Datasets:

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ftopal

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huggingface-datasets

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[ "region:us" ]

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[ "# Small NORB Azimuth (Test set only)\n\nOriginal paper: Learning Methods for Generic Object Recognition with Invariance to Pose and Lighting\n\nHomepage: URL\n\nBibtex:" ]

[ "TAGS\n#region-us \n", "# Small NORB Azimuth (Test set only)\n\nOriginal paper: Learning Methods for Generic Object Recognition with Invariance to Pose and Lighting\n\nHomepage: URL\n\nBibtex:" ]

[ "region:us" ]

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[ "# Small NORB Elevation (Test set only)\n\nOriginal paper: Learning Methods for Generic Object Recognition with Invariance to Pose and Lighting\n\nHomepage: URL\n\nBibtex:" ]

[ "TAGS\n#region-us \n", "# Small NORB Elevation (Test set only)\n\nOriginal paper: Learning Methods for Generic Object Recognition with Invariance to Pose and Lighting\n\nHomepage: URL\n\nBibtex:" ]

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

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[ "en" ]

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

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

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

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[ "en" ]

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

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

[ "region:us" ]

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[ "# SVHN (Test set only)\n\nOriginal paper: Reading Digits in Natural Images with Unsupervised Feature Learning\n\nHomepage: URL\n\nBibtex:" ]

[ "TAGS\n#region-us \n", "# SVHN (Test set only)\n\nOriginal paper: Reading Digits in Natural Images with Unsupervised Feature Learning\n\nHomepage: URL\n\nBibtex:" ]

[ "region:us" ]

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

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

[ "region:us" ]

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

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

[ "region:us" ]

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

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

[ "region:us" ]

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

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

[ "region:us" ]

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[ "# Dataset Card for \"text_summarization_dataset7\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"text_summarization_dataset7\"\n\nMore Information needed" ]

[ "region:us" ]

[]

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[ "# Dataset Card for \"text_summarization_dataset8\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"text_summarization_dataset8\"\n\nMore Information needed" ]

[ "region:us" ]

[]

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[ "# Dataset Card for \"text_summarization_dataset9\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"text_summarization_dataset9\"\n\nMore Information needed" ]

[ "region:us" ]

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

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

[ "region:us" ]

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

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

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

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Binary Text Classification\n* Model: autoevaluate/binary-classification\n* Dataset: glue\n* Config: sst2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Binary Text Classification\n* Model: autoevaluate/binary-classification\n* Dataset: glue\n* Config: sst2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

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

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Binary Text Classification\n* Model: autoevaluate/binary-classification\n* Dataset: glue\n* Config: sst2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Binary Text Classification\n* Model: autoevaluate/binary-classification\n* Dataset: glue\n* Config: sst2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

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

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Binary Text Classification\n* Model: autoevaluate/binary-classification\n* Dataset: glue\n* Config: sst2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Binary Text Classification\n* Model: autoevaluate/binary-classification\n* Dataset: glue\n* Config: sst2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "task_categories:question-answering", "task_categories:text-retrieval", "task_ids:open-domain-qa", "task_ids:extractive-qa", "task_ids:document-retrieval", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:sk", "license:cc-by-sa-4.0", "license:cc-by-4.0", "wikipedia", "region:us" ]

[]

[ "sk" ]

[ "### Dataset Summary\n\n\nSK-QuAD is the first QA dataset for the Slovak language.\nIt is manually annotated, so it has no distortion caused by\nmachine translation. The dataset is thematically diverse – it\ndoes not overlap with SQuAD – it brings new knowledge.\nIt passed the second round of annotation – each question\nand the answer were seen by at least two annotators.", "### Supported Tasks and Leaderboards\n\n\n* Question answering\n* Document retrieval", "### Languages\n\n\n* Slovak\n\n\nDataset Structure\n-----------------", "#### squad\\_v2\n\n\n* Size of downloaded dataset files: 44.34 MB\n* Size of the generated dataset: 122.57 MB\n* Total amount of disk used: 166.91 MB\n* \n\n\nAn example of 'validation' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### squad\\_v2\n\n\n* 'id': a 'string' feature.\n* 'title': a 'string' feature.\n* 'context': a 'string' feature.\n* 'question': a 'string' feature.\n* 'answers': a dictionary feature containing:\n\t+ 'text': a 'string' feature.\n\t+ 'answer\\_start': a 'int32' feature.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\n* Deutsche Telekom Systems Solutions Slovakia\n* Technical Univesity of Košice", "### Licensing Information\n\n\nAttribution-ShareAlike 4.0 International (CC BY-SA 4.0)", "### Contributions\n\n\nThanks to @github-username for adding this dataset." ]

[ "TAGS\n#task_categories-question-answering #task_categories-text-retrieval #task_ids-open-domain-qa #task_ids-extractive-qa #task_ids-document-retrieval #annotations_creators-crowdsourced #language_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Slovak #license-cc-by-sa-4.0 #license-cc-by-4.0 #wikipedia #region-us \n", "### Dataset Summary\n\n\nSK-QuAD is the first QA dataset for the Slovak language.\nIt is manually annotated, so it has no distortion caused by\nmachine translation. The dataset is thematically diverse – it\ndoes not overlap with SQuAD – it brings new knowledge.\nIt passed the second round of annotation – each question\nand the answer were seen by at least two annotators.", "### Supported Tasks and Leaderboards\n\n\n* Question answering\n* Document retrieval", "### Languages\n\n\n* Slovak\n\n\nDataset Structure\n-----------------", "#### squad\\_v2\n\n\n* Size of downloaded dataset files: 44.34 MB\n* Size of the generated dataset: 122.57 MB\n* Total amount of disk used: 166.91 MB\n* \n\n\nAn example of 'validation' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### squad\\_v2\n\n\n* 'id': a 'string' feature.\n* 'title': a 'string' feature.\n* 'context': a 'string' feature.\n* 'question': a 'string' feature.\n* 'answers': a dictionary feature containing:\n\t+ 'text': a 'string' feature.\n\t+ 'answer\\_start': a 'int32' feature.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\n* Deutsche Telekom Systems Solutions Slovakia\n* Technical Univesity of Košice", "### Licensing Information\n\n\nAttribution-ShareAlike 4.0 International (CC BY-SA 4.0)", "### Contributions\n\n\nThanks to @github-username for adding this dataset." ]

[ "region:us" ]

[]

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[ "# Dataset Card for \"github-discussion\"\n\nMore Information needed" ]

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

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Binary Text Classification\n* Model: autoevaluate/binary-classification-not-evaluated\n* Dataset: glue\n* Config: sst2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Binary Text Classification\n* Model: autoevaluate/binary-classification-not-evaluated\n* Dataset: glue\n* Config: sst2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

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

[]

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: autoevaluate/multi-class-classification-not-evaluated\n* Dataset: emotion\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: autoevaluate/multi-class-classification-not-evaluated\n* Dataset: emotion\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "region:us" ]

[]

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[ "# Dataset Card for \"IC-Satellites\"\n\nMore Information needed" ]

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

[ "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: Multi-class Image Classification\n* Model: autoevaluate/image-multi-class-classification-not-evaluated\n* Dataset: autoevaluate/mnist-sample\n* Config: autoevaluate--mnist-sample\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Image Classification\n* Model: autoevaluate/image-multi-class-classification-not-evaluated\n* Dataset: autoevaluate/mnist-sample\n* Config: autoevaluate--mnist-sample\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "task_categories:text-classification", "task_ids:intent-classification", "multilinguality:multilingual", "size_categories:1K<n<10K", "source_datasets:extended|deutsche-telekom/NLU-Evaluation-Data-en-de", "language:en", "language:de", "license:cc-by-4.0", "region:us" ]

[]

[ "en", "de" ]

[ "# NLU Few-shot Benchmark - English and German\nThis is a few-shot training dataset from the domain of human-robot interaction.\nIt contains texts in German and English language with 64 different utterances (classes).\nEach utterance (class) has exactly 20 samples in the training set.\nThis leads to a total of 1280 different training samples.\n\nThe dataset is intended to benchmark the intent classifiers of chat bots in English and especially in German language.\nWe are building on our\ndeutsche-telekom/NLU-Evaluation-Data-en-de\ndata set", "## Creator\n\nThis data set was compiled and open sourced by Philip May\nof Deutsche Telekom.", "## Processing Steps\n- drop 'NaN' values\n- drop duplicates in 'answer_de' and 'answer'\n- delete all rows where 'answer_de' has more than 70 characters\n- add column 'label': 'df[\"label\"] = df[\"scenario\"] + \"_\" + df[\"intent\"]'\n- remove classes ('label') with less than 25 samples:\n - 'audio_volume_other'\n - 'cooking_query'\n - 'general_greet'\n - 'music_dislikeness'\n- random selection for train set - exactly 20 samples for each class ('label')\n- rest for test set", "## Copyright\nCopyright (c) the authors of xliuhw/NLU-Evaluation-Data\\\nCopyright (c) 2022 Philip May, Deutsche Telekom AG\n\nAll data is released under the\nCreative Commons Attribution 4.0 International License (CC BY 4.0)." ]

[ "TAGS\n#task_categories-text-classification #task_ids-intent-classification #multilinguality-multilingual #size_categories-1K<n<10K #source_datasets-extended|deutsche-telekom/NLU-Evaluation-Data-en-de #language-English #language-German #license-cc-by-4.0 #region-us \n", "# NLU Few-shot Benchmark - English and German\nThis is a few-shot training dataset from the domain of human-robot interaction.\nIt contains texts in German and English language with 64 different utterances (classes).\nEach utterance (class) has exactly 20 samples in the training set.\nThis leads to a total of 1280 different training samples.\n\nThe dataset is intended to benchmark the intent classifiers of chat bots in English and especially in German language.\nWe are building on our\ndeutsche-telekom/NLU-Evaluation-Data-en-de\ndata set", "## Creator\n\nThis data set was compiled and open sourced by Philip May\nof Deutsche Telekom.", "## Processing Steps\n- drop 'NaN' values\n- drop duplicates in 'answer_de' and 'answer'\n- delete all rows where 'answer_de' has more than 70 characters\n- add column 'label': 'df[\"label\"] = df[\"scenario\"] + \"_\" + df[\"intent\"]'\n- remove classes ('label') with less than 25 samples:\n - 'audio_volume_other'\n - 'cooking_query'\n - 'general_greet'\n - 'music_dislikeness'\n- random selection for train set - exactly 20 samples for each class ('label')\n- rest for test set", "## Copyright\nCopyright (c) the authors of xliuhw/NLU-Evaluation-Data\\\nCopyright (c) 2022 Philip May, Deutsche Telekom AG\n\nAll data is released under the\nCreative Commons Attribution 4.0 International License (CC BY 4.0)." ]

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

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: autoevaluate/zero-shot-classification-not-evaluated\n* Dataset: autoevaluate/zero-shot-classification-sample\n* Config: autoevaluate--zero-shot-classification-sample\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: autoevaluate/zero-shot-classification-not-evaluated\n* Dataset: autoevaluate/zero-shot-classification-sample\n* Config: autoevaluate--zero-shot-classification-sample\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "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: Natural Language Inference\n* Model: autoevaluate/natural-language-inference-not-evaluated\n* Dataset: glue\n* Config: mrpc\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Natural Language Inference\n* Model: autoevaluate/natural-language-inference-not-evaluated\n* Dataset: glue\n* Config: mrpc\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

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

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: autoevaluate/entity-extraction-not-evaluated\n* Dataset: autoevaluate/conll2003-sample\n* Config: autoevaluate--conll2003-sample\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: autoevaluate/entity-extraction-not-evaluated\n* Dataset: autoevaluate/conll2003-sample\n* Config: autoevaluate--conll2003-sample\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "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: autoevaluate/distilbert-base-cased-distilled-squad\n* Dataset: autoevaluate/squad-sample\n* Config: autoevaluate--squad-sample\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

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

[ "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: autoevaluate/extractive-question-answering-not-evaluated\n* Dataset: autoevaluate/squad-sample\n* Config: autoevaluate--squad-sample\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: autoevaluate/extractive-question-answering-not-evaluated\n* Dataset: autoevaluate/squad-sample\n* Config: autoevaluate--squad-sample\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "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: autoevaluate/translation-not-evaluated\n* Dataset: autoevaluate/wmt16-ro-en-sample\n* Config: autoevaluate--wmt16-ro-en-sample\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Translation\n* Model: autoevaluate/translation-not-evaluated\n* Dataset: autoevaluate/wmt16-ro-en-sample\n* Config: autoevaluate--wmt16-ro-en-sample\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"imdb-movie-genres\"\n\nMDb (an acronym for Internet Movie Database) is an online database of information related to films, television programs, home videos, video games, and streaming content online – including cast, production crew and personal biographies, plot summaries, trivia, ratings, and fan and critical reviews. An additional fan feature, message boards, was abandoned in February 2017. Originally a fan-operated website, the database is now owned and operated by URL, Inc., a subsidiary of Amazon.\n\nAs of December 2020, IMDb has approximately 7.5 million titles (including episodes) and 10.4 million personalities in its database,[2] as well as 83 million registered users.\n\nIMDb began as a movie database on the Usenet group \"URL\" in 1990 and moved to the web in 1993.", "## Provenance : ftp://URL\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"imdb-movie-genres\"\n\nMDb (an acronym for Internet Movie Database) is an online database of information related to films, television programs, home videos, video games, and streaming content online – including cast, production crew and personal biographies, plot summaries, trivia, ratings, and fan and critical reviews. An additional fan feature, message boards, was abandoned in February 2017. Originally a fan-operated website, the database is now owned and operated by URL, Inc., a subsidiary of Amazon.\n\nAs of December 2020, IMDb has approximately 7.5 million titles (including episodes) and 10.4 million personalities in its database,[2] as well as 83 million registered users.\n\nIMDb began as a movie database on the Usenet group \"URL\" in 1990 and moved to the web in 1993.", "## Provenance : ftp://URL\n\nMore Information needed" ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: Artifact-AI/t5_base_courtlistener_billsum\n* Dataset: billsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Artifact-AI 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: Artifact-AI/t5_base_courtlistener_billsum\n* Dataset: billsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Artifact-AI for evaluating this model." ]

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

[ "2210.03992" ]

[ "en" ]

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

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

[ "task_categories:tabular-classification", "task_ids:tabular-single-column-regression", "language_creators:expert-generated", "size_categories:100K<n<1M", "license:cc-by-nc-sa-4.0", "wifi", "indoor-positioning", "indoor-localisation", "wifi-rssi", "rssi", "recurrent-neural-networks", "region:us" ]

[]

[]

[ "# WIFI RSSI Indoor Positioning Dataset\n\nA reliable and comprehensive public WiFi fingerprinting database for researchers to implement and compare the indoor localization’s methods.The database contains RSSI information from 6 APs conducted in different days with the support of autonomous robot.\n\nWe use an autonomous robot to collect the WiFi fingerprint data. Our 3-wheel robot has multiple sensors including wheel odometer, an inertial measurement unit (IMU), a LIDAR, sonar sensors and a color and depth (RGB-D) camera. The robot can navigate to a target location to collect WiFi fingerprints automatically. The localization accuracy of the robot is 0.07 m ± 0.02 m. The dimension of the area is 21 m × 16 m. It has three long corridors. There are six APs and five of them provide two distinct MAC address for 2.4- and 5-GHz communications channels, respectively, except for one that only operates on 2.4-GHz frequency. There is one router can provide CSI information.", "# Data Format\n\nX Position (m), Y Position (m), RSSI Feature 1 (dBm), RSSI Feature 2 (dBm), RSSI Feature 3 (dBm), RSSI Feature 4 (dBm), ..." ]

[ "TAGS\n#task_categories-tabular-classification #task_ids-tabular-single-column-regression #language_creators-expert-generated #size_categories-100K<n<1M #license-cc-by-nc-sa-4.0 #wifi #indoor-positioning #indoor-localisation #wifi-rssi #rssi #recurrent-neural-networks #region-us \n", "# WIFI RSSI Indoor Positioning Dataset\n\nA reliable and comprehensive public WiFi fingerprinting database for researchers to implement and compare the indoor localization’s methods.The database contains RSSI information from 6 APs conducted in different days with the support of autonomous robot.\n\nWe use an autonomous robot to collect the WiFi fingerprint data. Our 3-wheel robot has multiple sensors including wheel odometer, an inertial measurement unit (IMU), a LIDAR, sonar sensors and a color and depth (RGB-D) camera. The robot can navigate to a target location to collect WiFi fingerprints automatically. The localization accuracy of the robot is 0.07 m ± 0.02 m. The dimension of the area is 21 m × 16 m. It has three long corridors. There are six APs and five of them provide two distinct MAC address for 2.4- and 5-GHz communications channels, respectively, except for one that only operates on 2.4-GHz frequency. There is one router can provide CSI information.", "# Data Format\n\nX Position (m), Y Position (m), RSSI Feature 1 (dBm), RSSI Feature 2 (dBm), RSSI Feature 3 (dBm), RSSI Feature 4 (dBm), ..." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"2000dataset\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"2000dataset\"\n\nMore Information needed" ]

[ "task_categories:fill-mask", "task_categories:summarization", "task_categories:text-classification", "task_categories:token-classification", "task_ids:masked-language-modeling", "task_ids:multi-class-classification", "task_ids:topic-classification", "task_ids:named-entity-recognition", "language:en", "license:cc-by-sa-4.0", "patents", "arxiv:2207.04043", "region:us" ]

[ "2207.04043" ]

[ "en" ]

[ "# Dataset Card for The Harvard USPTO Patent Dataset (HUPD)\n!HUPD-Diagram", "## Dataset Description\n- Homepage: URL\n- Repository: HUPD GitHub repository\n- Paper: HUPD arXiv Submission\n- Point of Contact: Mirac Suzgun", "### Dataset Summary\nThe Harvard USPTO Dataset (HUPD) is a large-scale, well-structured, and multi-purpose corpus of English-language utility patent applications filed to the United States Patent and Trademark Office (USPTO) between January 2004 and December 2018.", "### Experiments and Tasks Considered in the Paper\n- Patent Acceptance Prediction: Given a section of a patent application (in particular, the abstract, claims, or description), predict whether the application will be accepted by the USPTO.\n- Automated Subject (IPC/CPC) Classification: Predict the primary IPC or CPC code of a patent application given (some subset of) the text of the application.\n- Language Modeling: Masked/autoregressive language modeling on the claims and description sections of patent applications.\n- Abstractive Summarization: Given the claims or claims section of a patent application, generate the abstract.", "### Languages\nThe dataset contains English text only.", "### Domain\nPatents (intellectual property).", "### Dataset Curators\nThe dataset was created by Mirac Suzgun, Luke Melas-Kyriazi, Suproteem K. Sarkar, Scott Duke Kominers, and Stuart M. Shieber.", "## Dataset Structure\nEach patent application is defined by a distinct JSON file, named after its application number, and includes information about \nthe application and publication numbers, \ntitle, \ndecision status, \nfiling and publication dates, \nprimary and secondary classification codes, \ninventor(s), \nexaminer,\nattorney,\nabstract, \nclaims, \nbackground, \nsummary, and \nfull description of the proposed invention, among other fields. There are also supplementary variables, such as the small-entity indicator (which denotes whether the applicant is considered to be a small entity by the USPTO) and the foreign-filing indicator (which denotes whether the application was originally filed in a foreign country).\nIn total, there are 34 data fields for each application. A full list of data fields used in the dataset is listed in the next section.", "### Data Instances\nEach patent application in our patent dataset is defined by a distinct JSON file (e.g., ''URL''), named after its unique application number. The format of the JSON files is as follows:", "## Usage", "### Loading the Dataset", "#### Sample (January 2016 Subset) \nThe following command can be used to load the 'sample' version of the dataset, which contains all the patent applications that were filed to the USPTO during the month of January in 2016. This small subset of the dataset can be used for debugging and exploration purposes.", "#### Full Dataset\n\nIf you would like to use the full version of the dataset, please make sure that change the 'name' field from 'sample' to 'all', specify the training and validation start and end dates carefully, and set 'force_extract' to be 'True' (so that you would only untar the files that you are interested in and not squander your disk storage space). In the following example, for instance, we set the training set year range to be [2011, 2016] (inclusive) and the validation set year range to be 2017.", "### Google Colab Notebook\nYou can also use the following Google Colab notebooks to explore HUPD. \n- ![Open In Colab](URL HUPD Examples: Loading the Dataset\n- ![Open In Colab](URL HUPD Examples: Loading HUPD By Using HuggingFace's Libraries\n- ![Open In Colab](URL HUPD Examples: Using the HUPD DistilRoBERTa Model\n- ![Open In Colab](URL HUPD Examples: Using the HUPD T5-Small Summarization Model", "## Dataset Creation", "### Source Data\n\nHUPD synthesizes multiple data sources from the USPTO: While the full patent application texts were obtained from the USPTO Bulk Data Storage System (Patent Application Data/XML Versions 4.0, 4.1, 4.2, 4.3, 4.4 ICE, as well as Version 1.5) as XML files, the bibliographic filing metadata were obtained from the USPTO Patent Examination Research Dataset (in February, 2021).", "### Annotations\n\nBeyond our patent decision label, for which construction details are provided in the paper, the dataset does not contain any human-written or computer-generated annotations beyond those produced by patent applicants or the USPTO.", "### Data Shift\n\nA major feature of HUPD is its structure, which allows it to demonstrate the evolution of concepts over time. As we illustrate in the paper, the criteria for patent acceptance evolve over time at different rates, depending on category. We believe this is an important feature of the dataset, not only because of the social scientific questions it raises, but also because it facilitates research on models that can accommodate concept shift in a real-world setting.", "### Personal and Sensitive Information\n\nThe dataset contains information about the inventor(s) and examiner of each patent application. These details are, however, already in the public domain and available on the USPTO's Patent Application Information Retrieval (PAIR) system, as well as on Google Patents and PatentsView.", "### Social Impact of the Dataset\n\nThe authors of the dataset hope that HUPD will have a positive social impact on the ML/NLP and Econ/IP communities. They discuss these considerations in more detail in the paper.", "### Impact on Underserved Communities and Discussion of Biases\nThe dataset contains patent applications in English, a language with heavy attention from the NLP community. However, innovation is spread across many languages, cultures, and communities that are not reflected in this dataset. HUPD is thus not representative of all kinds of innovation. Furthermore, patent applications require a fixed cost to draft and file and are not accessible to everyone. One goal of this dataset is to spur research that reduces the cost of drafting applications, potentially allowing for more people to seek intellectual property protection for their innovations.", "### Discussion of Biases\nSection 4 of the HUPD paper provides an examination of the dataset for potential biases. It shows, among other things, that female inventors are notably underrepresented in the U.S. patenting system, that small and micro entities (e.g., independent inventors, small companies, non-profit organizations) are less likely to have positive outcomes in patent obtaining than large entities (e.g., companies with more than 500 employees), and that patent filing and acceptance rates are not uniformly distributed across the US. Our empirical findings suggest that any study focusing on the acceptance prediction task, especially if it is using the inventor information or the small-entity indicator as part of the input, should be aware of the the potential biases present in the dataset and interpret their results carefully in light of those biases.\n\n- Please refer to Section 4 and Section D for an in-depth discussion of potential biases embedded in the dataset.", "### Licensing Information\n\nHUPD is released under the CreativeCommons Attribution-NonCommercial-ShareAlike 4.0 International." ]

[ "TAGS\n#task_categories-fill-mask #task_categories-summarization #task_categories-text-classification #task_categories-token-classification #task_ids-masked-language-modeling #task_ids-multi-class-classification #task_ids-topic-classification #task_ids-named-entity-recognition #language-English #license-cc-by-sa-4.0 #patents #arxiv-2207.04043 #region-us \n", "# Dataset Card for The Harvard USPTO Patent Dataset (HUPD)\n!HUPD-Diagram", "## Dataset Description\n- Homepage: URL\n- Repository: HUPD GitHub repository\n- Paper: HUPD arXiv Submission\n- Point of Contact: Mirac Suzgun", "### Dataset Summary\nThe Harvard USPTO Dataset (HUPD) is a large-scale, well-structured, and multi-purpose corpus of English-language utility patent applications filed to the United States Patent and Trademark Office (USPTO) between January 2004 and December 2018.", "### Experiments and Tasks Considered in the Paper\n- Patent Acceptance Prediction: Given a section of a patent application (in particular, the abstract, claims, or description), predict whether the application will be accepted by the USPTO.\n- Automated Subject (IPC/CPC) Classification: Predict the primary IPC or CPC code of a patent application given (some subset of) the text of the application.\n- Language Modeling: Masked/autoregressive language modeling on the claims and description sections of patent applications.\n- Abstractive Summarization: Given the claims or claims section of a patent application, generate the abstract.", "### Languages\nThe dataset contains English text only.", "### Domain\nPatents (intellectual property).", "### Dataset Curators\nThe dataset was created by Mirac Suzgun, Luke Melas-Kyriazi, Suproteem K. Sarkar, Scott Duke Kominers, and Stuart M. Shieber.", "## Dataset Structure\nEach patent application is defined by a distinct JSON file, named after its application number, and includes information about \nthe application and publication numbers, \ntitle, \ndecision status, \nfiling and publication dates, \nprimary and secondary classification codes, \ninventor(s), \nexaminer,\nattorney,\nabstract, \nclaims, \nbackground, \nsummary, and \nfull description of the proposed invention, among other fields. There are also supplementary variables, such as the small-entity indicator (which denotes whether the applicant is considered to be a small entity by the USPTO) and the foreign-filing indicator (which denotes whether the application was originally filed in a foreign country).\nIn total, there are 34 data fields for each application. A full list of data fields used in the dataset is listed in the next section.", "### Data Instances\nEach patent application in our patent dataset is defined by a distinct JSON file (e.g., ''URL''), named after its unique application number. The format of the JSON files is as follows:", "## Usage", "### Loading the Dataset", "#### Sample (January 2016 Subset) \nThe following command can be used to load the 'sample' version of the dataset, which contains all the patent applications that were filed to the USPTO during the month of January in 2016. This small subset of the dataset can be used for debugging and exploration purposes.", "#### Full Dataset\n\nIf you would like to use the full version of the dataset, please make sure that change the 'name' field from 'sample' to 'all', specify the training and validation start and end dates carefully, and set 'force_extract' to be 'True' (so that you would only untar the files that you are interested in and not squander your disk storage space). In the following example, for instance, we set the training set year range to be [2011, 2016] (inclusive) and the validation set year range to be 2017.", "### Google Colab Notebook\nYou can also use the following Google Colab notebooks to explore HUPD. \n- ![Open In Colab](URL HUPD Examples: Loading the Dataset\n- ![Open In Colab](URL HUPD Examples: Loading HUPD By Using HuggingFace's Libraries\n- ![Open In Colab](URL HUPD Examples: Using the HUPD DistilRoBERTa Model\n- ![Open In Colab](URL HUPD Examples: Using the HUPD T5-Small Summarization Model", "## Dataset Creation", "### Source Data\n\nHUPD synthesizes multiple data sources from the USPTO: While the full patent application texts were obtained from the USPTO Bulk Data Storage System (Patent Application Data/XML Versions 4.0, 4.1, 4.2, 4.3, 4.4 ICE, as well as Version 1.5) as XML files, the bibliographic filing metadata were obtained from the USPTO Patent Examination Research Dataset (in February, 2021).", "### Annotations\n\nBeyond our patent decision label, for which construction details are provided in the paper, the dataset does not contain any human-written or computer-generated annotations beyond those produced by patent applicants or the USPTO.", "### Data Shift\n\nA major feature of HUPD is its structure, which allows it to demonstrate the evolution of concepts over time. As we illustrate in the paper, the criteria for patent acceptance evolve over time at different rates, depending on category. We believe this is an important feature of the dataset, not only because of the social scientific questions it raises, but also because it facilitates research on models that can accommodate concept shift in a real-world setting.", "### Personal and Sensitive Information\n\nThe dataset contains information about the inventor(s) and examiner of each patent application. These details are, however, already in the public domain and available on the USPTO's Patent Application Information Retrieval (PAIR) system, as well as on Google Patents and PatentsView.", "### Social Impact of the Dataset\n\nThe authors of the dataset hope that HUPD will have a positive social impact on the ML/NLP and Econ/IP communities. They discuss these considerations in more detail in the paper.", "### Impact on Underserved Communities and Discussion of Biases\nThe dataset contains patent applications in English, a language with heavy attention from the NLP community. However, innovation is spread across many languages, cultures, and communities that are not reflected in this dataset. HUPD is thus not representative of all kinds of innovation. Furthermore, patent applications require a fixed cost to draft and file and are not accessible to everyone. One goal of this dataset is to spur research that reduces the cost of drafting applications, potentially allowing for more people to seek intellectual property protection for their innovations.", "### Discussion of Biases\nSection 4 of the HUPD paper provides an examination of the dataset for potential biases. It shows, among other things, that female inventors are notably underrepresented in the U.S. patenting system, that small and micro entities (e.g., independent inventors, small companies, non-profit organizations) are less likely to have positive outcomes in patent obtaining than large entities (e.g., companies with more than 500 employees), and that patent filing and acceptance rates are not uniformly distributed across the US. Our empirical findings suggest that any study focusing on the acceptance prediction task, especially if it is using the inventor information or the small-entity indicator as part of the input, should be aware of the the potential biases present in the dataset and interpret their results carefully in light of those biases.\n\n- Please refer to Section 4 and Section D for an in-depth discussion of potential biases embedded in the dataset.", "### Licensing Information\n\nHUPD is released under the CreativeCommons Attribution-NonCommercial-ShareAlike 4.0 International." ]

[ "region:us" ]

[]

[]

[]

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

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

[]

[ "en" ]

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

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

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

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

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

[ "license:mit", "arxiv:1806.05521", "arxiv:2012.11490", "region:us" ]

[ "1806.05521", "2012.11490" ]

[]

[]

[ "TAGS\n#license-mit #arxiv-1806.05521 #arxiv-2012.11490 #region-us \n" ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"Food-Prototype-Bruce\"\n\nMore Information needed" ]

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

[ "license:mit", "Reinforcement Learning", "Offline Reinforcement Learning", "Reinforcement Learning from Pixels", "DreamerV2", "DrQ+BC", "arxiv:2206.04779", "region:us" ]

[ "2206.04779" ]

[]

[ "# V-D4RL\n\nV-D4RL provides pixel-based analogues of the popular D4RL benchmarking tasks, derived from the 'dm_control' suite, along with natural extensions of two state-of-the-art online pixel-based continuous control algorithms, DrQ-v2 and DreamerV2, to the offline setting. For further details, please see the paper:\n\n_Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations_; Cong Lu*, Philip J. Ball*, Tim G. J. Rudner, Jack Parker-Holder, Michael A. Osborne, Yee Whye Teh.\n\n<p align=\"center\">\n <a href=URL on arXiv</a>\n</p>", "## Benchmarks\nThe V-D4RL datasets can be found in this repository under 'vd4rl'. These must be downloaded before running the code. Assuming the data is stored under 'vd4rl_data', the file structure is:", "## Baselines", "### Environment Setup\nRequirements are presented in conda environment files named 'conda_env.yml' within each folder. The command to create the environment is:\n\n\nAlternatively, dockerfiles are located under 'dockerfiles', replace '<<USER_ID>>' in the files with your own user ID from the command 'id -u'.", "### V-D4RL Main Evaluation\nExample run commands are given below, given an environment type and dataset identifier:", "#### Offline DV2", "#### DrQ+BC", "#### DrQ+CQL", "#### BC", "### Distracted and Multitask Experiments\nTo run the distracted and multitask experiments, it suffices to change the offline directory passed to the commands above.", "## Note on data collection and format\nWe follow the image sizes and dataset format of each algorithm's native codebase.\nThe means that Offline DV2 uses '*.npz' files with 64px images to store the offline data, whereas DrQ+BC uses '*.hdf5' with 84px images.\n\nThe data collection procedure is detailed in Appendix B of our paper, and we provide conversion scripts in 'conversion_scripts'.\nFor the original SAC policies to generate the data see here.\nSee here for distracted/multitask variants.\nWe used 'seed=0' for all data generation.", "## Acknowledgements\nV-D4RL builds upon many works and open-source codebases in both offline reinforcement learning and online pixel-based continuous control. We would like to particularly thank the authors of:\n- D4RL\n- DMControl\n- DreamerV2\n- DrQ-v2\n- LOMPO", "## Contact\nPlease contact Cong Lu or Philip Ball for any queries. We welcome any suggestions or contributions!" ]

[ "TAGS\n#license-mit #Reinforcement Learning #Offline Reinforcement Learning #Reinforcement Learning from Pixels #DreamerV2 #DrQ+BC #arxiv-2206.04779 #region-us \n", "# V-D4RL\n\nV-D4RL provides pixel-based analogues of the popular D4RL benchmarking tasks, derived from the 'dm_control' suite, along with natural extensions of two state-of-the-art online pixel-based continuous control algorithms, DrQ-v2 and DreamerV2, to the offline setting. For further details, please see the paper:\n\n_Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations_; Cong Lu*, Philip J. Ball*, Tim G. J. Rudner, Jack Parker-Holder, Michael A. Osborne, Yee Whye Teh.\n\n<p align=\"center\">\n <a href=URL on arXiv</a>\n</p>", "## Benchmarks\nThe V-D4RL datasets can be found in this repository under 'vd4rl'. These must be downloaded before running the code. Assuming the data is stored under 'vd4rl_data', the file structure is:", "## Baselines", "### Environment Setup\nRequirements are presented in conda environment files named 'conda_env.yml' within each folder. The command to create the environment is:\n\n\nAlternatively, dockerfiles are located under 'dockerfiles', replace '<<USER_ID>>' in the files with your own user ID from the command 'id -u'.", "### V-D4RL Main Evaluation\nExample run commands are given below, given an environment type and dataset identifier:", "#### Offline DV2", "#### DrQ+BC", "#### DrQ+CQL", "#### BC", "### Distracted and Multitask Experiments\nTo run the distracted and multitask experiments, it suffices to change the offline directory passed to the commands above.", "## Note on data collection and format\nWe follow the image sizes and dataset format of each algorithm's native codebase.\nThe means that Offline DV2 uses '*.npz' files with 64px images to store the offline data, whereas DrQ+BC uses '*.hdf5' with 84px images.\n\nThe data collection procedure is detailed in Appendix B of our paper, and we provide conversion scripts in 'conversion_scripts'.\nFor the original SAC policies to generate the data see here.\nSee here for distracted/multitask variants.\nWe used 'seed=0' for all data generation.", "## Acknowledgements\nV-D4RL builds upon many works and open-source codebases in both offline reinforcement learning and online pixel-based continuous control. We would like to particularly thank the authors of:\n- D4RL\n- DMControl\n- DreamerV2\n- DrQ-v2\n- LOMPO", "## Contact\nPlease contact Cong Lu or Philip Ball for any queries. We welcome any suggestions or contributions!" ]

[ "task_categories:summarization", "size_categories:100K<n<1M", "source_datasets:xsum", "language:en", "license:apache-2.0", "stacked summaries", "xsum", "doi:10.57967/hf/0390", "region:us" ]

[]

[ "en" ]

[ "# stacked-xsum-1024\n\n\na \"stacked\" version of 'xsum' \n\n1. Original Dataset: copy of the base dataset\n\n2. Stacked Rows: The original dataset is processed by stacking rows based on certain criteria:\n - Maximum Input Length: The maximum length for input sequences is 1024 tokens in the longt5 model tokenizer.\n - Maximum Output Length: The maximum length for output sequences is also 1024 tokens in the longt5 model tokenizer.\n\n3. Special Token: The dataset utilizes the '[NEXT_CONCEPT]' token to indicate a new topic within the same summary. It is recommended to explicitly add this special token to your model's tokenizer before training, ensuring that it is recognized and processed correctly during downstream usage.\n4.", "## updates \n\n- dec 3: upload initial version\n- dec 4: upload v2 with basic data quality fixes (i.e. the 'is_stacked' column)\n- dec 5 0500: upload v3 which has pre-randomised order and duplicate rows for document+summary dropped", "## stats\n\n\n!stats", "## dataset details\n\nsee the repo '.log' file for more details.\n\ntrain input\n\n\n\nstacked train:\n\n\n\nIf you find this useful in your work, please consider citing us." ]

[ "TAGS\n#task_categories-summarization #size_categories-100K<n<1M #source_datasets-xsum #language-English #license-apache-2.0 #stacked summaries #xsum #doi-10.57967/hf/0390 #region-us \n", "# stacked-xsum-1024\n\n\na \"stacked\" version of 'xsum' \n\n1. Original Dataset: copy of the base dataset\n\n2. Stacked Rows: The original dataset is processed by stacking rows based on certain criteria:\n - Maximum Input Length: The maximum length for input sequences is 1024 tokens in the longt5 model tokenizer.\n - Maximum Output Length: The maximum length for output sequences is also 1024 tokens in the longt5 model tokenizer.\n\n3. Special Token: The dataset utilizes the '[NEXT_CONCEPT]' token to indicate a new topic within the same summary. It is recommended to explicitly add this special token to your model's tokenizer before training, ensuring that it is recognized and processed correctly during downstream usage.\n4.", "## updates \n\n- dec 3: upload initial version\n- dec 4: upload v2 with basic data quality fixes (i.e. the 'is_stacked' column)\n- dec 5 0500: upload v3 which has pre-randomised order and duplicate rows for document+summary dropped", "## stats\n\n\n!stats", "## dataset details\n\nsee the repo '.log' file for more details.\n\ntrain input\n\n\n\nstacked train:\n\n\n\nIf you find this useful in your work, please consider citing us." ]

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

[]

[]

[ "# TU-Berlin Sketch Dataset\n\nThis is the full PNG dataset from TU-Berlin." ]

[ "TAGS\n#license-cc-by-4.0 #region-us \n", "# TU-Berlin Sketch Dataset\n\nThis is the full PNG dataset from TU-Berlin." ]

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

[]

[ "en" ]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"wmt16_sentence_lang\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"wmt16_sentence_lang\"\n\nMore Information needed" ]

[ "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:" ]

[ "task_categories:text-generation", "task_categories:question-answering", "task_ids:language-modeling", "task_ids:open-domain-qa", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:ru", "license:cc0-1.0", "region:us" ]

[]

[ "ru" ]

[ "# Dataset Card for Yandex.Q", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Languages\n- Dataset Structure\n - Data Fields\n - Data Splits\n- Dataset Creation\n- Additional Information\n - Dataset Curators\n - Citation Information", "## Dataset Description\n\n- Repository: URL", "### Dataset Summary\n\nThis is a dataset of questions and answers scraped from Yandex.Q. There are 836810 answered questions out of the total of 1297670.\nThe full dataset that includes all metadata returned by Yandex.Q APIs and contains unanswered questions can be found in 'URL'", "### Languages\n\nThe dataset is mostly in Russian, but there may be other languages present", "## Dataset Structure", "### Data Fields\n\nThe dataset consists of 3 fields:\n- 'question' - question title ('string')\n- 'description' - question description ('string' or 'null')\n- 'answer' - answer to the question ('string')", "### Data Splits\n\nAll 836810 examples are in the train split, there is no validation split.", "## Dataset Creation\n\nThe data was scraped through some \"hidden\" APIs using several scripts, located in my GitHub repository", "## Additional Information", "### Dataset Curators\n\n- URL" ]

[ "TAGS\n#task_categories-text-generation #task_categories-question-answering #task_ids-language-modeling #task_ids-open-domain-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-Russian #license-cc0-1.0 #region-us \n", "# Dataset Card for Yandex.Q", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Languages\n- Dataset Structure\n - Data Fields\n - Data Splits\n- Dataset Creation\n- Additional Information\n - Dataset Curators\n - Citation Information", "## Dataset Description\n\n- Repository: URL", "### Dataset Summary\n\nThis is a dataset of questions and answers scraped from Yandex.Q. There are 836810 answered questions out of the total of 1297670.\nThe full dataset that includes all metadata returned by Yandex.Q APIs and contains unanswered questions can be found in 'URL'", "### Languages\n\nThe dataset is mostly in Russian, but there may be other languages present", "## Dataset Structure", "### Data Fields\n\nThe dataset consists of 3 fields:\n- 'question' - question title ('string')\n- 'description' - question description ('string' or 'null')\n- 'answer' - answer to the question ('string')", "### Data Splits\n\nAll 836810 examples are in the train split, there is no validation split.", "## Dataset Creation\n\nThe data was scraped through some \"hidden\" APIs using several scripts, located in my GitHub repository", "## Additional Information", "### Dataset Curators\n\n- URL" ]

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

[]

[]

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

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

[ "task_categories:translation", "task_categories:text-generation", "size_categories:10K<n<100K", "language:bm", "language:fr", "region:us" ]

[]

[ "bm", "fr" ]

[ "### Maintenance\n\n\n* This dataset is supposed to be actively maintained.", "### Benchmarks:\n\n\n* 'Coming soon'", "### Sources\n\n\n* 'sources'", "### To note:\n\n\n* ʃ => (sh/shy) sound: Symbol left in the dataset, although not a part of bambara orthography nor French orthography.\n\n\nLicense\n-------\n\n\n* 'CC-BY-SA-4.0'\n\n\nVersion\n-------\n\n\n* '1.0.1'\n\n\nContacts\n--------\n\n\n* 'sdiarra robotsmali org'\n* 'aat3261 rit edu'" ]

[ "TAGS\n#task_categories-translation #task_categories-text-generation #size_categories-10K<n<100K #language-Bambara #language-French #region-us \n", "### Maintenance\n\n\n* This dataset is supposed to be actively maintained.", "### Benchmarks:\n\n\n* 'Coming soon'", "### Sources\n\n\n* 'sources'", "### To note:\n\n\n* ʃ => (sh/shy) sound: Symbol left in the dataset, although not a part of bambara orthography nor French orthography.\n\n\nLicense\n-------\n\n\n* 'CC-BY-SA-4.0'\n\n\nVersion\n-------\n\n\n* '1.0.1'\n\n\nContacts\n--------\n\n\n* 'sdiarra robotsmali org'\n* 'aat3261 rit edu'" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"Lab2scalable\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"Lab2scalable\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"librispeech5k-augmentated-train-prepared\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"librispeech5k-augmentated-train-prepared\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"id2223_whisper_swedish\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"id2223_whisper_swedish\"\n\nMore Information needed" ]

[ "task_categories:summarization", "size_categories:10K<n<100K", "source_datasets:samsum", "language:en", "license:apache-2.0", "stacked summaries", "region:us" ]

[]

[ "en" ]

[ "# stacked samsum 1024\n\nCreated with the 'stacked-booksum' repo version v0.25. It contains:\n\n1. Original Dataset: copy of the base dataset\n\n2. Stacked Rows: The original dataset is processed by stacking rows based on certain criteria:\n - Maximum Input Length: The maximum length for input sequences is 1024 tokens in the longt5 model tokenizer.\n - Maximum Output Length: The maximum length for output sequences is also 1024 tokens in the longt5 model tokenizer.\n\n3. Special Token: The dataset utilizes the '[NEXT_CONCEPT]' token to indicate a new topic within the same summary. It is recommended to explicitly add this special token to your model's tokenizer before training, ensuring that it is recognized and processed correctly during downstream usage.", "## stats\n\n!stacked-samsum-1024-trainstats", "## dataset details\n\nDefault (train):\n\n\n\nstacked (train)" ]

[ "TAGS\n#task_categories-summarization #size_categories-10K<n<100K #source_datasets-samsum #language-English #license-apache-2.0 #stacked summaries #region-us \n", "# stacked samsum 1024\n\nCreated with the 'stacked-booksum' repo version v0.25. It contains:\n\n1. Original Dataset: copy of the base dataset\n\n2. Stacked Rows: The original dataset is processed by stacking rows based on certain criteria:\n - Maximum Input Length: The maximum length for input sequences is 1024 tokens in the longt5 model tokenizer.\n - Maximum Output Length: The maximum length for output sequences is also 1024 tokens in the longt5 model tokenizer.\n\n3. Special Token: The dataset utilizes the '[NEXT_CONCEPT]' token to indicate a new topic within the same summary. It is recommended to explicitly add this special token to your model's tokenizer before training, ensuring that it is recognized and processed correctly during downstream usage.", "## stats\n\n!stacked-samsum-1024-trainstats", "## dataset details\n\nDefault (train):\n\n\n\nstacked (train)" ]

[ "task_categories:automatic-speech-recognition", "annotations_creators:expert-generated", "language_creators:machine-generated", "multilinguality:multilingual", "size_categories:100K<n<1M", "source_datasets:original", "license:mit", "arxiv:2208.11761", "region:us" ]

[ "2208.11761" ]

[]

[ "# Dataset Card for Kathbath", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:URL\n- Repository:URL\n- Paper:URL\n- Point of Contact:tahirjmakhdoomi@URL", "### Dataset Summary\n\nKathbath is an human-labeled ASR dataset containing 1,684 hours of labelled speech data across 12 Indian languages from 1,218 contributors located in 203 districts in India", "### Languages\n\n- Bengali\n- Gujarati\n- Kannada\n- Hindi\n- Malayalam\n- Marathi\n- Odia\n- Punjabi\n- Sanskrit\n- Tamil\n- Telugu\n- Urdu", "## Dataset Structure", "### Licensing Information\n\nThe IndicSUPERB dataset is released under this licensing scheme:\n\n- We do not own any of the raw text used in creating this dataset.\n- The text data comes from the IndicCorp dataset which is a crawl of publicly available websites.\n- The audio transcriptions of the raw text and labelled annotations of the datasets have been created by us.\n- We license the actual packaging of all this data under the Creative Commons CC0 license (“no rights reserved”).\n- To the extent possible under law, AI4Bharat has waived all copyright and related or neighboring rights to the IndicSUPERB dataset.\n- This work is published from: India.", "### Contributions\n\nWe would like to thank the Ministry of Electronics and Information Technology (MeitY) of the Government of India and the Centre for Development of Advanced Computing (C-DAC), Pune for generously supporting this work and providing us access to multiple GPU nodes on the Param Siddhi Supercomputer. We would like to thank the EkStep Foundation and Nilekani Philanthropies for their generous grant which went into hiring human resources as well as cloud resources needed for this work. We would like to thank DesiCrew for connecting us to native speakers for collecting data. We would like to thank Vivek Seshadri from Karya Inc. for helping setup the data collection infrastructure on the Karya platform. We would like to thank all the members of AI4Bharat team in helping create the Query by Example dataset." ]

[ "TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-expert-generated #language_creators-machine-generated #multilinguality-multilingual #size_categories-100K<n<1M #source_datasets-original #license-mit #arxiv-2208.11761 #region-us \n", "# Dataset Card for Kathbath", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:URL\n- Repository:URL\n- Paper:URL\n- Point of Contact:tahirjmakhdoomi@URL", "### Dataset Summary\n\nKathbath is an human-labeled ASR dataset containing 1,684 hours of labelled speech data across 12 Indian languages from 1,218 contributors located in 203 districts in India", "### Languages\n\n- Bengali\n- Gujarati\n- Kannada\n- Hindi\n- Malayalam\n- Marathi\n- Odia\n- Punjabi\n- Sanskrit\n- Tamil\n- Telugu\n- Urdu", "## Dataset Structure", "### Licensing Information\n\nThe IndicSUPERB dataset is released under this licensing scheme:\n\n- We do not own any of the raw text used in creating this dataset.\n- The text data comes from the IndicCorp dataset which is a crawl of publicly available websites.\n- The audio transcriptions of the raw text and labelled annotations of the datasets have been created by us.\n- We license the actual packaging of all this data under the Creative Commons CC0 license (“no rights reserved”).\n- To the extent possible under law, AI4Bharat has waived all copyright and related or neighboring rights to the IndicSUPERB dataset.\n- This work is published from: India.", "### Contributions\n\nWe would like to thank the Ministry of Electronics and Information Technology (MeitY) of the Government of India and the Centre for Development of Advanced Computing (C-DAC), Pune for generously supporting this work and providing us access to multiple GPU nodes on the Param Siddhi Supercomputer. We would like to thank the EkStep Foundation and Nilekani Philanthropies for their generous grant which went into hiring human resources as well as cloud resources needed for this work. We would like to thank DesiCrew for connecting us to native speakers for collecting data. We would like to thank Vivek Seshadri from Karya Inc. for helping setup the data collection infrastructure on the Karya platform. We would like to thank all the members of AI4Bharat team in helping create the Query by Example dataset." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"Kimetsu-no-Yaiba-Image-Dataset-01\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"Kimetsu-no-Yaiba-Image-Dataset-01\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"lcbsi-wbc\"\n\nMore Information needed" ]

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

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

[]

[]

[ "# Dataset Card for \"linustechtips\"", "## Table of Contents\n- 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 - Contributions", "## Dataset Description\n\n- Homepage: Whispering-GPT\n- Repository: whisper_gpt_pipeline\n- Paper: whisper and gpt\n- Point of Contact: Whispering-GPT organization", "### Dataset Summary\n\nThis dataset is created by applying whisper to the videos of the Youtube channel Linus Tech Tips. The dataset was created a medium size whisper model.", "### Languages\n\n- Language: English", "## Dataset Structure\n\nThe dataset contains all the transcripts plus the audio of the different videos of Linus Tech Tips.", "### Data Fields\n\nThe dataset is composed by:\n- id: Id of the youtube video.\n- channel: Name of the channel.\n- channel\\_id: Id of the youtube channel.\n- title: Title given to the video.\n- categories: Category of the video.\n- description: Description added by the author.\n- text: Whole transcript of the video.\n- segments: A list with the time and transcription of the video.\n - start: When started the trancription.\n - end: When the transcription ends.\n - text: The text of the transcription.\n- audio: the extracted audio of the video.", "### Data Splits\n- Train split.", "## Dataset Creation", "### Source Data\nThe transcriptions are from the videos of Linus Tech Tips Channel", "### Contributions\nThanks to Whispering-GPT organization for adding this dataset." ]

[ "TAGS\n#task_categories-automatic-speech-recognition #whisper #whispering #medium #region-us \n", "# Dataset Card for \"linustechtips\"", "## Table of Contents\n- 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 - Contributions", "## Dataset Description\n\n- Homepage: Whispering-GPT\n- Repository: whisper_gpt_pipeline\n- Paper: whisper and gpt\n- Point of Contact: Whispering-GPT organization", "### Dataset Summary\n\nThis dataset is created by applying whisper to the videos of the Youtube channel Linus Tech Tips. The dataset was created a medium size whisper model.", "### Languages\n\n- Language: English", "## Dataset Structure\n\nThe dataset contains all the transcripts plus the audio of the different videos of Linus Tech Tips.", "### Data Fields\n\nThe dataset is composed by:\n- id: Id of the youtube video.\n- channel: Name of the channel.\n- channel\\_id: Id of the youtube channel.\n- title: Title given to the video.\n- categories: Category of the video.\n- description: Description added by the author.\n- text: Whole transcript of the video.\n- segments: A list with the time and transcription of the video.\n - start: When started the trancription.\n - end: When the transcription ends.\n - text: The text of the transcription.\n- audio: the extracted audio of the video.", "### Data Splits\n- Train split.", "## Dataset Creation", "### Source Data\nThe transcriptions are from the videos of Linus Tech Tips Channel", "### Contributions\nThanks to Whispering-GPT organization for adding this dataset." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"librispeech-augmentated-validation-prepared\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"librispeech-augmentated-validation-prepared\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset for Multi Domain （Including Kitchen, Books, DVDs, and Electronics）\n\nMulti-Domain Sentiment Dataset by John Blitzer, Mark Dredze, Fernando Pereira.", "### Description：\nThe Multi-Domain Sentiment Dataset contains product reviews taken from URL from 4 product types (domains): Kitchen, Books, DVDs, and Electronics. Each domain has several thousand reviews, but the exact number varies by domain. Reviews contain star ratings (1 to 5 stars) that can be converted into binary labels if needed. This page contains some descriptions about the data. If you have questions, please email me directly (email found here).\n\nA few notes regarding the data.\n\n1) There are 4 directories corresponding to each of the four domains. Each directory contains 3 files called URL, URL and URL. (The books directory doesn't contain the unlabeled but the link is below.) While the positive and negative files contain positive and negative reviews, these aren't necessarily the splits we used in the experiments. We randomly drew from the three files ignoring the file names.\n\n2) Each file contains a pseudo XML scheme for encoding the reviews. Most of the fields are self explanatory. The reviews have a unique ID field that isn't very unique. If it has two unique id fields, ignore the one containing only a number.", "### Link to download the data:\n\nMulti-Domain Sentiment Dataset (30 MB) domain_sentiment_data.URL\n\nBooks unlabeled data (2 MB) URL" ]

[ "TAGS\n#region-us \n", "# Dataset for Multi Domain （Including Kitchen, Books, DVDs, and Electronics）\n\nMulti-Domain Sentiment Dataset by John Blitzer, Mark Dredze, Fernando Pereira.", "### Description：\nThe Multi-Domain Sentiment Dataset contains product reviews taken from URL from 4 product types (domains): Kitchen, Books, DVDs, and Electronics. Each domain has several thousand reviews, but the exact number varies by domain. Reviews contain star ratings (1 to 5 stars) that can be converted into binary labels if needed. This page contains some descriptions about the data. If you have questions, please email me directly (email found here).\n\nA few notes regarding the data.\n\n1) There are 4 directories corresponding to each of the four domains. Each directory contains 3 files called URL, URL and URL. (The books directory doesn't contain the unlabeled but the link is below.) While the positive and negative files contain positive and negative reviews, these aren't necessarily the splits we used in the experiments. We randomly drew from the three files ignoring the file names.\n\n2) Each file contains a pseudo XML scheme for encoding the reviews. Most of the fields are self explanatory. The reviews have a unique ID field that isn't very unique. If it has two unique id fields, ignore the one containing only a number.", "### Link to download the data:\n\nMulti-Domain Sentiment Dataset (30 MB) domain_sentiment_data.URL\n\nBooks unlabeled data (2 MB) URL" ]

[ "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: autoevaluate/summarization-not-evaluated\n* Dataset: autoevaluate/xsum-sample\n* Config: autoevaluate--xsum-sample\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: autoevaluate/summarization-not-evaluated\n* Dataset: autoevaluate/xsum-sample\n* Config: autoevaluate--xsum-sample\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "region:us" ]

[]

[]

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

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

[ "task_categories:automatic-speech-recognition", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:100K<n<1M", "language:sv", "license:cc0-1.0", "NST", "region:us" ]

[]

[ "sv" ]

[ "# Dataset Card for Babelbox Voice", "## 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 database was created by Nordic Language Technology for the development of automatic speech recognition and dictation in Swedish. \nIt is redistributed as a Hugging Face dataset for convienience.", "### Supported Tasks and Leaderboards", "### Languages\n\nSwedish", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @github-username for adding this dataset." ]

[ "TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-100K<n<1M #language-Swedish #license-cc0-1.0 #NST #region-us \n", "# Dataset Card for Babelbox Voice", "## 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 database was created by Nordic Language Technology for the development of automatic speech recognition and dictation in Swedish. \nIt is redistributed as a Hugging Face dataset for convienience.", "### Supported Tasks and Leaderboards", "### Languages\n\nSwedish", "## 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 \"english_wikipedia\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"met-ds-0\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"met-ds-0\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"tokenized-recipe-nlg-gpt2-ners-ingredients-only\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"tokenized-recipe-nlg-gpt2-ners-ingredients-only\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"github-issues\"\n\nMore Information needed" ]

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

[ "task_categories:automatic-speech-recognition", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "language:ta", "license:cc-by-4.0", "tamil asr", "nlp", "arxiv:2111.03945", "region:us" ]

[ "2111.03945" ]

[ "ta" ]

[ "# Dataset Card for [UCLA Bashini Tamil Dataset]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:: URL\n- Paper:: URL\n- Leaderboard:\n- Point of Contact:contact.bhashini@URL", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages\n\n- Tamil", "## 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 @parambharat for adding this dataset." ]

[ "TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #language-Tamil #license-cc-by-4.0 #tamil asr #nlp #arxiv-2111.03945 #region-us \n", "# Dataset Card for [UCLA Bashini Tamil Dataset]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:: URL\n- Paper:: URL\n- Leaderboard:\n- Point of Contact:contact.bhashini@URL", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages\n\n- Tamil", "## 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 @parambharat for adding this dataset." ]

[ "language:ru", "arxiv:2211.01786", "region:us" ]

[ "2211.01786" ]

[ "ru" ]

[ "# Dataset Summary\n\nRussian split for xP3.\nUsing the script in this repository as an example it is very easy to add new languages to xP3." ]

[ "TAGS\n#language-Russian #arxiv-2211.01786 #region-us \n", "# Dataset Summary\n\nRussian split for xP3.\nUsing the script in this repository as an example it is very easy to add new languages to xP3." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"vie-book\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

[]

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

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

[ "task_categories:automatic-speech-recognition", "multilinguality:monolingual", "language:en", "audio", "automatic-speech-recognition", "en-atc", "en", "noisy-speech-recognition", "speech-recognition", "arxiv:2211.04054", "region:us" ]

[ "2211.04054" ]

[ "en" ]

[ "# Dataset Card for ATCO2 test set corpus (1hr set)", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages and Other Details\n- Dataset Structure\n - Data Fields\n- Additional Information\n - Licensing Information\n - Citation Information", "## Dataset Description\n- Homepage: ATCO2 project homepage\n- Repository: ATCO2 corpus\n- Paper: ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications", "### Dataset Summary\n\nATCO2 project aims at developing a unique platform allowing to collect, organize and pre-process air-traffic control (voice communication) data from air space. This project has received funding from the Clean Sky 2 Joint Undertaking (JU) under grant agreement No 864702. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and the Clean Sky 2 JU members other than the Union.\n\nThe project collected the real-time voice communication between air-traffic controllers and pilots available either directly through publicly accessible radio frequency channels or indirectly from air-navigation service providers (ANSPs). In addition to the voice communication data, contextual information is available in a form of metadata (i.e. surveillance data). The dataset consists of two distinct packages:\n\n- A corpus of 5000+ hours (pseudo-transcribed) of air-traffic control speech collected across different airports (Sion, Bern, Zurich, etc.) in .wav format for speech recognition. Speaker distribution is 90/10% between males and females and the group contains native and non-native speakers of English.\n- A corpus of 4 hours (transcribed) of air-traffic control speech collected across different airports (Sion, Bern, Zurich, etc.) in .wav format for speech recognition. Speaker distribution is 90/10% between males and females and the group contains native and non-native speakers of English. This corpus has been transcribed with orthographic information in XML format with speaker noise information, SNR values and others. Read Less\n- A free sample of the 4 hours transcribed data is in ATCO2 project homepage", "### Supported Tasks and Leaderboards\n\n- 'automatic-speech-recognition'. Already adapted/fine-tuned models are available here --> Wav2Vec 2.0 LARGE mdel.", "### Languages and other details\n\nThe text and the recordings are in English. For more information see Table 3 and Table 4 of ATCO2 corpus paper", "## Dataset Structure", "### Data Fields\n\n- 'id (string)': a string of recording identifier for each example, corresponding to its.\n- 'audio (audio)': audio data for the given ID\n- 'text (string)': transcript of the file already normalized. Follow these repositories for more details w2v2-air-traffic and bert-text-diarization-atc\n- 'segment_start_time (float32)': segment start time (normally 0)\n- 'segment_end_time (float32): segment end time\n- 'duration (float32)': duration of the recording, compute as segment_end_time - segment_start_time", "## Additional Information", "### Licensing Information\n\nThe licensing status of the ATCO2-test-set-1h corpus is in the file ATCO2-ASRdataset-v1_beta - End-User Data Agreement in the data folder. Download the data in ATCO2 project homepage\n\n\n\nContributors who prepared, processed, normalized and uploaded the dataset in HuggingFace:" ]

[ "TAGS\n#task_categories-automatic-speech-recognition #multilinguality-monolingual #language-English #audio #automatic-speech-recognition #en-atc #en #noisy-speech-recognition #speech-recognition #arxiv-2211.04054 #region-us \n", "# Dataset Card for ATCO2 test set corpus (1hr set)", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages and Other Details\n- Dataset Structure\n - Data Fields\n- Additional Information\n - Licensing Information\n - Citation Information", "## Dataset Description\n- Homepage: ATCO2 project homepage\n- Repository: ATCO2 corpus\n- Paper: ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications", "### Dataset Summary\n\nATCO2 project aims at developing a unique platform allowing to collect, organize and pre-process air-traffic control (voice communication) data from air space. This project has received funding from the Clean Sky 2 Joint Undertaking (JU) under grant agreement No 864702. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and the Clean Sky 2 JU members other than the Union.\n\nThe project collected the real-time voice communication between air-traffic controllers and pilots available either directly through publicly accessible radio frequency channels or indirectly from air-navigation service providers (ANSPs). In addition to the voice communication data, contextual information is available in a form of metadata (i.e. surveillance data). The dataset consists of two distinct packages:\n\n- A corpus of 5000+ hours (pseudo-transcribed) of air-traffic control speech collected across different airports (Sion, Bern, Zurich, etc.) in .wav format for speech recognition. Speaker distribution is 90/10% between males and females and the group contains native and non-native speakers of English.\n- A corpus of 4 hours (transcribed) of air-traffic control speech collected across different airports (Sion, Bern, Zurich, etc.) in .wav format for speech recognition. Speaker distribution is 90/10% between males and females and the group contains native and non-native speakers of English. This corpus has been transcribed with orthographic information in XML format with speaker noise information, SNR values and others. Read Less\n- A free sample of the 4 hours transcribed data is in ATCO2 project homepage", "### Supported Tasks and Leaderboards\n\n- 'automatic-speech-recognition'. Already adapted/fine-tuned models are available here --> Wav2Vec 2.0 LARGE mdel.", "### Languages and other details\n\nThe text and the recordings are in English. For more information see Table 3 and Table 4 of ATCO2 corpus paper", "## Dataset Structure", "### Data Fields\n\n- 'id (string)': a string of recording identifier for each example, corresponding to its.\n- 'audio (audio)': audio data for the given ID\n- 'text (string)': transcript of the file already normalized. Follow these repositories for more details w2v2-air-traffic and bert-text-diarization-atc\n- 'segment_start_time (float32)': segment start time (normally 0)\n- 'segment_end_time (float32): segment end time\n- 'duration (float32)': duration of the recording, compute as segment_end_time - segment_start_time", "## Additional Information", "### Licensing Information\n\nThe licensing status of the ATCO2-test-set-1h corpus is in the file ATCO2-ASRdataset-v1_beta - End-User Data Agreement in the data folder. Download the data in ATCO2 project homepage\n\n\n\nContributors who prepared, processed, normalized and uploaded the dataset in HuggingFace:" ]

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

[]

[]

[ "### Dataset Summary\n\nAugmented part of the test data of the LibriSpeech dataset.\nAs a basis, the original part of the test was taken, and augmentation was carried out to add extraneous noise." ]

[ "TAGS\n#license-apache-2.0 #region-us \n", "### Dataset Summary\n\nAugmented part of the test data of the LibriSpeech dataset.\nAs a basis, the original part of the test was taken, and augmentation was carried out to add extraneous noise." ]

[ "region:us" ]

[]

[]

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

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

[ "task_categories:translation", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "size_categories:100K<n<1M", "source_datasets:original", "language:ar", "license:unknown", "region:us" ]

[]

[ "ar" ]

[ "# Dataset Card for DataTranslationDT", "## 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: None\n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\n\n'dataset = load_dataset(\"DataTranslationDT\", lang1=\"disfluent\", lang2=\"fluent\")'", "### 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" ]

[ "TAGS\n#task_categories-translation #annotations_creators-found #language_creators-found #multilinguality-multilingual #size_categories-100K<n<1M #source_datasets-original #language-Arabic #license-unknown #region-us \n", "# Dataset Card for DataTranslationDT", "## 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: None\n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\n\n'dataset = load_dataset(\"DataTranslationDT\", lang1=\"disfluent\", lang2=\"fluent\")'", "### 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" ]

[ "task_categories:automatic-speech-recognition", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:ta", "license:cc-by-2.0", "Tamil ASR", "Speech Recognition", "arxiv:2207.13331", "arxiv:2207.13333", "region:us" ]

[ "2207.13331", "2207.13333" ]

[ "ta" ]

[ "# Dataset Card for [Dataset Name]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nTamil transcribed speech corpus for ASR", "### Supported Tasks and Leaderboards", "### Languages\n\n- Tamil", "## 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\n\nAttribution 2.0 Generic (CC BY 2.0) \n\n\n@misc{mile_1,\n doi = {10.48550/ARXIV.2207.13331},\n url = {URL\n author = {A, Madhavaraj and Pilar, Bharathi and G, Ramakrishnan A},\n title = {Subword Dictionary Learning and Segmentation Techniques for Automatic Speech Recognition in Tamil and Kannada},\n publisher = {arXiv},\n year = {2022},\n}\n\n@misc{mile_2,\n doi = {10.48550/ARXIV.2207.13333},\n url = {URL\n author = {A, Madhavaraj and Pilar, Bharathi and G, Ramakrishnan A},\n title = {Knowledge-driven Subword Grammar Modeling for Automatic Speech Recognition in Tamil and Kannada},\n publisher = {arXiv},\n year = {2022},\n}", "### Contributions\n\nThanks to @parambharat for adding this dataset." ]

[ "TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Tamil #license-cc-by-2.0 #Tamil ASR #Speech Recognition #arxiv-2207.13331 #arxiv-2207.13333 #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: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nTamil transcribed speech corpus for ASR", "### Supported Tasks and Leaderboards", "### Languages\n\n- Tamil", "## 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\n\nAttribution 2.0 Generic (CC BY 2.0) \n\n\n@misc{mile_1,\n doi = {10.48550/ARXIV.2207.13331},\n url = {URL\n author = {A, Madhavaraj and Pilar, Bharathi and G, Ramakrishnan A},\n title = {Subword Dictionary Learning and Segmentation Techniques for Automatic Speech Recognition in Tamil and Kannada},\n publisher = {arXiv},\n year = {2022},\n}\n\n@misc{mile_2,\n doi = {10.48550/ARXIV.2207.13333},\n url = {URL\n author = {A, Madhavaraj and Pilar, Bharathi and G, Ramakrishnan A},\n title = {Knowledge-driven Subword Grammar Modeling for Automatic Speech Recognition in Tamil and Kannada},\n publisher = {arXiv},\n year = {2022},\n}", "### Contributions\n\nThanks to @parambharat for adding this dataset." ]

[ "task_categories:automatic-speech-recognition", "task_categories:text-to-speech", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:ha", "license:cc-by-sa-4.0", "bible", "arxiv:2207.03546", "region:us" ]

[ "2207.03546" ]

[ "ha" ]

[ "# Dataset Card for BibleTTS Hausa", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Languages\n- Dataset Structure\n - Data Fields\n - Data Splits\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL", "### Dataset Summary\n\nBibleTTS is a large high-quality open Text-to-Speech dataset with up to 80 hours of single speaker, studio quality 48kHz recordings.\nThis is a Hausa part of the dataset. Aligned hours: 86.6, aligned verses: 40,603.", "### Languages\n\nHausa", "## Dataset Structure", "### Data Fields\n\n- 'audio': audio path\n- 'sentence': transcription of the audio\n- 'locale': always set to 'ha'\n- 'book': 3-char book encoding\n- 'verse': verse id", "### Data Splits\n\n- 'dev': Book of Ezra (264 verses)\n- 'test': Book of Colossians (124 verses)\n- 'train': all other books (40215 verses)", "## Additional Information\n\n*See this notebook for the code on how the dataset was processed.", "### Dataset Curators\n\nThe dataset uploaded by vpetukhov who is not connected to the dataset authors. Please, see the project page for more info.", "### Licensing Information\n\nThe data is released under a commercial-friendly CC-BY-SA license.\n\n\n\nMeyer, Josh, et al. \"BibleTTS: a large, high-fidelity, multilingual, and uniquely African speech corpus.\" arXiv preprint arXiv:2207.03546 (2022)." ]

[ "TAGS\n#task_categories-automatic-speech-recognition #task_categories-text-to-speech #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Hausa #license-cc-by-sa-4.0 #bible #arxiv-2207.03546 #region-us \n", "# Dataset Card for BibleTTS Hausa", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Languages\n- Dataset Structure\n - Data Fields\n - Data Splits\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL", "### Dataset Summary\n\nBibleTTS is a large high-quality open Text-to-Speech dataset with up to 80 hours of single speaker, studio quality 48kHz recordings.\nThis is a Hausa part of the dataset. Aligned hours: 86.6, aligned verses: 40,603.", "### Languages\n\nHausa", "## Dataset Structure", "### Data Fields\n\n- 'audio': audio path\n- 'sentence': transcription of the audio\n- 'locale': always set to 'ha'\n- 'book': 3-char book encoding\n- 'verse': verse id", "### Data Splits\n\n- 'dev': Book of Ezra (264 verses)\n- 'test': Book of Colossians (124 verses)\n- 'train': all other books (40215 verses)", "## Additional Information\n\n*See this notebook for the code on how the dataset was processed.", "### Dataset Curators\n\nThe dataset uploaded by vpetukhov who is not connected to the dataset authors. Please, see the project page for more info.", "### Licensing Information\n\nThe data is released under a commercial-friendly CC-BY-SA license.\n\n\n\nMeyer, Josh, et al. \"BibleTTS: a large, high-fidelity, multilingual, and uniquely African speech corpus.\" arXiv preprint arXiv:2207.03546 (2022)." ]

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

[]

[]

[ "### Dataset Summary\n\nAugmented part of the test data of the Mozilla Common Voice (part 10, en, test) dataset.\nAs a basis, the original part of the test was taken, and augmentation was carried out to add extraneous noise.\n\nPart dataset: test" ]

[ "TAGS\n#license-apache-2.0 #region-us \n", "### Dataset Summary\n\nAugmented part of the test data of the Mozilla Common Voice (part 10, en, test) dataset.\nAs a basis, the original part of the test was taken, and augmentation was carried out to add extraneous noise.\n\nPart dataset: test" ]

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

[]

[]

[ "### Dataset Summary\n\nAugmented part of the test data of the Mozilla Common Voice (part 10, ru, test) dataset.\nAs a basis, the original part of the test was taken, and augmentation was carried out to add extraneous noise.\n\nPart dataset: test" ]

[ "TAGS\n#license-apache-2.0 #region-us \n", "### Dataset Summary\n\nAugmented part of the test data of the Mozilla Common Voice (part 10, ru, test) dataset.\nAs a basis, the original part of the test was taken, and augmentation was carried out to add extraneous noise.\n\nPart dataset: test" ]

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

[]

[]

[ "### Dataset Summary\n\nAugmented part of the test data of the Russian LibriSpeech (RuLS) test part dataset.\nAs a basis, the original part of the test was taken, and augmentation was carried out to add extraneous noise.\n\nPart dataset: test" ]

[ "TAGS\n#license-apache-2.0 #region-us \n", "### Dataset Summary\n\nAugmented part of the test data of the Russian LibriSpeech (RuLS) test part dataset.\nAs a basis, the original part of the test was taken, and augmentation was carried out to add extraneous noise.\n\nPart dataset: test" ]

[ "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: Multi-class Text Classification\n* Model: autoevaluate/multi-class-classification-not-evaluated\n* Dataset: emotion\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: autoevaluate/multi-class-classification-not-evaluated\n* Dataset: emotion\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

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

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

[ "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: Multi-class Text Classification\n* Model: lewtun/sagemaker-distilbert-emotion\n* Dataset: emotion\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

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

[ "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: Multi-class Text Classification\n* Model: lewtun/minilm-finetuned-emotion\n* Dataset: emotion\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

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

[ "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: Multi-class Text Classification\n* Model: lewtun/sagemaker-distilbert-emotion-1\n* Dataset: emotion\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: lewtun/sagemaker-distilbert-emotion-1\n* Dataset: emotion\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"pl-text-images-5000\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"pl-text-images-5000\"\n\nMore Information needed" ]

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:et", "license:cc-by-nc-4.0", "newspapers", "1997-2009", "arxiv:2101.09763", "arxiv:2102.13129", "region:us" ]

[ "2101.09763", "2102.13129" ]

[ "et" ]

[ "### Dataset Summary\n\n\nNoisyNER is a dataset for the evaluation of methods to handle noisy labels when training machine learning models.\n\n\n* Entity Types: 'PER', 'ORG', 'LOC'\n\n\nIt is from the NLP/Information Extraction domain and was created through a realistic distant supervision technique. Some highlights and interesting aspects of the data are:\n\n\n* Seven sets of labels with differing noise patterns to evaluate different noise levels on the same instances\n* Full parallel clean labels available to compute upper performance bounds or study scenarios where a small amount of gold-standard data can be leveraged\n* Skewed label distribution (typical for Named Entity Recognition tasks)\n* For some label sets: noise level higher than the true label probability\n* Sequential dependencies between the labels\n\n\nFor more details on the dataset and its creation process, please refer to the original author's publication URL (published at AAAI'21).\n\n\nThis dataset is based on the Estonian NER corpus. For more details see URL", "### Supported Tasks and Leaderboards", "### Languages\n\n\nThe language data in NoisyNER is in Estonian (BCP-47 et)\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'id': a 'string' feature.\n* 'tokens': a 'list' of 'string' features.\n* 'lemmas': a 'list' of 'string' features.\n* 'grammar': a 'list' of 'string' features.\n* 'ner\\_tags': a 'list' of classification labels ('int'). Full tagset with indices:", "### Data Splits\n\n\nThe splits are the same across all configurations.\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nTkachenko et al (2013) collected 572 news stories published in the local online newspapers Delfi and Postimees between 1997 and 2009. Selected articles cover both local and international news on a range of topics including politics, economics and sports. The raw text was preprocessed using the morphological disambiguator t3mesta (Kaalep and\nVaino, 1998) provided by Filosoft. The processing steps involve tokenization, lemmatization, part-of-speech tagging, grammatical and morphological analysis.", "#### Who are the source language producers?", "### Annotations", "#### Annotation process\n\n\nAccording to Tkachenko et al (2013) one of the authors manually tagged the corpus and the other author examined the tags, after which conflicting cases were resolved.\nThe total size of the corpus is 184,638 tokens. Tkachenko et al (2013) provide the following number of named entities in the corpus:\n\n\n\nHedderich et al (2021) obtained the noisy labels through a distant supervision/automatic annotation approach. They extracted lists of named entities from Wikidata and matched them against words in the text via the ANEA tool (Hedderich, Lange, and Klakow 2021). They also used heuristic functions to correct errors caused by non-complete lists of entities,\ngrammatical complexities of Estonian that do not allow simple string matching or entity lists in conflict with each other. For instance, they normalized the grammatical form of a word or excluded certain high false-positive words. They provide seven sets of labels that differ in the noise process. This results in 8 different configurations, when added to the original split with clean labels.", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions\n\n\nThanks to @phucdev for adding this dataset." ]

[ "TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Estonian #license-cc-by-nc-4.0 #newspapers #1997-2009 #arxiv-2101.09763 #arxiv-2102.13129 #region-us \n", "### Dataset Summary\n\n\nNoisyNER is a dataset for the evaluation of methods to handle noisy labels when training machine learning models.\n\n\n* Entity Types: 'PER', 'ORG', 'LOC'\n\n\nIt is from the NLP/Information Extraction domain and was created through a realistic distant supervision technique. Some highlights and interesting aspects of the data are:\n\n\n* Seven sets of labels with differing noise patterns to evaluate different noise levels on the same instances\n* Full parallel clean labels available to compute upper performance bounds or study scenarios where a small amount of gold-standard data can be leveraged\n* Skewed label distribution (typical for Named Entity Recognition tasks)\n* For some label sets: noise level higher than the true label probability\n* Sequential dependencies between the labels\n\n\nFor more details on the dataset and its creation process, please refer to the original author's publication URL (published at AAAI'21).\n\n\nThis dataset is based on the Estonian NER corpus. For more details see URL", "### Supported Tasks and Leaderboards", "### Languages\n\n\nThe language data in NoisyNER is in Estonian (BCP-47 et)\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'id': a 'string' feature.\n* 'tokens': a 'list' of 'string' features.\n* 'lemmas': a 'list' of 'string' features.\n* 'grammar': a 'list' of 'string' features.\n* 'ner\\_tags': a 'list' of classification labels ('int'). Full tagset with indices:", "### Data Splits\n\n\nThe splits are the same across all configurations.\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nTkachenko et al (2013) collected 572 news stories published in the local online newspapers Delfi and Postimees between 1997 and 2009. Selected articles cover both local and international news on a range of topics including politics, economics and sports. The raw text was preprocessed using the morphological disambiguator t3mesta (Kaalep and\nVaino, 1998) provided by Filosoft. The processing steps involve tokenization, lemmatization, part-of-speech tagging, grammatical and morphological analysis.", "#### Who are the source language producers?", "### Annotations", "#### Annotation process\n\n\nAccording to Tkachenko et al (2013) one of the authors manually tagged the corpus and the other author examined the tags, after which conflicting cases were resolved.\nThe total size of the corpus is 184,638 tokens. Tkachenko et al (2013) provide the following number of named entities in the corpus:\n\n\n\nHedderich et al (2021) obtained the noisy labels through a distant supervision/automatic annotation approach. They extracted lists of named entities from Wikidata and matched them against words in the text via the ANEA tool (Hedderich, Lange, and Klakow 2021). They also used heuristic functions to correct errors caused by non-complete lists of entities,\ngrammatical complexities of Estonian that do not allow simple string matching or entity lists in conflict with each other. For instance, they normalized the grammatical form of a word or excluded certain high false-positive words. They provide seven sets of labels that differ in the noise process. This results in 8 different configurations, when added to the original split with clean labels.", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions\n\n\nThanks to @phucdev for adding this dataset." ]

[ "region:us" ]

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[ "# Dataset Card for \"trecqa\"\n\nTREC-QA dataset for Answer Sentence Selection. The dataset contains 2 additional splits which are 'clean' versions of the original development and test sets. 'clean' versions contain only questions which have at least a positive and a negative answer candidate." ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"trecqa\"\n\nTREC-QA dataset for Answer Sentence Selection. The dataset contains 2 additional splits which are 'clean' versions of the original development and test sets. 'clean' versions contain only questions which have at least a positive and a negative answer candidate." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"wikiqa\"\n\nWikiQA dataset for Answer Sentence Selection. The dataset contains 2 additional splits which are 'clean' versions of the original development and test sets. 'clean' versions contain only questions which have at least a positive and a negative answer candidate." ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"wikiqa\"\n\nWikiQA dataset for Answer Sentence Selection. The dataset contains 2 additional splits which are 'clean' versions of the original development and test sets. 'clean' versions contain only questions which have at least a positive and a negative answer candidate." ]

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

[]

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[ "## Context\n\nI got inspiration for this dataset from the Rick&Morty Scripts by Andrada Olteanu but felt like dataset was a little small and outdated\n\nThis dataset includes almost all the episodes till Season 5. More data will be updated", "## Content\nRick and Morty Transcripts:\n\n- index: index of the row\n- speaker: the character's name\n- dialogue: the dialogue of the character", "## Acknowledgements\n\nThanks to the transcripts made available by \n\n- URL\n- URL" ]

[ "TAGS\n#license-mit #region-us \n", "## Context\n\nI got inspiration for this dataset from the Rick&Morty Scripts by Andrada Olteanu but felt like dataset was a little small and outdated\n\nThis dataset includes almost all the episodes till Season 5. More data will be updated", "## Content\nRick and Morty Transcripts:\n\n- index: index of the row\n- speaker: the character's name\n- dialogue: the dialogue of the character", "## Acknowledgements\n\nThanks to the transcripts made available by \n\n- URL\n- URL" ]

[ "region:us" ]

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[ "# Dataset Card for \"ESCWA\"\nCollected over two days of meetings of the United Nations Economic and Social Commission for West Asia (ESCWA) in 2019. The data includes intrasentential code alternation between Arabic and English. In the case of Algerian, Tunisian, and Moroccan native speakers, the switch is between Arabic and French. \nThe 2.8 hours ESCWA includes dialectal Arabic, with a Code Mixing Index (CMI) of ~28%. \nMore details about the ESCWA can be found URL\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"ESCWA\"\nCollected over two days of meetings of the United Nations Economic and Social Commission for West Asia (ESCWA) in 2019. The data includes intrasentential code alternation between Arabic and English. In the case of Algerian, Tunisian, and Moroccan native speakers, the switch is between Arabic and French. \nThe 2.8 hours ESCWA includes dialectal Arabic, with a Code Mixing Index (CMI) of ~28%. \nMore details about the ESCWA can be found URL\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"clinical_trial_texts\"\n\nThese are the text of clinical trials dowloaded from URL on Dec 3rd 2022.\n\nTotal trials is 434977\nNumber of tokens is 2,184,397,556 (2.1bn tokens).\n\nThe tokens here are from the default BERT tokenizer in hugginface.\n\nThis data can be used for pretraining in the clinical trial and biomedical domains.\n\nIf you use this data please acknowledge @domenicrosati and link to this dataset\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"clinical_trial_texts\"\n\nThese are the text of clinical trials dowloaded from URL on Dec 3rd 2022.\n\nTotal trials is 434977\nNumber of tokens is 2,184,397,556 (2.1bn tokens).\n\nThe tokens here are from the default BERT tokenizer in hugginface.\n\nThis data can be used for pretraining in the clinical trial and biomedical domains.\n\nIf you use this data please acknowledge @domenicrosati and link to this dataset\n\nMore Information needed" ]

[ "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: sshleifer/distilbart-xsum-1-1\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 @Buckeyes2019 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-1-1\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 @Buckeyes2019 for evaluating this model." ]