Datasets:

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ftopal

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huggingface-datasets

like 0

[ "region:us" ]

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[ "# Dataset Card for \"Calc-ape210k_selftrain_experiment\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"Calc-ape210k_selftrain_experiment\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of abhishekchohan/mistral-7B-forest-merge-v0.1\n\n\n\nDataset automatically created during the evaluation run of model abhishekchohan/mistral-7B-forest-merge-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 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 2024-01-22T17:22:14.145358(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 abhishekchohan/mistral-7B-forest-merge-v0.1\n\n\n\nDataset automatically created during the evaluation run of model abhishekchohan/mistral-7B-forest-merge-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 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 2024-01-22T17:22:14.145358(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" ]

[ "task_categories:text-generation", "language:fr", "ocr", "region:us" ]

[]

[ "fr" ]

[ "## Content\nAs of January 2024, the collection contains 289,000 books (16,407,292,362 words) from the French National Library (Gallica). Each parquet file has the full text of 2,000 books selected at random and few core metadatas (Gallica id, title, author, word counts…). The metadata can be easily expanded thanks to the BNF API.\n\nThis initial agregation was made possible thanks to the open data program of the French National Library and the consolidation of public domain status for cultural heritage works in the EU with the 2019 Copyright Directive (art. 14)\n\nThe composition of the dataset adheres to the criteria for public domain works in the EU and, consequently, all Berne-countries for EU authors: any publication whose author is dead for more than 70 years.", "## Uses\nThe primary use of the collection is for cultural analytics project on a wide scale. It is already in use by the Gallicagram project, an open and significantly enhanced version of ngram viewer.\n\nThe collection also aims to expand the availability of open works for the training of Large Language Models. The text can be used for model training and republished without restriction for reproducibility purposes.", "## License\nThe entire collection is in the public domain everywhere. This means that the patrimonial rights of each individual or collective rightholders have expired.\n\nThe French National Library claims additional rights in its terms of use and restricts commercial use: \"La réutilisation commerciale de ces contenus est payante et fait l'objet d'une licence. Est entendue par réutilisation commerciale la revente de contenus sous forme de produits élaborés ou de fourniture de service ou toute autre réutilisation des contenus générant directement des revenus.\"\n\nThere has been a debate for years in Europe over the definition of public domain and the possibility to restrict its use. Since 2019, the EU Copyright Directive states that \"Member States shall provide that, when the term of protection of a work of visual art has expired, any material resulting from an act of reproduction of that work is not subject to copyright or related rights, unless the material resulting from that act of reproduction is original in the sense that it is the author's own intellectual creation.\" (art. 14)", "## Future developments\nThis dataset is not a one time work but will continue to evolve significantly on three directions:\n* Correction of computer generated errors in the text. All the texts have been transcribed automatically through the use of Optical Character Recognition (OCR) software. The original files have been digitized over a long time period (since the mid-2000s) and some documents should be. Future versions will strive either to re-OCRize the original text or use experimental LLM models for partial OCR correction.\n* Enhancement of the structure/editorial presentation of the original text. Some parts of the original documents are likely unwanted for large scale analysis or model training (header, page count…). Additionally, some advanced document structures like tables or multi-column layout are unlikely to be well formatted. Major enhancements could be experted through applying new SOTA layout recognition models (like COLAF) on the original PDF files.\n* Expansion of the collection to other cultural heritage holdings, especially coming from Hathi Trust, Internet Archive and Google Books." ]

[ "TAGS\n#task_categories-text-generation #language-French #ocr #region-us \n", "## Content\nAs of January 2024, the collection contains 289,000 books (16,407,292,362 words) from the French National Library (Gallica). Each parquet file has the full text of 2,000 books selected at random and few core metadatas (Gallica id, title, author, word counts…). The metadata can be easily expanded thanks to the BNF API.\n\nThis initial agregation was made possible thanks to the open data program of the French National Library and the consolidation of public domain status for cultural heritage works in the EU with the 2019 Copyright Directive (art. 14)\n\nThe composition of the dataset adheres to the criteria for public domain works in the EU and, consequently, all Berne-countries for EU authors: any publication whose author is dead for more than 70 years.", "## Uses\nThe primary use of the collection is for cultural analytics project on a wide scale. It is already in use by the Gallicagram project, an open and significantly enhanced version of ngram viewer.\n\nThe collection also aims to expand the availability of open works for the training of Large Language Models. The text can be used for model training and republished without restriction for reproducibility purposes.", "## License\nThe entire collection is in the public domain everywhere. This means that the patrimonial rights of each individual or collective rightholders have expired.\n\nThe French National Library claims additional rights in its terms of use and restricts commercial use: \"La réutilisation commerciale de ces contenus est payante et fait l'objet d'une licence. Est entendue par réutilisation commerciale la revente de contenus sous forme de produits élaborés ou de fourniture de service ou toute autre réutilisation des contenus générant directement des revenus.\"\n\nThere has been a debate for years in Europe over the definition of public domain and the possibility to restrict its use. Since 2019, the EU Copyright Directive states that \"Member States shall provide that, when the term of protection of a work of visual art has expired, any material resulting from an act of reproduction of that work is not subject to copyright or related rights, unless the material resulting from that act of reproduction is original in the sense that it is the author's own intellectual creation.\" (art. 14)", "## Future developments\nThis dataset is not a one time work but will continue to evolve significantly on three directions:\n* Correction of computer generated errors in the text. All the texts have been transcribed automatically through the use of Optical Character Recognition (OCR) software. The original files have been digitized over a long time period (since the mid-2000s) and some documents should be. Future versions will strive either to re-OCRize the original text or use experimental LLM models for partial OCR correction.\n* Enhancement of the structure/editorial presentation of the original text. Some parts of the original documents are likely unwanted for large scale analysis or model training (header, page count…). Additionally, some advanced document structures like tables or multi-column layout are unlikely to be well formatted. Major enhancements could be experted through applying new SOTA layout recognition models (like COLAF) on the original PDF files.\n* Expansion of the collection to other cultural heritage holdings, especially coming from Hathi Trust, Internet Archive and Google Books." ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of silvercoder45/Mistral-7b-instruct-v0.2-summ-sft-dpo-e3\n\n\n\nDataset automatically created during the evaluation run of model silvercoder45/Mistral-7b-instruct-v0.2-summ-sft-dpo-e3 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 2024-01-22T17:40:11.975482(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 silvercoder45/Mistral-7b-instruct-v0.2-summ-sft-dpo-e3\n\n\n\nDataset automatically created during the evaluation run of model silvercoder45/Mistral-7b-instruct-v0.2-summ-sft-dpo-e3 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 2024-01-22T17:40:11.975482(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 robinsmits/Mistral-Instruct-7B-v0.2-ChatAlpaca-DPO2\n\n\n\nDataset automatically created during the evaluation run of model robinsmits/Mistral-Instruct-7B-v0.2-ChatAlpaca-DPO2 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 2024-01-22T17:51:45.656296(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 robinsmits/Mistral-Instruct-7B-v0.2-ChatAlpaca-DPO2\n\n\n\nDataset automatically created during the evaluation run of model robinsmits/Mistral-Instruct-7B-v0.2-ChatAlpaca-DPO2 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 2024-01-22T17:51:45.656296(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 silvercoder67/Mistral-7b-instruct-v0.2-summ-sft-dpo-e1\n\n\n\nDataset automatically created during the evaluation run of model silvercoder67/Mistral-7b-instruct-v0.2-summ-sft-dpo-e1 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 2024-01-23T04:42:00.925654(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 silvercoder67/Mistral-7b-instruct-v0.2-summ-sft-dpo-e1\n\n\n\nDataset automatically created during the evaluation run of model silvercoder67/Mistral-7b-instruct-v0.2-summ-sft-dpo-e1 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 2024-01-23T04:42:00.925654(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 ibivibiv/athene-noctua-13b\n\n\n\nDataset automatically created during the evaluation run of model ibivibiv/athene-noctua-13b 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 2024-01-22T17:56:03.589917(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 ibivibiv/athene-noctua-13b\n\n\n\nDataset automatically created during the evaluation run of model ibivibiv/athene-noctua-13b 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 2024-01-22T17:56:03.589917(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 AA051612/B0122\n\n\n\nDataset automatically created during the evaluation run of model AA051612/B0122 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 2024-01-22T18:14:13.351822(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 AA051612/B0122\n\n\n\nDataset automatically created during the evaluation run of model AA051612/B0122 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 2024-01-22T18:14:13.351822(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 silvercoder67/Mistral-7b-instruct-v0.2-summ-sft-e2m\n\n\n\nDataset automatically created during the evaluation run of model silvercoder67/Mistral-7b-instruct-v0.2-summ-sft-e2m 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 2024-01-22T18:17:22.033616(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 silvercoder67/Mistral-7b-instruct-v0.2-summ-sft-e2m\n\n\n\nDataset automatically created during the evaluation run of model silvercoder67/Mistral-7b-instruct-v0.2-summ-sft-e2m 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 2024-01-22T18:17:22.033616(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" ]

[]

[]

[ "# sample_tables" ]

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

[ "license:odc-by", "arxiv:2303.01432", "region:us" ]

[ "2303.01432" ]

[]

[ "# wice\n\nWiCE: Real-World Entailment for Claims in Wikipedia", "## Dataset Details", "### Dataset Description\n\nWiCE is a fine-grained textual entailment dataset built on natural claim and evidence pairs extracted from Wikipedia. Given a sentence in Wikipedia and the corresponding article(s) it cites, we annotate the entailment label, a list of sentences in the cited article(s) that support the claim sentence, and tokens in the claim that are unsupported by the article(s).\n\nThis is the 'entailment_retrieval' subset that includes the WiCE dataset for entailment and retrieval task. 'claim' includes data with the original claims and 'subclaim' includes data with the decomposed claims (finegrained annotation by using Claim-Split).", "### Dataset Sources\n\n\n\n- Repository: URL\n- Paper: Kamoi, Goyal, Rodriguez, and Durett(2023) WiCE: Real-World Entailment for Claims in Wikipedia\n." ]

[ "TAGS\n#license-odc-by #arxiv-2303.01432 #region-us \n", "# wice\n\nWiCE: Real-World Entailment for Claims in Wikipedia", "## Dataset Details", "### Dataset Description\n\nWiCE is a fine-grained textual entailment dataset built on natural claim and evidence pairs extracted from Wikipedia. Given a sentence in Wikipedia and the corresponding article(s) it cites, we annotate the entailment label, a list of sentences in the cited article(s) that support the claim sentence, and tokens in the claim that are unsupported by the article(s).\n\nThis is the 'entailment_retrieval' subset that includes the WiCE dataset for entailment and retrieval task. 'claim' includes data with the original claims and 'subclaim' includes data with the decomposed claims (finegrained annotation by using Claim-Split).", "### Dataset Sources\n\n\n\n- Repository: URL\n- Paper: Kamoi, Goyal, Rodriguez, and Durett(2023) WiCE: Real-World Entailment for Claims in Wikipedia\n." ]

[ "arxiv:2304.06364", "region:us" ]

[ "2304.06364" ]

[]

[ "# Dataset Card for \"agieval-math\"\n\n\nDataset taken from URL and processed as in that repo, following dmayhem93/agieval-* datasets on the HF hub.\n\nThis dataset contains the contents of the MATH subtask of AGIEval, as accessed in URL .\n\n\nCitation:\n\n\n@misc\n\n{zhong2023agieval,\ntitle={AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models},\nauthor={Wanjun Zhong and Ruixiang Cui and Yiduo Guo and Yaobo Liang and Shuai Lu and Yanlin Wang and Amin Saied and Weizhu Chen and Nan Duan},\nyear={2023},\neprint={2304.06364},\narchivePrefix={arXiv},\nprimaryClass={cs.CL}\n}" ]

[ "TAGS\n#arxiv-2304.06364 #region-us \n", "# Dataset Card for \"agieval-math\"\n\n\nDataset taken from URL and processed as in that repo, following dmayhem93/agieval-* datasets on the HF hub.\n\nThis dataset contains the contents of the MATH subtask of AGIEval, as accessed in URL .\n\n\nCitation:\n\n\n@misc\n\n{zhong2023agieval,\ntitle={AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models},\nauthor={Wanjun Zhong and Ruixiang Cui and Yiduo Guo and Yaobo Liang and Shuai Lu and Yanlin Wang and Amin Saied and Weizhu Chen and Nan Duan},\nyear={2023},\neprint={2304.06364},\narchivePrefix={arXiv},\nprimaryClass={cs.CL}\n}" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"uf_unsafe_v2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"uf_unsafe_v2\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"hub-report-dataset\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"cai-conversation-dev1705951688\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"cai-conversation-dev1705951688\"\n\nMore Information needed" ]

[ "region:us" ]

[]

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[ "# Dataset Card for \"hub-report-classlabels\"\n\nMore Information needed" ]

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

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

[]

[]

[ "# Dataset Card for \"cai-conversation-dev1705950597\"\n\nMore Information needed" ]

[ "TAGS\n#license-apache-2.0 #region-us \n", "# Dataset Card for \"cai-conversation-dev1705950597\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of silvercoder67/Mistral-7b-instruct-v0.2-summ-sft-dpo-e2\n\n\n\nDataset automatically created during the evaluation run of model silvercoder67/Mistral-7b-instruct-v0.2-summ-sft-dpo-e2 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 2024-01-23T04:48:23.031751(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 silvercoder67/Mistral-7b-instruct-v0.2-summ-sft-dpo-e2\n\n\n\nDataset automatically created during the evaluation run of model silvercoder67/Mistral-7b-instruct-v0.2-summ-sft-dpo-e2 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 2024-01-23T04:48:23.031751(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 silvercoder67/Mistral-7b-instruct-v0.2-summ-sft-dpo-e3\n\n\n\nDataset automatically created during the evaluation run of model silvercoder67/Mistral-7b-instruct-v0.2-summ-sft-dpo-e3 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 2024-01-22T20:28:36.623759(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 silvercoder67/Mistral-7b-instruct-v0.2-summ-sft-dpo-e3\n\n\n\nDataset automatically created during the evaluation run of model silvercoder67/Mistral-7b-instruct-v0.2-summ-sft-dpo-e3 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 2024-01-22T20:28:36.623759(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 freeCS-dot-org/ThetaWave-7B-v0.1\n\n\n\nDataset automatically created during the evaluation run of model freeCS-dot-org/ThetaWave-7B-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 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 2024-01-22T20:31:20.560284(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 freeCS-dot-org/ThetaWave-7B-v0.1\n\n\n\nDataset automatically created during the evaluation run of model freeCS-dot-org/ThetaWave-7B-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 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 2024-01-22T20:31:20.560284(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" ]

[ "task_categories:question-answering", "size_categories:n<1K", "language:en", "license:mit", "wikipedia", "synthetic data", "region:us" ]

[]

[ "en" ]

[ "# Methodology:\n\n1) Retrieve random article content from the HuggingFace Wikipedia English dataset.\n2) Construct a Chain of Thought (CoT) to generate a Multiple Choice Question (MCQ).\n3) Utilize a Large Language Model (LLM) to score the results then filter it.\n\nAll these steps are prompted in the 'URL' file located in the specified code folder.\n\nCode: URL\n\nFeel free to reach me on Linkedin (URL or Twitter (URL" ]

[ "TAGS\n#task_categories-question-answering #size_categories-n<1K #language-English #license-mit #wikipedia #synthetic data #region-us \n", "# Methodology:\n\n1) Retrieve random article content from the HuggingFace Wikipedia English dataset.\n2) Construct a Chain of Thought (CoT) to generate a Multiple Choice Question (MCQ).\n3) Utilize a Large Language Model (LLM) to score the results then filter it.\n\nAll these steps are prompted in the 'URL' file located in the specified code folder.\n\nCode: URL\n\nFeel free to reach me on Linkedin (URL or Twitter (URL" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of RatanRohith/NeuralPizza-7B-Merge-Slerp\n\n\n\nDataset automatically created during the evaluation run of model RatanRohith/NeuralPizza-7B-Merge-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 2024-01-22T21:47:50.776941(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 RatanRohith/NeuralPizza-7B-Merge-Slerp\n\n\n\nDataset automatically created during the evaluation run of model RatanRohith/NeuralPizza-7B-Merge-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 2024-01-22T21:47:50.776941(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" ]

[ "comma", "openpilot", "region:us" ]

[]

[]

[ "# Comma Car Segments\n\npublic dataset of sanetized car segments for each car that openpilot supports, only containing can data, pandaStates, and carParams. This allows you to perform validation across a large database when doing car ports and making changes for particular platforms." ]

[ "TAGS\n#comma #openpilot #region-us \n", "# Comma Car Segments\n\npublic dataset of sanetized car segments for each car that openpilot supports, only containing can data, pandaStates, and carParams. This allows you to perform validation across a large database when doing car ports and making changes for particular platforms." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of ewqr2130/7B_ppo_phiRM_2GPU_3e-7step_4000\n\n\n\nDataset automatically created during the evaluation run of model ewqr2130/7B_ppo_phiRM_2GPU_3e-7step_4000 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 2024-01-22T22:10:51.590429(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 ewqr2130/7B_ppo_phiRM_2GPU_3e-7step_4000\n\n\n\nDataset automatically created during the evaluation run of model ewqr2130/7B_ppo_phiRM_2GPU_3e-7step_4000 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 2024-01-22T22:10:51.590429(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 \"glaive-function-calling-v2-sharegpt\"\n\nThis dataset takes the glaive/glaive-function-calling-v2 dataset and formats it with ShareGPT using Lilac\n\nThe accompanying notebook can be found here.\n\nThe original columns \"system\" and \"chat\" still exist on the dataset.\n\nThere are 4 types of roles in the ShareGPT format:\n- system\n- user\n- human\n- function call\n\nThe original dataset has a column called 'chat' with the following structure:\n\n\nThis dataset is a version that converts it into the ShareGPT format:\n\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"glaive-function-calling-v2-sharegpt\"\n\nThis dataset takes the glaive/glaive-function-calling-v2 dataset and formats it with ShareGPT using Lilac\n\nThe accompanying notebook can be found here.\n\nThe original columns \"system\" and \"chat\" still exist on the dataset.\n\nThere are 4 types of roles in the ShareGPT format:\n- system\n- user\n- human\n- function call\n\nThe original dataset has a column called 'chat' with the following structure:\n\n\nThis dataset is a version that converts it into the ShareGPT format:\n\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of RatanRohith/NeuralPizza-Valor-7B-Merge-slerp\n\n\n\nDataset automatically created during the evaluation run of model RatanRohith/NeuralPizza-Valor-7B-Merge-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 2024-01-22T23:00:14.356889(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 RatanRohith/NeuralPizza-Valor-7B-Merge-slerp\n\n\n\nDataset automatically created during the evaluation run of model RatanRohith/NeuralPizza-Valor-7B-Merge-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 2024-01-22T23:00:14.356889(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" ]

[]

[]

[]

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

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

[]

[ "es" ]

[]

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

[ "Lilac", "region:us" ]

[]

[]

[ "# lilac/glaive-function-calling-v2\nThis dataset is a Lilac processed dataset. Original dataset: URL\n\nTo download the dataset to a local directory:\n\n\n\nor from python with:" ]

[ "TAGS\n#Lilac #region-us \n", "# lilac/glaive-function-calling-v2\nThis dataset is a Lilac processed dataset. Original dataset: URL\n\nTo download the dataset to a local directory:\n\n\n\nor from python with:" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of alexredna/Tukan-1.1B-Chat-reasoning-sft-COLA\n\n\n\nDataset automatically created during the evaluation run of model alexredna/Tukan-1.1B-Chat-reasoning-sft-COLA 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 2024-01-22T23:29:11.286981(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 alexredna/Tukan-1.1B-Chat-reasoning-sft-COLA\n\n\n\nDataset automatically created during the evaluation run of model alexredna/Tukan-1.1B-Chat-reasoning-sft-COLA 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 2024-01-22T23:29:11.286981(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" ]

[ "size_categories:n<1K", "rlfh", "argilla", "human-feedback", "region:us" ]

[]

[]

[ "### Dataset Summary\n\n\nThis dataset contains:\n\n\n* A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\\_huggingface' method in Argilla.\n* Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\\_huggingface' and can be loaded independently using the 'datasets' library via 'load\\_dataset'.\n* The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.", "### Load with Argilla\n\n\nTo load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code:", "### Load with 'datasets'\n\n\nTo load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code:", "### Supported Tasks and Leaderboards\n\n\nThis dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.\n\n\nThere are no leaderboards associated with this dataset.", "### Languages\n\n\nDataset Structure\n-----------------", "### Data in Argilla\n\n\nThe dataset is created in Argilla with: fields, questions, suggestions, metadata, vectors, and guidelines.\n\n\nThe fields are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.\n\n\n\nThe questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label\\_selection, multi\\_label\\_selection, or ranking.\n\n\n\nThe suggestions are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending \"-suggestion\" and \"-suggestion-metadata\" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with \"-suggestion\" and the metadata is appended with \"-suggestion-metadata\".\n\n\nThe metadata is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\\_properties' defined in the dataset configuration file in 'URL'.\n\n\n\nThe guidelines, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.", "### Data Instances\n\n\nAn example of a dataset instance in Argilla looks as follows:\n\n\nWhile the same record in HuggingFace 'datasets' looks as follows:", "### Data Fields\n\n\nAmong the dataset fields, we differentiate between the following:\n\n\n* Fields: These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.\n\n\n\t+ text is of type 'text'.\n* Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'.\n\n\n\t+ sentiment is of type 'label\\_selection' with the following allowed values ['positive', 'neutral', 'negative'].\n\t+ mixed-emotion is of type 'multi\\_label\\_selection' with the following allowed values ['joy', 'anger', 'sadness', 'fear', 'surprise', 'love'].\n* Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.\n\n\n\t+ (optional) sentiment-suggestion is of type 'label\\_selection' with the following allowed values ['positive', 'neutral', 'negative'].\n\t+ (optional) mixed-emotion-suggestion is of type 'multi\\_label\\_selection' with the following allowed values ['joy', 'anger', 'sadness', 'fear', 'surprise', 'love'].\n\n\nAdditionally, we also have two more fields that are optional and are the following:\n\n\n* metadata: This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\\_properties' defined in the dataset configuration file in 'URL'.\n* external\\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.", "### Data Splits\n\n\nThe dataset contains a single split, which is 'train'.\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation guidelines\n\n\nEmotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise.", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ "TAGS\n#size_categories-n<1K #rlfh #argilla #human-feedback #region-us \n", "### Dataset Summary\n\n\nThis dataset contains:\n\n\n* A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\\_huggingface' method in Argilla.\n* Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\\_huggingface' and can be loaded independently using the 'datasets' library via 'load\\_dataset'.\n* The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.", "### Load with Argilla\n\n\nTo load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code:", "### Load with 'datasets'\n\n\nTo load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code:", "### Supported Tasks and Leaderboards\n\n\nThis dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.\n\n\nThere are no leaderboards associated with this dataset.", "### Languages\n\n\nDataset Structure\n-----------------", "### Data in Argilla\n\n\nThe dataset is created in Argilla with: fields, questions, suggestions, metadata, vectors, and guidelines.\n\n\nThe fields are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.\n\n\n\nThe questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label\\_selection, multi\\_label\\_selection, or ranking.\n\n\n\nThe suggestions are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending \"-suggestion\" and \"-suggestion-metadata\" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with \"-suggestion\" and the metadata is appended with \"-suggestion-metadata\".\n\n\nThe metadata is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\\_properties' defined in the dataset configuration file in 'URL'.\n\n\n\nThe guidelines, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.", "### Data Instances\n\n\nAn example of a dataset instance in Argilla looks as follows:\n\n\nWhile the same record in HuggingFace 'datasets' looks as follows:", "### Data Fields\n\n\nAmong the dataset fields, we differentiate between the following:\n\n\n* Fields: These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.\n\n\n\t+ text is of type 'text'.\n* Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'.\n\n\n\t+ sentiment is of type 'label\\_selection' with the following allowed values ['positive', 'neutral', 'negative'].\n\t+ mixed-emotion is of type 'multi\\_label\\_selection' with the following allowed values ['joy', 'anger', 'sadness', 'fear', 'surprise', 'love'].\n* Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.\n\n\n\t+ (optional) sentiment-suggestion is of type 'label\\_selection' with the following allowed values ['positive', 'neutral', 'negative'].\n\t+ (optional) mixed-emotion-suggestion is of type 'multi\\_label\\_selection' with the following allowed values ['joy', 'anger', 'sadness', 'fear', 'surprise', 'love'].\n\n\nAdditionally, we also have two more fields that are optional and are the following:\n\n\n* metadata: This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\\_properties' defined in the dataset configuration file in 'URL'.\n* external\\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.", "### Data Splits\n\n\nThe dataset contains a single split, which is 'train'.\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation guidelines\n\n\nEmotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise.", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ "region:us" ]

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[ "# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "## 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 Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "## 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 CultriX/MergeTrix-7B-v2\n\n\n\nDataset automatically created during the evaluation run of model CultriX/MergeTrix-7B-v2 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 2024-01-23T01:33:45.756570(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 CultriX/MergeTrix-7B-v2\n\n\n\nDataset automatically created during the evaluation run of model CultriX/MergeTrix-7B-v2 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 2024-01-23T01:33:45.756570(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 Dataset Name", "## 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 Dataset Name", "## 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 adamo1139/Yi-34B-200K-rawrr1-LORA-DPO-experimental-r3\n\n\n\nDataset automatically created during the evaluation run of model adamo1139/Yi-34B-200K-rawrr1-LORA-DPO-experimental-r3 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 2024-01-23T02:09:10.146041(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 adamo1139/Yi-34B-200K-rawrr1-LORA-DPO-experimental-r3\n\n\n\nDataset automatically created during the evaluation run of model adamo1139/Yi-34B-200K-rawrr1-LORA-DPO-experimental-r3 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 2024-01-23T02:09:10.146041(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 LordNoah/Alpaca_spin_gpt2_e1_se0\n\n\n\nDataset automatically created during the evaluation run of model LordNoah/Alpaca_spin_gpt2_e1_se0 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 2024-01-23T02:15:43.434636(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 LordNoah/Alpaca_spin_gpt2_e1_se0\n\n\n\nDataset automatically created during the evaluation run of model LordNoah/Alpaca_spin_gpt2_e1_se0 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 2024-01-23T02:15:43.434636(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 LordNoah/Alpaca_refine_gpt2_e1_se0\n\n\n\nDataset automatically created during the evaluation run of model LordNoah/Alpaca_refine_gpt2_e1_se0 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 2024-01-23T02:16:01.366997(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 LordNoah/Alpaca_refine_gpt2_e1_se0\n\n\n\nDataset automatically created during the evaluation run of model LordNoah/Alpaca_refine_gpt2_e1_se0 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 2024-01-23T02:16:01.366997(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 \"ultrafeedback_binarized_relabelled_test\"\n\nMore Information needed" ]

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

[ "task_categories:audio-classification", "size_categories:n>1T", "license:cc-by-4.0", "pedestrian detection", "arxiv:2309.06531", "region:us" ]

[ "2309.06531" ]

[]

[ "# ASPED: An Audio Dataset for Detecting Pedestrians\n\nThis repo contains the data for the ASPED dataset, presented at ICASSP 2024.\n- Paper Link, Project Homepage\n\n- Pavan Seshadri, Chaeyeon Han, Bon-Woo Koo, Noah Posner, Suhbrajit Guhathakurta, Alexander Lerch", "## Usage\nThis dataset contains audio and video recordings of pedestrian activity collected at various locations in and around Georgia Tech. \n\nLabels of pedestrian counts per each second of audio/video are provided as well, calculated via a computer vision model (Mask2Former trained on msft-coco) using the video recordings.", "### Access\nIt is recommended to use the huggingface_hub library to download the dataset from this location. Info on downloading with huggingface_hub.\n\nDownloading the entire dataset can be done with the following code:\n\nAlternatively if you would like to download only the audio or video, pass the ignore_patterns flag to snapshot_download to avoid downloading the entire set. \n\nAudio Only\n\n\nVideo Only" ]

[ "TAGS\n#task_categories-audio-classification #size_categories-n>1T #license-cc-by-4.0 #pedestrian detection #arxiv-2309.06531 #region-us \n", "# ASPED: An Audio Dataset for Detecting Pedestrians\n\nThis repo contains the data for the ASPED dataset, presented at ICASSP 2024.\n- Paper Link, Project Homepage\n\n- Pavan Seshadri, Chaeyeon Han, Bon-Woo Koo, Noah Posner, Suhbrajit Guhathakurta, Alexander Lerch", "## Usage\nThis dataset contains audio and video recordings of pedestrian activity collected at various locations in and around Georgia Tech. \n\nLabels of pedestrian counts per each second of audio/video are provided as well, calculated via a computer vision model (Mask2Former trained on msft-coco) using the video recordings.", "### Access\nIt is recommended to use the huggingface_hub library to download the dataset from this location. Info on downloading with huggingface_hub.\n\nDownloading the entire dataset can be done with the following code:\n\nAlternatively if you would like to download only the audio or video, pass the ignore_patterns flag to snapshot_download to avoid downloading the entire set. \n\nAudio Only\n\n\nVideo Only" ]

[ "task_categories:text-generation", "language:zh", "license:cc-by-nc-sa-4.0", "not-for-all-audiences", "region:us" ]

[]

[ "zh" ]

[]

[ "TAGS\n#task_categories-text-generation #language-Chinese #license-cc-by-nc-sa-4.0 #not-for-all-audiences #region-us \n" ]

[ "region:us" ]

[]

[]

[]

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

[ "size_categories:100K<n<1M", "language:en", "license:apache-2.0", "face", "humanface", "ml", "face recognition", "Generated data", "dataset", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#size_categories-100K<n<1M #language-English #license-apache-2.0 #face #humanface #ml #face recognition #Generated data #dataset #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of ibivibiv/strix-rufipes-70b\n\n\n\nDataset automatically created during the evaluation run of model ibivibiv/strix-rufipes-70b 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 2024-01-23T03:56:38.126146(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 ibivibiv/strix-rufipes-70b\n\n\n\nDataset automatically created during the evaluation run of model ibivibiv/strix-rufipes-70b 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 2024-01-23T03:56:38.126146(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 BarryFutureman/WildWest-Variant3-7B\n\n\n\nDataset automatically created during the evaluation run of model BarryFutureman/WildWest-Variant3-7B 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 2024-01-23T04:01:23.522881(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 BarryFutureman/WildWest-Variant3-7B\n\n\n\nDataset automatically created during the evaluation run of model BarryFutureman/WildWest-Variant3-7B 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 2024-01-23T04:01:23.522881(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" ]

[ "task_categories:sentence-similarity", "size_categories:100K<n<1M", "language:en", "license:cc0-1.0", "GloVe", "embeddings", "semantic search", "hpi", "workshop", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card\n\nThis dataset is a UMAP 2D-projection of the glove.6B.50d embeddings from Stanford. It is intended as a fast reference for visualizing embeddings in a workshop from the AI Service Center Berlin-Brandenburg at the Hasso Plattner Institute.", "## Dataset Details", "### Dataset Description\n\nThe embeddings have a vocabulary of 400k tokens with 2 dimensions each token.\n\nCurated by: Mario Tormo Romero\n \nLicense: cc0-1.0", "### Dataset Sources\nThis Dataset has been created with UMAP from the glove.6B.50d embeddings.", "## Uses\n\nThis is a dataset created for pegagogical purposes, and is used in the Working with embeddings Workshop created and organized by the AI Service Center Berlin-Brandenburg at the Hasso Plattner Institute.", "## Dataset Creation", "### Curation Rationale\n\nWe want to provide with this dataset a fast way of obtaining the required data for our workshops, without having to process the data for long periods during the workshop." ]

[ "TAGS\n#task_categories-sentence-similarity #size_categories-100K<n<1M #language-English #license-cc0-1.0 #GloVe #embeddings #semantic search #hpi #workshop #region-us \n", "# Dataset Card\n\nThis dataset is a UMAP 2D-projection of the glove.6B.50d embeddings from Stanford. It is intended as a fast reference for visualizing embeddings in a workshop from the AI Service Center Berlin-Brandenburg at the Hasso Plattner Institute.", "## Dataset Details", "### Dataset Description\n\nThe embeddings have a vocabulary of 400k tokens with 2 dimensions each token.\n\nCurated by: Mario Tormo Romero\n \nLicense: cc0-1.0", "### Dataset Sources\nThis Dataset has been created with UMAP from the glove.6B.50d embeddings.", "## Uses\n\nThis is a dataset created for pegagogical purposes, and is used in the Working with embeddings Workshop created and organized by the AI Service Center Berlin-Brandenburg at the Hasso Plattner Institute.", "## Dataset Creation", "### Curation Rationale\n\nWe want to provide with this dataset a fast way of obtaining the required data for our workshops, without having to process the data for long periods during the workshop." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of binbi/MoMo-70B-V1.2_1\n\n\n\nDataset automatically created during the evaluation run of model binbi/MoMo-70B-V1.2_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 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 2024-01-23T05:09:38.161416(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/MoMo-70B-V1.2_1\n\n\n\nDataset automatically created during the evaluation run of model binbi/MoMo-70B-V1.2_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 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 2024-01-23T05:09:38.161416(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 jsfs11/West-Dare-7B\n\n\n\nDataset automatically created during the evaluation run of model jsfs11/West-Dare-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 2024-01-23T05:18:14.864719(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 jsfs11/West-Dare-7B\n\n\n\nDataset automatically created during the evaluation run of model jsfs11/West-Dare-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 2024-01-23T05:18:14.864719(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" ]

[]

[]

[ "# Version\n* 0.1: add korean history.\n\n---\nThis software/dataset is provided \"as is\" without express or implied warranty to the extent permitted by applicable law\n\nIf you used this dataset, specify in your model at the model-card.\n---" ]

[ "TAGS\n#region-us \n", "# Version\n* 0.1: add korean history.\n\n---\nThis software/dataset is provided \"as is\" without express or implied warranty to the extent permitted by applicable law\n\nIf you used this dataset, specify in your model at the model-card.\n---" ]

[ "language:en", "biology", "climate", "plants", "region:us" ]

[]

[ "en" ]

[]

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

[ "language:en", "license:apache-2.0", "python", "sourcecode", "algorithm", "pseudocode", "python3", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "## Dataset Details", "### Dataset Description\n\n\n\nThis dataset provides algorithms and corresponding Python source code which can be leveraged for any type of code conversion applications. \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#language-English #license-apache-2.0 #python #sourcecode #algorithm #pseudocode #python3 #region-us \n", "# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "## Dataset Details", "### Dataset Description\n\n\n\nThis dataset provides algorithms and corresponding Python source code which can be leveraged for any type of code conversion applications. \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" ]

[ "arxiv:2304.08485", "region:us" ]

[ "2304.08485" ]

[]

[ "# GNHK Synthetic OCR Dataset", "## Overview\n\nWelcome to the GNHK Synthetic OCR Dataset repository. Here I have generated synthetic data using GNHK Dataset, and Open Source LLMs like Mixtral. The dataset contains queries on the images and their answers.", "## What's Inside?\n\n- Dataset Folder: The Dataset Folder contains the images, and corresponding to each image, there is a JSON file which carries the ocr information of that image\n\n- Parquet File: For easy handling and analysis, the processed dataset is saved as a Parquet file ('dataset.parquet'). This file contains images, their OCR text, one probable question per image, and its likely answer.", "# Methodology for Generation", "## URL\n\nThis Python notebook interacts with a dataset provided by GNHK, stored on Google Drive. The dataset consists of images, each accompanied by a JSON file containing OCR information for that image. The purpose of ParseJSON is to extract information from these JSON files, convert it into text files, and store these files in a folder named 'parsed_dataset' on the same Google Drive.", "### What does it parse to?\n\n- ocr_data: It extracts OCR texts for words based on their 'line_index' and organizes them to represent the OCR text of the given image.\n\n- bbox_data: Another text file is generated by the parser, structuring information in this format:\n'word: [[x0, y0], [x1, y1], [x2, y2], [x3, y3]]'\n\n (where x0, y1, etc. are coordinates of bounding boxes)", "### Why do we need a parser?\nThe parser is necessary because models require OCR data and bounding boxes as input. If this information is in JSON format, creating a prompt for the models becomes complex and may lead to confusion, resulting in undesirable outputs. The parser simplifies the process by converting the data into easily understandable text files.", "## 2. URL\n\nThis notebook is the central tool for creating the dataset. In summary, it leverages OCR data and bounding boxes to prompt open-source LLMs, generating query-output tuples.\n\nThe methodology draws inspiration from the paper on Visual Instruction Tuning, which outlines the creation of three types of query-output tuples:\n\n1. Conversation Based: Simple question-answer pairs related to the given image, covering a broad range of straightforward inquiries. Multiple conversation-based query-output tuples are generated for a single image to ensure comprehensiveness.\n\n2. Description: This is not a typical question-answer pair. In this category the model generates detailed descriptions of the text depicted in the image.\n\n3. Complex Reasoning Based: These questions delve deeper, requiring thoughtful consideration. Answering them involves understanding the visual content, followed by applying background knowledge or reasoning to provide a detailed response. Only one question-answer tuple of this nature is generated for each image.", "## Output Parsing and Cleaning Functions\n\nVarious parsers are implemented to process the model-generated output. Due to the unpredictable nature of LLM outputs, these parsers aren't flawless. However, by incorporating few-shot prompting and identifying common patterns in the LLM outputs, these parsers can handle a significant number of cases. Their primary function is to convert the raw output into a structured format for inclusion in the final database.\n\n\n\nFinally, the dataset generated has the following format:", "### Model Used\n\nAfter multiple experiments, the most promising results were achieved using the Mixtral_8x7b model. It demonstrated superior performance compared to Llama-2 70b for the specific task at hand.\n\nTo execute these open-source models in the cloud, the services offered by URL have been employed.", "## Post Processing\n\nIn this experiment, the output generated from two Language Models (LLMs) was processed to enhance the dataset quality. The LLMs used were Platypus2 and Mixtral_8x7b. The process involved the following steps:", "### Step 1:\n1. Generation and Evaluation: Mixtral_8x7b generated the initial dataset, which was then evaluated and modified by Platypus2. Subsequently, the output from Platypus2 was further evaluated and modified by Mixtral_8x7b.", "### Step 2:\n2. Judgment and Selection: The outputs from both Mixtral_8x7b (final output of step 1) and Platypus2 (intermediate output of step 1) were assessed by Mixtral_8x7b_Instruct. The best output was selected, and the dataset was updated accordingly.\n\nThe pipeline can be summarized as follows:\n\n\nThe resulting dataset, after this process, is named 'post_processed_dataset.parquet'. Please note that only 50 data points were post-processed as part of this experiment.\n\nNote: While this post-processing experiment aimed to enhance the dataset's overall quality, manual observations did not reveal significant improvements." ]

[ "TAGS\n#arxiv-2304.08485 #region-us \n", "# GNHK Synthetic OCR Dataset", "## Overview\n\nWelcome to the GNHK Synthetic OCR Dataset repository. Here I have generated synthetic data using GNHK Dataset, and Open Source LLMs like Mixtral. The dataset contains queries on the images and their answers.", "## What's Inside?\n\n- Dataset Folder: The Dataset Folder contains the images, and corresponding to each image, there is a JSON file which carries the ocr information of that image\n\n- Parquet File: For easy handling and analysis, the processed dataset is saved as a Parquet file ('dataset.parquet'). This file contains images, their OCR text, one probable question per image, and its likely answer.", "# Methodology for Generation", "## URL\n\nThis Python notebook interacts with a dataset provided by GNHK, stored on Google Drive. The dataset consists of images, each accompanied by a JSON file containing OCR information for that image. The purpose of ParseJSON is to extract information from these JSON files, convert it into text files, and store these files in a folder named 'parsed_dataset' on the same Google Drive.", "### What does it parse to?\n\n- ocr_data: It extracts OCR texts for words based on their 'line_index' and organizes them to represent the OCR text of the given image.\n\n- bbox_data: Another text file is generated by the parser, structuring information in this format:\n'word: [[x0, y0], [x1, y1], [x2, y2], [x3, y3]]'\n\n (where x0, y1, etc. are coordinates of bounding boxes)", "### Why do we need a parser?\nThe parser is necessary because models require OCR data and bounding boxes as input. If this information is in JSON format, creating a prompt for the models becomes complex and may lead to confusion, resulting in undesirable outputs. The parser simplifies the process by converting the data into easily understandable text files.", "## 2. URL\n\nThis notebook is the central tool for creating the dataset. In summary, it leverages OCR data and bounding boxes to prompt open-source LLMs, generating query-output tuples.\n\nThe methodology draws inspiration from the paper on Visual Instruction Tuning, which outlines the creation of three types of query-output tuples:\n\n1. Conversation Based: Simple question-answer pairs related to the given image, covering a broad range of straightforward inquiries. Multiple conversation-based query-output tuples are generated for a single image to ensure comprehensiveness.\n\n2. Description: This is not a typical question-answer pair. In this category the model generates detailed descriptions of the text depicted in the image.\n\n3. Complex Reasoning Based: These questions delve deeper, requiring thoughtful consideration. Answering them involves understanding the visual content, followed by applying background knowledge or reasoning to provide a detailed response. Only one question-answer tuple of this nature is generated for each image.", "## Output Parsing and Cleaning Functions\n\nVarious parsers are implemented to process the model-generated output. Due to the unpredictable nature of LLM outputs, these parsers aren't flawless. However, by incorporating few-shot prompting and identifying common patterns in the LLM outputs, these parsers can handle a significant number of cases. Their primary function is to convert the raw output into a structured format for inclusion in the final database.\n\n\n\nFinally, the dataset generated has the following format:", "### Model Used\n\nAfter multiple experiments, the most promising results were achieved using the Mixtral_8x7b model. It demonstrated superior performance compared to Llama-2 70b for the specific task at hand.\n\nTo execute these open-source models in the cloud, the services offered by URL have been employed.", "## Post Processing\n\nIn this experiment, the output generated from two Language Models (LLMs) was processed to enhance the dataset quality. The LLMs used were Platypus2 and Mixtral_8x7b. The process involved the following steps:", "### Step 1:\n1. Generation and Evaluation: Mixtral_8x7b generated the initial dataset, which was then evaluated and modified by Platypus2. Subsequently, the output from Platypus2 was further evaluated and modified by Mixtral_8x7b.", "### Step 2:\n2. Judgment and Selection: The outputs from both Mixtral_8x7b (final output of step 1) and Platypus2 (intermediate output of step 1) were assessed by Mixtral_8x7b_Instruct. The best output was selected, and the dataset was updated accordingly.\n\nThe pipeline can be summarized as follows:\n\n\nThe resulting dataset, after this process, is named 'post_processed_dataset.parquet'. Please note that only 50 data points were post-processed as part of this experiment.\n\nNote: While this post-processing experiment aimed to enhance the dataset's overall quality, manual observations did not reveal significant improvements." ]

[ "Fuel", "Sri Lanka", "region:us" ]

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[ "TAGS\n#Fuel #Sri Lanka #region-us \n" ]

[ "region:us" ]

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[ "# OPV2V-H dataset\n\nBased on the original OPV2V dataset, we supplemented 16-line, 32-line lidar data for each agent, as well as 4 depth cameras. Annotations will be shared with the original OPV2V, so please download the original OPV2V dataset as well.\n\n\n\n1. Depth Data\n\n 'URL ' stores the depth camera data. You can directly uncompress them:\n\n \n\n \n\n2. LiDAR Data\n\n 'OPV2V-H-LiDAR-partxx' is a volume-compressed slice. They store 16-line and 32-line LiDAR data. You can unzip them using\n\n \n\n \n\nA desired output structure will be" ]

[ "TAGS\n#region-us \n", "# OPV2V-H dataset\n\nBased on the original OPV2V dataset, we supplemented 16-line, 32-line lidar data for each agent, as well as 4 depth cameras. Annotations will be shared with the original OPV2V, so please download the original OPV2V dataset as well.\n\n\n\n1. Depth Data\n\n 'URL ' stores the depth camera data. You can directly uncompress them:\n\n \n\n \n\n2. LiDAR Data\n\n 'OPV2V-H-LiDAR-partxx' is a volume-compressed slice. They store 16-line and 32-line LiDAR data. You can unzip them using\n\n \n\n \n\nA desired output structure will be" ]

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

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[]

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

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

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

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of abhishekchohan/mistral-7B-forest-v0.1\n\n\n\nDataset automatically created during the evaluation run of model abhishekchohan/mistral-7B-forest-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 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 2024-01-23T06:57:46.714861(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 abhishekchohan/mistral-7B-forest-v0.1\n\n\n\nDataset automatically created during the evaluation run of model abhishekchohan/mistral-7B-forest-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 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 2024-01-23T06:57:46.714861(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" ]

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[ "# Dataset Card for Dataset Name\n\n\n\nFragen und Antworten für die Theorieprüfung SBF Binnen Segeln.", "## 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#license-apache-2.0 #region-us \n", "# Dataset Card for Dataset Name\n\n\n\nFragen und Antworten für die Theorieprüfung SBF Binnen Segeln.", "## 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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[]

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

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of Epiculous/Crunchy-onion\n\n\n\nDataset automatically created during the evaluation run of model Epiculous/Crunchy-onion 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 2024-01-23T07:42:32.669813(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 Epiculous/Crunchy-onion\n\n\n\nDataset automatically created during the evaluation run of model Epiculous/Crunchy-onion 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 2024-01-23T07:42:32.669813(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 flemmingmiguel/MBX-7B-v2\n\n\n\nDataset automatically created during the evaluation run of model flemmingmiguel/MBX-7B-v2 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 6 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 2024-01-29T01:58:59.417725(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 flemmingmiguel/MBX-7B-v2\n\n\n\nDataset automatically created during the evaluation run of model flemmingmiguel/MBX-7B-v2 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 6 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 2024-01-29T01:58:59.417725(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 jsfs11/WestOrcaNeuralMarco-DPO-v2-DARETIES-7B\n\n\n\nDataset automatically created during the evaluation run of model jsfs11/WestOrcaNeuralMarco-DPO-v2-DARETIES-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 2024-01-23T07:53:57.376778(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 jsfs11/WestOrcaNeuralMarco-DPO-v2-DARETIES-7B\n\n\n\nDataset automatically created during the evaluation run of model jsfs11/WestOrcaNeuralMarco-DPO-v2-DARETIES-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 2024-01-23T07:53:57.376778(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" ]

[ "language:ind", "summarization", "region:us" ]

[]

[ "ind" ]

[ "# indosum\n\nINDOSUM is a new benchmark dataset for Indonesian text summarization. \n\nThe dataset consists of news articles and manually constructed summaries.", "## Dataset Usage\n\nRun 'pip install nusacrowd' before loading the dataset through HuggingFace's 'load_dataset'.", "## License\n\nApache License, Version 2.0", "## Homepage\n\nURL", "### NusaCatalogue\n\nFor easy indexing and metadata: URL" ]

[ "TAGS\n#language-Indonesian #summarization #region-us \n", "# indosum\n\nINDOSUM is a new benchmark dataset for Indonesian text summarization. \n\nThe dataset consists of news articles and manually constructed summaries.", "## Dataset Usage\n\nRun 'pip install nusacrowd' before loading the dataset through HuggingFace's 'load_dataset'.", "## License\n\nApache License, Version 2.0", "## Homepage\n\nURL", "### NusaCatalogue\n\nFor easy indexing and metadata: URL" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"quirky_modularaddition_increment0\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"quirky_modularaddition_increment0\"\n\nMore Information needed" ]

[ "size_categories:1B<n<10B", "license:apache-2.0", "region:us" ]

[]

[]

[]

[ "TAGS\n#size_categories-1B<n<10B #license-apache-2.0 #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of cloudyu/Yi-34Bx2-MoE-60B-DPO\n\n\n\nDataset automatically created during the evaluation run of model cloudyu/Yi-34Bx2-MoE-60B-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 2024-01-23T09:26:46.662482(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/Yi-34Bx2-MoE-60B-DPO\n\n\n\nDataset automatically created during the evaluation run of model cloudyu/Yi-34Bx2-MoE-60B-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 2024-01-23T09:26:46.662482(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/Mixtral-8x7B-Instruct-v0.1-DPO\n\n\n\nDataset automatically created during the evaluation run of model cloudyu/Mixtral-8x7B-Instruct-v0.1-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 2024-01-23T09:56:27.618926(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/Mixtral-8x7B-Instruct-v0.1-DPO\n\n\n\nDataset automatically created during the evaluation run of model cloudyu/Mixtral-8x7B-Instruct-v0.1-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 2024-01-23T09:56:27.618926(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" ]

[ "size_categories:n<1K", "license:gpl-3.0", "art", "region:us" ]

[]

[]

[]

[ "TAGS\n#size_categories-n<1K #license-gpl-3.0 #art #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of mathurinache/Odysseas-11B\n\n\n\nDataset automatically created during the evaluation run of model mathurinache/Odysseas-11B 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 2024-01-23T13:39:56.584949(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 mathurinache/Odysseas-11B\n\n\n\nDataset automatically created during the evaluation run of model mathurinache/Odysseas-11B 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 2024-01-23T13:39:56.584949(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 aloobun/slerp_bun_mistral_7b_v2\n\n\n\nDataset automatically created during the evaluation run of model aloobun/slerp_bun_mistral_7b_v2 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 2024-01-23T10:20:17.591541(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 aloobun/slerp_bun_mistral_7b_v2\n\n\n\nDataset automatically created during the evaluation run of model aloobun/slerp_bun_mistral_7b_v2 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 2024-01-23T10:20:17.591541(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 \"fashion_image_caption-100-v2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"fashion_image_caption-100-v2\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of cognitivecomputations/TinyDolphin-2.8-1.1b\n\n\n\nDataset automatically created during the evaluation run of model cognitivecomputations/TinyDolphin-2.8-1.1b 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 2024-01-23T11:30:41.082288(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 cognitivecomputations/TinyDolphin-2.8-1.1b\n\n\n\nDataset automatically created during the evaluation run of model cognitivecomputations/TinyDolphin-2.8-1.1b 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 2024-01-23T11:30:41.082288(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" ]

[ "vdf", "vector-io", "vector-dataset", "vector-embeddings", "region:us" ]

[]

[]

[]

[ "TAGS\n#vdf #vector-io #vector-dataset #vector-embeddings #region-us \n" ]

[ "language:en", "license:apache-2.0", "movies", "region:us" ]

[]

[ "en" ]

[ "## sample_mflix.embedded_movies\n\nThis data set contains details on movies with genres of Western, Action, or Fantasy. Each document contains a single movie, and information such as its title, release year, and cast.\n\nIn addition, documents in this collection include a plot_embedding field that contains embeddings created using OpenAI's text-embedding-ada-002 embedding model that you can use with the Atlas Search vector search feature.", "## Overview\n\nThis dataset offers a comprehensive collection of data on various movies. It includes details such as plot summaries, genres, runtime, ratings, cast, and more. This dataset is ideal for movie recommendation systems, film analysis, and educational purposes in film studies.", "## Dataset Structure\n\nEach record in the dataset represents a movie and includes the following fields:\n\n- '_id': A unique identifier for the movie.\n- 'plot': A brief summary of the movie's plot.\n- 'genres': A list of genres associated with the movie.\n- 'runtime': The runtime of the movie in minutes.\n- 'rated': The MPAA rating of the movie.\n- 'cast': A list of main actors in the movie.\n- 'num_mflix_comments': The number of comments on the movie in the mflix platform.\n- 'poster': A URL to the movie's poster image.\n- 'title': The title of the movie.\n- 'lastupdated': The last date and time when the movie information was updated.\n- 'languages': The languages available in the movie.\n- 'released': The release date of the movie.\n- 'directors': A list of directors of the movie.\n- 'writers': A list of writers of the movie.\n- 'awards': Information about awards won and nominations.\n- 'year': The release year of the movie.\n- 'imdb': IMDb rating, votes, and ID.\n- 'countries': A list of countries where the movie was produced.\n- 'type': The type of record, in this case, 'movie'.\n- 'tomatoes': Ratings and reviews from Rotten Tomatoes.\n- 'plot_embedding': An array of numerical values representing the plot embedding.", "## Field Details", "### Awards Object\n\n- 'wins': The number of awards won.\n- 'nominations': The number of awards the movie was nominated for.\n- 'text': A text summary of the awards and nominations.", "### IMDb Object\n\n- 'rating': The IMDb rating.\n- 'votes': The number of votes on IMDb.\n- 'id': The IMDb ID of the movie.", "### Tomatoes Object\n\n- Contains viewer and critic ratings, reviews count, DVD release date, and production details.", "### Plot Embedding\n\n- An array representing a numerical embedding of the movie's plot. Useful for machine learning applications, like content-based filtering in recommendation systems.", "## Usage\n\nThe dataset is suited for a range of applications, including:\n\n- Analyzing trends in film genres and ratings over time.\n- Building movie recommendation engines using plot embeddings and genres.\n- Studying the correlation between cast/directors and movie success.\n- Educational purposes in film studies and data analysis courses.", "## Notes\n\n- The data is provided as-is and intended for informational and educational purposes.\n- Users should verify the accuracy of the information for any critical use-cases.", "### Sample Document", "## Ingest Data\n\nThe small script 'URL' can be used to load the data into your MongoDB Atlas cluster. \n\n\nThe 'URL':" ]

[ "TAGS\n#language-English #license-apache-2.0 #movies #region-us \n", "## sample_mflix.embedded_movies\n\nThis data set contains details on movies with genres of Western, Action, or Fantasy. Each document contains a single movie, and information such as its title, release year, and cast.\n\nIn addition, documents in this collection include a plot_embedding field that contains embeddings created using OpenAI's text-embedding-ada-002 embedding model that you can use with the Atlas Search vector search feature.", "## Overview\n\nThis dataset offers a comprehensive collection of data on various movies. It includes details such as plot summaries, genres, runtime, ratings, cast, and more. This dataset is ideal for movie recommendation systems, film analysis, and educational purposes in film studies.", "## Dataset Structure\n\nEach record in the dataset represents a movie and includes the following fields:\n\n- '_id': A unique identifier for the movie.\n- 'plot': A brief summary of the movie's plot.\n- 'genres': A list of genres associated with the movie.\n- 'runtime': The runtime of the movie in minutes.\n- 'rated': The MPAA rating of the movie.\n- 'cast': A list of main actors in the movie.\n- 'num_mflix_comments': The number of comments on the movie in the mflix platform.\n- 'poster': A URL to the movie's poster image.\n- 'title': The title of the movie.\n- 'lastupdated': The last date and time when the movie information was updated.\n- 'languages': The languages available in the movie.\n- 'released': The release date of the movie.\n- 'directors': A list of directors of the movie.\n- 'writers': A list of writers of the movie.\n- 'awards': Information about awards won and nominations.\n- 'year': The release year of the movie.\n- 'imdb': IMDb rating, votes, and ID.\n- 'countries': A list of countries where the movie was produced.\n- 'type': The type of record, in this case, 'movie'.\n- 'tomatoes': Ratings and reviews from Rotten Tomatoes.\n- 'plot_embedding': An array of numerical values representing the plot embedding.", "## Field Details", "### Awards Object\n\n- 'wins': The number of awards won.\n- 'nominations': The number of awards the movie was nominated for.\n- 'text': A text summary of the awards and nominations.", "### IMDb Object\n\n- 'rating': The IMDb rating.\n- 'votes': The number of votes on IMDb.\n- 'id': The IMDb ID of the movie.", "### Tomatoes Object\n\n- Contains viewer and critic ratings, reviews count, DVD release date, and production details.", "### Plot Embedding\n\n- An array representing a numerical embedding of the movie's plot. Useful for machine learning applications, like content-based filtering in recommendation systems.", "## Usage\n\nThe dataset is suited for a range of applications, including:\n\n- Analyzing trends in film genres and ratings over time.\n- Building movie recommendation engines using plot embeddings and genres.\n- Studying the correlation between cast/directors and movie success.\n- Educational purposes in film studies and data analysis courses.", "## Notes\n\n- The data is provided as-is and intended for informational and educational purposes.\n- Users should verify the accuracy of the information for any critical use-cases.", "### Sample Document", "## Ingest Data\n\nThe small script 'URL' can be used to load the data into your MongoDB Atlas cluster. \n\n\nThe 'URL':" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of xformAI/opt-125m-gqa-ub-6-best-for-KV-cache\n\n\n\nDataset automatically created during the evaluation run of model xformAI/opt-125m-gqa-ub-6-best-for-KV-cache 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 2024-01-23T12:11:33.435491(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 xformAI/opt-125m-gqa-ub-6-best-for-KV-cache\n\n\n\nDataset automatically created during the evaluation run of model xformAI/opt-125m-gqa-ub-6-best-for-KV-cache 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 2024-01-23T12:11:33.435491(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 \"SlimOrca-tr\"\n\nMore Information needed" ]

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

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of uukuguy/speechless-zephyr-code-functionary-7b\n\n\n\nDataset automatically created during the evaluation run of model uukuguy/speechless-zephyr-code-functionary-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 2024-01-23T12:18:03.845597(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 uukuguy/speechless-zephyr-code-functionary-7b\n\n\n\nDataset automatically created during the evaluation run of model uukuguy/speechless-zephyr-code-functionary-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 2024-01-23T12:18:03.845597(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 xformAI/facebook-opt-125m-qcqa-ub-6-best-for-KV-cache\n\n\n\nDataset automatically created during the evaluation run of model xformAI/facebook-opt-125m-qcqa-ub-6-best-for-KV-cache 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 2024-01-23T12:20:26.154798(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 xformAI/facebook-opt-125m-qcqa-ub-6-best-for-KV-cache\n\n\n\nDataset automatically created during the evaluation run of model xformAI/facebook-opt-125m-qcqa-ub-6-best-for-KV-cache 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 2024-01-23T12:20:26.154798(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" ]

[ "task_categories:tabular-regression", "size_categories:n<1K", "chemistry", "region:us" ]

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[ "### Dataset Description\n\nPredict URL given URL. This is a signal unmixing problem because the observations are a weighted sum of the chemical concentrations and pure spectra." ]

[ "TAGS\n#task_categories-tabular-regression #size_categories-n<1K #chemistry #region-us \n", "### Dataset Description\n\nPredict URL given URL. This is a signal unmixing problem because the observations are a weighted sum of the chemical concentrations and pure spectra." ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of zhengr/MixTAO-7Bx2-MoE-Instruct-v5.0\n\n\n\nDataset automatically created during the evaluation run of model zhengr/MixTAO-7Bx2-MoE-Instruct-v5.0 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 2024-01-23T12:36:33.042804(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 zhengr/MixTAO-7Bx2-MoE-Instruct-v5.0\n\n\n\nDataset automatically created during the evaluation run of model zhengr/MixTAO-7Bx2-MoE-Instruct-v5.0 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 2024-01-23T12:36:33.042804(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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[]

[ "### Specific doomains\n\n\nThe textbook dataset generated with LLM in French covers large academical domains. Here I list the name of the book grouped by its domain.\n\n\nHistory:\n\n\n* \"Contre-histoire du libéralisme\"\n* \"Histoire de l'Émigration pendant la Révolution Française\"\n* \"Histoire de la littérature française.\"\n* \"Histoire des mouvements sociaux en France\"\n* \"Histoire du surréalisme\"\n* \"La guerre froide\"\n* \"Les Chaînes de l'Esclavage\"\n* \"Les Femmes Avant le Patriarcat\"\n* \"Patrimoine\\_ une histoire vraie\"\n\n\nScientific:\n\n\n* \"Anthropologie\"\n* \"Classes préparatoires\"\n* \"Fondamentaux de la vie sociale\"\n* \"Histoire de la Physique et Chimie\"\n* \"Le carbone renouvelable\"\n\n\nPolitics:\n\n\n* \"Capitalisme et liberté\"\n* \"Gouvernance Le management totalitaire\"\n* \"Introduction à l'économie politique\"\n* \"Introduction à la politique comparée\"\n* \"L anarchisme de droite\"\n* \"Le socialisme démocratique\"\n* \"Les relations internationales\"\n\n\nMedical:\n\n\n* \"Clinique de l'écriture\"\n* \"Introduction à l'étude de la médecine expérimentale\"\n* \"Physiologie et thérapie\"\n\n\nEconomics and Finance:\n\n\n* \"Comprendre léconomie et la finance\"\n* \"Discours sur la Dette\"\n* \"Ecologie et capitalisme\"\n* \"Economie monétaire Théories et politiques\"\n* \"Etat du monde\"\n* \"Introduction à l'économie\"\n* \"Le Magicien de la finance\"\n* \"Les seigneurs de l'argent\\_ Des Médicis au Bitcoin\"\n\n\nLaw:\n\n\n* \"Droit des contrats spéciaux\"\n* \"Droit international des relations diplomatiques\"\n* \"Droit pénal général\"\n* \"Le globe et la loi\"\n\n\nLiterature:\n\n\n* \"Histoire littéraire d'Italie 4\"\n* \"La Préparation du roman\"\n* \"Le Démon de la théorie\"\n* \"Les Origines de la Culture\"\n\n\nMusical:\n\n\n* \" Introduction au langage musical \"\n\n\nPhilosophy:\n\n\n* \"Introduction à la métaphysique de Maurice Blondel\"\n* \"Introduction à la pensée chinoise\"\n* \"Introduction à la philosophie analytics\"\n* \"Introduction à la philosophie de l'histoire\"\n* \"Libertés et droits fondamentaux\"\n\n\nMedia:\n\n\n* \"Les médias sociaux en entreprise\"" ]

[ "TAGS\n#region-us \n", "### Specific doomains\n\n\nThe textbook dataset generated with LLM in French covers large academical domains. Here I list the name of the book grouped by its domain.\n\n\nHistory:\n\n\n* \"Contre-histoire du libéralisme\"\n* \"Histoire de l'Émigration pendant la Révolution Française\"\n* \"Histoire de la littérature française.\"\n* \"Histoire des mouvements sociaux en France\"\n* \"Histoire du surréalisme\"\n* \"La guerre froide\"\n* \"Les Chaînes de l'Esclavage\"\n* \"Les Femmes Avant le Patriarcat\"\n* \"Patrimoine\\_ une histoire vraie\"\n\n\nScientific:\n\n\n* \"Anthropologie\"\n* \"Classes préparatoires\"\n* \"Fondamentaux de la vie sociale\"\n* \"Histoire de la Physique et Chimie\"\n* \"Le carbone renouvelable\"\n\n\nPolitics:\n\n\n* \"Capitalisme et liberté\"\n* \"Gouvernance Le management totalitaire\"\n* \"Introduction à l'économie politique\"\n* \"Introduction à la politique comparée\"\n* \"L anarchisme de droite\"\n* \"Le socialisme démocratique\"\n* \"Les relations internationales\"\n\n\nMedical:\n\n\n* \"Clinique de l'écriture\"\n* \"Introduction à l'étude de la médecine expérimentale\"\n* \"Physiologie et thérapie\"\n\n\nEconomics and Finance:\n\n\n* \"Comprendre léconomie et la finance\"\n* \"Discours sur la Dette\"\n* \"Ecologie et capitalisme\"\n* \"Economie monétaire Théories et politiques\"\n* \"Etat du monde\"\n* \"Introduction à l'économie\"\n* \"Le Magicien de la finance\"\n* \"Les seigneurs de l'argent\\_ Des Médicis au Bitcoin\"\n\n\nLaw:\n\n\n* \"Droit des contrats spéciaux\"\n* \"Droit international des relations diplomatiques\"\n* \"Droit pénal général\"\n* \"Le globe et la loi\"\n\n\nLiterature:\n\n\n* \"Histoire littéraire d'Italie 4\"\n* \"La Préparation du roman\"\n* \"Le Démon de la théorie\"\n* \"Les Origines de la Culture\"\n\n\nMusical:\n\n\n* \" Introduction au langage musical \"\n\n\nPhilosophy:\n\n\n* \"Introduction à la métaphysique de Maurice Blondel\"\n* \"Introduction à la pensée chinoise\"\n* \"Introduction à la philosophie analytics\"\n* \"Introduction à la philosophie de l'histoire\"\n* \"Libertés et droits fondamentaux\"\n\n\nMedia:\n\n\n* \"Les médias sociaux en entreprise\"" ]

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

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of Charlie911/MultiLora-drop-sharegpt\n\n\n\nDataset automatically created during the evaluation run of model Charlie911/MultiLora-drop-sharegpt 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 2024-01-23T20:13:52.401722(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 Charlie911/MultiLora-drop-sharegpt\n\n\n\nDataset automatically created during the evaluation run of model Charlie911/MultiLora-drop-sharegpt 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 2024-01-23T20:13:52.401722(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" ]

[ "task_categories:other", "task_ids:parsing", "size_categories:0<n<1k", "license:other", "other", "region:us" ]

[]

[]

[ "# Testing Datasets", "### How to use the dataset" ]

[ "TAGS\n#task_categories-other #task_ids-parsing #size_categories-0<n<1k #license-other #other #region-us \n", "# Testing Datasets", "### How to use the dataset" ]

[ "license:apache-2.0", "arxiv:2402.04335", "region:us" ]

[ "2402.04335" ]

[]

[ "## Overview\nLegalLensNER is a dedicated dataset designed for Named Entity Recognition (NER) in the legal domain, with a specific emphasis on detecting legal violations in unstructured texts.", "## Data Fields\n\n- id: (int) A unique identifier for each record.\n- word: (str) The specific word or token in the text.\n- label: (str) The entity class assigned to the word, one of Law, Violation, Violated By, or Violated On.\n- start: (int) The starting character index of the word in the text.\n- end: (int) The ending character index of the word in the text.", "## Data Generation\nThe LegalLensNER dataset was generated through a detailed process involving automated data generation with GPT-4 for synthetic data production and manual reviews by experienced legal annotators.", "## Collaborations and Contributions\nThe LegalLensNER dataset stands out as a specialized resource for NER tasks within the legal domain, providing an extensive foundation for legal text analysis, information extraction, and fostering the progression of legal NLP research and applications.\nLegalLensNER is open for further enrichment through contributions and collaborations. Researchers and practitioners with an interest in legal NLP are encouraged to contribute or engage in collaborative projects to enhance the dataset's breadth and depth.", "## Curation Rationale\nThe dataset was curated by URL (2023).", "## Data Instances\nTo access the dataset, the following code snippet can be used:\n\n\n\n\n*TBD \n*LegalLens: Leveraging LLMs for Legal Violation Identification in Unstructured Text*\n*Proceedings of the 2024 European Chapter of the Association for Computational Linguistics. Malta. 2024*" ]

[ "TAGS\n#license-apache-2.0 #arxiv-2402.04335 #region-us \n", "## Overview\nLegalLensNER is a dedicated dataset designed for Named Entity Recognition (NER) in the legal domain, with a specific emphasis on detecting legal violations in unstructured texts.", "## Data Fields\n\n- id: (int) A unique identifier for each record.\n- word: (str) The specific word or token in the text.\n- label: (str) The entity class assigned to the word, one of Law, Violation, Violated By, or Violated On.\n- start: (int) The starting character index of the word in the text.\n- end: (int) The ending character index of the word in the text.", "## Data Generation\nThe LegalLensNER dataset was generated through a detailed process involving automated data generation with GPT-4 for synthetic data production and manual reviews by experienced legal annotators.", "## Collaborations and Contributions\nThe LegalLensNER dataset stands out as a specialized resource for NER tasks within the legal domain, providing an extensive foundation for legal text analysis, information extraction, and fostering the progression of legal NLP research and applications.\nLegalLensNER is open for further enrichment through contributions and collaborations. Researchers and practitioners with an interest in legal NLP are encouraged to contribute or engage in collaborative projects to enhance the dataset's breadth and depth.", "## Curation Rationale\nThe dataset was curated by URL (2023).", "## Data Instances\nTo access the dataset, the following code snippet can be used:\n\n\n\n\n*TBD \n*LegalLens: Leveraging LLMs for Legal Violation Identification in Unstructured Text*\n*Proceedings of the 2024 European Chapter of the Association for Computational Linguistics. Malta. 2024*" ]

[ "task_categories:text-classification", "license:apache-2.0", "code", "arxiv:2001.00059", "region:us" ]

[ "2001.00059" ]

[]

[ "# CuBERT ETH150 Open Benchmarks\n\nThis is an unofficial HuggingFace upload of the CuBERT ETH150 Open Benchmarks. This dataset was released along with Learning and Evaluating Contextual Embedding of Source Code.\n\n---", "## Benchmarks and Fine-Tuned Models\n\nHere we describe the 6 Python benchmarks we created. All 6 benchmarks were derived from ETH Py150 Open. All examples are stored as sharded text files. Each text line corresponds to a separate example encoded as a JSON object. For each dataset, we release separate training/validation/testing splits along the same boundaries that ETH Py150 Open splits its files to the corresponding splits. The fine-tuned models are the checkpoints of each model with the highest validation accuracy.\n\n1. Function-docstring classification. Combinations of functions with their correct or incorrect documentation string, used to train a classifier that can tell which pairs go together. The JSON fields are:\n * 'function': string, the source code of a function as text\n * 'docstring': string, the documentation string for that function. Note that the string is unquoted. To be able to properly tokenize it with the CuBERT tokenizers, you have to wrap it in quotes first. For example, in Python, use 'string_to_tokenize = f'\"\"\"{docstring}\"\"\"''.\n * 'label': string, one of (“Incorrect”, “Correct”), the label of the example.\n * 'info': string, an unformatted description of how the example was constructed, including the source dataset (always “ETHPy150Open”), the repository and filepath, the function name and, for “Incorrect” examples, the function whose docstring was substituted.\n1. Exception classification. Combinations of functions where one exception type has been masked, along with a label indicating the masked exception type. The JSON fields are:\n * 'function': string, the source code of a function as text, in which one exception type has been replaced with the special token “__HOLE__”\n * 'label': string, one of ('ValueError', 'KeyError', 'AttributeError', 'TypeError', 'OSError', 'IOError', 'ImportError', 'IndexError', 'DoesNotExist', 'KeyboardInterrupt', 'StopIteration', 'AssertionError', 'SystemExit', 'RuntimeError', 'HTTPError', 'UnicodeDecodeError', 'NotImplementedError', 'ValidationError', 'ObjectDoesNotExist', 'NameError', 'None'), the masked exception type. Note that 'None' never occurs in the data and will be removed in a future release.\n * 'info': string, an unformatted description of how the example was constructed, including the source dataset (always “ETHPy150Open”), the repository and filepath, and the fully-qualified function name.\n1. Variable-misuse classification. Combinations of functions where one use of a variable may have been replaced with another variable defined in the same context, along with a label indicating if this bug-injection has occurred. The JSON fields are:\n * 'function': string, the source code of a function as text.\n * 'label': string, one of (“Correct”, “Variable misuse”) indicating if this is a buggy or bug-free example.\n * 'info': string, an unformatted description of how the example was constructed, including the source dataset (always “ETHPy150Open”), the repository and filepath, the function, and whether the example is bugfree (marked “original”) or the variable substitution that has occurred (e.g., “correct_variable” → “incorrect_variable”).\n1. Swapped-operand classification. Combinations of functions where one use binary operator’s arguments have been swapped, to create a buggy example, or left undisturbed, along with a label indicating if this bug-injection has occurred. The JSON fields are:\n * 'function': string, the source code of a function as text.\n * 'label': string, one of (“Correct”, “Swapped operands”) indicating if this is a buggy or bug-free example.\n * 'info': string, an unformatted description of how the example was constructed, including the source dataset (always “ETHPy150Open”), the repository and filepath, the function, and whether the example is bugfree (marked “original”) or the operand swap has occurred (e.g., “swapped operands of 'not in'”).\n1. Wrong-binary-operator classification. Combinations of functions where one binary operator has been swapped with another, to create a buggy example, or left undisturbed, along with a label indicating if this bug-injection has occurred. The JSON fields are:\n * 'function': string, the source code of a function as text.\n * 'label': string, one of (“Correct”, “Wrong binary operator”) indicating if this is a buggy or bug-free example.\n * 'info': string, an unformatted description of how the example was constructed, including the source dataset (always “ETHPy150Open”), the repository and filepath, the function, and whether the example is bugfree (marked “original”) or the operator replacement has occurred (e.g., “'=='-> '!='”).\n1. Variable-misuse localization and repair. Combinations of functions where one use of a variable may have been replaced with another variable defined in the same context, along with information that can be used to localize and repair the bug, as well as the location of the bug if such a bug exists. The JSON fields are:\n * 'function': a list of strings, the source code of a function, tokenized with the vocabulary from item b. Note that, unlike other task datasets, this dataset gives a tokenized function, rather than the code as a single string.\n * 'target_mask': a list of integers (0 or 1). If the integer at some position is 1, then the token at the corresponding position of the function token list is a correct repair for the introduced bug. If a variable has been split into multiple tokens, only the first subtoken is marked in this mask. If the example is bug-free, all integers are 0.\n * 'error_location_mask': a list of integers (0 or 1). If the integer at some position is 1, then there is a variable-misuse bug at the corresponding location of the tokenized function. In a bug-free example, the first integer is 1. There is exactly one integer set to 1 for all examples. If a variable has been split into multiple tokens, only the first subtoken is marked in this mask.\n * 'candidate_mask': a list of integers (0 or 1). If the integer at some position is 1, then the variable starting at that position in the tokenized function is a candidate to consider when repairing a bug. Candidates are all variables defined in the function parameters or via variable declarations in the function. If a variable has been split into multiple tokens, only the first subtoken is marked in this mask, for each candidate.\n * 'provenance': string, an unformatted description of how the example was constructed, including the source dataset (always “ETHPy150Open”), the repository and filepath, the function, and whether the example is bugfree (marked “original”) or the buggy/repair token positions and variables (e.g., “16/18 'kwargs' → 'self'”). 16 is the position of the introduced error, 18 is the location of the repair." ]

[ "TAGS\n#task_categories-text-classification #license-apache-2.0 #code #arxiv-2001.00059 #region-us \n", "# CuBERT ETH150 Open Benchmarks\n\nThis is an unofficial HuggingFace upload of the CuBERT ETH150 Open Benchmarks. This dataset was released along with Learning and Evaluating Contextual Embedding of Source Code.\n\n---", "## Benchmarks and Fine-Tuned Models\n\nHere we describe the 6 Python benchmarks we created. All 6 benchmarks were derived from ETH Py150 Open. All examples are stored as sharded text files. Each text line corresponds to a separate example encoded as a JSON object. For each dataset, we release separate training/validation/testing splits along the same boundaries that ETH Py150 Open splits its files to the corresponding splits. The fine-tuned models are the checkpoints of each model with the highest validation accuracy.\n\n1. Function-docstring classification. Combinations of functions with their correct or incorrect documentation string, used to train a classifier that can tell which pairs go together. The JSON fields are:\n * 'function': string, the source code of a function as text\n * 'docstring': string, the documentation string for that function. Note that the string is unquoted. To be able to properly tokenize it with the CuBERT tokenizers, you have to wrap it in quotes first. For example, in Python, use 'string_to_tokenize = f'\"\"\"{docstring}\"\"\"''.\n * 'label': string, one of (“Incorrect”, “Correct”), the label of the example.\n * 'info': string, an unformatted description of how the example was constructed, including the source dataset (always “ETHPy150Open”), the repository and filepath, the function name and, for “Incorrect” examples, the function whose docstring was substituted.\n1. Exception classification. Combinations of functions where one exception type has been masked, along with a label indicating the masked exception type. The JSON fields are:\n * 'function': string, the source code of a function as text, in which one exception type has been replaced with the special token “__HOLE__”\n * 'label': string, one of ('ValueError', 'KeyError', 'AttributeError', 'TypeError', 'OSError', 'IOError', 'ImportError', 'IndexError', 'DoesNotExist', 'KeyboardInterrupt', 'StopIteration', 'AssertionError', 'SystemExit', 'RuntimeError', 'HTTPError', 'UnicodeDecodeError', 'NotImplementedError', 'ValidationError', 'ObjectDoesNotExist', 'NameError', 'None'), the masked exception type. Note that 'None' never occurs in the data and will be removed in a future release.\n * 'info': string, an unformatted description of how the example was constructed, including the source dataset (always “ETHPy150Open”), the repository and filepath, and the fully-qualified function name.\n1. Variable-misuse classification. Combinations of functions where one use of a variable may have been replaced with another variable defined in the same context, along with a label indicating if this bug-injection has occurred. The JSON fields are:\n * 'function': string, the source code of a function as text.\n * 'label': string, one of (“Correct”, “Variable misuse”) indicating if this is a buggy or bug-free example.\n * 'info': string, an unformatted description of how the example was constructed, including the source dataset (always “ETHPy150Open”), the repository and filepath, the function, and whether the example is bugfree (marked “original”) or the variable substitution that has occurred (e.g., “correct_variable” → “incorrect_variable”).\n1. Swapped-operand classification. Combinations of functions where one use binary operator’s arguments have been swapped, to create a buggy example, or left undisturbed, along with a label indicating if this bug-injection has occurred. The JSON fields are:\n * 'function': string, the source code of a function as text.\n * 'label': string, one of (“Correct”, “Swapped operands”) indicating if this is a buggy or bug-free example.\n * 'info': string, an unformatted description of how the example was constructed, including the source dataset (always “ETHPy150Open”), the repository and filepath, the function, and whether the example is bugfree (marked “original”) or the operand swap has occurred (e.g., “swapped operands of 'not in'”).\n1. Wrong-binary-operator classification. Combinations of functions where one binary operator has been swapped with another, to create a buggy example, or left undisturbed, along with a label indicating if this bug-injection has occurred. The JSON fields are:\n * 'function': string, the source code of a function as text.\n * 'label': string, one of (“Correct”, “Wrong binary operator”) indicating if this is a buggy or bug-free example.\n * 'info': string, an unformatted description of how the example was constructed, including the source dataset (always “ETHPy150Open”), the repository and filepath, the function, and whether the example is bugfree (marked “original”) or the operator replacement has occurred (e.g., “'=='-> '!='”).\n1. Variable-misuse localization and repair. Combinations of functions where one use of a variable may have been replaced with another variable defined in the same context, along with information that can be used to localize and repair the bug, as well as the location of the bug if such a bug exists. The JSON fields are:\n * 'function': a list of strings, the source code of a function, tokenized with the vocabulary from item b. Note that, unlike other task datasets, this dataset gives a tokenized function, rather than the code as a single string.\n * 'target_mask': a list of integers (0 or 1). If the integer at some position is 1, then the token at the corresponding position of the function token list is a correct repair for the introduced bug. If a variable has been split into multiple tokens, only the first subtoken is marked in this mask. If the example is bug-free, all integers are 0.\n * 'error_location_mask': a list of integers (0 or 1). If the integer at some position is 1, then there is a variable-misuse bug at the corresponding location of the tokenized function. In a bug-free example, the first integer is 1. There is exactly one integer set to 1 for all examples. If a variable has been split into multiple tokens, only the first subtoken is marked in this mask.\n * 'candidate_mask': a list of integers (0 or 1). If the integer at some position is 1, then the variable starting at that position in the tokenized function is a candidate to consider when repairing a bug. Candidates are all variables defined in the function parameters or via variable declarations in the function. If a variable has been split into multiple tokens, only the first subtoken is marked in this mask, for each candidate.\n * 'provenance': string, an unformatted description of how the example was constructed, including the source dataset (always “ETHPy150Open”), the repository and filepath, the function, and whether the example is bugfree (marked “original”) or the buggy/repair token positions and variables (e.g., “16/18 'kwargs' → 'self'”). 16 is the position of the introduced error, 18 is the location of the repair." ]

[ "task_categories:text-classification", "task_ids:intent-classification", "multilinguality:monolingual", "size_categories:1K<n<10K", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for \"kmb-ai/vaia-ic\"", "## Dataset Description\n\n- Repository: vaia-ner\n- Dataset: vaia-ic\n- Domain: Hotel Q&A\n- Number of Classes: 7", "### Dataset Summary\nvaia-ic is an intent classification dataset created as part of Vaia project.", "## Dataset Structure", "### Data Instances\nExamples of 'train' looks as follows." ]

[ "TAGS\n#task_categories-text-classification #task_ids-intent-classification #multilinguality-monolingual #size_categories-1K<n<10K #language-English #license-other #region-us \n", "# Dataset Card for \"kmb-ai/vaia-ic\"", "## Dataset Description\n\n- Repository: vaia-ner\n- Dataset: vaia-ic\n- Domain: Hotel Q&A\n- Number of Classes: 7", "### Dataset Summary\nvaia-ic is an intent classification dataset created as part of Vaia project.", "## Dataset Structure", "### Data Instances\nExamples of 'train' looks as follows." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"Trial7\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"Trial7\"\n\nMore Information needed" ]

[ "task_categories:text-classification", "size_categories:1K<n<10K", "language:en", "license:mit", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for \"10-dim\"", "### Map labels to strings\n\n\n\nMore Information needed" ]

[ "TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-English #license-mit #region-us \n", "# Dataset Card for \"10-dim\"", "### Map labels to strings\n\n\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"trial8\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"trial8\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "## 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 Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "## 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 \"testonly\"\n\nMore Information needed" ]

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

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of xformAI/facebook-opt-125m-qcqa-ub-6-best-for-q-loss\n\n\n\nDataset automatically created during the evaluation run of model xformAI/facebook-opt-125m-qcqa-ub-6-best-for-q-loss 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 2024-01-23T14:48:42.032735(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 xformAI/facebook-opt-125m-qcqa-ub-6-best-for-q-loss\n\n\n\nDataset automatically created during the evaluation run of model xformAI/facebook-opt-125m-qcqa-ub-6-best-for-q-loss 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 2024-01-23T14:48:42.032735(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 Cartinoe5930/TIES-Merging\n\n\n\nDataset automatically created during the evaluation run of model Cartinoe5930/TIES-Merging 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 2024-01-23T14:48:42.091163(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 Cartinoe5930/TIES-Merging\n\n\n\nDataset automatically created during the evaluation run of model Cartinoe5930/TIES-Merging 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 2024-01-23T14:48:42.091163(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 \"multishort_id_rename_filtered\"\n\nMore Information needed" ]

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

[ "region:us" ]

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

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

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of abhinand/telugu-llama-7b-instruct-v0.1\n\n\n\nDataset automatically created during the evaluation run of model abhinand/telugu-llama-7b-instruct-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 2024-01-23T18:07:17.451860(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 abhinand/telugu-llama-7b-instruct-v0.1\n\n\n\nDataset automatically created during the evaluation run of model abhinand/telugu-llama-7b-instruct-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 2024-01-23T18:07:17.451860(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 \"multilong_id_rename_filtered\"\n\nMore Information needed" ]

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

[ "region:us" ]

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

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