the splits weren't originally part of the paper that presents this dataset. The same goes for the k-shot prompts. Added\nby @LauraRuis.", "## Dataset Creation", "### Curation Rationale\n\nPragmatic language understanding is a crucial aspect of human communication, and implicatures are the primary object of study in this field.\nWe want computational models of language to understand all the speakers implications.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\"Conversational implicatures in English dialogue: Annotated dataset\", Elizabeth Jasmi George and Radhika Mamidi 2020.\n\nLink to paper", "#### Who are the source language producers?\n\nThese written representations of the utterances are collected manually by scraping and transcribing from relevant sources from August, 2019 to August, 2020. The source of dialogues in the data include TOEFL listening comprehension short conversations, movie dialogues from IMSDb and websites explaining idioms, similes, metaphors and hyperboles. The implicatures are annotated manually.", "### Annotations", "#### Annotation process\n\nManually annotated by dataset collectors.", "#### Who are the annotators?\n\nAuthors of the original paper.", "### Personal and Sensitive Information\n\nAll the data is public and not sensitive.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nAny application that requires communicating with humans requires pragmatic language understanding.", "### Discussion of Biases\n\nImplicatures can be biased to specific cultures. For example, whether the Pope is Catholic (a common used response implicature to indicate \"yes\") might not be common knowledge for everyone.\nImplicatures are also language-specific, the way people use pragmatic language depends on the language. This dataset only focuses on the English language.", "### Other Known Limitations\n\nNone yet.", "## Additional Information", "### Dataset Curators\n\nElizabeth Jasmi George and Radhika Mamidi", "### Licensing Information\n\nlicense", "### Contributions\n\nThanks to @LauraRuis for adding this dataset." ]

[ "TAGS\n#task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-cc-by-4.0 #implicature #pragmatics #language #llm #conversation #dialogue #region-us \n", "# Dataset Card for LUDWIG", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository: URL\n- Paper: TODO\n- Leaderboard: TODO\n- Point of Contact: Laura Ruis", "### Dataset Summary\n\nLUDWIG (Language Understanding With Implied meaninG) is a dataset containing English conversational implicatures.\nImplicature is the act of meaning or implying one thing by saying something else. \nThere's different types of implicatures, from simple ones like \"Some guests came to the party\" \n(implying not all guests came) to more complicated implicatures that depend on context like \n\"A: Are you going to the party this Friday? B: There's a global pandemic.\", implying no. Implicatures serve a wide range of\ngoals in communication: efficiency, style, navigating social interactions, and more. We cannot fully\nunderstand utterances without understanding their implications. \nThe implicatures in this dataset are conversational because they come in utterance-response tuples. \nEach tuple has an implicature associated with it,\nwhich is the implied meaning of the response. For example:\n\nUtterance: Are you going to the party this Friday? \nResponse: There's a global pandemic. \nImplicature: No. \n\nThis dataset can be used to evaluate language models on their pragmatic language understanding.", "### Supported Tasks and Leaderboards\n\n- : The dataset can be used to evaluate a models ability to generate the correct next token, i.e. \"yes\" or \"no\", depending on the implicature. For example, if you pass the model an example wrapped in a template like \"Esther asked 'Are you coming to the party this Friday' and Juan responded 'There's a global pandemic', which means\" the correct completion would be \"no\". Success in this task can be determined by the ability to generate the correct answer or by the ability to give the right token a higher likelihood than the wrong token, e.g. p(\"no\") > p(\"yes\").\n- : The dataset can be used to evaluate a models ability to fill the correct token, i.e. \"yes\" or \"no\", depending on the implicature. For example, if you pass the model an example wrapped in a template like \"Esther asked 'Are you coming to the party this Friday' and Juan responded 'There's a global pandemic', which means [mask]\" the correct mask-fill would be \"no\". Success in this task can be determined by the ability to fill the correct answer or by the ability to give the right token a higher likelihood than the wrong token, e.g. p(\"no\") > p(\"yes\").", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances\n\nFind below an example of a 1-shot example instance (1-shot because there's 1 prompt example).", "### Data Fields", "### Data Splits\n\nValidation: 118 instances that can be used for finetuning or few-shot learning \nTest: 600 instances that can be used for evaluating models.\n\nNB: the splits weren't originally part of the paper that presents this dataset. The same goes for the k-shot prompts. Added\nby @LauraRuis.", "## Dataset Creation", "### Curation Rationale\n\nPragmatic language understanding is a crucial aspect of human communication, and implicatures are the primary object of study in this field.\nWe want computational models of language to understand all the speakers implications.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\"Conversational implicatures in English dialogue: Annotated dataset\", Elizabeth Jasmi George and Radhika Mamidi 2020.\n\nLink to paper", "#### Who are the source language producers?\n\nThese written representations of the utterances are collected manually by scraping and transcribing from relevant sources from August, 2019 to August, 2020. The source of dialogues in the data include TOEFL listening comprehension short conversations, movie dialogues from IMSDb and websites explaining idioms, similes, metaphors and hyperboles. The implicatures are annotated manually.", "### Annotations", "#### Annotation process\n\nManually annotated by dataset collectors.", "#### Who are the annotators?\n\nAuthors of the original paper.", "### Personal and Sensitive Information\n\nAll the data is public and not sensitive.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nAny application that requires communicating with humans requires pragmatic language understanding.", "### Discussion of Biases\n\nImplicatures can be biased to specific cultures. For example, whether the Pope is Catholic (a common used response implicature to indicate \"yes\") might not be common knowledge for everyone.\nImplicatures are also language-specific, the way people use pragmatic language depends on the language. This dataset only focuses on the English language.", "### Other Known Limitations\n\nNone yet.", "## Additional Information", "### Dataset Curators\n\nElizabeth Jasmi George and Radhika Mamidi", "### Licensing Information\n\nlicense", "### Contributions\n\nThanks to @LauraRuis for adding this dataset." ]

[ 136, 9, 125, 32, 255, 312, 5, 6, 27, 5, 81, 5, 48, 4, 40, 97, 5, 16, 16, 17, 8, 22, 82, 11, 5, 16, 7, 18 ]

[ "passage: TAGS\n#task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-cc-by-4.0 #implicature #pragmatics #language #llm #conversation #dialogue #region-us \n# Dataset Card for LUDWIG## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions## Dataset Description\n\n- Homepage:\n- Repository: URL\n- Paper: TODO\n- Leaderboard: TODO\n- Point of Contact: Laura Ruis", "passage: ### Dataset Summary\n\nLUDWIG (Language Understanding With Implied meaninG) is a dataset containing English conversational implicatures.\nImplicature is the act of meaning or implying one thing by saying something else. \nThere's different types of implicatures, from simple ones like \"Some guests came to the party\" \n(implying not all guests came) to more complicated implicatures that depend on context like \n\"A: Are you going to the party this Friday? B: There's a global pandemic.\", implying no. Implicatures serve a wide range of\ngoals in communication: efficiency, style, navigating social interactions, and more. We cannot fully\nunderstand utterances without understanding their implications. \nThe implicatures in this dataset are conversational because they come in utterance-response tuples. \nEach tuple has an implicature associated with it,\nwhich is the implied meaning of the response. For example:\n\nUtterance: Are you going to the party this Friday? \nResponse: There's a global pandemic. \nImplicature: No. \n\nThis dataset can be used to evaluate language models on their pragmatic language understanding.### Supported Tasks and Leaderboards\n\n- : The dataset can be used to evaluate a models ability to generate the correct next token, i.e. \"yes\" or \"no\", depending on the implicature. For example, if you pass the model an example wrapped in a template like \"Esther asked 'Are you coming to the party this Friday' and Juan responded 'There's a global pandemic', which means\" the correct completion would be \"no\". Success in this task can be determined by the ability to generate the correct answer or by the ability to give the right token a higher likelihood than the wrong token, e.g. p(\"no\") > p(\"yes\").\n- : The dataset can be used to evaluate a models ability to fill the correct token, i.e. \"yes\" or \"no\", depending on the implicature. For example, if you pass the model an example wrapped in a template like \"Esther asked 'Are you coming to the party this Friday' and Juan responded 'There's a global pandemic', which means [mask]\" the correct mask-fill would be \"no\". Success in this task can be determined by the ability to fill the correct answer or by the ability to give the right token a higher likelihood than the wrong token, e.g. p(\"no\") > p(\"yes\").### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nFind below an example of a 1-shot example instance (1-shot because there's 1 prompt example).### Data Fields### Data Splits\n\nValidation: 118 instances that can be used for finetuning or few-shot learning \nTest: 600 instances that can be used for evaluating models.\n\nNB: the splits weren't originally part of the paper that presents this dataset. The same goes for the k-shot prompts. Added\nby @LauraRuis.## Dataset Creation### Curation Rationale\n\nPragmatic language understanding is a crucial aspect of human communication, and implicatures are the primary object of study in this field.\nWe want computational models of language to understand all the speakers implications.### Source Data" ]

[ "arxiv:2208.05545", "region:us" ]

[ "2208.05545" ]

[]

[ "# Dataset Card for MFRC", "## 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\nReddit posts annotated for moral foundations", "### Supported Tasks and Leaderboards", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances", "### Data Fields\n\n- text\n- subreddit\n- bucket \n- annotator\n- annotation\n- confidence", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\ncc-by-4.0", "### Contributions" ]

[ "TAGS\n#arxiv-2208.05545 #region-us \n", "# Dataset Card for MFRC", "## 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\nReddit posts annotated for moral foundations", "### Supported Tasks and Leaderboards", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances", "### Data Fields\n\n- text\n- subreddit\n- bucket \n- annotator\n- annotation\n- confidence", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\ncc-by-4.0", "### Contributions" ]

[ 14, 7, 125, 24, 15, 10, 5, 6, 6, 23, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 12, 5 ]

[ "passage: TAGS\n#arxiv-2208.05545 #region-us \n# Dataset Card for MFRC## 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\nReddit posts annotated for moral foundations### Supported Tasks and Leaderboards### Languages\n\nEnglish## Dataset Structure### Data Instances### Data Fields\n\n- text\n- subreddit\n- bucket \n- annotator\n- annotation\n- confidence### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information\n\ncc-by-4.0### Contributions" ]

[ "region:us" ]

[]

[]

[]

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

[ 6 ]

[ "passage: 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: datien228/distilbart-cnn-12-6-ftn-multi_news\n* Dataset: multi_news\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @ccdv 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: datien228/distilbart-cnn-12-6-ftn-multi_news\n* Dataset: multi_news\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @ccdv for evaluating this model." ]

[ 13, 95, 15 ]

[ "passage: 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: datien228/distilbart-cnn-12-6-ftn-multi_news\n* Dataset: multi_news\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @ccdv for evaluating this model." ]

[ "region:us" ]

[]

[]

[]

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

[ 6 ]

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

[ "language:en", "language:ar", "language:ca", "language:cy", "language:de", "language:es", "language:et", "language:fa", "language:fr", "language:id", "language:it", "language:ja", "language:lv", "language:mn", "language:nl", "language:pt", "language:ru", "language:sl", "language:sv", "language:ta", "language:tr", "language:zh", "license:cc-by-4.0", "arxiv:2201.03713", "region:us" ]

[ "2201.03713" ]

[ "en", "ar", "ca", "cy", "de", "es", "et", "fa", "fr", "id", "it", "ja", "lv", "mn", "nl", "pt", "ru", "sl", "sv", "ta", "tr", "zh" ]

[ "# CVSS: A Massively Multilingual Speech-to-Speech Translation Corpus\n\n\n*CVSS* is a massively multilingual-to-English speech-to-speech translation corpus, covering sentence-level parallel speech-to-speech translation pairs from 21 languages into English. CVSS is derived from the Common Voice speech corpus and the CoVoST 2 speech-to-text translation corpus. The translation speech in CVSS is synthesized with two state-of-the-art TTS models trained on the LibriTTS corpus.\n\nCVSS includes two versions of spoken translation for all the 21 x-en language pairs from CoVoST 2, with each version providing unique values:\n\n - *CVSS-C*: All the translation speeches are in a single canonical speaker's voice. Despite being synthetic, these speeches are of very high naturalness and cleanness, as well as having a consistent speaking style. These properties ease the modeling of the target speech and enable models to produce high quality translation speech suitable for user-facing applications.\n\n - *CVSS-T*: The translation speeches are in voices transferred from the corresponding source speeches. Each translation pair has similar voices on the two sides despite being in different languages, making this dataset suitable for building models that preserve speakers' voices when translating speech into different languages.\n\nTogether with the source speeches originated from Common Voice, they make two multilingual speech-to-speech translation datasets each with about 1,900 hours of speech.\n\nIn addition to translation speech, CVSS also provides normalized translation text matching the pronunciation in the translation speech (e.g. on numbers, currencies, acronyms, etc.), which can be used for both model training as well as standardizing evaluation.\n\nPlease check out our paper for the detailed description of this corpus, as well as the baseline models we trained on both datasets.", "# Load the data\n\n\nThe following example loads the translation speech (i.e. target speech) and the normalized translation text (i.e. target text) released in CVSS corpus. You'll need to load the source speech and optionally the source text from Common Voice v4.0 separately, and join them by the file names.", "# License\n\nCVSS is released under the very permissive Creative Commons Attribution 4.0 International (CC BY 4.0) license.\n\n\nPlease cite this paper when referencing the CVSS corpus:" ]

[ "TAGS\n#language-English #language-Arabic #language-Catalan #language-Welsh #language-German #language-Spanish #language-Estonian #language-Persian #language-French #language-Indonesian #language-Italian #language-Japanese #language-Latvian #language-Mongolian #language-Dutch #language-Portuguese #language-Russian #language-Slovenian #language-Swedish #language-Tamil #language-Turkish #language-Chinese #license-cc-by-4.0 #arxiv-2201.03713 #region-us \n", "# CVSS: A Massively Multilingual Speech-to-Speech Translation Corpus\n\n\n*CVSS* is a massively multilingual-to-English speech-to-speech translation corpus, covering sentence-level parallel speech-to-speech translation pairs from 21 languages into English. CVSS is derived from the Common Voice speech corpus and the CoVoST 2 speech-to-text translation corpus. The translation speech in CVSS is synthesized with two state-of-the-art TTS models trained on the LibriTTS corpus.\n\nCVSS includes two versions of spoken translation for all the 21 x-en language pairs from CoVoST 2, with each version providing unique values:\n\n - *CVSS-C*: All the translation speeches are in a single canonical speaker's voice. Despite being synthetic, these speeches are of very high naturalness and cleanness, as well as having a consistent speaking style. These properties ease the modeling of the target speech and enable models to produce high quality translation speech suitable for user-facing applications.\n\n - *CVSS-T*: The translation speeches are in voices transferred from the corresponding source speeches. Each translation pair has similar voices on the two sides despite being in different languages, making this dataset suitable for building models that preserve speakers' voices when translating speech into different languages.\n\nTogether with the source speeches originated from Common Voice, they make two multilingual speech-to-speech translation datasets each with about 1,900 hours of speech.\n\nIn addition to translation speech, CVSS also provides normalized translation text matching the pronunciation in the translation speech (e.g. on numbers, currencies, acronyms, etc.), which can be used for both model training as well as standardizing evaluation.\n\nPlease check out our paper for the detailed description of this corpus, as well as the baseline models we trained on both datasets.", "# Load the data\n\n\nThe following example loads the translation speech (i.e. target speech) and the normalized translation text (i.e. target text) released in CVSS corpus. You'll need to load the source speech and optionally the source text from Common Voice v4.0 separately, and join them by the file names.", "# License\n\nCVSS is released under the very permissive Creative Commons Attribution 4.0 International (CC BY 4.0) license.\n\n\nPlease cite this paper when referencing the CVSS corpus:" ]

[ 140, 436, 72, 35 ]

[ "passage: TAGS\n#language-English #language-Arabic #language-Catalan #language-Welsh #language-German #language-Spanish #language-Estonian #language-Persian #language-French #language-Indonesian #language-Italian #language-Japanese #language-Latvian #language-Mongolian #language-Dutch #language-Portuguese #language-Russian #language-Slovenian #language-Swedish #language-Tamil #language-Turkish #language-Chinese #license-cc-by-4.0 #arxiv-2201.03713 #region-us \n" ]