Datasets:

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ftopal

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huggingface-datasets

like 0

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

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-7b1\n* Dataset: futin/guess\n* Config: en_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: bigscience/bloom-7b1\n* Dataset: futin/guess\n* Config: en_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloom-1b7\n* Dataset: futin/guess\n* Config: en_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: bigscience/bloom-1b7\n* Dataset: futin/guess\n* Config: en_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: Zero-Shot Text Classification\n* Model: bigscience/bloom-560m\n* Dataset: futin/guess\n* Config: en_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin 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: bigscience/bloom-560m\n* Dataset: futin/guess\n* Config: en_3\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @futin for evaluating this model." ]

[ "region:us" ]

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[ "# Dataset Card for \"zalo-ai-train\"\n\nMore Information needed" ]

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

[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:no-annotation", "language_creators:crowdsourced", "multilinguality:multilingual", "size_categories:n<1K", "size_categories:1K<n<10K", "size_categories:10K<n<100K", "size_categories:100K<n<1M", "size_categories:1M<n<10M", "source_datasets:original", "language:bg", "language:cs", "language:da", "language:de", "language:el", "language:en", "language:es", "language:et", "language:fi", "language:fr", "language:ga", "language:hr", "language:hu", "language:it", "language:lt", "language:lv", "language:mt", "language:nl", "language:pl", "language:pt", "language:ro", "language:sk", "language:sl", "language:sv", "license:cc-by-sa-3.0", "license:gfdl", "region:us" ]

[]

[ "bg", "cs", "da", "de", "el", "en", "es", "et", "fi", "fr", "ga", "hr", "hu", "it", "lt", "lv", "mt", "nl", "pl", "pt", "ro", "sk", "sl", "sv" ]

[ "# Dataset Card for Wikipedia\n\nThis repo is a wrapper around olm/wikipedia that just concatenates data from the EU languages.\nPlease refer to it for a complete data card.\n\nThe EU languages we include are:\n - bg\n - cs\n - da\n - de\n - el\n - en\n - es\n - et\n - fi\n - fr\n - ga\n - hr\n - hu\n - it\n - lt\n - lv\n - mt\n - nl\n - pl\n - pt\n - ro\n - sk\n - sl\n - sv\n\n\nAs with 'olm/wikipedia' you will need to install a few dependencies:\n\n\n\n\n\n\nPlease refer to the original olm/wikipedia for a complete data card." ]

[ "TAGS\n#task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-crowdsourced #multilinguality-multilingual #size_categories-n<1K #size_categories-1K<n<10K #size_categories-10K<n<100K #size_categories-100K<n<1M #size_categories-1M<n<10M #source_datasets-original #language-Bulgarian #language-Czech #language-Danish #language-German #language-Modern Greek (1453-) #language-English #language-Spanish #language-Estonian #language-Finnish #language-French #language-Irish #language-Croatian #language-Hungarian #language-Italian #language-Lithuanian #language-Latvian #language-Maltese #language-Dutch #language-Polish #language-Portuguese #language-Romanian #language-Slovak #language-Slovenian #language-Swedish #license-cc-by-sa-3.0 #license-gfdl #region-us \n", "# Dataset Card for Wikipedia\n\nThis repo is a wrapper around olm/wikipedia that just concatenates data from the EU languages.\nPlease refer to it for a complete data card.\n\nThe EU languages we include are:\n - bg\n - cs\n - da\n - de\n - el\n - en\n - es\n - et\n - fi\n - fr\n - ga\n - hr\n - hu\n - it\n - lt\n - lv\n - mt\n - nl\n - pl\n - pt\n - ro\n - sk\n - sl\n - sv\n\n\nAs with 'olm/wikipedia' you will need to install a few dependencies:\n\n\n\n\n\n\nPlease refer to the original olm/wikipedia for a complete data card." ]

[ "region:us" ]

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[ "# VietNews-Abs-Sum\nA dataset for Vietnamese Abstractive Summarization task. \nIt includes all articles from Vietnews (VNDS) dataset which was released by Van-Hau Nguyen et al. \nThe articles were collected from URL, URL, and URL online newspaper by the authors.", "# Introduction\nThis dataset was extracted from Train/Val/Test split of Vietnews dataset. All files from *test_tokenized*, *train_tokenized* and *val_tokenized* directories are fetched and preprocessed with punctuation normalization. The subsets then are stored in the *raw* director with 3 files *URL*, *URL*, and *URL* accordingly. These files will be considered as the original raw dataset as nothing changes except the punctuation normalization.\n\nAs pointed out in *BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese*, there are lots of duplicated samples across subsets. Therefore, we do another preprocessing process to remove all the duplicated samples. The process includes the following steps:\n- First, remove all duplicates from each subset\n- Second, merge all subsets into 1 set with the following order: test + val + train\n- Finally, remove all duplicates from that merged set and then split out into 3 new subsets\n\nThe final subsets are the same to the orignal subsets but all duplicates were removed. Each subset now has total samples as follows:\n- train_no_dups.tsv: 99134 samples\n- valid_no_dups.tsv: 22184 samples\n- test_no_dups.tsv: 22498 samples\n\nTotally, we have 99134 + 22184 + 22498 = 143816 samples after filtering! \nNote that this result is not the same as the number of samples reported in BARTpho paper, but there is no duplicate inside each subset or across subsets anymore.\n\nThese filtered subsets are also exported into JSONLINE format to support future training script that requires this data format.", "# Directory structure\n- raw: contains 3 raw subset files fetched from Vietnews directories\n - URL\n - URL\n - URL\n- processed: contains duplicates filtered subsets\n - URL\n - URL\n - URL\n - URL\n - URL\n - URL\n - [and other variants]", "# Credits\n- Special thanks to Vietnews (VNDS) authors: URL" ]

[ "TAGS\n#region-us \n", "# VietNews-Abs-Sum\nA dataset for Vietnamese Abstractive Summarization task. \nIt includes all articles from Vietnews (VNDS) dataset which was released by Van-Hau Nguyen et al. \nThe articles were collected from URL, URL, and URL online newspaper by the authors.", "# Introduction\nThis dataset was extracted from Train/Val/Test split of Vietnews dataset. All files from *test_tokenized*, *train_tokenized* and *val_tokenized* directories are fetched and preprocessed with punctuation normalization. The subsets then are stored in the *raw* director with 3 files *URL*, *URL*, and *URL* accordingly. These files will be considered as the original raw dataset as nothing changes except the punctuation normalization.\n\nAs pointed out in *BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese*, there are lots of duplicated samples across subsets. Therefore, we do another preprocessing process to remove all the duplicated samples. The process includes the following steps:\n- First, remove all duplicates from each subset\n- Second, merge all subsets into 1 set with the following order: test + val + train\n- Finally, remove all duplicates from that merged set and then split out into 3 new subsets\n\nThe final subsets are the same to the orignal subsets but all duplicates were removed. Each subset now has total samples as follows:\n- train_no_dups.tsv: 99134 samples\n- valid_no_dups.tsv: 22184 samples\n- test_no_dups.tsv: 22498 samples\n\nTotally, we have 99134 + 22184 + 22498 = 143816 samples after filtering! \nNote that this result is not the same as the number of samples reported in BARTpho paper, but there is no duplicate inside each subset or across subsets anymore.\n\nThese filtered subsets are also exported into JSONLINE format to support future training script that requires this data format.", "# Directory structure\n- raw: contains 3 raw subset files fetched from Vietnews directories\n - URL\n - URL\n - URL\n- processed: contains duplicates filtered subsets\n - URL\n - URL\n - URL\n - URL\n - URL\n - URL\n - [and other variants]", "# Credits\n- Special thanks to Vietnews (VNDS) authors: URL" ]

[ "region:us" ]

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

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

[ "region:us" ]

[]

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[ "# Dataset Card for \"logo-blip\"\n\nMore Information needed" ]

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

[ "task_categories:text-to-image", "annotations_creators:machine-generated", "language_creators:other", "multilinguality:monolingual", "size_categories:n<1K", "language:en", "license:cc-by-nc-sa-4.0", "region:us" ]

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[ "en" ]

[ "# Dataset Card for \"ChristmasClaymation-blip-captions\"\n\nAll captions end with the suffix \", Christmas claymation style\"" ]

[ "TAGS\n#task_categories-text-to-image #annotations_creators-machine-generated #language_creators-other #multilinguality-monolingual #size_categories-n<1K #language-English #license-cc-by-nc-sa-4.0 #region-us \n", "# Dataset Card for \"ChristmasClaymation-blip-captions\"\n\nAll captions end with the suffix \", Christmas claymation style\"" ]

[ "region:us" ]

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[ "# viWikiHow-Abs-Sum\nA dataset for Vietnamese Abstractive Summarization task.\nIt includes all Vietnamese posts from WikiHow which was released in WikiLingua dataset.", "# Introduction\nThis dataset was extracted from Train/Test split of WikiLingua dataset. As the target language is Vietnamese, we remove all other files, just keep train.\\*.vi, test.\\*.vi, and val.\\*.vi for Vietnamese Abstractive Summarization task. The raw files then are stored in the *raw* director and after that, we run the python script to generate ready-to-use data files in TSV and JSONLINE formats which are stored in *processed* directory to be easily used for future training scripts.", "# Directory structure\n- raw: contains raw text files from WikiLingua\n - URL\n - URL\n - URL\n - URL\n - URL\n - URL\n- processed: contains generated TSV and JSONLINE files\n - URL\n - URL\n - URL\n - URL\n - URL\n - URL\n - [and other variants]", "# Credits\n- Special thanks to WikiLingua authors: URL\n- Article provided by <a href=\"URL target=\"_blank\">wikiHow</a>, a wiki that is building the world's largest and highest quality how-to manual. Please edit this article and find author credits at the original wikiHow article on How to Tie a Tie. Content on wikiHow can be shared under a <a href=\"URL target=\"_blank\">Creative Commons License</a>." ]

[ "TAGS\n#region-us \n", "# viWikiHow-Abs-Sum\nA dataset for Vietnamese Abstractive Summarization task.\nIt includes all Vietnamese posts from WikiHow which was released in WikiLingua dataset.", "# Introduction\nThis dataset was extracted from Train/Test split of WikiLingua dataset. As the target language is Vietnamese, we remove all other files, just keep train.\\*.vi, test.\\*.vi, and val.\\*.vi for Vietnamese Abstractive Summarization task. The raw files then are stored in the *raw* director and after that, we run the python script to generate ready-to-use data files in TSV and JSONLINE formats which are stored in *processed* directory to be easily used for future training scripts.", "# Directory structure\n- raw: contains raw text files from WikiLingua\n - URL\n - URL\n - URL\n - URL\n - URL\n - URL\n- processed: contains generated TSV and JSONLINE files\n - URL\n - URL\n - URL\n - URL\n - URL\n - URL\n - [and other variants]", "# Credits\n- Special thanks to WikiLingua authors: URL\n- Article provided by <a href=\"URL target=\"_blank\">wikiHow</a>, a wiki that is building the world's largest and highest quality how-to manual. Please edit this article and find author credits at the original wikiHow article on How to Tie a Tie. Content on wikiHow can be shared under a <a href=\"URL target=\"_blank\">Creative Commons License</a>." ]

[ "region:us" ]

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

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

[ "whisper", "region:us" ]

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

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

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

[]

[]

[ "# Dataset Card for Twitter US Airline Sentiment", "## 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:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n*This data originally came from Crowdflower's Data for Everyone library.*\n\nAs the original source says,\n\n> A sentiment analysis job about the problems of each major U.S. airline. Twitter data was scraped from February of 2015 and contributors were asked to first classify positive, negative, and neutral tweets, followed by categorizing negative reasons (such as \"late flight\" or \"rude service\").\n\nThe data we're providing on Kaggle is a slightly reformatted version of the original source. It includes both a CSV file and SQLite database. The code that does these transformations is available on GitHub\n\nFor example, it contains whether the sentiment of the tweets in this set was positive, neutral, or negative for six US airlines:\n\n![airline sentiment graph](URL", "### 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\n\nThis dataset was shared by @crowdflower", "### Licensing Information\n\nThe license for this dataset is cc-by-nc-sa-4.0", "### Contributions" ]

[ "TAGS\n#license-cc-by-nc-sa-4.0 #region-us \n", "# Dataset Card for Twitter US Airline Sentiment", "## 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:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n*This data originally came from Crowdflower's Data for Everyone library.*\n\nAs the original source says,\n\n> A sentiment analysis job about the problems of each major U.S. airline. Twitter data was scraped from February of 2015 and contributors were asked to first classify positive, negative, and neutral tweets, followed by categorizing negative reasons (such as \"late flight\" or \"rude service\").\n\nThe data we're providing on Kaggle is a slightly reformatted version of the original source. It includes both a CSV file and SQLite database. The code that does these transformations is available on GitHub\n\nFor example, it contains whether the sentiment of the tweets in this set was positive, neutral, or negative for six US airlines:\n\n![airline sentiment graph](URL", "### 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\n\nThis dataset was shared by @crowdflower", "### Licensing Information\n\nThe license for this dataset is cc-by-nc-sa-4.0", "### Contributions" ]

[ "region:us" ]

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[ "# Dataset Card for \"my-image-captioning-dataset\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"my-image-captioning-dataset1\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"my-image-captioning-dataset1\"\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: Token Classification\n* Model: 51la5/bert-large-NER\n* Dataset: conll2003\n* Config: conll2003\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @aniketrawat97 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: 51la5/bert-large-NER\n* Dataset: conll2003\n* Config: conll2003\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @aniketrawat97 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: 51la5/distilbert-base-NER\n* Dataset: conll2003\n* Config: conll2003\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @aniketrawat97 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: 51la5/distilbert-base-NER\n* Dataset: conll2003\n* Config: conll2003\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @aniketrawat97 for evaluating this model." ]

[ "task_categories:text-to-speech", "task_categories:automatic-speech-recognition", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:ml", "license:cc-by-sa-4.0", "arxiv:2211.12796", "region:us" ]

[ "2211.12796" ]

[ "ml" ]

[ "### Data Instances\n\n\nAn example instance is given below:", "### Data Fields\n\n\n* text (str): Transcription of the audio file\n* speaker (str): The name of the speaker\n* audio (dict): Audio object including loaded audio array, sampling rate and path to audio (always None)", "### Data Splits\n\n\nWe provide all the data in a single 'train' split. The loaded dataset object thus looks like this:", "### Dataset Creation\n\n\nThe text is sourced from Malayalam Wikipedia, and read by our speakers in studio conditions. Extensive error correction was conducted to provide a clean, accurate database. Further details are given in our paper, accessible at URL\n\n\nAdditional Information\n----------------------", "### Licensing\n\n\nThe corpus is made available under the Creative Commons license (CC BY-SA 4.0)." ]

[ "TAGS\n#task_categories-text-to-speech #task_categories-automatic-speech-recognition #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Malayalam #license-cc-by-sa-4.0 #arxiv-2211.12796 #region-us \n", "### Data Instances\n\n\nAn example instance is given below:", "### Data Fields\n\n\n* text (str): Transcription of the audio file\n* speaker (str): The name of the speaker\n* audio (dict): Audio object including loaded audio array, sampling rate and path to audio (always None)", "### Data Splits\n\n\nWe provide all the data in a single 'train' split. The loaded dataset object thus looks like this:", "### Dataset Creation\n\n\nThe text is sourced from Malayalam Wikipedia, and read by our speakers in studio conditions. Extensive error correction was conducted to provide a clean, accurate database. Further details are given in our paper, accessible at URL\n\n\nAdditional Information\n----------------------", "### Licensing\n\n\nThe corpus is made available under the Creative Commons license (CC BY-SA 4.0)." ]

[ "region:us" ]

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[]

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

[ "region:us" ]

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[ "# Dataset Card for \"github-issues\"\nannotations_creators:\n- expert-generated\nlanguage:\n- en\nlanguage_creators:\n- found\nlicense:\n- cc-by-nc-sa-3.0\nmultilinguality:\n- monolingual\npaperswithcode_id: null\npretty_name: Hugging Face GitHub Issues\nsize_categories:\n- 1K<n<10K\nsource_datasets:\n- original\ntags:\n- bio\n- paper\ntask_categories:\n- text-classification\n- table-to-text\ntask_ids:\n- multi-class-classification\n- sentiment-classification\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"github-issues\"\nannotations_creators:\n- expert-generated\nlanguage:\n- en\nlanguage_creators:\n- found\nlicense:\n- cc-by-nc-sa-3.0\nmultilinguality:\n- monolingual\npaperswithcode_id: null\npretty_name: Hugging Face GitHub Issues\nsize_categories:\n- 1K<n<10K\nsource_datasets:\n- original\ntags:\n- bio\n- paper\ntask_categories:\n- text-classification\n- table-to-text\ntask_ids:\n- multi-class-classification\n- sentiment-classification\nMore Information needed" ]

[ "task_categories:text-classification", "task_ids:multi-class-classification", "annotations_creators:other", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:ne", "license:apache-2.0", "nepali-newsgroups", "nepali-20-newsgroups", "np20ng", "nepali text classification", "natural language processing", "news", "headline", "region:us" ]

[]

[ "ne" ]

[ "# Dataset Card for [np20ng]", "## Table of Contents\n- [Dataset Card for [np20ng]](#dataset-card-for-dataset-name)\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 - Initial Data Collection and Normalization\n - Who are the source language producers?\n - Annotations\n - Annotation process\n - Who are the annotators?\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: To be updated\n- Repository: To be updated\n- Paper: Submitted for review\n- Leaderboard: To be updated\n- Point of Contact: To be updated", "### Dataset Summary\n\nThis is a multi-class Nepali text classification dataset. Text are the news documents and labels are the news categories. It consists over 200,000 documents categorized into 20 different Nepali news groups. News documents from 10 different news sources are compiled into this dataset. Labeling is done using the category-specific news from the respective news portals.", "### Supported Tasks and Leaderboards\n\n- Multi-class text classification from news document\n- Multi-class text classification from news headings\n- News heading generation from news document", "### Languages\n\n- Nepali", "## Dataset Structure", "### Data Instances\n\nThe dataset consists over 200,000 Nepali news documents categorized into 20 different news categories.", "### Data Fields\n\n- category: News category\n- content: News document (main text)\n- headline: News headline\n- source: News source from where the news is taken from", "### Data Splits\n\nThe dataset is a whole dataset and is not splitted.", "## Dataset Creation", "### Curation Rationale\n\nTo develop and create a large-scale Nepali text classification dataset and releasing it to the public for further research and developments", "### Source Data", "#### Initial Data Collection and Normalization\n\nData are scraped from popular Nepali news portals such as Onlinekhabar, Nepalkhabar, Ekantipur, Ratopati, Gorkhapatra, Nepalipatra, Educationpati, Crimenews, etc.", "#### Who are the source language producers?\n\nNews portals", "### Annotations", "#### Annotation process\n\nCategory labeling of news documents are automatically done as the documents are scraped from category-specific URLs of particular news source", "#### Who are the annotators?\n\nNews portals", "### Personal and Sensitive Information\n\nThis dataset does not possess any personal and sensitive information. However, the news content can possess some biasness and irregular information which might be sensitive and not quite related with the original author of the dataset", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nNo issues.", "### Discussion of Biases\n\nCategories can be depended on how news portals have categorized them. Otherwise could cause some bias between them.", "### Other Known Limitations\n\nNews summary are not included", "## Additional Information", "### Dataset Curators\n\nMe myself.", "### Licensing Information\n\nApache-2.0\n\n\n\nTo be updated later (Paper submission in process)", "### Contributions\n\nThanks to @Suyogyart for adding this dataset." ]

[ "TAGS\n#task_categories-text-classification #task_ids-multi-class-classification #annotations_creators-other #language_creators-machine-generated #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-Nepali (macrolanguage) #license-apache-2.0 #nepali-newsgroups #nepali-20-newsgroups #np20ng #nepali text classification #natural language processing #news #headline #region-us \n", "# Dataset Card for [np20ng]", "## Table of Contents\n- [Dataset Card for [np20ng]](#dataset-card-for-dataset-name)\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 - Initial Data Collection and Normalization\n - Who are the source language producers?\n - Annotations\n - Annotation process\n - Who are the annotators?\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: To be updated\n- Repository: To be updated\n- Paper: Submitted for review\n- Leaderboard: To be updated\n- Point of Contact: To be updated", "### Dataset Summary\n\nThis is a multi-class Nepali text classification dataset. Text are the news documents and labels are the news categories. It consists over 200,000 documents categorized into 20 different Nepali news groups. News documents from 10 different news sources are compiled into this dataset. Labeling is done using the category-specific news from the respective news portals.", "### Supported Tasks and Leaderboards\n\n- Multi-class text classification from news document\n- Multi-class text classification from news headings\n- News heading generation from news document", "### Languages\n\n- Nepali", "## Dataset Structure", "### Data Instances\n\nThe dataset consists over 200,000 Nepali news documents categorized into 20 different news categories.", "### Data Fields\n\n- category: News category\n- content: News document (main text)\n- headline: News headline\n- source: News source from where the news is taken from", "### Data Splits\n\nThe dataset is a whole dataset and is not splitted.", "## Dataset Creation", "### Curation Rationale\n\nTo develop and create a large-scale Nepali text classification dataset and releasing it to the public for further research and developments", "### Source Data", "#### Initial Data Collection and Normalization\n\nData are scraped from popular Nepali news portals such as Onlinekhabar, Nepalkhabar, Ekantipur, Ratopati, Gorkhapatra, Nepalipatra, Educationpati, Crimenews, etc.", "#### Who are the source language producers?\n\nNews portals", "### Annotations", "#### Annotation process\n\nCategory labeling of news documents are automatically done as the documents are scraped from category-specific URLs of particular news source", "#### Who are the annotators?\n\nNews portals", "### Personal and Sensitive Information\n\nThis dataset does not possess any personal and sensitive information. However, the news content can possess some biasness and irregular information which might be sensitive and not quite related with the original author of the dataset", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nNo issues.", "### Discussion of Biases\n\nCategories can be depended on how news portals have categorized them. Otherwise could cause some bias between them.", "### Other Known Limitations\n\nNews summary are not included", "## Additional Information", "### Dataset Curators\n\nMe myself.", "### Licensing Information\n\nApache-2.0\n\n\n\nTo be updated later (Paper submission in process)", "### Contributions\n\nThanks to @Suyogyart for adding this dataset." ]

[ "task_categories:text-classification", "task_ids:semantic-similarity-scoring", "task_ids:text-scoring", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "language:es", "region:us" ]

[]

[ "es" ]

[ "# STS-es", "## 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- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n- Homepage: URL \n- Point of Contact: Aitor Gonzalez", "### Dataset Summary\n\nFor Semantic Text Similarity, we collected the Spanish test sets from SemEval-2014 (Agirre et al., 2014) and SemEval-2015 (Agirre et al., 2015). Since no training data was provided for the Spanish subtask, we randomly sampled both datasets into 1,321 sentences for the train set, 78 sentences for the development set, and 156 sentences for the test set. To make the task harder for the models, we purposely made the development set smaller than the test set.\n\nWe use this corpus as part of the EvalEs Spanish language benchmark.", "### Supported Tasks and Leaderboards\n\nSemantic Text Similarity Scoring", "### Languages\n\nThe dataset is in Spanish ('es-ES')", "## Dataset Structure", "### Data Instances", "### Data Fields\n\n- sentence1: String\n- sentence2: String\n- label: Float", "### Data Splits\n\n- train: 1,321 instances\n- dev: 78 instances\n- test: 156 instances", "## Dataset Creation", "### Curation Rationale\n[N/A]", "### Source Data\n\nThe source data came from the Spanish Wikipedia (2013 dump) and texts from Spanish news (2014).\n\nFor more information visit the paper from the SemEval-2014 Shared Task (Agirre et al., 2014) and the SemEval-2015 Shared Task (Agirre et al., 2015).", "#### Initial Data Collection and Normalization\n\nFor more information visit the paper from the SemEval-2014 Shared Task (Agirre et al., 2014) and the SemEval-2015 Shared Task (Agirre et al., 2015).", "#### Who are the source language producers?\n\nJournalists and Wikipedia contributors.", "### Annotations", "#### Annotation process\n\nFor more information visit the paper from the SemEval-2014 Shared Task (Agirre et al., 2014) and the SemEval-2015 Shared Task (Agirre et al., 2015).", "#### Who are the annotators?\n\nFor more information visit the paper from the SemEval-2014 Shared Task (Agirre et al., 2014) and the SemEval-2015 Shared Task (Agirre et al., 2015).", "### Personal and Sensitive Information\n\nNo personal or sensitive information included.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThis dataset contributes to the development of language models in Spanish.", "### Discussion of Biases\n\nNo postprocessing steps were applied to mitigate potential social biases.", "## Additional Information\n\n\n\n\nThe following papers must be cited when using this corpus:" ]

[ "TAGS\n#task_categories-text-classification #task_ids-semantic-similarity-scoring #task_ids-text-scoring #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #language-Spanish #region-us \n", "# STS-es", "## 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- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n- Homepage: URL \n- Point of Contact: Aitor Gonzalez", "### Dataset Summary\n\nFor Semantic Text Similarity, we collected the Spanish test sets from SemEval-2014 (Agirre et al., 2014) and SemEval-2015 (Agirre et al., 2015). Since no training data was provided for the Spanish subtask, we randomly sampled both datasets into 1,321 sentences for the train set, 78 sentences for the development set, and 156 sentences for the test set. To make the task harder for the models, we purposely made the development set smaller than the test set.\n\nWe use this corpus as part of the EvalEs Spanish language benchmark.", "### Supported Tasks and Leaderboards\n\nSemantic Text Similarity Scoring", "### Languages\n\nThe dataset is in Spanish ('es-ES')", "## Dataset Structure", "### Data Instances", "### Data Fields\n\n- sentence1: String\n- sentence2: String\n- label: Float", "### Data Splits\n\n- train: 1,321 instances\n- dev: 78 instances\n- test: 156 instances", "## Dataset Creation", "### Curation Rationale\n[N/A]", "### Source Data\n\nThe source data came from the Spanish Wikipedia (2013 dump) and texts from Spanish news (2014).\n\nFor more information visit the paper from the SemEval-2014 Shared Task (Agirre et al., 2014) and the SemEval-2015 Shared Task (Agirre et al., 2015).", "#### Initial Data Collection and Normalization\n\nFor more information visit the paper from the SemEval-2014 Shared Task (Agirre et al., 2014) and the SemEval-2015 Shared Task (Agirre et al., 2015).", "#### Who are the source language producers?\n\nJournalists and Wikipedia contributors.", "### Annotations", "#### Annotation process\n\nFor more information visit the paper from the SemEval-2014 Shared Task (Agirre et al., 2014) and the SemEval-2015 Shared Task (Agirre et al., 2015).", "#### Who are the annotators?\n\nFor more information visit the paper from the SemEval-2014 Shared Task (Agirre et al., 2014) and the SemEval-2015 Shared Task (Agirre et al., 2015).", "### Personal and Sensitive Information\n\nNo personal or sensitive information included.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThis dataset contributes to the development of language models in Spanish.", "### Discussion of Biases\n\nNo postprocessing steps were applied to mitigate potential social biases.", "## Additional Information\n\n\n\n\nThe following papers must be cited when using this corpus:" ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:automatic-speech-recognition", "task_categories:audio-classification", "task_ids:speaker-identification", "annotations_creators:expert-generated", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:cc-by-4.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nThis is a truncated version of the LibriSpeech dataset. It contains 20 samples from each of the splits. To view the full dataset, visit: URL\n\n\nLibriSpeech is a corpus of approximately 1000 hours of 16kHz read English speech, prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read audiobooks from the LibriVox project, and has been carefully segmented and aligned.", "### Supported Tasks and Leaderboards\n\n\n* 'automatic-speech-recognition', 'audio-speaker-identification': The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active Hugging Face leaderboard which can be found at URL The leaderboard ranks models uploaded to the Hub based on their WER. An external leaderboard at URL ranks the latest models from research and academia.", "### Languages\n\n\nThe audio is in English. There are two configurations: 'clean' and 'other'.\nThe speakers in the corpus were ranked according to the WER of the transcripts of a model trained on\na different dataset, and were divided roughly in the middle,\nwith the lower-WER speakers designated as \"clean\" and the higher WER speakers designated as \"other\".\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA typical data point comprises the path to the audio file, usually called 'file' and its transcription, called 'text'. Some additional information about the speaker and the passage which contains the transcription is provided.", "### Data Fields\n\n\n* file: A path to the downloaded audio file in .flac format.\n* audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling\\_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n* text: the transcription of the audio file.\n* id: unique id of the data sample.\n* speaker\\_id: unique id of the speaker. The same speaker id can be found for multiple data samples.\n* chapter\\_id: id of the audiobook chapter which includes the transcription.", "### Data Splits\n\n\nThe size of the corpus makes it impractical, or at least inconvenient\nfor some users, to distribute it as a single large archive. Thus the\ntraining portion of the corpus is split into three subsets, with approximate size 100, 360 and 500 hours respectively.\nA simple automatic\nprocedure was used to select the audio in the first two sets to be, on\naverage, of higher recording quality and with accents closer to US\nEnglish. An acoustic model was trained on WSJ’s si-84 data subset\nand was used to recognize the audio in the corpus, using a bigram\nLM estimated on the text of the respective books. We computed the\nWord Error Rate (WER) of this automatic transcript relative to our\nreference transcripts obtained from the book texts.\nThe speakers in the corpus were ranked according to the WER of\nthe WSJ model’s transcripts, and were divided roughly in the middle,\nwith the lower-WER speakers designated as \"clean\" and the higher-WER speakers designated as \"other\".\n\n\nFor \"clean\", the data is split into train, validation, and test set. The train set is further split into train.100 and train.360\nrespectively accounting for 100h and 360h of the training data.\nFor \"other\", the data is split into train, validation, and test set. The train set contains approximately 500h of recorded speech.\n\n\n\nDataset Creation\n----------------", "### Personal and Sensitive Information\n\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe dataset was initially created by Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur.", "### Licensing Information\n\n\nCC BY 4.0" ]

[ "TAGS\n#task_categories-automatic-speech-recognition #task_categories-audio-classification #task_ids-speaker-identification #annotations_creators-expert-generated #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-cc-by-4.0 #region-us \n", "### Dataset Summary\n\n\nThis is a truncated version of the LibriSpeech dataset. It contains 20 samples from each of the splits. To view the full dataset, visit: URL\n\n\nLibriSpeech is a corpus of approximately 1000 hours of 16kHz read English speech, prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read audiobooks from the LibriVox project, and has been carefully segmented and aligned.", "### Supported Tasks and Leaderboards\n\n\n* 'automatic-speech-recognition', 'audio-speaker-identification': The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active Hugging Face leaderboard which can be found at URL The leaderboard ranks models uploaded to the Hub based on their WER. An external leaderboard at URL ranks the latest models from research and academia.", "### Languages\n\n\nThe audio is in English. There are two configurations: 'clean' and 'other'.\nThe speakers in the corpus were ranked according to the WER of the transcripts of a model trained on\na different dataset, and were divided roughly in the middle,\nwith the lower-WER speakers designated as \"clean\" and the higher WER speakers designated as \"other\".\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA typical data point comprises the path to the audio file, usually called 'file' and its transcription, called 'text'. Some additional information about the speaker and the passage which contains the transcription is provided.", "### Data Fields\n\n\n* file: A path to the downloaded audio file in .flac format.\n* audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling\\_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n* text: the transcription of the audio file.\n* id: unique id of the data sample.\n* speaker\\_id: unique id of the speaker. The same speaker id can be found for multiple data samples.\n* chapter\\_id: id of the audiobook chapter which includes the transcription.", "### Data Splits\n\n\nThe size of the corpus makes it impractical, or at least inconvenient\nfor some users, to distribute it as a single large archive. Thus the\ntraining portion of the corpus is split into three subsets, with approximate size 100, 360 and 500 hours respectively.\nA simple automatic\nprocedure was used to select the audio in the first two sets to be, on\naverage, of higher recording quality and with accents closer to US\nEnglish. An acoustic model was trained on WSJ’s si-84 data subset\nand was used to recognize the audio in the corpus, using a bigram\nLM estimated on the text of the respective books. We computed the\nWord Error Rate (WER) of this automatic transcript relative to our\nreference transcripts obtained from the book texts.\nThe speakers in the corpus were ranked according to the WER of\nthe WSJ model’s transcripts, and were divided roughly in the middle,\nwith the lower-WER speakers designated as \"clean\" and the higher-WER speakers designated as \"other\".\n\n\nFor \"clean\", the data is split into train, validation, and test set. The train set is further split into train.100 and train.360\nrespectively accounting for 100h and 360h of the training data.\nFor \"other\", the data is split into train, validation, and test set. The train set contains approximately 500h of recorded speech.\n\n\n\nDataset Creation\n----------------", "### Personal and Sensitive Information\n\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe dataset was initially created by Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur.", "### Licensing Information\n\n\nCC BY 4.0" ]

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

[]

[ "en" ]

[ "# Dataset Card for MNIST", "## 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: \n- Paper: MNIST handwritten digit database by Yann LeCun, Corinna Cortes, and CJ Burges\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThe MNIST dataset consists of 70,000 28x28 black-and-white images of handwritten digits extracted from two NIST databases. There are 60,000 images in the training dataset and 10,000 images in the validation dataset, one class per digit so a total of 10 classes, with 7,000 images (6,000 train images and 1,000 test images) per class.\nHalf of the image were drawn by Census Bureau employees and the other half by high school students (this split is evenly distributed in the training and testing sets).", "### Supported Tasks and Leaderboards\n\n- 'image-classification': The goal of this task is to classify a given image of a handwritten digit into one of 10 classes representing integer values from 0 to 9, inclusively. The leaderboard is available here.", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances\n\nA data point comprises an image and its label:", "### Data Fields\n\n- 'image': A 'PIL.Image.Image' object containing the 28x28 image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'label': an integer between 0 and 9 representing the digit.", "### Data Splits\n\nThe data is split into training and test set. All the images in the test set were drawn by different individuals than the images in the training set. The training set contains 60,000 images and the test set 10,000 images.", "## Dataset Creation", "### Curation Rationale\n\nThe MNIST database was created to provide a testbed for people wanting to try pattern recognition methods or machine learning algorithms while spending minimal efforts on preprocessing and formatting. Images of the original dataset (NIST) were in two groups, one consisting of images drawn by Census Bureau employees and one consisting of images drawn by high school students. In NIST, the training set was built by grouping all the images of the Census Bureau employees, and the test set was built by grouping the images form the high school students.\nThe goal in building MNIST was to have a training and test set following the same distributions, so the training set contains 30,000 images drawn by Census Bureau employees and 30,000 images drawn by high school students, and the test set contains 5,000 images of each group. The curators took care to make sure all the images in the test set were drawn by different individuals than the images in the training set.", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe original images from NIST were size normalized to fit a 20x20 pixel box while preserving their aspect ratio. The resulting images contain grey levels (i.e., pixels don't simply have a value of black and white, but a level of greyness from 0 to 255) as a result of the anti-aliasing technique used by the normalization algorithm. The images were then centered in a 28x28 image by computing the center of mass of the pixels, and translating the image so as to position this point at the center of the 28x28 field.", "#### Who are the source language producers?\n\nHalf of the source images were drawn by Census Bureau employees, half by high school students. According to the dataset curator, the images from the first group are more easily recognizable.", "### Annotations", "#### Annotation process\n\nThe images were not annotated after their creation: the image creators annotated their images with the corresponding label after drawing them.", "#### Who are the annotators?\n\nSame as the source data creators.", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nChris Burges, Corinna Cortes and Yann LeCun", "### Licensing Information\n\nMIT Licence", "### Contributions\n\nThanks to @sgugger for adding this dataset." ]

[ "TAGS\n#task_categories-image-classification #task_ids-multi-class-image-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|other-nist #language-English #license-mit #region-us \n", "# Dataset Card for MNIST", "## 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: \n- Paper: MNIST handwritten digit database by Yann LeCun, Corinna Cortes, and CJ Burges\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThe MNIST dataset consists of 70,000 28x28 black-and-white images of handwritten digits extracted from two NIST databases. There are 60,000 images in the training dataset and 10,000 images in the validation dataset, one class per digit so a total of 10 classes, with 7,000 images (6,000 train images and 1,000 test images) per class.\nHalf of the image were drawn by Census Bureau employees and the other half by high school students (this split is evenly distributed in the training and testing sets).", "### Supported Tasks and Leaderboards\n\n- 'image-classification': The goal of this task is to classify a given image of a handwritten digit into one of 10 classes representing integer values from 0 to 9, inclusively. The leaderboard is available here.", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances\n\nA data point comprises an image and its label:", "### Data Fields\n\n- 'image': A 'PIL.Image.Image' object containing the 28x28 image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'label': an integer between 0 and 9 representing the digit.", "### Data Splits\n\nThe data is split into training and test set. All the images in the test set were drawn by different individuals than the images in the training set. The training set contains 60,000 images and the test set 10,000 images.", "## Dataset Creation", "### Curation Rationale\n\nThe MNIST database was created to provide a testbed for people wanting to try pattern recognition methods or machine learning algorithms while spending minimal efforts on preprocessing and formatting. Images of the original dataset (NIST) were in two groups, one consisting of images drawn by Census Bureau employees and one consisting of images drawn by high school students. In NIST, the training set was built by grouping all the images of the Census Bureau employees, and the test set was built by grouping the images form the high school students.\nThe goal in building MNIST was to have a training and test set following the same distributions, so the training set contains 30,000 images drawn by Census Bureau employees and 30,000 images drawn by high school students, and the test set contains 5,000 images of each group. The curators took care to make sure all the images in the test set were drawn by different individuals than the images in the training set.", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe original images from NIST were size normalized to fit a 20x20 pixel box while preserving their aspect ratio. The resulting images contain grey levels (i.e., pixels don't simply have a value of black and white, but a level of greyness from 0 to 255) as a result of the anti-aliasing technique used by the normalization algorithm. The images were then centered in a 28x28 image by computing the center of mass of the pixels, and translating the image so as to position this point at the center of the 28x28 field.", "#### Who are the source language producers?\n\nHalf of the source images were drawn by Census Bureau employees, half by high school students. According to the dataset curator, the images from the first group are more easily recognizable.", "### Annotations", "#### Annotation process\n\nThe images were not annotated after their creation: the image creators annotated their images with the corresponding label after drawing them.", "#### Who are the annotators?\n\nSame as the source data creators.", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nChris Burges, Corinna Cortes and Yann LeCun", "### Licensing Information\n\nMIT Licence", "### Contributions\n\nThanks to @sgugger for adding this dataset." ]

[ "region:us" ]

[]

[]

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

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

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

[]

[ "en" ]

[]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"olm-CC-MAIN-2022-40-sampling-ratio-0.0001-ne-language\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"olm-CC-MAIN-2022-40-sampling-ratio-0.0001-ne-language\"\n\nMore Information needed" ]

[ "language:en", "license:creativeml-openrail-m", "stable-diffusion", "text-to-image", "image-to-image", "region:us" ]

[]

[ "en" ]

[ "# Stripe Style Embedding / Textual Inversion\n\n<img alt=\"Showcase\" src=\"URL", "## Usage\n\nTo use this embedding you have to download the file aswell as drop it into the \"\\stable-diffusion-webui\\embeddings\" folder\n\nTo use it in a prompt: \n\nPersonally, I would recommend to use my embeddings with a strength of 0.8, like \n\nI trained the embedding two epochs until 5000 steps.\n\nI hope you enjoy the embedding. If you have any questions, you can ask me anything via Discord: \"Nerfgun3#7508\"", "## License\n\nThis embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.\nThe CreativeML OpenRAIL License specifies: \n\n1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content \n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)\nPlease read the full license here" ]

[ "TAGS\n#language-English #license-creativeml-openrail-m #stable-diffusion #text-to-image #image-to-image #region-us \n", "# Stripe Style Embedding / Textual Inversion\n\n<img alt=\"Showcase\" src=\"URL", "## Usage\n\nTo use this embedding you have to download the file aswell as drop it into the \"\\stable-diffusion-webui\\embeddings\" folder\n\nTo use it in a prompt: \n\nPersonally, I would recommend to use my embeddings with a strength of 0.8, like \n\nI trained the embedding two epochs until 5000 steps.\n\nI hope you enjoy the embedding. If you have any questions, you can ask me anything via Discord: \"Nerfgun3#7508\"", "## License\n\nThis embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.\nThe CreativeML OpenRAIL License specifies: \n\n1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content \n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)\nPlease read the full license here" ]

[ "task_categories:text-retrieval", "multilinguality:multilingual", "language:af", "language:am", "language:arz", "language:ha", "language:ig", "language:ary", "language:nso", "language:sn", "language:so", "language:sw", "language:ti", "language:tw", "language:wo", "language:yo", "language:zu", "license:apache-2.0", "region:us" ]

[]

[ "af", "am", "arz", "ha", "ig", "ary", "nso", "sn", "so", "sw", "ti", "tw", "wo", "yo", "zu" ]

[ "# Dataset Summary\nAfriCLIRMatrix is a test collection for cross-lingual information retrieval research in 15 diverse African languages. This resource comprises English queries with query–document relevance judgments in 15 African languages automatically mined from Wikipedia\n\nThis dataset stores documents of AfriCLIRMatrix. To access the queries and judgments, please refer to castorini/africlirmatrix.", "# Dataset Structure\nThe only configuration here is the 'language'.\n\nAn example of document data entry looks as follows:", "# Load Dataset\nAn example to load the dataset:" ]

[ "TAGS\n#task_categories-text-retrieval #multilinguality-multilingual #language-Afrikaans #language-Amharic #language-Egyptian Arabic #language-Hausa #language-Igbo #language-Moroccan Arabic #language-Pedi #language-Shona #language-Somali #language-Swahili (macrolanguage) #language-Tigrinya #language-Twi #language-Wolof #language-Yoruba #language-Zulu #license-apache-2.0 #region-us \n", "# Dataset Summary\nAfriCLIRMatrix is a test collection for cross-lingual information retrieval research in 15 diverse African languages. This resource comprises English queries with query–document relevance judgments in 15 African languages automatically mined from Wikipedia\n\nThis dataset stores documents of AfriCLIRMatrix. To access the queries and judgments, please refer to castorini/africlirmatrix.", "# Dataset Structure\nThe only configuration here is the 'language'.\n\nAn example of document data entry looks as follows:", "# Load Dataset\nAn example to load the dataset:" ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"hackathon_pil_v2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"hackathon_pil_v2\"\n\nMore Information needed" ]

[ "task_categories:fill-mask", "annotations_creators:other", "language_creators:found", "multilinguality:multilingual", "size_categories:10M<n<100M", "source_datasets:original", "language:bg", "language:cs", "language:da", "language:de", "language:el", "language:en", "language:es", "language:et", "language:fi", "language:fr", "language:ga", "language:hr", "language:hu", "language:it", "language:lt", "language:lv", "language:mt", "language:nl", "language:pl", "language:pt", "language:ro", "language:sk", "language:sl", "language:sv", "license:cc-by-4.0", "region:us" ]

[]

[ "bg", "cs", "da", "de", "el", "en", "es", "et", "fi", "fr", "ga", "hr", "hu", "it", "lt", "lv", "mt", "nl", "pl", "pt", "ro", "sk", "sl", "sv" ]

[ "# Dataset Card for MultiLegalPile_Wikipedia_Filtered: A filtered version of the MultiLegalPile dataset, together with wikipedia articles", "## Table of Contents\n\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: Joel Niklaus", "### Dataset Summary\n\nThe Multi_Legal_Pile is a large-scale multilingual legal dataset suited for pretraining language models.\nIt spans over 24 languages and four legal text types.", "### Supported Tasks and Leaderboards\n\nThe dataset supports the tasks of fill-mask.", "### Languages\n\nThe following languages are supported: \nbg, cs, da, de, el, en, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv", "## Dataset Structure\n\nIt is structured in the following format: {language}_{text_type}_{shard}.URL\n\ntext_type is one of the following:\n\n- caselaw\n- contracts\n- legislation\n- other\n- wikipedia\n\n\nUse the dataset like this:\n\n\n'config' is a combination of language and text_type, e.g. 'en_contracts' or 'de_caselaw'.\nTo load all the languages or all the text_types, use 'all' instead of the language or text_type (e.g., '\nall_legislation').", "### Data Instances\n\nThe file format is URL and there is a 'train' and 'validation' split available. \nSince some configurations are very small or non-existent, they might not contain a train split or not be present at all.\n\nThe complete dataset consists of five large subsets:\n- Native Multi Legal Pile\n- Eurlex Resources \n- MC4 Legal\n- Pile of Law\n- EU Wikipedias", "### Data Fields", "### Data Splits", "## Dataset Creation\n\nThis dataset has been created by combining the following datasets:\nNative Multi Legal Pile, Eurlex Resources, MC4 Legal, Pile of Law, EU Wikipedias.\nIt has been filtered to remove short documents (less than 64 whitespace-separated tokens) and \ndocuments with more than 30% punctuation or numbers (see prepare_legal_data.py for more details).", "### 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 @JoelNiklaus for adding this dataset." ]

[ "TAGS\n#task_categories-fill-mask #annotations_creators-other #language_creators-found #multilinguality-multilingual #size_categories-10M<n<100M #source_datasets-original #language-Bulgarian #language-Czech #language-Danish #language-German #language-Modern Greek (1453-) #language-English #language-Spanish #language-Estonian #language-Finnish #language-French #language-Irish #language-Croatian #language-Hungarian #language-Italian #language-Lithuanian #language-Latvian #language-Maltese #language-Dutch #language-Polish #language-Portuguese #language-Romanian #language-Slovak #language-Slovenian #language-Swedish #license-cc-by-4.0 #region-us \n", "# Dataset Card for MultiLegalPile_Wikipedia_Filtered: A filtered version of the MultiLegalPile dataset, together with wikipedia articles", "## Table of Contents\n\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: Joel Niklaus", "### Dataset Summary\n\nThe Multi_Legal_Pile is a large-scale multilingual legal dataset suited for pretraining language models.\nIt spans over 24 languages and four legal text types.", "### Supported Tasks and Leaderboards\n\nThe dataset supports the tasks of fill-mask.", "### Languages\n\nThe following languages are supported: \nbg, cs, da, de, el, en, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv", "## Dataset Structure\n\nIt is structured in the following format: {language}_{text_type}_{shard}.URL\n\ntext_type is one of the following:\n\n- caselaw\n- contracts\n- legislation\n- other\n- wikipedia\n\n\nUse the dataset like this:\n\n\n'config' is a combination of language and text_type, e.g. 'en_contracts' or 'de_caselaw'.\nTo load all the languages or all the text_types, use 'all' instead of the language or text_type (e.g., '\nall_legislation').", "### Data Instances\n\nThe file format is URL and there is a 'train' and 'validation' split available. \nSince some configurations are very small or non-existent, they might not contain a train split or not be present at all.\n\nThe complete dataset consists of five large subsets:\n- Native Multi Legal Pile\n- Eurlex Resources \n- MC4 Legal\n- Pile of Law\n- EU Wikipedias", "### Data Fields", "### Data Splits", "## Dataset Creation\n\nThis dataset has been created by combining the following datasets:\nNative Multi Legal Pile, Eurlex Resources, MC4 Legal, Pile of Law, EU Wikipedias.\nIt has been filtered to remove short documents (less than 64 whitespace-separated tokens) and \ndocuments with more than 30% punctuation or numbers (see prepare_legal_data.py for more details).", "### 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 @JoelNiklaus for adding this dataset." ]

[ "language:en", "license:creativeml-openrail-m", "stable-diffusion", "text-to-image", "image-to-image", "region:us" ]

[]

[ "en" ]

[ "# Negative Embedding / Textual Inversion\n\n<img alt=\"Showcase\" src=\"URL", "## Idea\n\nThe idea behind this embedding was to somehow train the negative prompt as an embedding, thus unifying the basis of the negative prompt into one word or embedding. \n\nSide note: Embedding has proven to be very helpful for the generation of hands! :)", "## Usage\n\nTo use this embedding you have to download the file aswell as drop it into the \"\\stable-diffusion-webui\\embeddings\" folder.\n\nPlease put the embedding in the negative prompt to get the right results!\n\nFor special negative tags such as \"malformed sword\", you still need to add them yourself. The negative embedding is trained on a basic skeleton for the negative prompt, which should provide a high-resolution image as a result.", "### Version 1:\n\nIssue: Changing the style to much.\n\nTo use it in the negative prompt: \n\nPersonally, I would recommend to use my embeddings with a strength of 0.8 even the negative embeddings, like", "### Version 2:\n\nWith this version I tried to reduce the amount of vectors used, aswell as the issue with the changing artstyle. The newer version is still a work in progress, but its already way better than the first version. Its in files section!\n\nI hope you enjoy the embedding. If you have any questions, you can ask me anything via Discord: \"Nerfgun3#7508\"", "## License\n\nThis embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.\nThe CreativeML OpenRAIL License specifies: \n\n1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content \n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)\nPlease read the full license here" ]

[ "TAGS\n#language-English #license-creativeml-openrail-m #stable-diffusion #text-to-image #image-to-image #region-us \n", "# Negative Embedding / Textual Inversion\n\n<img alt=\"Showcase\" src=\"URL", "## Idea\n\nThe idea behind this embedding was to somehow train the negative prompt as an embedding, thus unifying the basis of the negative prompt into one word or embedding. \n\nSide note: Embedding has proven to be very helpful for the generation of hands! :)", "## Usage\n\nTo use this embedding you have to download the file aswell as drop it into the \"\\stable-diffusion-webui\\embeddings\" folder.\n\nPlease put the embedding in the negative prompt to get the right results!\n\nFor special negative tags such as \"malformed sword\", you still need to add them yourself. The negative embedding is trained on a basic skeleton for the negative prompt, which should provide a high-resolution image as a result.", "### Version 1:\n\nIssue: Changing the style to much.\n\nTo use it in the negative prompt: \n\nPersonally, I would recommend to use my embeddings with a strength of 0.8 even the negative embeddings, like", "### Version 2:\n\nWith this version I tried to reduce the amount of vectors used, aswell as the issue with the changing artstyle. The newer version is still a work in progress, but its already way better than the first version. Its in files section!\n\nI hope you enjoy the embedding. If you have any questions, you can ask me anything via Discord: \"Nerfgun3#7508\"", "## License\n\nThis embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.\nThe CreativeML OpenRAIL License specifies: \n\n1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content \n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)\nPlease read the full license here" ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

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

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

[ "arxiv:2211.07514", "region:us" ]

[ "2211.07514" ]

[]

[ "# Dataset Card for \"hinglish_top\"\n\nLicense: URL\nOriginal Repo: URL\nPaper Link For Citation: URL\n\nMore Information needed" ]

[ "TAGS\n#arxiv-2211.07514 #region-us \n", "# Dataset Card for \"hinglish_top\"\n\nLicense: URL\nOriginal Repo: URL\nPaper Link For Citation: URL\n\nMore Information needed" ]

[ "task_categories:automatic-speech-recognition", "language:sv", "license:cc0-1.0", "region:us" ]

[]

[ "sv" ]

[]

[ "TAGS\n#task_categories-automatic-speech-recognition #language-Swedish #license-cc0-1.0 #region-us \n" ]

[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended|other", "language:en", "license:unknown", "haiku", "poem", "poetry", "reddit", "keybert", "generation", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for \"Reddit Haiku\"\n\nThis dataset contains haikus from the subreddit /r/haiku scraped and filtered between October 19th and 10th 2022, combined with a previous dump of that same subreddit packaged by ConvoKit as part of the Subreddit Corpus, which is itself a subset of URL's big dump.\n\nA main motivation for this dataset was to collect an alternative haiku dataset for evaluation, in particular for evaluating Fabian Mueller's Deep Haiku model which was trained on the Haiku datasets of hjhalani30 and bfbarry, which are also available on huggingface hub.", "## Fields\nThe fields are post id ('id'), the content of the haiku ('processed_title'), upvotes ('ups'), and topic keywords ('keywords'). Topic keywords for each haiku have been extracted with the KeyBERT library and truncated to top-5 keywords.", "## Usage\n\nThis dataset is intended for evaluation, hence there is only one split which is 'test'.\n\n\n\nThere is code for scraping and processing in 'processing_code', and a subset of the data with more fields such as author Karma, downvotes and posting time at 'processing_code/URL'." ]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|other #language-English #license-unknown #haiku #poem #poetry #reddit #keybert #generation #region-us \n", "# Dataset Card for \"Reddit Haiku\"\n\nThis dataset contains haikus from the subreddit /r/haiku scraped and filtered between October 19th and 10th 2022, combined with a previous dump of that same subreddit packaged by ConvoKit as part of the Subreddit Corpus, which is itself a subset of URL's big dump.\n\nA main motivation for this dataset was to collect an alternative haiku dataset for evaluation, in particular for evaluating Fabian Mueller's Deep Haiku model which was trained on the Haiku datasets of hjhalani30 and bfbarry, which are also available on huggingface hub.", "## Fields\nThe fields are post id ('id'), the content of the haiku ('processed_title'), upvotes ('ups'), and topic keywords ('keywords'). Topic keywords for each haiku have been extracted with the KeyBERT library and truncated to top-5 keywords.", "## Usage\n\nThis dataset is intended for evaluation, hence there is only one split which is 'test'.\n\n\n\nThere is code for scraping and processing in 'processing_code', and a subset of the data with more fields such as author Karma, downvotes and posting time at 'processing_code/URL'." ]

[ "region:us" ]

[]

[]

[]

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

[ "license:mit", "arxiv:2302.12433", "region:us" ]

[ "2302.12433" ]

[]

[ "### Dataset Summary\n\n\nProofNet is a benchmark for autoformalization and formal proving of undergraduate-level mathematics. The ProofNet benchmarks consists of 371 examples, each consisting of a formal theorem statement in Lean 3, a natural language theorem statement, and a natural language proof. The problems are primarily drawn from popular undergraduate pure mathematics textbooks and cover topics such as real and complex analysis, linear algebra, abstract algebra, and topology. We intend for ProofNet to be a challenging benchmark that will drive progress in autoformalization and automatic theorem proving.\n\n\nCitation:", "### Leaderboard\n\n\nStatement Autoformalization\n\n\nModel: Davinci-code-002 (prompt retrieval), Typecheck Rate: 45.2, Accuracy: 16.1\nModel: Davinci-code-002 (in-context learning), Typecheck Rate: 23.7, Accuracy: 13.4\nModel: proofGPT-1.3B, Typecheck Rate: 10.7, Accuracy: 3.2\n\n\nStatement Informalization", "### Data Fields\n\n\n* 'id': Unique string identifier for the problem.\n* 'nl\\_statement': Natural language theorem statement.\n* 'nl\\_proof': Natural language proof, in LaTeX. Depends on 'amsthm, amsmath, amssymb' packages.\n* 'formal\\_statement': Formal theorem statement in Lean 3.\n* 'src\\_header': File header including imports, namespaces, and locales required for the formal statement. Note that local import of URL, which has to be manually downloaded and place in the same directory as your '.lean' file containing the formal statement.", "### Authors\n\n\nZhangir Azerbayev, Bartosz Piotrowski, Jeremy Avigad" ]

[ "TAGS\n#license-mit #arxiv-2302.12433 #region-us \n", "### Dataset Summary\n\n\nProofNet is a benchmark for autoformalization and formal proving of undergraduate-level mathematics. The ProofNet benchmarks consists of 371 examples, each consisting of a formal theorem statement in Lean 3, a natural language theorem statement, and a natural language proof. The problems are primarily drawn from popular undergraduate pure mathematics textbooks and cover topics such as real and complex analysis, linear algebra, abstract algebra, and topology. We intend for ProofNet to be a challenging benchmark that will drive progress in autoformalization and automatic theorem proving.\n\n\nCitation:", "### Leaderboard\n\n\nStatement Autoformalization\n\n\nModel: Davinci-code-002 (prompt retrieval), Typecheck Rate: 45.2, Accuracy: 16.1\nModel: Davinci-code-002 (in-context learning), Typecheck Rate: 23.7, Accuracy: 13.4\nModel: proofGPT-1.3B, Typecheck Rate: 10.7, Accuracy: 3.2\n\n\nStatement Informalization", "### Data Fields\n\n\n* 'id': Unique string identifier for the problem.\n* 'nl\\_statement': Natural language theorem statement.\n* 'nl\\_proof': Natural language proof, in LaTeX. Depends on 'amsthm, amsmath, amssymb' packages.\n* 'formal\\_statement': Formal theorem statement in Lean 3.\n* 'src\\_header': File header including imports, namespaces, and locales required for the formal statement. Note that local import of URL, which has to be manually downloaded and place in the same directory as your '.lean' file containing the formal statement.", "### Authors\n\n\nZhangir Azerbayev, Bartosz Piotrowski, Jeremy Avigad" ]

[ "region:us" ]

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[ "# Dataset Card for \"top_v2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"top_v2\"\n\nMore Information needed" ]

[ "region:us" ]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"parsed_sst2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"parsed_sst2\"\n\nMore Information needed" ]

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

[]

[]

[ "# Dataset Card for \"Yannic-Kilcher\"\n\nMore Information needed" ]

[ "TAGS\n#task_categories-automatic-speech-recognition #whisper #whispering #region-us \n", "# Dataset Card for \"Yannic-Kilcher\"\n\nMore Information needed" ]

[ "region:us" ]

[]

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[ "# Dataset Card for \"urgent-triage-samples\"\n\nMore Information needed" ]

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

[ "whisper", "whispering", "region:us" ]

[]

[]

[ "# Dataset Card for \"Yannic-Kilcher\"\n\nMore Information needed" ]

[ "TAGS\n#whisper #whispering #region-us \n", "# Dataset Card for \"Yannic-Kilcher\"\n\nMore Information needed" ]

[ "region:us" ]

[]

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[ "# Dataset Card for \"task-pages\"\n\nMore Information needed" ]

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

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

[]

[]

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

[ "TAGS\n#task_categories-automatic-speech-recognition #whisper #whispering #region-us \n", "# Dataset Card for \"azure\"\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." ]

[ "task_categories:text-classification", "task_ids:multi-class-classification", "annotations_creators:automatically-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:unknown", "language:es", "license:cc-by-sa-3.0", "region:us" ]

[]

[ "es" ]

[ "# WikiCAT_es: Spanish Text Classification dataset", "## Dataset Description\n\n- Paper: \n\n- Point of Contact: carlos.rodriguez1@URL\n\n\nRepository", "### Dataset Summary\n\nWikiCAT_ca is a Spanish corpus for thematic Text Classification tasks. It is created automatically from Wikipedia and Wikidata sources, and contains 8401 articles from the Viquipedia classified under 12 different categories.\n\nThis dataset was developed by BSC TeMU as part of the PlanTL project, and intended as an evaluation of LT capabilities to generate useful synthetic corpus.", "### Supported Tasks and Leaderboards\n\nText classification, Language Model", "### Languages\n\nES- Spanish", "## Dataset Structure", "### Data Instances\n\nTwo json files, one for each split.", "### Data Fields\n\nWe used a simple model with the article text and associated labels, without further metadata.", "#### Example:\n\n<pre>\n{'sentence': 'La economía de Reunión se ha basado tradicionalmente en la agricultura. La caña de azúcar ha sido el cultivo principal durante más de un siglo, y en algunos años representa el 85% de las exportaciones. El gobierno ha estado impulsando el desarrollo de una industria turística para aliviar el alto desempleo, que representa más del 40% de la fuerza laboral.(...) El PIB total de la isla fue de 18.800 millones de dólares EE.UU. en 2007., 'label': 'Economía'}\n\n\n</pre>", "#### Labels\n\n'Religión', 'Entretenimiento', 'Música', 'Ciencia_y_Tecnología', 'Política', 'Economía', 'Matemáticas', 'Humanidades', 'Deporte', 'Derecho', 'Historia', 'Filosofía'", "### Data Splits\n\n* hfeval_esv5.json: 1681 label-document pairs\n* hftrain_esv5.json: 6716 label-document pairs", "## Dataset Creation", "### Methodology\n\nLa páginas de \"Categoría\" representan los temas.\npara cada tema, extraemos las páginas asociadas a ese primer nivel de la jerarquía, y utilizamos el resúmen (\"summary\") como texto representativo.", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe source data are thematic categories in the different Wikipedias", "#### Who are the source language producers?", "### Annotations", "#### Annotation process\nAutomatic annotation", "#### Who are the annotators?\n\n[N/A]", "### Personal and Sensitive Information\n\nNo personal or sensitive information included.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nWe hope this corpus contributes to the development of language models in Spanish.", "### Discussion of Biases\n\nWe are aware that this data might contain biases. We have not applied any steps to reduce their impact.", "### Other Known Limitations\n\n[N/A]", "## Additional Information", "### Dataset Curators\n\nText Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@URL). \n\nFor further information, send an email to (plantl-gob-es@URL).\n\nThis work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL.", "### Licensing Information\n\nThis work is licensed under CC Attribution 4.0 International License.\n\nCopyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)", "### Contributions\n\n[N/A]" ]

[ "TAGS\n#task_categories-text-classification #task_ids-multi-class-classification #annotations_creators-automatically-generated #language_creators-found #multilinguality-monolingual #size_categories-unknown #language-Spanish #license-cc-by-sa-3.0 #region-us \n", "# WikiCAT_es: Spanish Text Classification dataset", "## Dataset Description\n\n- Paper: \n\n- Point of Contact: carlos.rodriguez1@URL\n\n\nRepository", "### Dataset Summary\n\nWikiCAT_ca is a Spanish corpus for thematic Text Classification tasks. It is created automatically from Wikipedia and Wikidata sources, and contains 8401 articles from the Viquipedia classified under 12 different categories.\n\nThis dataset was developed by BSC TeMU as part of the PlanTL project, and intended as an evaluation of LT capabilities to generate useful synthetic corpus.", "### Supported Tasks and Leaderboards\n\nText classification, Language Model", "### Languages\n\nES- Spanish", "## Dataset Structure", "### Data Instances\n\nTwo json files, one for each split.", "### Data Fields\n\nWe used a simple model with the article text and associated labels, without further metadata.", "#### Example:\n\n<pre>\n{'sentence': 'La economía de Reunión se ha basado tradicionalmente en la agricultura. La caña de azúcar ha sido el cultivo principal durante más de un siglo, y en algunos años representa el 85% de las exportaciones. El gobierno ha estado impulsando el desarrollo de una industria turística para aliviar el alto desempleo, que representa más del 40% de la fuerza laboral.(...) El PIB total de la isla fue de 18.800 millones de dólares EE.UU. en 2007., 'label': 'Economía'}\n\n\n</pre>", "#### Labels\n\n'Religión', 'Entretenimiento', 'Música', 'Ciencia_y_Tecnología', 'Política', 'Economía', 'Matemáticas', 'Humanidades', 'Deporte', 'Derecho', 'Historia', 'Filosofía'", "### Data Splits\n\n* hfeval_esv5.json: 1681 label-document pairs\n* hftrain_esv5.json: 6716 label-document pairs", "## Dataset Creation", "### Methodology\n\nLa páginas de \"Categoría\" representan los temas.\npara cada tema, extraemos las páginas asociadas a ese primer nivel de la jerarquía, y utilizamos el resúmen (\"summary\") como texto representativo.", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe source data are thematic categories in the different Wikipedias", "#### Who are the source language producers?", "### Annotations", "#### Annotation process\nAutomatic annotation", "#### Who are the annotators?\n\n[N/A]", "### Personal and Sensitive Information\n\nNo personal or sensitive information included.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nWe hope this corpus contributes to the development of language models in Spanish.", "### Discussion of Biases\n\nWe are aware that this data might contain biases. We have not applied any steps to reduce their impact.", "### Other Known Limitations\n\n[N/A]", "## Additional Information", "### Dataset Curators\n\nText Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@URL). \n\nFor further information, send an email to (plantl-gob-es@URL).\n\nThis work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL.", "### Licensing Information\n\nThis work is licensed under CC Attribution 4.0 International License.\n\nCopyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)", "### Contributions\n\n[N/A]" ]

[ "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." ]

[ "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." ]

[ "region:us" ]

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[ "# Dataset Card for \"sidewalk-imagery\"\n\nMore Information needed" ]

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

[ "region:us" ]

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[]

[ "# Dataset Card for Dataset Name", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "### 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#region-us \n", "# Dataset Card for Dataset Name", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "### 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" ]

[ "region:us" ]

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

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

[ "license:creativeml-openrail-m", "region:us" ]

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[ "#### Usage\n\n\nTo use this embedding you have to download the file and put it into the \"\\stable-diffusion-webui\\embeddings\" folder\nTo use it in a prompt add\n*art by rogue\\_style* \n\n\nadd **[ ]** around it to reduce its weight.", "#### Included Files\n\n\n* 500 steps *Usage: art by rogue\\_style-500*\n* 3500 steps *Usage: art by rogue\\_style-3500*\n* 6500 steps *Usage: art by rogue\\_style*\n\n\ncheers \n\nWipeout", "#### Example Pictures", "#### prompt comparison\n\n\n *click the image to enlarge*\n[<img height=\"50%\" width=\"50%\" src=\"https://i.URL](https://i.URL target=)" ]

[ "TAGS\n#license-creativeml-openrail-m #region-us \n", "#### Usage\n\n\nTo use this embedding you have to download the file and put it into the \"\\stable-diffusion-webui\\embeddings\" folder\nTo use it in a prompt add\n*art by rogue\\_style* \n\n\nadd **[ ]** around it to reduce its weight.", "#### Included Files\n\n\n* 500 steps *Usage: art by rogue\\_style-500*\n* 3500 steps *Usage: art by rogue\\_style-3500*\n* 6500 steps *Usage: art by rogue\\_style*\n\n\ncheers \n\nWipeout", "#### Example Pictures", "#### prompt comparison\n\n\n *click the image to enlarge*\n[<img height=\"50%\" width=\"50%\" src=\"https://i.URL](https://i.URL target=)" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"highways-hacktum\"\n\nMore Information needed" ]

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

[ "task_categories:automatic-speech-recognition", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:fo", "license:cc-by-4.0", "faroe islands", "faroese", "ravnur project", "speech recognition in faroese", "region:us" ]

[]

[ "fo" ]

[ "# Dataset Card for ravnursson_asr", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n- Homepage: Ravnursson Faroese Speech and Transcripts\n- Repository: URL\n- Paper: ASR Language Resources for Faroese\n- Paper: Creating a basic language resource kit for faroese.\n- Point of Contact: Annika Simonsen, Carlos Mena", "### Dataset Summary\nThe corpus \"RAVNURSSON FAROESE SPEECH AND TRANSCRIPTS\" (or RAVNURSSON Corpus for short) is a collection of speech recordings with transcriptions intended for Automatic Speech Recognition (ASR) applications in the language that is spoken at the Faroe Islands (Faroese). It was curated at the Reykjavík University (RU) in 2022.\n\nThe RAVNURSSON Corpus is an extract of the \"Basic Language Resource Kit 1.0\" (BLARK 1.0) [1] developed by the Ravnur Project from the Faroe Islands [2]. As a matter of fact, the name RAVNURSSON comes from Ravnur (a tribute to the Ravnur Project) and the suffix \"son\" which in Icelandic means \"son of\". Therefore, the name \"RAVNURSSON\" means \"The (Icelandic) son of Ravnur\". The double \"ss\" is just for aesthetics.\n\nThe audio was collected by recording speakers reading texts. The participants are aged 15-83, divided into 3 age groups: 15-35, 36-60 and 61+.\n\nThe speech files are made of 249 female speakers and 184 male speakers; 433 speakers total. The recordings were made on TASCAM DR-40 Linear PCM audio recorders using the built-in stereo microphones in WAVE 16 bit with a sample rate of 48kHz, but then, downsampled to 16kHz@16bit mono for this corpus.\n\n[1] Simonsen, A., Debess, I. N., Lamhauge, S. S., & Henrichsen, P. J. Creating a basic language resource kit for Faroese. In LREC 2022. 13th International Conference on Language Resources and Evaluation.\n \n[2] Website. The Project Ravnur under the Talutøkni Foundation URL", "### Example Usage\nThe RAVNURSSON Corpus is divided in 3 splits: train, validation and test. To load a specific split pass its name as a config name:\n\nTo load an specific split (for example, the validation split) do:", "### Supported Tasks\nautomatic-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).", "### Languages\nThe audio is in Faroese.\nThe reading prompts for the RAVNURSSON Corpus have been generated by expert linguists. The whole corpus was balanced for phonetic and dialectal coverage; Test and Dev subsets are gender-balanced. Tabular computer-searchable information is included as well as written documentation.", "## Dataset Structure", "### Data Instances", "### Data Fields\n* 'audio_id' (string) - id of audio segment\n* 'audio' (datasets.Audio) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally).\n* 'speaker_id' (string) - id of speaker\n* 'gender' (string) - gender of speaker (male or female)\n* 'age' (string) - range of age of the speaker: Younger (15-35), Middle-aged (36-60) or Elderly (61+).\n* 'duration' (float32) - duration of the audio file in seconds.\n* 'normalized_text' (string) - normalized audio segment transcription\n* 'dialect' (string) - dialect group, for example \"Suðuroy\" or \"Sandoy\".", "### Data Splits\nThe speech material has been subdivided into portions for training (train), development (evaluation) and testing (test). Lengths of each portion are: train = 100h08m, test = 4h30m, dev (evaluation)=4h30m.\n\nTo load an specific portion please see the above section \"Example Usage\".\n\nThe development and test portions have exactly 10 male and 10 female speakers each and both portions have exactly the same size in hours (4.5h each).", "## Dataset Creation", "### Curation Rationale\n\nThe directory called \"speech\" contains all the speech files of the corpus. The files in the speech directory are divided in three directories: train, dev and test. The train portion is sub-divided in three types of recordings: RDATA1O, RDATA1OP and RDATA2; this is due to the organization of the recordings in the original BLARK 1.0. There, the recordings are divided in Rdata1 and Rdata2.\n\nOne main difference between Rdata1 and Rdata2 is that the reading environment for Rdata2 was controlled by a software called \"PushPrompt\" which is included in the original BLARK 1.0. Another main difference is that in Rdata1 there are some available transcriptions labeled at the phoneme level. For this reason the audio files in the speech directory of the RAVNURSSON corpus are divided in the folders RDATA1O where \"O\" is for \"Orthographic\" and RDATA1OP where \"O\" is for Orthographic and \"P\" is for phonetic.\n\nIn the case of the dev and test portions, the data come only from Rdata2 which does not have labels at the phonetic level.\n\nIt is important to clarify that the RAVNURSSON Corpus only includes transcriptions at the orthographic level.", "### Source Data", "#### Initial Data Collection and Normalization\nThe dataset was released with normalized text only at an orthographic level in lower-case. The normalization process was performed by automatically removing punctuation marks and characters that are not present in the Faroese alphabet.", "#### Who are the source language producers?\n\n* The utterances were recorded using a TASCAM DR-40.\n\n* Participants self-reported their age group, gender, native language and dialect.\n\n* Participants are aged between 15 to 83 years. \n \n* The corpus contains 71949 speech files from 433 speakers, totalling 109 hours and 9 minutes.", "### Annotations", "#### Annotation process\n\nMost of the reading prompts were selected by experts from a Faroese text corpus (news, blogs, Wikipedia etc.) and were edited to fit the format. Reading prompts that are within specific domains (such as Faroese place names, numbers, license plates, telling time etc.) were written by the Ravnur Project. Then, a software tool called PushPrompt were used for reading sessions (voice recordings). PushPromt presents the text items in the reading material to the reader, allowing him/her to manage the session interactively (adjusting the reading tempo, repeating speech productions at wish, inserting short breaks as needed, etc.). When the reading session is completed, a log file (with time stamps for each production) is written as a data table compliant with the TextGrid-format.", "#### Who are the annotators?\nThe corpus was annotated by the Ravnur Project", "### Personal and Sensitive Information\nThe dataset consists of people who have donated their voice. You agree to not attempt to determine the identity of speakers in this dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\nThis is the first ASR corpus in Faroese.", "### Discussion of Biases\nAs the number of reading prompts was limited, the common denominator in the RAVNURSSON corpus is that one prompt is read by more than one speaker. This is relevant because is a common practice in ASR to create a language model using the prompts that are found in the train portion of the corpus. That is not recommended for the RAVNURSSON Corpus as it counts with many prompts shared by all the portions and that will produce an important bias in the language modeling task.\n\nIn this section we present some statistics about the repeated prompts through all the portions of the corpus.\n\n- In the train portion:\n\t* Total number of prompts = 65616\n\t* Number of unique prompts = 38646\nThere are 26970 repeated prompts in the train portion. In other words, 41.1% of the prompts are repeated.\n\n- In the test portion:\n\t* Total number of prompts = 3002\n\t* Number of unique prompts = 2887\nThere are 115 repeated prompts in the test portion. In other words, 3.83% of the prompts are repeated.\n\n- In the dev portion:\n\t* Total number of prompts = 3331\n\t* Number of unique prompts = 3302\nThere are 29 repeated prompts in the dev portion. In other words, 0.87% of the prompts are repeated.\n\n- Considering the corpus as a whole:\n\t* Total number of prompts = 71949\n\t* Number of unique prompts = 39945\nThere are 32004 repeated prompts in the whole corpus. In other words, 44.48% of the prompts are repeated.\n\nNOTICE!: It is also important to clarify that none of the 3 portions of the corpus share speakers.", "### Other Known Limitations\n\"RAVNURSSON FAROESE SPEECH AND TRANSCRIPTS\" by Carlos Daniel Hernández Mena and Annika Simonsen is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License with the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.", "## Additional Information", "### Dataset Curators\nThe dataset was collected by Annika Simonsen and curated by Carlos Daniel Hernández Mena.", "### Licensing Information\nCC-BY-4.0", "### Contributions\nThis project was made possible under the umbrella of the Language Technology Programme for Icelandic 2019-2023. The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture.\n\nSpecial thanks to Dr. Jón Guðnason, professor at Reykjavík University and head of the Language and Voice Lab (LVL) for providing computational resources." ]

[ "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-Faroese #license-cc-by-4.0 #faroe islands #faroese #ravnur project #speech recognition in faroese #region-us \n", "# Dataset Card for ravnursson_asr", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n- Homepage: Ravnursson Faroese Speech and Transcripts\n- Repository: URL\n- Paper: ASR Language Resources for Faroese\n- Paper: Creating a basic language resource kit for faroese.\n- Point of Contact: Annika Simonsen, Carlos Mena", "### Dataset Summary\nThe corpus \"RAVNURSSON FAROESE SPEECH AND TRANSCRIPTS\" (or RAVNURSSON Corpus for short) is a collection of speech recordings with transcriptions intended for Automatic Speech Recognition (ASR) applications in the language that is spoken at the Faroe Islands (Faroese). It was curated at the Reykjavík University (RU) in 2022.\n\nThe RAVNURSSON Corpus is an extract of the \"Basic Language Resource Kit 1.0\" (BLARK 1.0) [1] developed by the Ravnur Project from the Faroe Islands [2]. As a matter of fact, the name RAVNURSSON comes from Ravnur (a tribute to the Ravnur Project) and the suffix \"son\" which in Icelandic means \"son of\". Therefore, the name \"RAVNURSSON\" means \"The (Icelandic) son of Ravnur\". The double \"ss\" is just for aesthetics.\n\nThe audio was collected by recording speakers reading texts. The participants are aged 15-83, divided into 3 age groups: 15-35, 36-60 and 61+.\n\nThe speech files are made of 249 female speakers and 184 male speakers; 433 speakers total. The recordings were made on TASCAM DR-40 Linear PCM audio recorders using the built-in stereo microphones in WAVE 16 bit with a sample rate of 48kHz, but then, downsampled to 16kHz@16bit mono for this corpus.\n\n[1] Simonsen, A., Debess, I. N., Lamhauge, S. S., & Henrichsen, P. J. Creating a basic language resource kit for Faroese. In LREC 2022. 13th International Conference on Language Resources and Evaluation.\n \n[2] Website. The Project Ravnur under the Talutøkni Foundation URL", "### Example Usage\nThe RAVNURSSON Corpus is divided in 3 splits: train, validation and test. To load a specific split pass its name as a config name:\n\nTo load an specific split (for example, the validation split) do:", "### Supported Tasks\nautomatic-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).", "### Languages\nThe audio is in Faroese.\nThe reading prompts for the RAVNURSSON Corpus have been generated by expert linguists. The whole corpus was balanced for phonetic and dialectal coverage; Test and Dev subsets are gender-balanced. Tabular computer-searchable information is included as well as written documentation.", "## Dataset Structure", "### Data Instances", "### Data Fields\n* 'audio_id' (string) - id of audio segment\n* 'audio' (datasets.Audio) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally).\n* 'speaker_id' (string) - id of speaker\n* 'gender' (string) - gender of speaker (male or female)\n* 'age' (string) - range of age of the speaker: Younger (15-35), Middle-aged (36-60) or Elderly (61+).\n* 'duration' (float32) - duration of the audio file in seconds.\n* 'normalized_text' (string) - normalized audio segment transcription\n* 'dialect' (string) - dialect group, for example \"Suðuroy\" or \"Sandoy\".", "### Data Splits\nThe speech material has been subdivided into portions for training (train), development (evaluation) and testing (test). Lengths of each portion are: train = 100h08m, test = 4h30m, dev (evaluation)=4h30m.\n\nTo load an specific portion please see the above section \"Example Usage\".\n\nThe development and test portions have exactly 10 male and 10 female speakers each and both portions have exactly the same size in hours (4.5h each).", "## Dataset Creation", "### Curation Rationale\n\nThe directory called \"speech\" contains all the speech files of the corpus. The files in the speech directory are divided in three directories: train, dev and test. The train portion is sub-divided in three types of recordings: RDATA1O, RDATA1OP and RDATA2; this is due to the organization of the recordings in the original BLARK 1.0. There, the recordings are divided in Rdata1 and Rdata2.\n\nOne main difference between Rdata1 and Rdata2 is that the reading environment for Rdata2 was controlled by a software called \"PushPrompt\" which is included in the original BLARK 1.0. Another main difference is that in Rdata1 there are some available transcriptions labeled at the phoneme level. For this reason the audio files in the speech directory of the RAVNURSSON corpus are divided in the folders RDATA1O where \"O\" is for \"Orthographic\" and RDATA1OP where \"O\" is for Orthographic and \"P\" is for phonetic.\n\nIn the case of the dev and test portions, the data come only from Rdata2 which does not have labels at the phonetic level.\n\nIt is important to clarify that the RAVNURSSON Corpus only includes transcriptions at the orthographic level.", "### Source Data", "#### Initial Data Collection and Normalization\nThe dataset was released with normalized text only at an orthographic level in lower-case. The normalization process was performed by automatically removing punctuation marks and characters that are not present in the Faroese alphabet.", "#### Who are the source language producers?\n\n* The utterances were recorded using a TASCAM DR-40.\n\n* Participants self-reported their age group, gender, native language and dialect.\n\n* Participants are aged between 15 to 83 years. \n \n* The corpus contains 71949 speech files from 433 speakers, totalling 109 hours and 9 minutes.", "### Annotations", "#### Annotation process\n\nMost of the reading prompts were selected by experts from a Faroese text corpus (news, blogs, Wikipedia etc.) and were edited to fit the format. Reading prompts that are within specific domains (such as Faroese place names, numbers, license plates, telling time etc.) were written by the Ravnur Project. Then, a software tool called PushPrompt were used for reading sessions (voice recordings). PushPromt presents the text items in the reading material to the reader, allowing him/her to manage the session interactively (adjusting the reading tempo, repeating speech productions at wish, inserting short breaks as needed, etc.). When the reading session is completed, a log file (with time stamps for each production) is written as a data table compliant with the TextGrid-format.", "#### Who are the annotators?\nThe corpus was annotated by the Ravnur Project", "### Personal and Sensitive Information\nThe dataset consists of people who have donated their voice. You agree to not attempt to determine the identity of speakers in this dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\nThis is the first ASR corpus in Faroese.", "### Discussion of Biases\nAs the number of reading prompts was limited, the common denominator in the RAVNURSSON corpus is that one prompt is read by more than one speaker. This is relevant because is a common practice in ASR to create a language model using the prompts that are found in the train portion of the corpus. That is not recommended for the RAVNURSSON Corpus as it counts with many prompts shared by all the portions and that will produce an important bias in the language modeling task.\n\nIn this section we present some statistics about the repeated prompts through all the portions of the corpus.\n\n- In the train portion:\n\t* Total number of prompts = 65616\n\t* Number of unique prompts = 38646\nThere are 26970 repeated prompts in the train portion. In other words, 41.1% of the prompts are repeated.\n\n- In the test portion:\n\t* Total number of prompts = 3002\n\t* Number of unique prompts = 2887\nThere are 115 repeated prompts in the test portion. In other words, 3.83% of the prompts are repeated.\n\n- In the dev portion:\n\t* Total number of prompts = 3331\n\t* Number of unique prompts = 3302\nThere are 29 repeated prompts in the dev portion. In other words, 0.87% of the prompts are repeated.\n\n- Considering the corpus as a whole:\n\t* Total number of prompts = 71949\n\t* Number of unique prompts = 39945\nThere are 32004 repeated prompts in the whole corpus. In other words, 44.48% of the prompts are repeated.\n\nNOTICE!: It is also important to clarify that none of the 3 portions of the corpus share speakers.", "### Other Known Limitations\n\"RAVNURSSON FAROESE SPEECH AND TRANSCRIPTS\" by Carlos Daniel Hernández Mena and Annika Simonsen is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License with the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.", "## Additional Information", "### Dataset Curators\nThe dataset was collected by Annika Simonsen and curated by Carlos Daniel Hernández Mena.", "### Licensing Information\nCC-BY-4.0", "### Contributions\nThis project was made possible under the umbrella of the Language Technology Programme for Icelandic 2019-2023. The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture.\n\nSpecial thanks to Dr. Jón Guðnason, professor at Reykjavík University and head of the Language and Voice Lab (LVL) for providing computational resources." ]

[ "task_categories:other", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:en", "license:gpl-3.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for kodak", "## Table of Contents\n\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:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThe pictures below link to lossless, true color (24 bits per pixel, aka \"full\ncolor\") images. It is my understanding they have been released by the Eastman\nKodak Company for unrestricted usage. Many sites use them as a standard test\nsuite for compression testing, etc. Prior to this site, they were only\navailable in the Sun Raster format via ftp. This meant that the images could\nnot be previewed before downloading. Since their release, however, the lossless\nPNG format has been incorporated into all the major browsers. Since PNG\nsupports 24-bit lossless color (which GIF and JPEG do not), it is now possible\nto offer this browser-friendly access to the images.", "### Supported Tasks and Leaderboards\n\n- Image compression", "### Languages\n\n- en", "## Dataset Structure", "### Data Instances\n\n- ![kodak01](URL\n- ![kodak02](URL\n- ![kodak03](URL\n- ![kodak04](URL\n- ![kodak05](URL\n- ![kodak06](URL\n- ![kodak07](URL\n- ![kodak08](URL\n- ![kodak09](URL\n- ![kodak10](URL\n- ![kodak11](URL\n- ![kodak12](URL\n- ![kodak13](URL\n- ![kodak14](URL\n- ![kodak15](URL\n- ![kodak16](URL\n- ![kodak17](URL\n- ![kodak18](URL\n- ![kodak19](URL\n- ![kodak20](URL\n- ![kodak21](URL\n- ![kodak22](URL\n- ![kodak23](URL\n- ![kodak24](URL", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?\n\n<URL>", "### 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\nLICENSE", "### Contributions\n\nThanks to @Freed-Wu for adding this dataset." ]

[ "TAGS\n#task_categories-other #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-gpl-3.0 #region-us \n", "# Dataset Card for kodak", "## Table of Contents\n\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:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThe pictures below link to lossless, true color (24 bits per pixel, aka \"full\ncolor\") images. It is my understanding they have been released by the Eastman\nKodak Company for unrestricted usage. Many sites use them as a standard test\nsuite for compression testing, etc. Prior to this site, they were only\navailable in the Sun Raster format via ftp. This meant that the images could\nnot be previewed before downloading. Since their release, however, the lossless\nPNG format has been incorporated into all the major browsers. Since PNG\nsupports 24-bit lossless color (which GIF and JPEG do not), it is now possible\nto offer this browser-friendly access to the images.", "### Supported Tasks and Leaderboards\n\n- Image compression", "### Languages\n\n- en", "## Dataset Structure", "### Data Instances\n\n- ![kodak01](URL\n- ![kodak02](URL\n- ![kodak03](URL\n- ![kodak04](URL\n- ![kodak05](URL\n- ![kodak06](URL\n- ![kodak07](URL\n- ![kodak08](URL\n- ![kodak09](URL\n- ![kodak10](URL\n- ![kodak11](URL\n- ![kodak12](URL\n- ![kodak13](URL\n- ![kodak14](URL\n- ![kodak15](URL\n- ![kodak16](URL\n- ![kodak17](URL\n- ![kodak18](URL\n- ![kodak19](URL\n- ![kodak20](URL\n- ![kodak21](URL\n- ![kodak22](URL\n- ![kodak23](URL\n- ![kodak24](URL", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?\n\n<URL>", "### 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\nLICENSE", "### Contributions\n\nThanks to @Freed-Wu for adding this dataset." ]

[ "region:us" ]

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[ "# Dataset Card for \"koikatsu-cards\"\n\nMore Information needed" ]

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

[ "region:us" ]

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

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

[ "region:us" ]

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[ "# Dataset Card for \"gal_yair_83000_256x256\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"gal_yair_83000_256x256\"\n\nMore Information needed" ]

[ "region:us" ]

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

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

[ "region:us" ]

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

[ "region:us" ]

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[ "# Dataset Card for \"L1_poleval_korpus_pelny_train\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"L1_poleval_korpus_pelny_train\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"L1_poleval_korpus_pelny_test\"\n\nMore Information needed" ]

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[ "# Dataset Card for \"L1_poleval_korpus_wzorcowy_train\"\n\nMore Information needed" ]

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[ "region:us" ]

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[ "# Dataset Card for \"L1_poleval_korpus_wzorcowy_test\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"L1_poleval_korpus_wzorcowy_test\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"gal_yair_83000_100x100\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"gal_yair_83000_100x100\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for FIB", "## Dataset Summary\n\nThe FIB benchmark consists of 3579 examples for evaluating the factual inconsistency of large language models. Each example consists of a document and a pair of summaries: a factually consistent one and a factually inconsistent one. It is based on documents and summaries from XSum and CNN/DM.\nSince this dataset is intended to evaluate the factual inconsistency of large language models, there is only a test split. \n\nAccuracies should be reported separately for examples from XSum and for examples from CNN/DM. This is because the behavior of models on XSum and CNN/DM are expected to be very different. The factually inconsistent summaries are model-extracted from the document for CNN/DM but are model-generated for XSum.", "### Licensing Information\n\nlicense: cc-by-4.0" ]

[ "TAGS\n#region-us \n", "# Dataset Card for FIB", "## Dataset Summary\n\nThe FIB benchmark consists of 3579 examples for evaluating the factual inconsistency of large language models. Each example consists of a document and a pair of summaries: a factually consistent one and a factually inconsistent one. It is based on documents and summaries from XSum and CNN/DM.\nSince this dataset is intended to evaluate the factual inconsistency of large language models, there is only a test split. \n\nAccuracies should be reported separately for examples from XSum and for examples from CNN/DM. This is because the behavior of models on XSum and CNN/DM are expected to be very different. The factually inconsistent summaries are model-extracted from the document for CNN/DM but are model-generated for XSum.", "### Licensing Information\n\nlicense: cc-by-4.0" ]

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

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

[ "region:us" ]

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[ "# Dataset Card for \"L1_scraped_korpus_pelny_train\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"L1_scraped_korpus_pelny_train\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"L1_scraped_korpus_pelny_test\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"L1_scraped_korpus_pelny_test\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"L1_scraped_korpus_wzorcowy_train\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"L1_scraped_korpus_wzorcowy_train\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"L1_scraped_korpus_wzorcowy_test\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"L1_scraped_korpus_wzorcowy_test\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"L1_scraped_korpus_wzorcowy\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"L1_scraped_korpus_wzorcowy\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"L1_scraped_korpus_pelny\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"L1_scraped_korpus_pelny\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"L1_poleval_korpus_pelny\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"L1_poleval_korpus_pelny\"\n\nMore Information needed" ]

[ "region:us" ]

[]

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[ "# Dataset Card for \"L1_poleval_korpus_wzorcowy\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"L1_poleval_korpus_wzorcowy\"\n\nMore Information needed" ]

[ "task_categories:unconditional-image-generation", "annotations_creators:expert-generated", "size_categories:n<1K", "source_datasets:original", "license:cc-by-nc-4.0", "texture-synthesis", "photography", "non-infringing", "region:us" ]

[]

[]

[ "# TD 01: Natural Ground Textures\n\nThis dataset contains multi-photo texture captures in outdoor nature scenes — all focusing on the ground. Each set has different photos that showcase texture variety, making them ideal for training a domain-specific image generator!\n\nOverall information about this dataset:\n\n* Format — JPEG-XL, lossless RGB\n* Resolution — 4032 × 2268\n* Device — mobile camera\n* Technique — hand-held\n* Orientation — portrait or landscape\n* Author: Alex J. Champandard\n* Configurations: 4K, 2K (default), 1K\n\nTo load the medium- and high-resolution images of the dataset, you'll need to install 'jxlpy' from PyPI with 'pip install jxlpy':\n\n\n\nThe lowest-resolution images are available as PNG with a regular installation of 'pillow':\n\n\n\nUse built-in dataset 'filter()' and 'select()' to narrow down the loaded dataset for training, or to ease with development.", "## Set #1: Rock and Gravel\n\n!preview of the files in Set #1\n\n* Description:\n - surface rocks with gravel and coarse sand\n - strong sunlight from the left, sharp shadows\n* Number of Photos:\n - 7 train\n - 2 test\n* Edits:\n - rotated photos to align sunlight\n - removed infrequent objects\n* Size: 77.8 Mb", "## Set #2: Dry Grass with Pine Needles\n\n!preview of the files in Set #2\n\n* Description:\n - field of dry grass and pine needles\n - sunlight from the top right, some shadows\n* Number of Photos:\n - 6 train\n - 1 test\n* Edits:\n - removed dry leaves and large plants\n - removed sticks, rocks and sporadic daisies\n* Size: 95.2 Mb", "## Set #3: Chipped Stones, Broken Leaves and Twiglets\n\n!preview of the files in Set #3\n\n* Description:\n - autumn path with chipped stones and dry broken leaves\n - diffuse light on a cloudy day, very soft shadows\n* Number of Photos:\n - 9 train\n - 3 test\n* Edits:\n - removed anything that looks green, fresh leaves\n - removed long sticks and large/odd stones\n* Size: 126.9 Mb", "## Set #4: Grass Clumps and Cracked Dirt\n\n!preview of the files in Set #4\n\n* Description:\n - clumps of green grass, clover and patches of cracked dirt\n - diffuse light on cloudy day, shadows under large blades of grass\n* Number of Photos:\n - 9 train\n - 2 test\n* Edits:\n - removed dry leaves, sporadic dandelions, and large objects\n - histogram matching for two of the photos so the colors look similar\n* Size: 126.8 Mb", "## Set #5: Dirt, Stones, Rock, Twigs...\n\n!preview of the files in Set #5\n\n* Description:\n - intricate micro-scene with grey dirt, surface rock, stones, twigs and organic debris\n - diffuse light on cloudy day, soft shadows around the larger objects\n* Number of Photos:\n - 9 train\n - 3 test\n* Edits:\n - removed odd objects that felt out-of-distribution\n* Size: 102.1 Mb", "## Set #6: Plants with Flowers on Dry Leaves\n\n!preview of the files in Set #6\n\n* Description:\n - leafy plants with white flowers on a bed of dry brown leaves\n - soft diffuse light, shaded areas under the plants\n* Number of Photos:\n - 9 train\n - 2 test\n* Edits:\n - none yet, inpainting doesn't work well enough\n - would remove long sticks and pieces of wood\n* Size: 105.1 Mb", "## Set #7: Frozen Footpath with Snow\n\n!preview of the files in Set #7\n\n* Description:\n - frozen ground on a path with footprints\n - areas with snow and dark brown ground beneath\n - diffuse lighting on a cloudy day\n* Number of Photos:\n - 11 train\n - 3 test\n* Size: 95.5 Mb", "## Set #8: Pine Needles Forest Floor\n\n!preview of the files in Set #8\n\n* Description:\n - forest floor with a mix of brown soil and grass\n - variety of dry white leaves, sticks, pinecones, pine needles\n - diffuse lighting on a cloudy day\n* Number of Photos:\n - 15 train\n - 4 test\n* Size: 160.6 Mb", "## Set #9: Snow on Grass and Dried Leaves\n\n!preview of the files in Set #9\n\n* Description:\n - field in a park with short green grass\n - large dried brown leaves and fallen snow on top\n - diffuse lighting on a cloudy day\n* Number of Photos:\n - 8 train\n - 3 test\n* Size: 99.8 Mb", "## Set #10: Brown Leaves on Wet Ground\n\n!preview of the files in Set #10\n\n* Description:\n - fallew brown leaves on wet ground\n - occasional tree root and twiglets\n - diffuse lighting on a rainy day\n* Number of Photos:\n - 17 train\n - 4 test\n* Size: 186.2 Mb", "## Set #11: Wet Sand Path with Debris\n\n!preview of the files in Set #11\n\n* Description:\n - hard sandy path in the rain\n - decomposing leaves and other organic debris\n - diffuse lighting on a rainy day\n* Number of Photos:\n - 17 train\n - 4 test\n* Size: 186.2 Mb", "## Set #12: Wood Chips & Sawdust Sprinkled on Forest Path\n\n!preview of the files in Set #12\n\n* Description:\n - wood chips, sawdust, twigs and roots on forest path\n - intermittent sunlight with shadows of trees\n* Number of Photos:\n - 8 train\n - 2 test\n* Size: 110.4 Mb", "## Set #13: Young Grass Growing in the Dog Park\n\n!preview of the files in Set #13\n\n* Description:\n - young grass growing in a dog park after overnight rain\n - occasional stones, sticks and twigs, pine needles\n - diffuse lighting on a cloudy day\n* Number of Photos:\n - 17 train\n - 4 test\n* Size: 193.4 Mb", "## Set #14: Wavy Wet Beach Sand\n\n!preview of the files in Set #14\n\n* Description:\n - wavy wet sand on the beach after the tide retreated\n - some dirt and large pieces algae debris\n - diffuse lighting on a cloudy day\n* Number of Photos:\n - 11 train\n - 3 test\n* Size: 86.5 Mb", "## Set #15: Dry Dirt Road and Debris from Trees\n\n!preview of the files in Set #15\n\n* Description:\n - dirt road of dry compacted sand with debris on top\n - old pine needles and dry brown leaves\n - diffuse lighting on a cloudy day\n* Number of Photos:\n - 8 train\n - 2 test\n* Size: 86.9 Mb", "## Set #16: Sandy Beach Path with Grass Clumps\n\n!preview of the files in Set #17\n\n* Description:\n - path with sand and clumps grass heading towards the beach\n - occasional blueish stones, leafy weeds, and yellow flowers\n - diffuse lighting on a cloudy day\n* Number of Photos:\n - 10 train\n - 3 test\n* Size: 118.8 Mb", "## Set #17: Pine Needles and Brown Leaves on Park Floor\n\n!preview of the files in Set #16\n\n* Description:\n - park floor with predominantly pine needles\n - brown leaves from nearby trees, green grass underneath\n - diffuse lighting on a cloudy day\n* Number of Photos:\n - 8 train\n - 2 test\n* Size: 99.9 Mb" ]

[ "TAGS\n#task_categories-unconditional-image-generation #annotations_creators-expert-generated #size_categories-n<1K #source_datasets-original #license-cc-by-nc-4.0 #texture-synthesis #photography #non-infringing #region-us \n", "# TD 01: Natural Ground Textures\n\nThis dataset contains multi-photo texture captures in outdoor nature scenes — all focusing on the ground. Each set has different photos that showcase texture variety, making them ideal for training a domain-specific image generator!\n\nOverall information about this dataset:\n\n* Format — JPEG-XL, lossless RGB\n* Resolution — 4032 × 2268\n* Device — mobile camera\n* Technique — hand-held\n* Orientation — portrait or landscape\n* Author: Alex J. Champandard\n* Configurations: 4K, 2K (default), 1K\n\nTo load the medium- and high-resolution images of the dataset, you'll need to install 'jxlpy' from PyPI with 'pip install jxlpy':\n\n\n\nThe lowest-resolution images are available as PNG with a regular installation of 'pillow':\n\n\n\nUse built-in dataset 'filter()' and 'select()' to narrow down the loaded dataset for training, or to ease with development.", "## Set #1: Rock and Gravel\n\n!preview of the files in Set #1\n\n* Description:\n - surface rocks with gravel and coarse sand\n - strong sunlight from the left, sharp shadows\n* Number of Photos:\n - 7 train\n - 2 test\n* Edits:\n - rotated photos to align sunlight\n - removed infrequent objects\n* Size: 77.8 Mb", "## Set #2: Dry Grass with Pine Needles\n\n!preview of the files in Set #2\n\n* Description:\n - field of dry grass and pine needles\n - sunlight from the top right, some shadows\n* Number of Photos:\n - 6 train\n - 1 test\n* Edits:\n - removed dry leaves and large plants\n - removed sticks, rocks and sporadic daisies\n* Size: 95.2 Mb", "## Set #3: Chipped Stones, Broken Leaves and Twiglets\n\n!preview of the files in Set #3\n\n* Description:\n - autumn path with chipped stones and dry broken leaves\n - diffuse light on a cloudy day, very soft shadows\n* Number of Photos:\n - 9 train\n - 3 test\n* Edits:\n - removed anything that looks green, fresh leaves\n - removed long sticks and large/odd stones\n* Size: 126.9 Mb", "## Set #4: Grass Clumps and Cracked Dirt\n\n!preview of the files in Set #4\n\n* Description:\n - clumps of green grass, clover and patches of cracked dirt\n - diffuse light on cloudy day, shadows under large blades of grass\n* Number of Photos:\n - 9 train\n - 2 test\n* Edits:\n - removed dry leaves, sporadic dandelions, and large objects\n - histogram matching for two of the photos so the colors look similar\n* Size: 126.8 Mb", "## Set #5: Dirt, Stones, Rock, Twigs...\n\n!preview of the files in Set #5\n\n* Description:\n - intricate micro-scene with grey dirt, surface rock, stones, twigs and organic debris\n - diffuse light on cloudy day, soft shadows around the larger objects\n* Number of Photos:\n - 9 train\n - 3 test\n* Edits:\n - removed odd objects that felt out-of-distribution\n* Size: 102.1 Mb", "## Set #6: Plants with Flowers on Dry Leaves\n\n!preview of the files in Set #6\n\n* Description:\n - leafy plants with white flowers on a bed of dry brown leaves\n - soft diffuse light, shaded areas under the plants\n* Number of Photos:\n - 9 train\n - 2 test\n* Edits:\n - none yet, inpainting doesn't work well enough\n - would remove long sticks and pieces of wood\n* Size: 105.1 Mb", "## Set #7: Frozen Footpath with Snow\n\n!preview of the files in Set #7\n\n* Description:\n - frozen ground on a path with footprints\n - areas with snow and dark brown ground beneath\n - diffuse lighting on a cloudy day\n* Number of Photos:\n - 11 train\n - 3 test\n* Size: 95.5 Mb", "## Set #8: Pine Needles Forest Floor\n\n!preview of the files in Set #8\n\n* Description:\n - forest floor with a mix of brown soil and grass\n - variety of dry white leaves, sticks, pinecones, pine needles\n - diffuse lighting on a cloudy day\n* Number of Photos:\n - 15 train\n - 4 test\n* Size: 160.6 Mb", "## Set #9: Snow on Grass and Dried Leaves\n\n!preview of the files in Set #9\n\n* Description:\n - field in a park with short green grass\n - large dried brown leaves and fallen snow on top\n - diffuse lighting on a cloudy day\n* Number of Photos:\n - 8 train\n - 3 test\n* Size: 99.8 Mb", "## Set #10: Brown Leaves on Wet Ground\n\n!preview of the files in Set #10\n\n* Description:\n - fallew brown leaves on wet ground\n - occasional tree root and twiglets\n - diffuse lighting on a rainy day\n* Number of Photos:\n - 17 train\n - 4 test\n* Size: 186.2 Mb", "## Set #11: Wet Sand Path with Debris\n\n!preview of the files in Set #11\n\n* Description:\n - hard sandy path in the rain\n - decomposing leaves and other organic debris\n - diffuse lighting on a rainy day\n* Number of Photos:\n - 17 train\n - 4 test\n* Size: 186.2 Mb", "## Set #12: Wood Chips & Sawdust Sprinkled on Forest Path\n\n!preview of the files in Set #12\n\n* Description:\n - wood chips, sawdust, twigs and roots on forest path\n - intermittent sunlight with shadows of trees\n* Number of Photos:\n - 8 train\n - 2 test\n* Size: 110.4 Mb", "## Set #13: Young Grass Growing in the Dog Park\n\n!preview of the files in Set #13\n\n* Description:\n - young grass growing in a dog park after overnight rain\n - occasional stones, sticks and twigs, pine needles\n - diffuse lighting on a cloudy day\n* Number of Photos:\n - 17 train\n - 4 test\n* Size: 193.4 Mb", "## Set #14: Wavy Wet Beach Sand\n\n!preview of the files in Set #14\n\n* Description:\n - wavy wet sand on the beach after the tide retreated\n - some dirt and large pieces algae debris\n - diffuse lighting on a cloudy day\n* Number of Photos:\n - 11 train\n - 3 test\n* Size: 86.5 Mb", "## Set #15: Dry Dirt Road and Debris from Trees\n\n!preview of the files in Set #15\n\n* Description:\n - dirt road of dry compacted sand with debris on top\n - old pine needles and dry brown leaves\n - diffuse lighting on a cloudy day\n* Number of Photos:\n - 8 train\n - 2 test\n* Size: 86.9 Mb", "## Set #16: Sandy Beach Path with Grass Clumps\n\n!preview of the files in Set #17\n\n* Description:\n - path with sand and clumps grass heading towards the beach\n - occasional blueish stones, leafy weeds, and yellow flowers\n - diffuse lighting on a cloudy day\n* Number of Photos:\n - 10 train\n - 3 test\n* Size: 118.8 Mb", "## Set #17: Pine Needles and Brown Leaves on Park Floor\n\n!preview of the files in Set #16\n\n* Description:\n - park floor with predominantly pine needles\n - brown leaves from nearby trees, green grass underneath\n - diffuse lighting on a cloudy day\n* Number of Photos:\n - 8 train\n - 2 test\n* Size: 99.9 Mb" ]

[ "task_categories:text-classification", "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#task_categories-text-classification #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 \"stefano-finetune\"\n\nMore Information needed" ]

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

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

[]

[]

[ "# Dataset Card for \"two-minute-papers\"\n\nMore Information needed" ]

[ "TAGS\n#task_categories-automatic-speech-recognition #whisper #whispering #base #region-us \n", "# Dataset Card for \"two-minute-papers\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"gal_yair_8300_100x100_fixed\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"gal_yair_8300_100x100_fixed\"\n\nMore Information needed" ]

[ "Diff-SVC", "Genshin", "Genshin Impact", "Voice Data", "Voice Dataset", "region:us" ]

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[]

[ "### Windows平台\n\n\n1. 下载自己需要的数据集\n2. 解压到 Diff-SVC 根目录，如果提示覆盖，请直接覆盖。\n3. 依次输入如下指令进行预处理\n4. 按照教程进行加载预训练模型并训练：\n\n\n教程地址：URL", "### Linux平台\n\n\n1. 输入如下命令下载需要的数据集（这里以纳西妲为例，其它角色可以从下表右击链接复制）\n2. 输入如下命令解压到 Diff-SVC 根目录，如果提示覆盖请输入 y\n3. 依次输入如下指令进行预处理\n4. 按照教程进行加载预训练模型并训练：\n\n\n教程地址：URL\n\n\n下载地址\n----" ]

[ "TAGS\n#Diff-SVC #Genshin #Genshin Impact #Voice Data #Voice Dataset #region-us \n", "### Windows平台\n\n\n1. 下载自己需要的数据集\n2. 解压到 Diff-SVC 根目录，如果提示覆盖，请直接覆盖。\n3. 依次输入如下指令进行预处理\n4. 按照教程进行加载预训练模型并训练：\n\n\n教程地址：URL", "### Linux平台\n\n\n1. 输入如下命令下载需要的数据集（这里以纳西妲为例，其它角色可以从下表右击链接复制）\n2. 输入如下命令解压到 Diff-SVC 根目录，如果提示覆盖请输入 y\n3. 依次输入如下指令进行预处理\n4. 按照教程进行加载预训练模型并训练：\n\n\n教程地址：URL\n\n\n下载地址\n----" ]

[ "task_categories:fill-mask", "annotations_creators:other", "language_creators:found", "multilinguality:multilingual", "size_categories:10M<n<100M", "source_datasets:original", "language:bg", "language:cs", "language:da", "language:de", "language:el", "language:en", "language:es", "language:et", "language:fi", "language:fr", "language:ga", "language:hr", "language:hu", "language:it", "language:lt", "language:lv", "language:mt", "language:nl", "language:pl", "language:pt", "language:ro", "language:sk", "language:sl", "language:sv", "license:cc-by-4.0", "region:us" ]

[]

[ "bg", "cs", "da", "de", "el", "en", "es", "et", "fi", "fr", "ga", "hr", "hu", "it", "lt", "lv", "mt", "nl", "pl", "pt", "ro", "sk", "sl", "sv" ]

[ "### Dataset Summary\n\n\nWikipedia dataset containing cleaned articles of all languages.\nThe datasets are built from the Wikipedia dump\n(URL with one split per language. Each example\ncontains the content of one full Wikipedia article with cleaning to strip\nmarkdown and unwanted sections (references, etc.).", "### Supported Tasks and Leaderboards\n\n\nThe dataset supports the tasks of fill-mask.", "### Languages\n\n\nThe following languages are supported:\nbg, cs, da, de, el, en, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv\n\n\nDataset Structure\n-----------------\n\n\nIt is structured in the following format: {date}/{language}\\_{shard}.URL\nAt the moment only the date '20221120' is supported.\n\n\nUse the dataset like this:", "### Data Instances\n\n\nThe file format is URL and there is one split available ('train').", "### Data Fields", "### Data Splits\n\n\nDataset Creation\n----------------\n\n\nThis dataset has been created by downloading the wikipedias using olm/wikipedia for the 24 EU languages.\nFor more information about the creation of the dataset please refer to prepare\\_wikipedias.py", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions\n\n\nThanks to @JoelNiklaus for adding this dataset." ]

[ "TAGS\n#task_categories-fill-mask #annotations_creators-other #language_creators-found #multilinguality-multilingual #size_categories-10M<n<100M #source_datasets-original #language-Bulgarian #language-Czech #language-Danish #language-German #language-Modern Greek (1453-) #language-English #language-Spanish #language-Estonian #language-Finnish #language-French #language-Irish #language-Croatian #language-Hungarian #language-Italian #language-Lithuanian #language-Latvian #language-Maltese #language-Dutch #language-Polish #language-Portuguese #language-Romanian #language-Slovak #language-Slovenian #language-Swedish #license-cc-by-4.0 #region-us \n", "### Dataset Summary\n\n\nWikipedia dataset containing cleaned articles of all languages.\nThe datasets are built from the Wikipedia dump\n(URL with one split per language. Each example\ncontains the content of one full Wikipedia article with cleaning to strip\nmarkdown and unwanted sections (references, etc.).", "### Supported Tasks and Leaderboards\n\n\nThe dataset supports the tasks of fill-mask.", "### Languages\n\n\nThe following languages are supported:\nbg, cs, da, de, el, en, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv\n\n\nDataset Structure\n-----------------\n\n\nIt is structured in the following format: {date}/{language}\\_{shard}.URL\nAt the moment only the date '20221120' is supported.\n\n\nUse the dataset like this:", "### Data Instances\n\n\nThe file format is URL and there is one split available ('train').", "### Data Fields", "### Data Splits\n\n\nDataset Creation\n----------------\n\n\nThis dataset has been created by downloading the wikipedias using olm/wikipedia for the 24 EU languages.\nFor more information about the creation of the dataset please refer to prepare\\_wikipedias.py", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions\n\n\nThanks to @JoelNiklaus for adding this dataset." ]

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

[]

[]

[ "# Dataset Card for \"goog-tech-talks\"\n\nMore Information needed" ]

[ "TAGS\n#task_categories-automatic-speech-recognition #whisper #whispering #base #region-us \n", "# Dataset Card for \"goog-tech-talks\"\n\nMore Information needed" ]

[ "doi:10.57967/hf/0123", "region:us" ]

[]

[]

[ "# Dataset Card for \"gpk-captions\"\n\nMore Information needed" ]

[ "TAGS\n#doi-10.57967/hf/0123 #region-us \n", "# Dataset Card for \"gpk-captions\"\n\nMore Information needed" ]

[ "license:creativeml-openrail-m", "region:us" ]

[]

[]

[]

[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]

[ "multilinguality:monolingual", "size_categories:1K<n<10K", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\n*IMPORTANT*: This is the same dataset as relbert/semeval2012\\_relational\\_similarity,\nbut with a different dataset construction.\n\n\nRelational similarity dataset from SemEval2012 task 2, compiled to fine-tune RelBERT model.\nThe dataset contains a list of positive and negative word pair from 89 pre-defined relations.\nThe relation types are constructed on top of following 10 parent relation types.\n\n\nEach of the parent relation is further grouped into child relation types where the definition can be found here.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'train' looks as follows.", "### Data Splits", "### Number of Positive/Negative Word-pairs in each Split" ]

[ "TAGS\n#multilinguality-monolingual #size_categories-1K<n<10K #language-English #license-other #region-us \n", "### Dataset Summary\n\n\n*IMPORTANT*: This is the same dataset as relbert/semeval2012\\_relational\\_similarity,\nbut with a different dataset construction.\n\n\nRelational similarity dataset from SemEval2012 task 2, compiled to fine-tune RelBERT model.\nThe dataset contains a list of positive and negative word pair from 89 pre-defined relations.\nThe relation types are constructed on top of following 10 parent relation types.\n\n\nEach of the parent relation is further grouped into child relation types where the definition can be found here.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'train' looks as follows.", "### Data Splits", "### Number of Positive/Negative Word-pairs in each Split" ]

[ "multilinguality:monolingual", "size_categories:1K<n<10K", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\n*IMPORTANT*: This is the same dataset as relbert/semeval2012\\_relational\\_similarity,\nbut with a different dataset construction.\n\n\nRelational similarity dataset from SemEval2012 task 2, compiled to fine-tune RelBERT model.\nThe dataset contains a list of positive and negative word pair from 89 pre-defined relations.\nThe relation types are constructed on top of following 10 parent relation types.\n\n\nEach of the parent relation is further grouped into child relation types where the definition can be found here.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'train' looks as follows.", "### Data Splits", "### Number of Positive/Negative Word-pairs in each Split" ]

[ "TAGS\n#multilinguality-monolingual #size_categories-1K<n<10K #language-English #license-other #region-us \n", "### Dataset Summary\n\n\n*IMPORTANT*: This is the same dataset as relbert/semeval2012\\_relational\\_similarity,\nbut with a different dataset construction.\n\n\nRelational similarity dataset from SemEval2012 task 2, compiled to fine-tune RelBERT model.\nThe dataset contains a list of positive and negative word pair from 89 pre-defined relations.\nThe relation types are constructed on top of following 10 parent relation types.\n\n\nEach of the parent relation is further grouped into child relation types where the definition can be found here.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'train' looks as follows.", "### Data Splits", "### Number of Positive/Negative Word-pairs in each Split" ]

[ "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." ]

[ "region:us" ]

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[ "# Dataset Card for \"text_summarization_dataset5\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"text_summarization_dataset5\"\n\nMore Information needed" ]