Datasets:

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ftopal

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huggingface-datasets

like 0

[ "task_categories:conversational", "language:ko", "region:us" ]

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[ "ko" ]

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[ "TAGS\n#task_categories-conversational #language-Korean #region-us \n" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of Stopwolf/Cerberus-7B-slerp\n\n\n\nDataset automatically created during the evaluation run of model Stopwolf/Cerberus-7B-slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T09:08:12.484477(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of Stopwolf/Cerberus-7B-slerp\n\n\n\nDataset automatically created during the evaluation run of model Stopwolf/Cerberus-7B-slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T09:08:12.484477(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of Eric111/Mayo\n\n\n\nDataset automatically created during the evaluation run of model Eric111/Mayo on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T09:14:00.989301(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of Eric111/Mayo\n\n\n\nDataset automatically created during the evaluation run of model Eric111/Mayo on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T09:14:00.989301(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

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[ "# Dataset Card for \"find_marker_both_sent_train_400_eval_40_in_context\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"find_marker_both_sent_train_400_eval_40_in_context\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of paulml/NeuralOmniWestBeaglake-7B\n\n\n\nDataset automatically created during the evaluation run of model paulml/NeuralOmniWestBeaglake-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T09:58:00.255850(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of paulml/NeuralOmniWestBeaglake-7B\n\n\n\nDataset automatically created during the evaluation run of model paulml/NeuralOmniWestBeaglake-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T09:58:00.255850(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

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[]

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

[ "size_categories:1M<n<10M", "arxiv:2309.09400", "arxiv:2201.06642", "arxiv:2203.05437", "arxiv:2104.05596", "region:us" ]

[ "2309.09400", "2201.06642", "2203.05437", "2104.05596" ]

[]

[ "# CultureaX\n - Licesnse - ODC-By, CC0, Paper\n - Source - URL\n - 49M tokens, 2.9M sentences", "# IndicQA\n - License - cc-by-4.0\n - Source - URL\n - 0.23M tokens, 15K sentences", "# Odiaencorp \n - License - CC BY-NC-SA 4.0\n - Source - URL\n - 1.1M tokens, 85K sentences", "# Oscar\n - License - cc0-1.0, Paper\n - Source - URL\n - 25.7M tokens, 1.2M sentences", "# Paraphrasing\n - License - cc-by-nc-4.0, Paper\n - Source - URL\n - 2.3M tokens, 0.105M sentences", "# PMO\n - License - cc-by-nc-4.0\n - Source - PMO website\n - 2.2M tokens, 0.131M sentences", "# Samanantar\n - License - cc-by-nc-4.0, Paper\n - Source - URL\n - 10.25M Tokens, 0.909M Sentences", "# Sentiment Analysis\n - License - Unspecified\n - Source - URL\n - 34K Tokens, 1000 Sentences", "# Wiki\n - License - cc-by-sa-3.0, gfdl\n - Source - URL\n - 4.1M tokens, 0.253M sentences", "# XP3\n - License - apache-2.0\n - Source - URL\n - 4.9M tokens, 0.26M sentences" ]

[ "TAGS\n#size_categories-1M<n<10M #arxiv-2309.09400 #arxiv-2201.06642 #arxiv-2203.05437 #arxiv-2104.05596 #region-us \n", "# CultureaX\n - Licesnse - ODC-By, CC0, Paper\n - Source - URL\n - 49M tokens, 2.9M sentences", "# IndicQA\n - License - cc-by-4.0\n - Source - URL\n - 0.23M tokens, 15K sentences", "# Odiaencorp \n - License - CC BY-NC-SA 4.0\n - Source - URL\n - 1.1M tokens, 85K sentences", "# Oscar\n - License - cc0-1.0, Paper\n - Source - URL\n - 25.7M tokens, 1.2M sentences", "# Paraphrasing\n - License - cc-by-nc-4.0, Paper\n - Source - URL\n - 2.3M tokens, 0.105M sentences", "# PMO\n - License - cc-by-nc-4.0\n - Source - PMO website\n - 2.2M tokens, 0.131M sentences", "# Samanantar\n - License - cc-by-nc-4.0, Paper\n - Source - URL\n - 10.25M Tokens, 0.909M Sentences", "# Sentiment Analysis\n - License - Unspecified\n - Source - URL\n - 34K Tokens, 1000 Sentences", "# Wiki\n - License - cc-by-sa-3.0, gfdl\n - Source - URL\n - 4.1M tokens, 0.253M sentences", "# XP3\n - License - apache-2.0\n - Source - URL\n - 4.9M tokens, 0.26M sentences" ]

[ "region:us" ]

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[ "# Dataset Card for \"Wish-Summarization-Llama\"\n\nMore Information needed" ]

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

[ "region:us" ]

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[ "# Dataset Card for \"Wish-QA-NQ-Falcon\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"Wish-QA-NQ-Falcon\"\n\nMore Information needed" ]

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:1K<n<10K", "source_datasets:original", "language:bm", "language:bbj", "language:ee", "language:fon", "language:ha", "language:ig", "language:rw", "language:lg", "language:luo", "language:mos", "language:ny", "language:pcm", "language:sn", "language:sw", "language:tn", "language:tw", "language:wo", "language:xh", "language:yo", "language:zu", "license:afl-3.0", "pos", "masakhapos", "masakhane", "region:us" ]

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[ "bm", "bbj", "ee", "fon", "ha", "ig", "rw", "lg", "luo", "mos", "ny", "pcm", "sn", "sw", "tn", "tw", "wo", "xh", "yo", "zu" ]

[ "### Dataset Summary\n\n\nMasakhaPOS is the largest publicly available high-quality dataset for part-of-speech (POS) tagging in 20 African languages. The languages covered are:\n\n\nThe train/validation/test sets are available for all the 20 languages.\n\n\nFor more details see URL", "### Supported Tasks and Leaderboards\n\n\n* 'Part-of-speech': The performance in this task is measured with accuracy (higher is better).", "### Languages\n\n\nThere are 20 languages available :\n\n\n* Bambara (bam)\n* Ghomala (bbj)\n* Ewe (ewe)\n* Fon (fon)\n* Hausa (hau)\n* Igbo (ibo)\n* Kinyarwanda (kin)\n* Luganda (lug)\n* Dholuo (luo)\n* Mossi (mos)\n* Chichewa (nya)\n* Nigerian Pidgin\n* chShona (sna)\n* Kiswahili (swą)\n* Setswana (tsn)\n* Twi (twi)\n* Wolof (wol)\n* isiXhosa (xho)\n* Yorùbá (yor)\n* isiZulu (zul)\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nThe examples look like this for Yorùbá:", "### Data Fields\n\n\n* 'id': id of the sample\n* 'tokens': the tokens of the example text\n* 'upos': the POS tags of each token\n\n\nThe POS tags correspond to this list:\n\n\nThe definition of the tags can be found on UD website", "### Data Splits\n\n\nFor all languages, there are three splits.\n\n\nThe original splits were named 'train', 'dev' and 'test' and they correspond to the 'train', 'validation' and 'test' splits.\n\n\nThe splits have the following sizes :\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThe dataset was introduced to introduce new resources to 20 languages that were under-served for natural language processing.", "### Source Data\n\n\nThe source of the data is from the news domain, details can be found here URL", "#### Initial Data Collection and Normalization\n\n\nThe articles were word-tokenized, information on the exact pre-processing pipeline is unavailable.", "#### Who are the source language producers?\n\n\nThe source language was produced by journalists and writers employed by the news agency and newspaper mentioned above.", "### Annotations", "#### Annotation process\n\n\nDetails can be found here URL", "#### Who are the annotators?\n\n\nAnnotators were recruited from Masakhane", "### Personal and Sensitive Information\n\n\nThe data is sourced from newspaper source and only contains mentions of public figures or individuals\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nUsers should keep in mind that the dataset only contains news text, which might limit the applicability of the developed systems to other domains.\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nThe licensing status of the data is CC 4.0 Non-Commercial\n\n\nProvide the BibTex-formatted reference for the dataset. For example:", "### Contributions\n\n\nThanks to @dadelani for adding this dataset." ]

[ "TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-multilingual #size_categories-1K<n<10K #source_datasets-original #language-Bambara #language-Ghomálá' #language-Ewe #language-Fon #language-Hausa #language-Igbo #language-Kinyarwanda #language-Ganda #language-Luo (Kenya and Tanzania) #language-Mossi #language-Nyanja #language-Nigerian Pidgin #language-Shona #language-Swahili (macrolanguage) #language-Tswana #language-Twi #language-Wolof #language-Xhosa #language-Yoruba #language-Zulu #license-afl-3.0 #pos #masakhapos #masakhane #region-us \n", "### Dataset Summary\n\n\nMasakhaPOS is the largest publicly available high-quality dataset for part-of-speech (POS) tagging in 20 African languages. The languages covered are:\n\n\nThe train/validation/test sets are available for all the 20 languages.\n\n\nFor more details see URL", "### Supported Tasks and Leaderboards\n\n\n* 'Part-of-speech': The performance in this task is measured with accuracy (higher is better).", "### Languages\n\n\nThere are 20 languages available :\n\n\n* Bambara (bam)\n* Ghomala (bbj)\n* Ewe (ewe)\n* Fon (fon)\n* Hausa (hau)\n* Igbo (ibo)\n* Kinyarwanda (kin)\n* Luganda (lug)\n* Dholuo (luo)\n* Mossi (mos)\n* Chichewa (nya)\n* Nigerian Pidgin\n* chShona (sna)\n* Kiswahili (swą)\n* Setswana (tsn)\n* Twi (twi)\n* Wolof (wol)\n* isiXhosa (xho)\n* Yorùbá (yor)\n* isiZulu (zul)\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nThe examples look like this for Yorùbá:", "### Data Fields\n\n\n* 'id': id of the sample\n* 'tokens': the tokens of the example text\n* 'upos': the POS tags of each token\n\n\nThe POS tags correspond to this list:\n\n\nThe definition of the tags can be found on UD website", "### Data Splits\n\n\nFor all languages, there are three splits.\n\n\nThe original splits were named 'train', 'dev' and 'test' and they correspond to the 'train', 'validation' and 'test' splits.\n\n\nThe splits have the following sizes :\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThe dataset was introduced to introduce new resources to 20 languages that were under-served for natural language processing.", "### Source Data\n\n\nThe source of the data is from the news domain, details can be found here URL", "#### Initial Data Collection and Normalization\n\n\nThe articles were word-tokenized, information on the exact pre-processing pipeline is unavailable.", "#### Who are the source language producers?\n\n\nThe source language was produced by journalists and writers employed by the news agency and newspaper mentioned above.", "### Annotations", "#### Annotation process\n\n\nDetails can be found here URL", "#### Who are the annotators?\n\n\nAnnotators were recruited from Masakhane", "### Personal and Sensitive Information\n\n\nThe data is sourced from newspaper source and only contains mentions of public figures or individuals\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nUsers should keep in mind that the dataset only contains news text, which might limit the applicability of the developed systems to other domains.\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nThe licensing status of the data is CC 4.0 Non-Commercial\n\n\nProvide the BibTex-formatted reference for the dataset. For example:", "### Contributions\n\n\nThanks to @dadelani for adding this dataset." ]

[ "region:us" ]

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[ "# Dataset Card for \"Wish-QA-ASQA-Llama\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"Wish-QA-ASQA-Llama\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"Wish-QA-ELI5-Falcon\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"Wish-QA-ELI5-Falcon\"\n\nMore Information needed" ]

[ "region:us" ]

[]

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[ "# Dataset Card for \"Wish-QA-Falcon\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

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[ "# Dataset Card for \"Wish-Summarization-Falcon\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

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[ "# Dataset Card for \"Wish-QA-ELI5-Llama\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"Wish-QA-ELI5-Llama\"\n\nMore Information needed" ]

[ "region:us" ]

[]

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[ "# Dataset Card for \"Wish-QA-MED-Llama\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"Wish-QA-MED-Llama\"\n\nMore Information needed" ]

[ "region:us" ]

[]

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[ "# Dataset Card for \"Wish-QA-MED-Falcon\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"Wish-QA-MED-Falcon\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"Wish-IE-Falcon\"\n\nMore Information needed" ]

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

[ "region:us" ]

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[ "# Dataset Card for \"Wish-QA-ASQA-Falcon\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"Wish-QA-ASQA-Falcon\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"Wish-QA-NQ-Llama\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"Wish-QA-NQ-Llama\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"high_vs_random_min_length_500\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"high_vs_random_min_length_500\"\n\nMore Information needed" ]

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

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[]

[ "# rebase\n 2110 git pull\n 2111 git commit -m 'merge' -a\n 2112 git push\n 2113 cp ~/.gitignore_templates/Emacs.gitignore .gitingnore\n 2114 cp ~/.gitignore_templates/Emacs.gitignore .gitignore\n 2115 git status\n 2116 git add .gitignore \n 2117 git commit -m 'clean' -a\n 2118 ls\n 2119 mkdir -p 2016/09/27/Heidelberg/HLF2015/Unimath\n 2120 mv Unimath* 2016/09/27/Heidelberg/HLF2015/Unimath/\n 2121 git status\n 2122 git add 2016\n 2123 git commit -m 'moving' -a\n 2124 git push\n 2125 mv 2016/09/27/Heidelberg/HLF2015 016/09/27/Heidelberg/HLF2016\n 2126 git add 2016/\n 2127 git commit -m 'move' -a\n 2128 git push\n 2129 history" ]

[ "TAGS\n#license-creativeml-openrail-m #region-us \n", "# rebase\n 2110 git pull\n 2111 git commit -m 'merge' -a\n 2112 git push\n 2113 cp ~/.gitignore_templates/Emacs.gitignore .gitingnore\n 2114 cp ~/.gitignore_templates/Emacs.gitignore .gitignore\n 2115 git status\n 2116 git add .gitignore \n 2117 git commit -m 'clean' -a\n 2118 ls\n 2119 mkdir -p 2016/09/27/Heidelberg/HLF2015/Unimath\n 2120 mv Unimath* 2016/09/27/Heidelberg/HLF2015/Unimath/\n 2121 git status\n 2122 git add 2016\n 2123 git commit -m 'moving' -a\n 2124 git push\n 2125 mv 2016/09/27/Heidelberg/HLF2015 016/09/27/Heidelberg/HLF2016\n 2126 git add 2016/\n 2127 git commit -m 'move' -a\n 2128 git push\n 2129 history" ]

[ "task_categories:text-generation", "language:en", "license:cc", "code", "arxiv:2306.03091", "region:us" ]

[ "2306.03091" ]

[ "en" ]

[]

[ "TAGS\n#task_categories-text-generation #language-English #license-cc #code #arxiv-2306.03091 #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of KnutJaegersberg/Deita-20b\n\n\n\nDataset automatically created during the evaluation run of model KnutJaegersberg/Deita-20b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T12:16:25.639871(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of KnutJaegersberg/Deita-20b\n\n\n\nDataset automatically created during the evaluation run of model KnutJaegersberg/Deita-20b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T12:16:25.639871(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of Kquant03/Azathoth-16x7B-bf16\n\n\n\nDataset automatically created during the evaluation run of model Kquant03/Azathoth-16x7B-bf16 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T13:02:29.525872(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of Kquant03/Azathoth-16x7B-bf16\n\n\n\nDataset automatically created during the evaluation run of model Kquant03/Azathoth-16x7B-bf16 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T13:02:29.525872(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

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

[]

[]

[ "# Dataset: PhilEO Pre-train\n\nA novel 500GB Sentinel-2 dataset of the PhilEO Suite for model pre-training.", "## Dataset Details", "### Dataset Description\n\nThe PhilEO Pre-train dataset is a 500GB global dataset of Sentinel-2 images.\n\nThe data contain 11 bands at 10m resolution in the following order: 0-SCL, 1-B02, 2-B03, 3-B04, 4-B08, 5-B05, 6-B06, 7-B07, 8-B8A, 9-B11, and 10-B12 where SCL is the Scene Classification Layer.\n\n- Curated by: ESA Phi-lab and Leonardo Labs\n- License: MIT", "## Uses\n\nThe dataset can be used to pre-train models, i.e. train EO Foundation Models.", "### Dataset Sources\n\nThe basic links for the dataset:\n\n- Repository: URL \n\nCasper Fibaek, Luke Camilleri, Andreas Luyts, Nikolaos Dionelis, Bertrand Le Saux, Bagaglini Leonardo, Cascarano Giacomo Donato, and Giorgio Pasquali, “The PhilEO Geospatial Foundation Model Suite,” To appear, 2024." ]

[ "TAGS\n#license-mit #region-us \n", "# Dataset: PhilEO Pre-train\n\nA novel 500GB Sentinel-2 dataset of the PhilEO Suite for model pre-training.", "## Dataset Details", "### Dataset Description\n\nThe PhilEO Pre-train dataset is a 500GB global dataset of Sentinel-2 images.\n\nThe data contain 11 bands at 10m resolution in the following order: 0-SCL, 1-B02, 2-B03, 3-B04, 4-B08, 5-B05, 6-B06, 7-B07, 8-B8A, 9-B11, and 10-B12 where SCL is the Scene Classification Layer.\n\n- Curated by: ESA Phi-lab and Leonardo Labs\n- License: MIT", "## Uses\n\nThe dataset can be used to pre-train models, i.e. train EO Foundation Models.", "### Dataset Sources\n\nThe basic links for the dataset:\n\n- Repository: URL \n\nCasper Fibaek, Luke Camilleri, Andreas Luyts, Nikolaos Dionelis, Bertrand Le Saux, Bagaglini Leonardo, Cascarano Giacomo Donato, and Giorgio Pasquali, “The PhilEO Geospatial Foundation Model Suite,” To appear, 2024." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of Radiantloom/radiantloom-mixtral-8x7b-fusion-dpo\n\n\n\nDataset automatically created during the evaluation run of model Radiantloom/radiantloom-mixtral-8x7b-fusion-dpo on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T13:38:34.370463(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of Radiantloom/radiantloom-mixtral-8x7b-fusion-dpo\n\n\n\nDataset automatically created during the evaluation run of model Radiantloom/radiantloom-mixtral-8x7b-fusion-dpo on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T13:38:34.370463(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of binbi/Ein-72B-v0.1\n\n\n\nDataset automatically created during the evaluation run of model binbi/Ein-72B-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T14:07:01.496774(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of binbi/Ein-72B-v0.1\n\n\n\nDataset automatically created during the evaluation run of model binbi/Ein-72B-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T14:07:01.496774(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of cloudyu/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO\n\n\n\nDataset automatically created during the evaluation run of model cloudyu/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T13:57:06.982400(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of cloudyu/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO\n\n\n\nDataset automatically created during the evaluation run of model cloudyu/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T13:57:06.982400(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"openaccess-ai-collective-oo-gpt4-filtered\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"openaccess-ai-collective-oo-gpt4-filtered\"\n\nMore Information needed" ]

[ "task_categories:text-classification", "size_categories:100K<n<1M", "language:de", "region:us" ]

[]

[ "de" ]

[ "### Dataset Description\n\n\n* Curated by: DFKI, Talking Robots Group at MLT\n* Language(s) (NLP): German\n* License:\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields", "### Data Splits\n\n\nThis dataset contains 3525 dialogue turns in total. The data are split as follows: 2610 turns for training, 310 for development and 605 for test. The data represent a continuous conversation, i.e., the previous id refers to the previous turn in the dialogue.", "### Label Description and Statistics\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThe dataset is based on the recordings from the emergency response domain that use radio communication protocol. The goal of the conversation is to coordinate rescue operations in a robot-assisted disaster response.", "### Source Data\n\n\nThe data are based on human-human communication in robot-assisted disaster response. The dialogues are task-oriented, focused on collaborative execution of a mission by a team that uses robots to to explore some area, find hazardous materials, locate fires, damage or victims.", "#### Data Collection and Processing\n\n\nThe initial audio recordings were collected during the TRADR and ADRZ projects, transcribed and annotated by the Talking Robots Group, DFKI", "### Annotations\n\n\nThe annotations include dialogue intents relevant for communication in the emergency response domain: 'call', 'call\\_response', 'info\\_request', 'info\\_provide', 'confirm', 'disconfirm', 'order' and 'other'.\n\n\nNote the interpretation of the intent depends on the context. E.g., the following examples illustrate how very similar responses (\"Warten\", \"Wait\") are annotated differently depending on the previous turn:", "#### Annotation process\n\n\nThe recordings were manually transcribed and annotated with emergency response intents. There are 3525 dialogue turns in total with 6.3 tokens per turn on average.", "#### Who are the annotators?\n\n\nAll annotations were done by the research assistants of the Talking Robots Group, DFKI", "#### Personal and Sensitive Information\n\n\nThe dataset does not include any real names, addresses or other personal information. The recordings were done during training sessions with simulations of the emergency situation.\n\n\nBias, Risks, and Limitations\n----------------------------\n\n\nThe dataset covers only a subset of possible emergency situations, focusing mainly on fire, building collapse and chemical leakage. It does not address many other situations, e.g., traffic accidents, floods or explosions.\n\n\nPart of this dataset has been introduced in the following paper. However, the current version includes more annotated turns due to additional data collection.\n\n\nBibTeX:\n\n\nAPA:\n\n\nGlossary\n--------\n\n\nAbbrevations used for the speakers:\n\n\nUGV: Unmanned Ground Vehicle\n\n\nUAV: Unmanned Aerial Vehicle\n\n\nMC: Mission Commander\n\n\nTL: Team Leader\n\n\nRobLW: Robotikleitwagen (robotic lead vehicle)\n\n\nZF: Zugführer (fire brigade commander)\n\n\nGF: Gruppenführer (group leader)\n\n\nELW: Einsatzleitwagen (emergency command vehicle)\n\n\nGW-DUK: Gerätewagen-Daten-und-Kommunikation (vehicle for transporting robots and equipment)" ]

[ "TAGS\n#task_categories-text-classification #size_categories-100K<n<1M #language-German #region-us \n", "### Dataset Description\n\n\n* Curated by: DFKI, Talking Robots Group at MLT\n* Language(s) (NLP): German\n* License:\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields", "### Data Splits\n\n\nThis dataset contains 3525 dialogue turns in total. The data are split as follows: 2610 turns for training, 310 for development and 605 for test. The data represent a continuous conversation, i.e., the previous id refers to the previous turn in the dialogue.", "### Label Description and Statistics\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThe dataset is based on the recordings from the emergency response domain that use radio communication protocol. The goal of the conversation is to coordinate rescue operations in a robot-assisted disaster response.", "### Source Data\n\n\nThe data are based on human-human communication in robot-assisted disaster response. The dialogues are task-oriented, focused on collaborative execution of a mission by a team that uses robots to to explore some area, find hazardous materials, locate fires, damage or victims.", "#### Data Collection and Processing\n\n\nThe initial audio recordings were collected during the TRADR and ADRZ projects, transcribed and annotated by the Talking Robots Group, DFKI", "### Annotations\n\n\nThe annotations include dialogue intents relevant for communication in the emergency response domain: 'call', 'call\\_response', 'info\\_request', 'info\\_provide', 'confirm', 'disconfirm', 'order' and 'other'.\n\n\nNote the interpretation of the intent depends on the context. E.g., the following examples illustrate how very similar responses (\"Warten\", \"Wait\") are annotated differently depending on the previous turn:", "#### Annotation process\n\n\nThe recordings were manually transcribed and annotated with emergency response intents. There are 3525 dialogue turns in total with 6.3 tokens per turn on average.", "#### Who are the annotators?\n\n\nAll annotations were done by the research assistants of the Talking Robots Group, DFKI", "#### Personal and Sensitive Information\n\n\nThe dataset does not include any real names, addresses or other personal information. The recordings were done during training sessions with simulations of the emergency situation.\n\n\nBias, Risks, and Limitations\n----------------------------\n\n\nThe dataset covers only a subset of possible emergency situations, focusing mainly on fire, building collapse and chemical leakage. It does not address many other situations, e.g., traffic accidents, floods or explosions.\n\n\nPart of this dataset has been introduced in the following paper. However, the current version includes more annotated turns due to additional data collection.\n\n\nBibTeX:\n\n\nAPA:\n\n\nGlossary\n--------\n\n\nAbbrevations used for the speakers:\n\n\nUGV: Unmanned Ground Vehicle\n\n\nUAV: Unmanned Aerial Vehicle\n\n\nMC: Mission Commander\n\n\nTL: Team Leader\n\n\nRobLW: Robotikleitwagen (robotic lead vehicle)\n\n\nZF: Zugführer (fire brigade commander)\n\n\nGF: Gruppenführer (group leader)\n\n\nELW: Einsatzleitwagen (emergency command vehicle)\n\n\nGW-DUK: Gerätewagen-Daten-und-Kommunikation (vehicle for transporting robots and equipment)" ]

[ "region:us" ]

[]

[]

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

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

[ "task_categories:text-generation", "language:en", "license:cc", "code", "arxiv:2306.03091", "region:us" ]

[ "2306.03091" ]

[ "en" ]

[]

[ "TAGS\n#task_categories-text-generation #language-English #license-cc #code #arxiv-2306.03091 #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"lmind_nq_train10000_eval6489_v1_qa\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"lmind_nq_train10000_eval6489_v1_qa\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"lmind_nq_train10000_eval6489_v1_doc\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"lmind_nq_train10000_eval6489_v1_doc\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"lmind_nq_train10000_eval6489_v1_docidx\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"lmind_nq_train10000_eval6489_v1_docidx\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"lmind_nq_train10000_eval6489_v1_doc_qa\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"lmind_nq_train10000_eval6489_v1_doc_qa\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"lmind_nq_train10000_eval6489_v1_recite_qa\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"lmind_nq_train10000_eval6489_v1_recite_qa\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"lmind_nq_train10000_eval6489_v1_reciteonly_qa\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"lmind_nq_train10000_eval6489_v1_reciteonly_qa\"\n\nMore Information needed" ]

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

[]

[]

[ "# Dr. Nicefollows's Worry Free AIQA Chat Dataset", "## Overview\nThis dataset contains high-quality chat samples focused on AI-related (or other computer cience) questions and answers. It is designed following the LIMA: Less Is More for Alignment principle from MetaAI: emphasizing the importance of quality over quantity in training data. Despite its modest size, the dataset's quality ensures its effectiveness in training and fine-tuning conversational AI models.\nIn this version, each chat has one user query and assistant answer. In the next version, it will become a conversation of multiple rounds.", "## Dataset Format\nThe dataset is structured in the Vicuna 1.1 format, featuring one-round chats. This format is chosen for its compatibility with various conversational AI training paradigms and its efficiency in representing dialogues.", "## Volume\nThe dataset comprises a few thousand chat samples. Each sample has been carefully curated to ensure the highest quality, aligning with the LIMA principle.", "## Licensing\nOur dataset is worry-free regarding proprietary issues, as it is not automatically generated by a proprietary chatbot. This dataset is released under the Apache License 2.0. This license allows for broad freedom in usage and modification, provided that proper credit is given and changes are documented. For full license terms, please refer to the LICENSE file.", "## Use Case\nThis dataset is ideal for training conversational AI models, specifically those focused on AI topics. It can help in developing chatbots or virtual assistants capable of handling a wide range of travel-related queries with high accuracy. To use the dataset for finetuning a model with Axolotl, simply add the following to the .yml file:\ndatasets:\n - path: DrNicefellow/Quality_WorryFree_AIQA_Chat_Dataset-v1\n type: completion", "## Feeling Generous? \nEager to buy me a cup of 2$ coffe or iced tea? Sure, here is the link: URL Please add a note on which one you want me to drink?" ]

[ "TAGS\n#license-apache-2.0 #region-us \n", "# Dr. Nicefollows's Worry Free AIQA Chat Dataset", "## Overview\nThis dataset contains high-quality chat samples focused on AI-related (or other computer cience) questions and answers. It is designed following the LIMA: Less Is More for Alignment principle from MetaAI: emphasizing the importance of quality over quantity in training data. Despite its modest size, the dataset's quality ensures its effectiveness in training and fine-tuning conversational AI models.\nIn this version, each chat has one user query and assistant answer. In the next version, it will become a conversation of multiple rounds.", "## Dataset Format\nThe dataset is structured in the Vicuna 1.1 format, featuring one-round chats. This format is chosen for its compatibility with various conversational AI training paradigms and its efficiency in representing dialogues.", "## Volume\nThe dataset comprises a few thousand chat samples. Each sample has been carefully curated to ensure the highest quality, aligning with the LIMA principle.", "## Licensing\nOur dataset is worry-free regarding proprietary issues, as it is not automatically generated by a proprietary chatbot. This dataset is released under the Apache License 2.0. This license allows for broad freedom in usage and modification, provided that proper credit is given and changes are documented. For full license terms, please refer to the LICENSE file.", "## Use Case\nThis dataset is ideal for training conversational AI models, specifically those focused on AI topics. It can help in developing chatbots or virtual assistants capable of handling a wide range of travel-related queries with high accuracy. To use the dataset for finetuning a model with Axolotl, simply add the following to the .yml file:\ndatasets:\n - path: DrNicefellow/Quality_WorryFree_AIQA_Chat_Dataset-v1\n type: completion", "## Feeling Generous? \nEager to buy me a cup of 2$ coffe or iced tea? Sure, here is the link: URL Please add a note on which one you want me to drink?" ]

[ "language:en", "license:cc-by-sa-4.0", "arxiv:2205.09209", "region:us" ]

[ "2205.09209" ]

[ "en" ]

[ "# Usage\n\nWhen downloading, specify which files you want to download and set the split to 'train' (required by 'datasets').", "# Dataset Card for Holistic Bias\n\nThis dataset contains the source data of the Holistic Bias dataset as described by Smith et. al. (2022). The dataset contains noun phrases and sentences used to measure the likelihood bias of various models. The original dataset is released on GitHub.\n\nDisclaimer: this re-release of the dataset is not associated with the original authors. The dataset is released under the CC-BY-SA-4.0 license.", "## Dataset Details\n\nThe data is generated from the official generation script. The data is the v1.0 data from the original paper.\n\nFor details on the methodology, please refer to the original paper. This dataset is contributed to Hugging\nFace as part of the FairNLP 'fairscore' library.", "### Dataset Sources\n\n- Paper: URL\n\nBibTeX:" ]

[ "TAGS\n#language-English #license-cc-by-sa-4.0 #arxiv-2205.09209 #region-us \n", "# Usage\n\nWhen downloading, specify which files you want to download and set the split to 'train' (required by 'datasets').", "# Dataset Card for Holistic Bias\n\nThis dataset contains the source data of the Holistic Bias dataset as described by Smith et. al. (2022). The dataset contains noun phrases and sentences used to measure the likelihood bias of various models. The original dataset is released on GitHub.\n\nDisclaimer: this re-release of the dataset is not associated with the original authors. The dataset is released under the CC-BY-SA-4.0 license.", "## Dataset Details\n\nThe data is generated from the official generation script. The data is the v1.0 data from the original paper.\n\nFor details on the methodology, please refer to the original paper. This dataset is contributed to Hugging\nFace as part of the FairNLP 'fairscore' library.", "### Dataset Sources\n\n- Paper: URL\n\nBibTeX:" ]

[ "region:us" ]

[]

[]

[ "# openhermes\n\nThis is a cleansed version of teknium/openhermes", "## Usage" ]

[ "TAGS\n#region-us \n", "# openhermes\n\nThis is a cleansed version of teknium/openhermes", "## Usage" ]

[ "region:us" ]

[]

[]

[]

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

[ "size_categories:10K<n<100K", "language:ro", "license:apache-2.0", "dataset", "romanian", "stories", "region:us" ]

[]

[ "ro" ]

[]

[ "TAGS\n#size_categories-10K<n<100K #language-Romanian #license-apache-2.0 #dataset #romanian #stories #region-us \n" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

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[ "## Usage\n\nFor testing purpose, you can use the hosted dummy dataset ('dummy_data') as follows:\n\n\n\nFor using the COCO dataset (2017), you need to download it manually first:\n\n\nThen to load the dataset:" ]

[ "TAGS\n#region-us \n", "## Usage\n\nFor testing purpose, you can use the hosted dummy dataset ('dummy_data') as follows:\n\n\n\nFor using the COCO dataset (2017), you need to download it manually first:\n\n\nThen to load the dataset:" ]

[ "task_categories:table-question-answering", "size_categories:1K<n<10K", "language:en", "license:apache-2.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for Car Crash Dataset", "## Dataset Details\n\n- NetID: zm83\n- Repository: Access the Car Crash Dataset on Kaggle\n- License: Apache 2.0\n- Expected Update Frequency: Never (Last updated 22 days ago)", "## Dataset Description\n\nThe Car Crash Dataset provides a comprehensive collection of detailed records on traffic accidents from 2003 to 2015 in Monroe County. It offers insights into various factors influencing road accidents, including collision severity, weather conditions, road types, and other contributing factors. This dataset is crucial for analyses aimed at improving road safety and implementing preventive measures.", "### Collaborators\n\n- Jackson Divakar R (Owner)", "### Provenance\n\nData for the Car Crash Dataset come from:\n- Official traffic incident reports\n- Law enforcement records\n- Insurance claims\n\nThese sources ensure a comprehensive and accurate representation of the factors contributing to road accidents.", "### Collection Methodology\n\nA consistent methodology was applied during the compilation of data from various sources to ensure the accuracy and reliability of the dataset.", "## Dataset Summary\n\nThe dataset is a meticulous aggregation of data points that delve into the factors influencing road accidents, documenting various aspects of car crashes. It is designed to facilitate the development of predictive models, safety analytics, and enhanced traffic management systems.", "## Dataset Uses\n\nThe Car Crash Dataset can be used for a variety of applications, including traffic safety analysis, urban planning, machine learning, policy development, public health, and insurance analysis. It supports in-depth research into the causes and consequences of road traffic accidents.", "### Possible Topics for Analysis\n\nThe dataset supports a range of analysis topics, such as:\n- The effect of weather conditions on road accidents\n- Correlations between traffic volume and accident frequency\n- The effectiveness of road safety laws and regulations\n- Geographic analysis of accident hotspots\n- Human factors versus environmental factors in accidents\n- Vehicle type and collision severity", "### Curation Motivation\n\nThe dataset was curated to provide a resource for stakeholders to understand and mitigate the factors behind road accidents, aiming to reduce their frequency and severity, and to support the development of road safety strategies. Researchers and analysts are invited to use this dataset to explore various topics and generate actionable insights for community safety and well-being.", "### Data Instances\n\nA typical entry in the dataset might look like the following (example in JSON format):\n\n\n\nAdditional fields in the dataset may include but are not limited to:" ]

[ "TAGS\n#task_categories-table-question-answering #size_categories-1K<n<10K #language-English #license-apache-2.0 #region-us \n", "# Dataset Card for Car Crash Dataset", "## Dataset Details\n\n- NetID: zm83\n- Repository: Access the Car Crash Dataset on Kaggle\n- License: Apache 2.0\n- Expected Update Frequency: Never (Last updated 22 days ago)", "## Dataset Description\n\nThe Car Crash Dataset provides a comprehensive collection of detailed records on traffic accidents from 2003 to 2015 in Monroe County. It offers insights into various factors influencing road accidents, including collision severity, weather conditions, road types, and other contributing factors. This dataset is crucial for analyses aimed at improving road safety and implementing preventive measures.", "### Collaborators\n\n- Jackson Divakar R (Owner)", "### Provenance\n\nData for the Car Crash Dataset come from:\n- Official traffic incident reports\n- Law enforcement records\n- Insurance claims\n\nThese sources ensure a comprehensive and accurate representation of the factors contributing to road accidents.", "### Collection Methodology\n\nA consistent methodology was applied during the compilation of data from various sources to ensure the accuracy and reliability of the dataset.", "## Dataset Summary\n\nThe dataset is a meticulous aggregation of data points that delve into the factors influencing road accidents, documenting various aspects of car crashes. It is designed to facilitate the development of predictive models, safety analytics, and enhanced traffic management systems.", "## Dataset Uses\n\nThe Car Crash Dataset can be used for a variety of applications, including traffic safety analysis, urban planning, machine learning, policy development, public health, and insurance analysis. It supports in-depth research into the causes and consequences of road traffic accidents.", "### Possible Topics for Analysis\n\nThe dataset supports a range of analysis topics, such as:\n- The effect of weather conditions on road accidents\n- Correlations between traffic volume and accident frequency\n- The effectiveness of road safety laws and regulations\n- Geographic analysis of accident hotspots\n- Human factors versus environmental factors in accidents\n- Vehicle type and collision severity", "### Curation Motivation\n\nThe dataset was curated to provide a resource for stakeholders to understand and mitigate the factors behind road accidents, aiming to reduce their frequency and severity, and to support the development of road safety strategies. Researchers and analysts are invited to use this dataset to explore various topics and generate actionable insights for community safety and well-being.", "### Data Instances\n\nA typical entry in the dataset might look like the following (example in JSON format):\n\n\n\nAdditional fields in the dataset may include but are not limited to:" ]

[ "region:us" ]

[]

[]

[]

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

[ "license:apache-2.0", "code", "art", "music", "legal", "finance", "biology", "chemistry", "SlimOrca", "ChatML", "region:us" ]

[]

[]

[]

[ "TAGS\n#license-apache-2.0 #code #art #music #legal #finance #biology #chemistry #SlimOrca #ChatML #region-us \n" ]

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

[]

[ "hu" ]

[ "# Hungarian llm testing\n\nThis is a really simple data-set to test fine-tuning a language model on Hungarian text." ]

[ "TAGS\n#language-Hungarian #license-mit #region-us \n", "# Hungarian llm testing\n\nThis is a really simple data-set to test fine-tuning a language model on Hungarian text." ]

[ "region:us" ]

[]

[]

[]

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

[ "size_categories:10K<n<100K", "language:en", "conversational", "image-to-text", "vision", "convAI", "region:us" ]

[]

[ "en" ]

[ "# WebLINX: Real-World Website Navigation with Multi-Turn Dialogue\n\nWARNING: This is not the main WebLINX data card! You might want to use the main WebLINX data card instead: \n\n> WebLINX: Real-World Website Navigation with Multi-Turn Dialogue" ]

[ "TAGS\n#size_categories-10K<n<100K #language-English #conversational #image-to-text #vision #convAI #region-us \n", "# WebLINX: Real-World Website Navigation with Multi-Turn Dialogue\n\nWARNING: This is not the main WebLINX data card! You might want to use the main WebLINX data card instead: \n\n> WebLINX: Real-World Website Navigation with Multi-Turn Dialogue" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"VNTL-v3-1k\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"VNTL-v3-1k\"\n\nMore Information needed" ]

[ "task_categories:text-classification", "language:en", "license:apache-2.0", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-text-classification #language-English #license-apache-2.0 #region-us \n" ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"formal-logic-simple-order-new-objects-paired-bigger-5000\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"formal-logic-simple-order-new-objects-paired-bigger-5000\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"EGY_BCD\"\n\nThis is an unofficial repo for the change detection dataset EGY-BCD. The dataset was randomly (seed=8888) split into subsets of train/val/test with a ratio of 6:2:2.\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"EGY_BCD\"\n\nThis is an unofficial repo for the change detection dataset EGY-BCD. The dataset was randomly (seed=8888) split into subsets of train/val/test with a ratio of 6:2:2.\n\nMore Information needed" ]

[ "region:us" ]

[]

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

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

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

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

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

[ "source_datasets:https://github.com/linouk23/NBA-Player-Movements", "language:en", "basketball", "nba", "sports", "tracking", "region:us" ]

[]

[ "en" ]

[ "# 2015-2016 Raw Tracking Data from SportVU\n\nThe modern era of basketball is characterized by the use of data to analyze performance and make decisions both on and off the court.", "## Dataset Details", "### Dataset Descriptions\n\nTracking data is the finest level of basketball data, whereas play-by-play and box score data are also used. This dataset gives raw SportVU tracking data from each game of the 2015-2016 NBA season. This was the last season with publically available tracking data. This data has the coordinates of all players at all moments of the game, for each game in the season. There are also identifiers for player id and team id, so that further analysis can be performed.\n- Collected By: SportVU\n- Shared By: Kostya Linou, Dzmitryi Linou, Martijn De Boer", "### Dataset Source\n\n- Repository: URL", "## Uses\n\nThis dataset has many potential uses. Primarily, visualization of plays, as illustrated in the initial repository is possible, creating a comprehensive view for analyzing actions on court. Beyond that, models could be trained to recognize certain play types or actions, as illustrated in previous papers (see Stephanos et al., 2022). Even further, video data could be connected to each moment of collection to create a model where video frames are mapped to tracked coordinates, increasing the accessibility of tracking data as only publically available video footage is necessary. \n\n- Stephanos et al.: URL", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale\n\nThe reason for uploading this data to huggingface, is that in its current .7z form, the data is less accessible, and requires unzipping many files and then combining to access. Also, more sources for easily accessible tracking data, even if also available elsewhere, increase the chances of long-term preservation and accessibility for future NBA fans.", "### Source Data\n\nFrom creator StatsPerform, \"the SportVU camera system is installed in basketball arenas to track the real-time positions of players and the ball at 25 times per second.\" These methods were used to capture the data in this dataset.", "## Bias, Risks, and Limitations\n\nSince this data is not up-to-date, and the tracking data for the last eight seasons is private and unreleased, the continued spread of this specific data may not be representative of the current state of NBA tracking data. Thus, users that learn how to manipulate it may not be adequately prepared for work in basketball organizations. Further, analyses performed on the dataset may not be reflective of the current state of professional basketball. However, since this was the last iteration of publicly available tracking data, I believe increasing its availability is important.", "## Dataset Card Author\n\nDonald Cayton; dcayton9@URL" ]

[ "TAGS\n#source_datasets-https-//github.com/linouk23/NBA-Player-Movements #language-English #basketball #nba #sports #tracking #region-us \n", "# 2015-2016 Raw Tracking Data from SportVU\n\nThe modern era of basketball is characterized by the use of data to analyze performance and make decisions both on and off the court.", "## Dataset Details", "### Dataset Descriptions\n\nTracking data is the finest level of basketball data, whereas play-by-play and box score data are also used. This dataset gives raw SportVU tracking data from each game of the 2015-2016 NBA season. This was the last season with publically available tracking data. This data has the coordinates of all players at all moments of the game, for each game in the season. There are also identifiers for player id and team id, so that further analysis can be performed.\n- Collected By: SportVU\n- Shared By: Kostya Linou, Dzmitryi Linou, Martijn De Boer", "### Dataset Source\n\n- Repository: URL", "## Uses\n\nThis dataset has many potential uses. Primarily, visualization of plays, as illustrated in the initial repository is possible, creating a comprehensive view for analyzing actions on court. Beyond that, models could be trained to recognize certain play types or actions, as illustrated in previous papers (see Stephanos et al., 2022). Even further, video data could be connected to each moment of collection to create a model where video frames are mapped to tracked coordinates, increasing the accessibility of tracking data as only publically available video footage is necessary. \n\n- Stephanos et al.: URL", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale\n\nThe reason for uploading this data to huggingface, is that in its current .7z form, the data is less accessible, and requires unzipping many files and then combining to access. Also, more sources for easily accessible tracking data, even if also available elsewhere, increase the chances of long-term preservation and accessibility for future NBA fans.", "### Source Data\n\nFrom creator StatsPerform, \"the SportVU camera system is installed in basketball arenas to track the real-time positions of players and the ball at 25 times per second.\" These methods were used to capture the data in this dataset.", "## Bias, Risks, and Limitations\n\nSince this data is not up-to-date, and the tracking data for the last eight seasons is private and unreleased, the continued spread of this specific data may not be representative of the current state of NBA tracking data. Thus, users that learn how to manipulate it may not be adequately prepared for work in basketball organizations. Further, analyses performed on the dataset may not be reflective of the current state of professional basketball. However, since this was the last iteration of publicly available tracking data, I believe increasing its availability is important.", "## Dataset Card Author\n\nDonald Cayton; dcayton9@URL" ]

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

[]

[]

[ "# danbooru tag txt\n \nThis is for non-AI developers. \nIt's a simple list of tags in a human-friendly format. \n \nAI developers should use other datasets, including the output from this dataset.", "## dataset \n \nExtracted from the following data set \nURL" ]

[ "TAGS\n#license-mit #region-us \n", "# danbooru tag txt\n \nThis is for non-AI developers. \nIt's a simple list of tags in a human-friendly format. \n \nAI developers should use other datasets, including the output from this dataset.", "## dataset \n \nExtracted from the following data set \nURL" ]

[ "source_datasets:hieunguyenminh/roleplay", "language:en", "region:us" ]

[]

[ "en" ]

[ "# Changes\n1. Reformatted into ShareGPT.\n2. Removed the few duplicate characters.\n3. Removed samples with no messages.\n4. Any messages where the character is talking in third person is fixed to be first person, as it should be." ]

[ "TAGS\n#source_datasets-hieunguyenminh/roleplay #language-English #region-us \n", "# Changes\n1. Reformatted into ShareGPT.\n2. Removed the few duplicate characters.\n3. Removed samples with no messages.\n4. Any messages where the character is talking in third person is fixed to be first person, as it should be." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"EleutherAI_pythia-1b-deduped__dpo_on_policy__tldr\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"EleutherAI_pythia-1b-deduped__dpo_on_policy__tldr\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of TomGrc/FN-OpenLLM_2x72B_MoE\n\n\n\nDataset automatically created during the evaluation run of model TomGrc/FN-OpenLLM_2x72B_MoE on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-06T01:52:10.589662(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of TomGrc/FN-OpenLLM_2x72B_MoE\n\n\n\nDataset automatically created during the evaluation run of model TomGrc/FN-OpenLLM_2x72B_MoE on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-06T01:52:10.589662(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

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

[]

[]

[ "# Load Dataset" ]

[ "TAGS\n#license-apache-2.0 #region-us \n", "# Load Dataset" ]

[ "region:us" ]

[]

[]

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

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

[ "task_categories:robotics", "size_categories:100K<n<1M", "language:en", "license:cc0-1.0", "drone", "ecology", "zebra", "Grevy's zebra", "conservation", "UAV", "doi:10.57967/hf/1745", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for KABR Telemetry: In-Situ Dataset for Kenyan Animal Behavior Recognition from Drone Videos", "## Dataset Details", "### Dataset Description\n\nThis dataset contains the drone telemetry data associated with the KABR dataset. The KABR dataset contains annotated video behavior of zebras and giraffes at the Mpala Research Centre. This telemetry dataset contains information about the status drone during the missions, including location and altitude, along with the bounding box dimensions of the wildlife in the frame and behavior annotation information. Please see the \"kabr_telemetry_metadata.csv\" for more details.\n\n- Paper: Integrating Biological Data into Autonomous Remote Sensing Systems for In Situ Imageomics: A Case Study for Kenyan Animal Behavior Sensing with Unmanned Aerial Vehicles (UAVs).pdf)\n- Point of Contact: Jenna Kline, kline.377@URL \n\n- Curated by: Jenna Kline, Maksim Kholiavchenko, Otto Brookes, Tanya Berger-Wolf, Charles V. Stewart, and Christopher Stewart\n- Funded by: Imageomics\n- Shared by: Jenna Kline", "## Uses\n\nThis dataset is intended to be used to provide guidance on executing wildlife behavior collection missions with drones, which can be conducted by drone pilots manually, or integrated into an autonomous navigation framework.", "## Dataset Creation", "### Curation Rationale\n\nThis dataset was curated to provide additional context to the KABR dataset, and provide spatial information which can be used to develop autonomous navigation algorithms for wildlife data collection.", "#### Data Collection and Processing\n\nThis data was collected at the Mpala Research Centre in Laikipia, Kenya in January 2023. A DJI Mavic Air 2 drone was used to collect the data, and AirData was used to process DJI telemetry files.", "### Annotations\n\nPlease refer to the KABR) dataset and associated paper for details on the annotation process.", "## Additional Information", "### Authors\n\n* Jenna Kline (The Ohio State University)\n* Maksim Kholiavchenko (Rensselaer Polytechnic Institute) - ORCID: 0000-0001-6757-1957\n* Otto Brookes (University of Bristol)\n* Tanya Berger-Wolf (The Ohio State University) - ORCID: 0000-0001-7610-1412\n* Charles V. Stewart (Rensselaer Polytechnic Institute)\n* Christopher Stewart (The Ohio State University)", "### Licensing Information\n\nThis dataset is dedicated to the public domain for the benefit of scientific pursuits. We ask that you cite the dataset and journal paper using the below citations if you make use of it in your research.", "#### Dataset", "#### Paper", "### Contributions\n\nThe Imageomics Institute is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under Award #2118240 (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation." ]

[ "TAGS\n#task_categories-robotics #size_categories-100K<n<1M #language-English #license-cc0-1.0 #drone #ecology #zebra #Grevy's zebra #conservation #UAV #doi-10.57967/hf/1745 #region-us \n", "# Dataset Card for KABR Telemetry: In-Situ Dataset for Kenyan Animal Behavior Recognition from Drone Videos", "## Dataset Details", "### Dataset Description\n\nThis dataset contains the drone telemetry data associated with the KABR dataset. The KABR dataset contains annotated video behavior of zebras and giraffes at the Mpala Research Centre. This telemetry dataset contains information about the status drone during the missions, including location and altitude, along with the bounding box dimensions of the wildlife in the frame and behavior annotation information. Please see the \"kabr_telemetry_metadata.csv\" for more details.\n\n- Paper: Integrating Biological Data into Autonomous Remote Sensing Systems for In Situ Imageomics: A Case Study for Kenyan Animal Behavior Sensing with Unmanned Aerial Vehicles (UAVs).pdf)\n- Point of Contact: Jenna Kline, kline.377@URL \n\n- Curated by: Jenna Kline, Maksim Kholiavchenko, Otto Brookes, Tanya Berger-Wolf, Charles V. Stewart, and Christopher Stewart\n- Funded by: Imageomics\n- Shared by: Jenna Kline", "## Uses\n\nThis dataset is intended to be used to provide guidance on executing wildlife behavior collection missions with drones, which can be conducted by drone pilots manually, or integrated into an autonomous navigation framework.", "## Dataset Creation", "### Curation Rationale\n\nThis dataset was curated to provide additional context to the KABR dataset, and provide spatial information which can be used to develop autonomous navigation algorithms for wildlife data collection.", "#### Data Collection and Processing\n\nThis data was collected at the Mpala Research Centre in Laikipia, Kenya in January 2023. A DJI Mavic Air 2 drone was used to collect the data, and AirData was used to process DJI telemetry files.", "### Annotations\n\nPlease refer to the KABR) dataset and associated paper for details on the annotation process.", "## Additional Information", "### Authors\n\n* Jenna Kline (The Ohio State University)\n* Maksim Kholiavchenko (Rensselaer Polytechnic Institute) - ORCID: 0000-0001-6757-1957\n* Otto Brookes (University of Bristol)\n* Tanya Berger-Wolf (The Ohio State University) - ORCID: 0000-0001-7610-1412\n* Charles V. Stewart (Rensselaer Polytechnic Institute)\n* Christopher Stewart (The Ohio State University)", "### Licensing Information\n\nThis dataset is dedicated to the public domain for the benefit of scientific pursuits. We ask that you cite the dataset and journal paper using the below citations if you make use of it in your research.", "#### Dataset", "#### Paper", "### Contributions\n\nThe Imageomics Institute is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under Award #2118240 (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation." ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"lmind_nq_train10000_eval6489_v1_ic_qa\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"lmind_nq_train10000_eval6489_v1_ic_qa\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"metatree_waveform_5000\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"metatree_waveform_5000\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"metatree_Hyperplane_10_1E_3\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"metatree_Hyperplane_10_1E_3\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"metatree_Hyperplane_10_1E_4\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"metatree_Hyperplane_10_1E_4\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"metatree_RandomRBF_0_0\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"metatree_RandomRBF_0_0\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"metatree_RandomRBF_10_1E_3\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"metatree_RandomRBF_10_1E_3\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"metatree_RandomRBF_50_1E_3\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"metatree_RandomRBF_50_1E_3\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"metatree_RandomRBF_10_1E_4\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"metatree_RandomRBF_10_1E_4\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"metatree_RandomRBF_50_1E_4\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"metatree_RandomRBF_50_1E_4\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"metatree_SEA_50_\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"metatree_SEA_50_\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"metatree_SEA_50000_\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"metatree_SEA_50000_\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"metatree_BNG_bridges_version1_\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"metatree_BNG_bridges_version1_\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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