Datasets:

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ftopal

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huggingface-datasets

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[ "license:cc-by-sa-4.0", "korean", "hate-speech", "hate-sentence", "region:us" ]

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[ "# SJ-Donald/kor-hate-sentence\n\nSJ-Donald/kor-hate-sentence is merged dataset from fllow", "## Datasets\n\n* smilegate-ai/kor_unsmile\n* korean-hate-speech\n* Curse-detection-data\n* korean-malicious-comments-dataset\n\nMerge datasets from above and drop duplicates.", "## How to use" ]

[ "TAGS\n#license-cc-by-sa-4.0 #korean #hate-speech #hate-sentence #region-us \n", "# SJ-Donald/kor-hate-sentence\n\nSJ-Donald/kor-hate-sentence is merged dataset from fllow", "## Datasets\n\n* smilegate-ai/kor_unsmile\n* korean-hate-speech\n* Curse-detection-data\n* korean-malicious-comments-dataset\n\nMerge datasets from above and drop duplicates.", "## How to use" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of alignment-handbook/zephyr-7b-sft-full\n\n\n\nDataset automatically created during the evaluation run of model alignment-handbook/zephyr-7b-sft-full 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 2024-01-16T04:10:47.293422(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 alignment-handbook/zephyr-7b-sft-full\n\n\n\nDataset automatically created during the evaluation run of model alignment-handbook/zephyr-7b-sft-full 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 2024-01-16T04:10:47.293422(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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[ "TAGS\n#license-apache-2.0 #region-us \n" ]

[ "region:us" ]

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

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO\n\n\n\nDataset automatically created during the evaluation run of model NousResearch/Nous-Hermes-2-Mixtral-8x7B-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 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 2024-01-22T17:09:50.643842(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 NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO\n\n\n\nDataset automatically created during the evaluation run of model NousResearch/Nous-Hermes-2-Mixtral-8x7B-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 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 2024-01-22T17:09:50.643842(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 fionazhang/mistral-environment-all\n\n\n\nDataset automatically created during the evaluation run of model fionazhang/mistral-environment-all 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 2024-01-16T05:12:37.264031(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 fionazhang/mistral-environment-all\n\n\n\nDataset automatically created during the evaluation run of model fionazhang/mistral-environment-all 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 2024-01-16T05:12:37.264031(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:cc-by-4.0", "region:us" ]

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[ "# Dataset Card for FairFace", "## Table of Contents\n- Dataset Card Creation Guide\n - Table of Contents\n - Dataset Description\n - Dataset Summary\n - Dataset Structure\n - Data Instances\n - Data Fields\n - Additional Information\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Repository: URL\n- Paper: FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age for Bias Measurement and Mitigation", "### Dataset Summary\n\nA dataset of human faces annotated with discrete categories for the photographed person's age, sex, and race. Please consider prioritizing a previously created Hugging Face dataset repository for Fair Face as this new dataset repository was only made for downloading issues that may already be resolved.\n\nFor complete details on the dataset's construction and intended uses, please refer to the dataset's official repository or paper.", "## Dataset Structure", "### Data Instances\n\nEach instance contains an image and discrete categories for age, gender, and race.", "### Data Fields\n\n- 'file': an image of a human face with either padding = 0.25 or padding = 1.25 depending on the dataset config\n- 'age': a category describing the age of the person in the image limited to *0-2*, *3-9*, *10-19*, *20-29*, *30-39*, *40-49*, *50-59*, *60-69*, and *more than 70*\n- 'gender': a category describing the sex of the person in the image limited to *Male* and *Female*\n- 'race': a category describing the race of the person in the image limited to *East Asian*, *Indian*, *Black*, *White*, *Middle Eastern*, *Latino_Hispanic*, and *Southeast Asian*\n- 'service_test': please refer to this issue from the dataset's official repository", "## Additional Information", "### Licensing Information\n\nAccording to the official repository, FairFace is licensed under CC BY 4.0." ]

[ "TAGS\n#license-cc-by-4.0 #region-us \n", "# Dataset Card for FairFace", "## Table of Contents\n- Dataset Card Creation Guide\n - Table of Contents\n - Dataset Description\n - Dataset Summary\n - Dataset Structure\n - Data Instances\n - Data Fields\n - Additional Information\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Repository: URL\n- Paper: FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age for Bias Measurement and Mitigation", "### Dataset Summary\n\nA dataset of human faces annotated with discrete categories for the photographed person's age, sex, and race. Please consider prioritizing a previously created Hugging Face dataset repository for Fair Face as this new dataset repository was only made for downloading issues that may already be resolved.\n\nFor complete details on the dataset's construction and intended uses, please refer to the dataset's official repository or paper.", "## Dataset Structure", "### Data Instances\n\nEach instance contains an image and discrete categories for age, gender, and race.", "### Data Fields\n\n- 'file': an image of a human face with either padding = 0.25 or padding = 1.25 depending on the dataset config\n- 'age': a category describing the age of the person in the image limited to *0-2*, *3-9*, *10-19*, *20-29*, *30-39*, *40-49*, *50-59*, *60-69*, and *more than 70*\n- 'gender': a category describing the sex of the person in the image limited to *Male* and *Female*\n- 'race': a category describing the race of the person in the image limited to *East Asian*, *Indian*, *Black*, *White*, *Middle Eastern*, *Latino_Hispanic*, and *Southeast Asian*\n- 'service_test': please refer to this issue from the dataset's official repository", "## Additional Information", "### Licensing Information\n\nAccording to the official repository, FairFace is licensed under CC BY 4.0." ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of stanford-oval/Llama-2-7b-WikiChat-fused\n\n\n\nDataset automatically created during the evaluation run of model stanford-oval/Llama-2-7b-WikiChat-fused 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 2024-01-16T05:29:49.524111(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 stanford-oval/Llama-2-7b-WikiChat-fused\n\n\n\nDataset automatically created during the evaluation run of model stanford-oval/Llama-2-7b-WikiChat-fused 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 2024-01-16T05:29:49.524111(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\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 FelixChao/ExtremeDolphin-MoE\n\n\n\nDataset automatically created during the evaluation run of model FelixChao/ExtremeDolphin-MoE on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-16T06:21:06.917044(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 FelixChao/ExtremeDolphin-MoE\n\n\n\nDataset automatically created during the evaluation run of model FelixChao/ExtremeDolphin-MoE on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-16T06:21:06.917044(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

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

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

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

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

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

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

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[]

[ "# Apache-2.0 + WhiteRabbitNeo Extended Version", "# Licence: Usage Restrictions" ]

[ "TAGS\n#license-other #region-us \n", "# Apache-2.0 + WhiteRabbitNeo Extended Version", "# Licence: Usage Restrictions" ]

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

[]

[]

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

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

[ "task_categories:question-answering", "task_categories:conversational", "size_categories:10K<n<100K", "language:en", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-question-answering #task_categories-conversational #size_categories-10K<n<100K #language-English #region-us \n" ]

[ "task_categories:question-answering", "task_categories:conversational", "size_categories:10K<n<100K", "language:en", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-question-answering #task_categories-conversational #size_categories-10K<n<100K #language-English #region-us \n" ]

[ "task_categories:question-answering", "task_categories:conversational", "size_categories:1K<n<10K", "language:en", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-question-answering #task_categories-conversational #size_categories-1K<n<10K #language-English #region-us \n" ]

[ "license:cc-by-nc-4.0", "arxiv:2207.03051", "region:us" ]

[ "2207.03051" ]

[]

[ "### Click dataset\n\n\nname: query\\_id, dtype: string, description: Baidu query\\_id\nname: query\\_md5, dtype: string, description: MD5 hash of query text\nname: url\\_md5, dtype: List[string], description: MD5 hash of document url, most reliable document identifier\nname: text\\_md5, dtype: List[string], description: MD5 hash of document title and abstract\nname: query\\_document\\_embedding, dtype: Tensor[float16], description: BERT CLS token\nname: click, dtype: Tensor[int32], description: Click / no click on a document\nname: n, dtype: int32, description: Number of documents for current query, useful for padding\nname: position, dtype: Tensor[int32], description: Position in ranking (does not always match original item position)\nname: media\\_type, dtype: Tensor[int32], description: Document type (label encoding recommended as ids do not occupy a continous integer range)\nname: displayed\\_time, dtype: Tensor[float32], description: Seconds a document was displayed on screen\nname: serp\\_height, dtype: Tensor[int32], description: Pixel height of a document on screen\nname: slipoff\\_count\\_after\\_click, dtype: Tensor[int32], description: Number of times a document was scrolled off screen after previously clicking on it", "### Expert annotation dataset\n\n\nname: query\\_id, dtype: string, description: Baidu query\\_id\nname: query\\_md5, dtype: string, description: MD5 hash of query text\nname: text\\_md5, dtype: List[string], description: MD5 hash of document title and abstract\nname: query\\_document\\_embedding, dtype: Tensor[float16], description: BERT CLS token\nname: label, dtype: Tensor[int32], description: Relevance judgment on a scale from 0 (bad) to 4 (excellent)\nname: n, dtype: int32, description: Number of documents for current query, useful for padding\nname: frequency\\_bucket, dtype: int32, description: Monthly frequency of query (bucket) from 0 (high frequency) to 9 (low frequency)\n\n\nExample PyTorch collate function\n--------------------------------\n\n\nEach sample in the dataset is a single query with multiple documents.\nThe following example demonstrates how to create a batch containing multiple queries with varying numbers of documents by applying padding:" ]

[ "TAGS\n#license-cc-by-nc-4.0 #arxiv-2207.03051 #region-us \n", "### Click dataset\n\n\nname: query\\_id, dtype: string, description: Baidu query\\_id\nname: query\\_md5, dtype: string, description: MD5 hash of query text\nname: url\\_md5, dtype: List[string], description: MD5 hash of document url, most reliable document identifier\nname: text\\_md5, dtype: List[string], description: MD5 hash of document title and abstract\nname: query\\_document\\_embedding, dtype: Tensor[float16], description: BERT CLS token\nname: click, dtype: Tensor[int32], description: Click / no click on a document\nname: n, dtype: int32, description: Number of documents for current query, useful for padding\nname: position, dtype: Tensor[int32], description: Position in ranking (does not always match original item position)\nname: media\\_type, dtype: Tensor[int32], description: Document type (label encoding recommended as ids do not occupy a continous integer range)\nname: displayed\\_time, dtype: Tensor[float32], description: Seconds a document was displayed on screen\nname: serp\\_height, dtype: Tensor[int32], description: Pixel height of a document on screen\nname: slipoff\\_count\\_after\\_click, dtype: Tensor[int32], description: Number of times a document was scrolled off screen after previously clicking on it", "### Expert annotation dataset\n\n\nname: query\\_id, dtype: string, description: Baidu query\\_id\nname: query\\_md5, dtype: string, description: MD5 hash of query text\nname: text\\_md5, dtype: List[string], description: MD5 hash of document title and abstract\nname: query\\_document\\_embedding, dtype: Tensor[float16], description: BERT CLS token\nname: label, dtype: Tensor[int32], description: Relevance judgment on a scale from 0 (bad) to 4 (excellent)\nname: n, dtype: int32, description: Number of documents for current query, useful for padding\nname: frequency\\_bucket, dtype: int32, description: Monthly frequency of query (bucket) from 0 (high frequency) to 9 (low frequency)\n\n\nExample PyTorch collate function\n--------------------------------\n\n\nEach sample in the dataset is a single query with multiple documents.\nThe following example demonstrates how to create a batch containing multiple queries with varying numbers of documents by applying padding:" ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of Technoculture/Medtulu-2x7b\n\n\n\nDataset automatically created during the evaluation run of model Technoculture/Medtulu-2x7b 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 2024-01-16T08:08:44.091130(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 Technoculture/Medtulu-2x7b\n\n\n\nDataset automatically created during the evaluation run of model Technoculture/Medtulu-2x7b 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 2024-01-16T08:08:44.091130(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" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"repo-codegen-py-py-context-path-distance\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"repo-codegen-py-py-context-path-distance\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"repo-codegen-py-non-py-context-path-distance\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"repo-codegen-py-non-py-context-path-distance\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"repo-codegen-py-all-context-path-distance\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"repo-codegen-py-all-context-path-distance\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of Technoculture/Medorca-2x7b\n\n\n\nDataset automatically created during the evaluation run of model Technoculture/Medorca-2x7b 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 2024-01-16T08:17:44.723678(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 Technoculture/Medorca-2x7b\n\n\n\nDataset automatically created during the evaluation run of model Technoculture/Medorca-2x7b 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 2024-01-16T08:17:44.723678(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 \"myridade_dbg_aligned_ontologie\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of Technoculture/Mediquad-4x7b\n\n\n\nDataset automatically created during the evaluation run of model Technoculture/Mediquad-4x7b 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 2024-01-16T08:24:28.609699(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 Technoculture/Mediquad-4x7b\n\n\n\nDataset automatically created during the evaluation run of model Technoculture/Mediquad-4x7b 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 2024-01-16T08:24:28.609699(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 of proteins created by diffusion models" ]

[ "TAGS\n#region-us \n", "# Dataset of proteins created by diffusion models" ]

[ "task_categories:text-retrieval", "task_ids:document-retrieval", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:miracl", "language:ko", "text-retrieval", "region:us" ]

[]

[ "ko" ]

[ "# Ko-miracl\n\nThis dataset represents a conversion of the Korean (Ko) section from the miracl dataset into the BeIR format, making it compatible for use with mteb." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-document-retrieval #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-miracl #language-Korean #text-retrieval #region-us \n", "# Ko-miracl\n\nThis dataset represents a conversion of the Korean (Ko) section from the miracl dataset into the BeIR format, making it compatible for use with mteb." ]

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

[]

[]

[ "# first try\ntransfer amazing dataset" ]

[ "TAGS\n#license-apache-2.0 #region-us \n", "# first try\ntransfer amazing dataset" ]

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

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "### Please give the repo a :star:\n\n\n\nUsage\n=====\n\n\nURL\n\n\nContributing\n============\n\n\nContributions are welcome!\n\n\nCheck out the repo's issue backlog for source requests and bug reports.\n\n\nLicense\n-------\n\n\n\n```\nCopyright 2015 Javier Tomás\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\nURL\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n\n```\n\nDisclaimer\n----------\n\n\nThis project is not affiliated with Tachiyomi. Don't ask for help about these extensions at the official support means of Tachiyomi. All credits to the codebase goes to the original contributors." ]

[ "TAGS\n#region-us \n", "### Please give the repo a :star:\n\n\n\nUsage\n=====\n\n\nURL\n\n\nContributing\n============\n\n\nContributions are welcome!\n\n\nCheck out the repo's issue backlog for source requests and bug reports.\n\n\nLicense\n-------\n\n\n\n```\nCopyright 2015 Javier Tomás\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\nURL\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n\n```\n\nDisclaimer\n----------\n\n\nThis project is not affiliated with Tachiyomi. Don't ask for help about these extensions at the official support means of Tachiyomi. All credits to the codebase goes to the original contributors." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"esc50_synth\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"esc50_synth\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"DUC2004\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"DUC2004\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of Technoculture/Medtulu-4x7B\n\n\n\nDataset automatically created during the evaluation run of model Technoculture/Medtulu-4x7B 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 2024-01-16T09:26:06.099420(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 Technoculture/Medtulu-4x7B\n\n\n\nDataset automatically created during the evaluation run of model Technoculture/Medtulu-4x7B 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 2024-01-16T09:26:06.099420(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 Technoculture/Medorca-4x7b\n\n\n\nDataset automatically created during the evaluation run of model Technoculture/Medorca-4x7b 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 2024-01-16T09:31:13.388605(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 Technoculture/Medorca-4x7b\n\n\n\nDataset automatically created during the evaluation run of model Technoculture/Medorca-4x7b 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 2024-01-16T09:31:13.388605(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:image-classification", "size_categories:1K<n<10K", "language:en", "license:mit", "arxiv:2205.15480", "region:us" ]

[ "2205.15480" ]

[ "en" ]

[]

[ "TAGS\n#task_categories-image-classification #size_categories-1K<n<10K #language-English #license-mit #arxiv-2205.15480 #region-us \n" ]

[ "region:us" ]

[]

[]

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

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

[ "task_categories:question-answering", "task_categories:text-generation", "task_categories:conversational", "size_categories:1M<n<10M", "language:en", "license:apache-2.0", "health", "medical", "therapy", "hypnosis", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-question-answering #task_categories-text-generation #task_categories-conversational #size_categories-1M<n<10M #language-English #license-apache-2.0 #health #medical #therapy #hypnosis #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of qnguyen3/quan-1.8b-chat\n\n\n\nDataset automatically created during the evaluation run of model qnguyen3/quan-1.8b-chat 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 2024-01-16T11:13:19.271174(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 qnguyen3/quan-1.8b-chat\n\n\n\nDataset automatically created during the evaluation run of model qnguyen3/quan-1.8b-chat 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 2024-01-16T11:13:19.271174(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 kevin009/Llamafia\n\n\n\nDataset automatically created during the evaluation run of model kevin009/Llamafia 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 2024-01-16T11:18:55.714824(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 kevin009/Llamafia\n\n\n\nDataset automatically created during the evaluation run of model kevin009/Llamafia 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 2024-01-16T11:18:55.714824(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:10M<n<100M", "license:mit", "Imitation Learning", "Expert Trajectory", "region:us" ]

[]

[]

[ "# LunarLander-v2 - Imitation Learning Datasets\n\nThis is a dataset created by Imitation Learning Datasets project. \nIt was created by using Stable Baselines weights from a PPO policy from HuggingFace.", "## Description\n\nThe dataset consists of 1,000 episodes with an average episodic reward of 500.\nEach entry consists of:", "## Usage\n\nFeel free to download and use the 'URL' dataset as you please.\nIf you are interested in using our PyTorch Dataset implementation, feel free to check the IL Datasets project.\nThere, we implement a base Dataset that downloads this dataset and all other datasets directly from HuggingFace.\nThe Baseline Dataset also allows for more control over train and test splits and how many episodes you want to use (in cases where the 1k episodes are not necessary).\n\nComing soon." ]

[ "TAGS\n#size_categories-10M<n<100M #license-mit #Imitation Learning #Expert Trajectory #region-us \n", "# LunarLander-v2 - Imitation Learning Datasets\n\nThis is a dataset created by Imitation Learning Datasets project. \nIt was created by using Stable Baselines weights from a PPO policy from HuggingFace.", "## Description\n\nThe dataset consists of 1,000 episodes with an average episodic reward of 500.\nEach entry consists of:", "## Usage\n\nFeel free to download and use the 'URL' dataset as you please.\nIf you are interested in using our PyTorch Dataset implementation, feel free to check the IL Datasets project.\nThere, we implement a base Dataset that downloads this dataset and all other datasets directly from HuggingFace.\nThe Baseline Dataset also allows for more control over train and test splits and how many episodes you want to use (in cases where the 1k episodes are not necessary).\n\nComing soon." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of ycros/BagelMIsteryTour-8x7B\n\n\n\nDataset automatically created during the evaluation run of model ycros/BagelMIsteryTour-8x7B 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 2024-01-16T11:57:17.024146(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 ycros/BagelMIsteryTour-8x7B\n\n\n\nDataset automatically created during the evaluation run of model ycros/BagelMIsteryTour-8x7B 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 2024-01-16T11:57:17.024146(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 xDAN2099/xDAN-L2-moe-2x-v1\n\n\n\nDataset automatically created during the evaluation run of model xDAN2099/xDAN-L2-moe-2x-v1 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 2024-01-16T11:58:02.756350(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 xDAN2099/xDAN-L2-moe-2x-v1\n\n\n\nDataset automatically created during the evaluation run of model xDAN2099/xDAN-L2-moe-2x-v1 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 2024-01-16T11:58:02.756350(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 \"training_v0.1.0-public\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"training_v0.1.0-public\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"vsums_synthetic_wikipedia_seed_10k\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"vsums_synthetic_wikipedia_seed_10k\"\n\nMore Information needed" ]

[ "region:us" ]

[]

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[ "# Dataset Card for Evaluation run of adamo1139/yi-34b-200k-rawrr-dpo-1\n\n\n\nDataset automatically created during the evaluation run of model adamo1139/yi-34b-200k-rawrr-dpo-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 2024-01-16T12:24:51.812406(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-rawrr-dpo-1\n\n\n\nDataset automatically created during the evaluation run of model adamo1139/yi-34b-200k-rawrr-dpo-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 2024-01-16T12:24:51.812406(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:token-classification", "task_categories:text-generation", "size_categories:1K<n<10K", "language:en", "license:apache-2.0", "synthetic", "search-queries", "e-commerce", "online-shops", "travel-agencies", "educational-institutions-ai", "job-recruitment-automation", "banking-digital-services", "investment-ai-analysis", "insurance-tech-innovation", "financial-advisory-ai", "credit-services-automation", "payment-processing-tech", "mortgage-tech-solutions", "real-estate-digital-solutions", "taxation-tech-services", "risk-management-ai", "compliance-automation", "digital-banking-innovation", "mobile-banking-tech", "online-retail-tech", "offline-retail-automation", "automotive-dealership-tech", "restaurant-automation-tech", "food-delivery-ai", "entertainment-platforms-ai", "media-platforms-tech", "government-services-automation", "travel-tech-innovation", "consumer-analytics-ai", "logistics-tech-automation", "supply-chain-ai", "customer-support-tech", "market-research-ai", "mobile-app-dev-tech", "game-dev-ai", "cloud-computing-services", "data-analytics-ai", "business-intelligence-ai", "cybersecurity-software-tech", "ui-ux-design-ai", "iot-development-tech", "project-management-tools-ai", "version-control-systems-tech", "ci-cd-automation", "issue-tracking-ai", "bug-reporting-automation", "collaborative-dev-environments", "team-communication-tech", "task-time-management-ai", "customer-feedback-ai", "cloud-based-dev-tech", "image-stock-platforms-ai", "video-hosting-tech", "social-networks-ai", "professional-social-networks-ai", "dating-apps-tech", "region:us" ]

[]

[ "en" ]

[ "# Synthetic Search Filters\n\nThis is generated with GPT-4 Turbo possible search filters and theirs representations for the given business/service categories:\n\n\nThis is a parsed in the way each row is an unique pair filter - represantation version of 'EmbeddingStudio/synthetic-search-filters-raw'.", "## Columns description\n\n* category (type: Optional[str]) - business/service category name.\n* category_description (type: Optional[str]) - longer description of business/service.\n* filter_name (type: Optional[str]) - meaningful name of filter.\n* representation_name (type: Optional[str]) - name of filter representation.\n* representation_type (type: Optional[str]) - python-like type of representation value (str, int, float, bool)\n* representation_enum (type: (Optional[List[str]])) - is represntation is an enumertation, this is a list of possible values.\n* representation_examples (type: List[Union[str, int, float]])) - exmaples of expected representation values.\n* representation_pattern (type: Optional[str]) - if representation is a pattern-like (e.g. 'dd/mm/YYYY'), this is a pattern to follow.", "## What are representations?\n\nIt's easier to understand with an exmaple. Imagine, you have a filter named 'Rating', so it can be represented as:\n* Integer or float value in 1-5 scale\n* Integer or float value in 1-10 scale\n* Integer or float value in 1-100 scale\n* As the enumeration with values (*, , *, , *)\n* As the enumeration with values (bad, medium, good, the best)", "## Train / test splitting principles\n\nAs we are trying to fine-tune LLM to follow zero-shot query parsing instructions, so we want to test:\n\n* Ability to work well with unseen domain\n* Ability to work well with unseen filters\n* Ability to work well with unseen queries\n\nFor these purposes we:\n\n1. We put into test split 5 categories, completely separared from train: Telecommunication Companies, Legal Services, Enterprise Software Development, Artificial Intelligence and Machine Learning, Documentation and Knowledge Sharing.\n2. Also out of each appearing in train company categories, we put aside / removed one filter and queries related to it.", "# How to use it\n\n\nEmbedding Studio team uses this filters to generate queries and theirs parsed version for Falcon-7B-Instruct fine-tuning to follow Zero-Shot search queries parsing instructions." ]

[ "TAGS\n#task_categories-token-classification #task_categories-text-generation #size_categories-1K<n<10K #language-English #license-apache-2.0 #synthetic #search-queries #e-commerce #online-shops #travel-agencies #educational-institutions-ai #job-recruitment-automation #banking-digital-services #investment-ai-analysis #insurance-tech-innovation #financial-advisory-ai #credit-services-automation #payment-processing-tech #mortgage-tech-solutions #real-estate-digital-solutions #taxation-tech-services #risk-management-ai #compliance-automation #digital-banking-innovation #mobile-banking-tech #online-retail-tech #offline-retail-automation #automotive-dealership-tech #restaurant-automation-tech #food-delivery-ai #entertainment-platforms-ai #media-platforms-tech #government-services-automation #travel-tech-innovation #consumer-analytics-ai #logistics-tech-automation #supply-chain-ai #customer-support-tech #market-research-ai #mobile-app-dev-tech #game-dev-ai #cloud-computing-services #data-analytics-ai #business-intelligence-ai #cybersecurity-software-tech #ui-ux-design-ai #iot-development-tech #project-management-tools-ai #version-control-systems-tech #ci-cd-automation #issue-tracking-ai #bug-reporting-automation #collaborative-dev-environments #team-communication-tech #task-time-management-ai #customer-feedback-ai #cloud-based-dev-tech #image-stock-platforms-ai #video-hosting-tech #social-networks-ai #professional-social-networks-ai #dating-apps-tech #region-us \n", "# Synthetic Search Filters\n\nThis is generated with GPT-4 Turbo possible search filters and theirs representations for the given business/service categories:\n\n\nThis is a parsed in the way each row is an unique pair filter - represantation version of 'EmbeddingStudio/synthetic-search-filters-raw'.", "## Columns description\n\n* category (type: Optional[str]) - business/service category name.\n* category_description (type: Optional[str]) - longer description of business/service.\n* filter_name (type: Optional[str]) - meaningful name of filter.\n* representation_name (type: Optional[str]) - name of filter representation.\n* representation_type (type: Optional[str]) - python-like type of representation value (str, int, float, bool)\n* representation_enum (type: (Optional[List[str]])) - is represntation is an enumertation, this is a list of possible values.\n* representation_examples (type: List[Union[str, int, float]])) - exmaples of expected representation values.\n* representation_pattern (type: Optional[str]) - if representation is a pattern-like (e.g. 'dd/mm/YYYY'), this is a pattern to follow.", "## What are representations?\n\nIt's easier to understand with an exmaple. Imagine, you have a filter named 'Rating', so it can be represented as:\n* Integer or float value in 1-5 scale\n* Integer or float value in 1-10 scale\n* Integer or float value in 1-100 scale\n* As the enumeration with values (*, , *, , *)\n* As the enumeration with values (bad, medium, good, the best)", "## Train / test splitting principles\n\nAs we are trying to fine-tune LLM to follow zero-shot query parsing instructions, so we want to test:\n\n* Ability to work well with unseen domain\n* Ability to work well with unseen filters\n* Ability to work well with unseen queries\n\nFor these purposes we:\n\n1. We put into test split 5 categories, completely separared from train: Telecommunication Companies, Legal Services, Enterprise Software Development, Artificial Intelligence and Machine Learning, Documentation and Knowledge Sharing.\n2. Also out of each appearing in train company categories, we put aside / removed one filter and queries related to it.", "# How to use it\n\n\nEmbedding Studio team uses this filters to generate queries and theirs parsed version for Falcon-7B-Instruct fine-tuning to follow Zero-Shot search queries parsing instructions." ]

[ "task_categories:token-classification", "task_categories:text-generation", "size_categories:1K<n<10K", "language:en", "license:apache-2.0", "synthetic", "search-queries", "e-commerce", "online-shops", "travel-agencies", "educational-institutions-ai", "job-recruitment-automation", "banking-digital-services", "investment-ai-analysis", "insurance-tech-innovation", "financial-advisory-ai", "credit-services-automation", "payment-processing-tech", "mortgage-tech-solutions", "real-estate-digital-solutions", "taxation-tech-services", "risk-management-ai", "compliance-automation", "digital-banking-innovation", "mobile-banking-tech", "online-retail-tech", "offline-retail-automation", "automotive-dealership-tech", "restaurant-automation-tech", "food-delivery-ai", "entertainment-platforms-ai", "media-platforms-tech", "government-services-automation", "travel-tech-innovation", "consumer-analytics-ai", "logistics-tech-automation", "supply-chain-ai", "customer-support-tech", "market-research-ai", "mobile-app-dev-tech", "game-dev-ai", "cloud-computing-services", "data-analytics-ai", "business-intelligence-ai", "cybersecurity-software-tech", "ui-ux-design-ai", "iot-development-tech", "project-management-tools-ai", "version-control-systems-tech", "ci-cd-automation", "issue-tracking-ai", "bug-reporting-automation", "collaborative-dev-environments", "team-communication-tech", "task-time-management-ai", "customer-feedback-ai", "cloud-based-dev-tech", "image-stock-platforms-ai", "video-hosting-tech", "social-networks-ai", "professional-social-networks-ai", "dating-apps-tech", "region:us" ]

[]

[ "en" ]

[ "# Synthetic Search Filters Raw\n\nThis is the raw version of EmbeddingStudio/synthetic-search-filters dataset.\n\nThis is generated with GPT-4 Turbo possible search filters and theirs representations for the given business/service categories:", "## Columns description\n\n* category (type: str) - name of a business / service.\n* filters (type: str) - JSON parsable filters schema\n\nFilters schema is JSON-readable line in the format (we highly recommend you to use it): \nList of filters (dict):\n\n* Name - name of filter (better to be meaningful).\n* Representations - list of possible filter formats (dict):\n * Name - name of representation (better to be meaningful).\n * Type - python base type (int, float, str, bool).\n * Examples - list of examples.\n * Enum - if a representation is enumeration, provide a list of possible values, LLM should map parsed value into this list.\n * Pattern - if a representation is pattern-like (datetime, regexp, etc.) provide a pattern text in any format.", "## What are representations?\n\nIt's easier to understand with an exmaple. Imagine, you have a filter named 'Rating', so it can be represented as:\n* Integer or float value in 1-5 scale\n* Integer or float value in 1-10 scale\n* Integer or float value in 1-100 scale\n* As the enumeration with values (*, , *, , *)\n* As the enumeration with values (bad, medium, good, the best)", "## Train / test splitting principles\n\nAs we are trying to fine-tune LLM to follow zero-shot query parsing instructions, so we want to test:\n\n* Ability to work well with unseen domain\n* Ability to work well with unseen filters\n* Ability to work well with unseen queries\n\nFor these purposes we:\n\n1. We put into test split 5 categories, completely separared from train: Telecommunication Companies, Legal Services, Enterprise Software Development, Artificial Intelligence and Machine Learning, Documentation and Knowledge Sharing.\n2. Also out of each appearing in train company categories, we put aside / removed one filter and queries related to it.", "# How to use it\n\n\nEmbedding Studio team uses this filters to generate queries and theirs parsed version for Falcon-7B-Instruct fine-tuning to follow Zero-Shot search queries parsing instructions." ]

[ "TAGS\n#task_categories-token-classification #task_categories-text-generation #size_categories-1K<n<10K #language-English #license-apache-2.0 #synthetic #search-queries #e-commerce #online-shops #travel-agencies #educational-institutions-ai #job-recruitment-automation #banking-digital-services #investment-ai-analysis #insurance-tech-innovation #financial-advisory-ai #credit-services-automation #payment-processing-tech #mortgage-tech-solutions #real-estate-digital-solutions #taxation-tech-services #risk-management-ai #compliance-automation #digital-banking-innovation #mobile-banking-tech #online-retail-tech #offline-retail-automation #automotive-dealership-tech #restaurant-automation-tech #food-delivery-ai #entertainment-platforms-ai #media-platforms-tech #government-services-automation #travel-tech-innovation #consumer-analytics-ai #logistics-tech-automation #supply-chain-ai #customer-support-tech #market-research-ai #mobile-app-dev-tech #game-dev-ai #cloud-computing-services #data-analytics-ai #business-intelligence-ai #cybersecurity-software-tech #ui-ux-design-ai #iot-development-tech #project-management-tools-ai #version-control-systems-tech #ci-cd-automation #issue-tracking-ai #bug-reporting-automation #collaborative-dev-environments #team-communication-tech #task-time-management-ai #customer-feedback-ai #cloud-based-dev-tech #image-stock-platforms-ai #video-hosting-tech #social-networks-ai #professional-social-networks-ai #dating-apps-tech #region-us \n", "# Synthetic Search Filters Raw\n\nThis is the raw version of EmbeddingStudio/synthetic-search-filters dataset.\n\nThis is generated with GPT-4 Turbo possible search filters and theirs representations for the given business/service categories:", "## Columns description\n\n* category (type: str) - name of a business / service.\n* filters (type: str) - JSON parsable filters schema\n\nFilters schema is JSON-readable line in the format (we highly recommend you to use it): \nList of filters (dict):\n\n* Name - name of filter (better to be meaningful).\n* Representations - list of possible filter formats (dict):\n * Name - name of representation (better to be meaningful).\n * Type - python base type (int, float, str, bool).\n * Examples - list of examples.\n * Enum - if a representation is enumeration, provide a list of possible values, LLM should map parsed value into this list.\n * Pattern - if a representation is pattern-like (datetime, regexp, etc.) provide a pattern text in any format.", "## What are representations?\n\nIt's easier to understand with an exmaple. Imagine, you have a filter named 'Rating', so it can be represented as:\n* Integer or float value in 1-5 scale\n* Integer or float value in 1-10 scale\n* Integer or float value in 1-100 scale\n* As the enumeration with values (*, , *, , *)\n* As the enumeration with values (bad, medium, good, the best)", "## Train / test splitting principles\n\nAs we are trying to fine-tune LLM to follow zero-shot query parsing instructions, so we want to test:\n\n* Ability to work well with unseen domain\n* Ability to work well with unseen filters\n* Ability to work well with unseen queries\n\nFor these purposes we:\n\n1. We put into test split 5 categories, completely separared from train: Telecommunication Companies, Legal Services, Enterprise Software Development, Artificial Intelligence and Machine Learning, Documentation and Knowledge Sharing.\n2. Also out of each appearing in train company categories, we put aside / removed one filter and queries related to it.", "# How to use it\n\n\nEmbedding Studio team uses this filters to generate queries and theirs parsed version for Falcon-7B-Instruct fine-tuning to follow Zero-Shot search queries parsing instructions." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of Weyaxi/Astralis-4x34B\n\n\n\nDataset automatically created during the evaluation run of model Weyaxi/Astralis-4x34B 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 2024-01-16T12:46:01.846891(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 Weyaxi/Astralis-4x34B\n\n\n\nDataset automatically created during the evaluation run of model Weyaxi/Astralis-4x34B 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 2024-01-16T12:46:01.846891(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 \"myridade_dbg_aligned_ontologie_filter_myriade_int_label\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of gagan3012/Multilingual-mistral\n\n\n\nDataset automatically created during the evaluation run of model gagan3012/Multilingual-mistral 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 2024-01-16T13:00:17.256624(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 gagan3012/Multilingual-mistral\n\n\n\nDataset automatically created during the evaluation run of model gagan3012/Multilingual-mistral 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 2024-01-16T13:00:17.256624(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "license:mit", "arxiv:2305.15727", "region:us" ]

[ "2305.15727" ]

[]

[]

[ "TAGS\n#license-mit #arxiv-2305.15727 #region-us \n" ]

[ "task_categories:text-classification", "language:en", "license:cc-by-sa-4.0", "esg reports", "sustainability", "corporate governance", "environmental", "region:us" ]

[]

[ "en" ]

[ "# Company Reports Dataset", "## Description\n\nThis dataset contains ESG (Environmental, Social, and Governance) sustainability reports from various companies. It includes data like company details, report categories, textual analysis of the reports, and more.", "## Dataset Structure\n\n- 'id': Unique identifier for each report entry.\n- 'document_category': Classification of the document (e.g., ESG sustainability report).\n- 'year': Publication year of the report.\n- 'company_name': Name of the respective company.\n- 'company_description': A concise description of the company.\n- 'company_website': The official website URL of the company.\n- 'economic_activity': Sector of economic activity.\n- 'file_name': Filename of the report.\n- 'url': Direct URL to access the report.\n- 'downloaded_at': Date and time when the report was downloaded.\n- 'text_analyzed': The analyzed text extracted from the report.\n- 'tot_text_cleaned': The cleaned version of the report text.\n- 'tot_text_raw': The original, unedited text of the report.\n- 'documents_description': A short description of the documents.", "## Data Processing Versions\n\nDifferent versions of the dataset are available, each processed with specific methodologies:", "### Version: 2024_01_19\n\n- Extraction Methodology: Utilizing 'unstructured' package with the following parameters:\n - Partition method with strategy = fast\n- Translation Methodology:\n - Individual element_id translation using the multilingual model 'facebook/m2m100_418M'\n- Feature Extraction Methodology:\n - Concatenation of cleaned and translated element_ids, with GPT-3.5-turbo model applied to the first 2500 characters for JSON extraction.", "### Versions: 2024_01_21, 2024_01_22\n\n- Extraction Methodology: Employing 'unstructured' package with the following parameters:\n - Partition_pdf method with strategy = auto\n- Translation Methodology:\n - Aggregating into chunks, translating each chunk using Helsinki-NLP/opus-mt-{source_lang}-{target_lang} models. Language detection on each chunk is performed using URL(text_cleaned[:100]).\n- Feature Extraction Methodology:\n - Concatenation of cleaned and translated chunks, with GPT-3.5-turbo model applied to the first 2500 characters for JSON extraction.", "### Version: 2024_01_23\n\n- [Details to be updated]", "## Collection Methodology\n\nThe dataset was collected from publicly available ESG reports of various companies to represent a wide range of industries.", "## Intended Use\n\nThis dataset is suitable for tasks like text analysis, ESG metric analysis, corporate sustainability practice research, and more.", "## Useful link\n\nURL", "## Licensing\n\nThe dataset is available under the CC-BY-SA-4.0 license. Please ensure to adhere to the terms of this license when using or distributing this dataset." ]

[ "TAGS\n#task_categories-text-classification #language-English #license-cc-by-sa-4.0 #esg reports #sustainability #corporate governance #environmental #region-us \n", "# Company Reports Dataset", "## Description\n\nThis dataset contains ESG (Environmental, Social, and Governance) sustainability reports from various companies. It includes data like company details, report categories, textual analysis of the reports, and more.", "## Dataset Structure\n\n- 'id': Unique identifier for each report entry.\n- 'document_category': Classification of the document (e.g., ESG sustainability report).\n- 'year': Publication year of the report.\n- 'company_name': Name of the respective company.\n- 'company_description': A concise description of the company.\n- 'company_website': The official website URL of the company.\n- 'economic_activity': Sector of economic activity.\n- 'file_name': Filename of the report.\n- 'url': Direct URL to access the report.\n- 'downloaded_at': Date and time when the report was downloaded.\n- 'text_analyzed': The analyzed text extracted from the report.\n- 'tot_text_cleaned': The cleaned version of the report text.\n- 'tot_text_raw': The original, unedited text of the report.\n- 'documents_description': A short description of the documents.", "## Data Processing Versions\n\nDifferent versions of the dataset are available, each processed with specific methodologies:", "### Version: 2024_01_19\n\n- Extraction Methodology: Utilizing 'unstructured' package with the following parameters:\n - Partition method with strategy = fast\n- Translation Methodology:\n - Individual element_id translation using the multilingual model 'facebook/m2m100_418M'\n- Feature Extraction Methodology:\n - Concatenation of cleaned and translated element_ids, with GPT-3.5-turbo model applied to the first 2500 characters for JSON extraction.", "### Versions: 2024_01_21, 2024_01_22\n\n- Extraction Methodology: Employing 'unstructured' package with the following parameters:\n - Partition_pdf method with strategy = auto\n- Translation Methodology:\n - Aggregating into chunks, translating each chunk using Helsinki-NLP/opus-mt-{source_lang}-{target_lang} models. Language detection on each chunk is performed using URL(text_cleaned[:100]).\n- Feature Extraction Methodology:\n - Concatenation of cleaned and translated chunks, with GPT-3.5-turbo model applied to the first 2500 characters for JSON extraction.", "### Version: 2024_01_23\n\n- [Details to be updated]", "## Collection Methodology\n\nThe dataset was collected from publicly available ESG reports of various companies to represent a wide range of industries.", "## Intended Use\n\nThis dataset is suitable for tasks like text analysis, ESG metric analysis, corporate sustainability practice research, and more.", "## Useful link\n\nURL", "## Licensing\n\nThe dataset is available under the CC-BY-SA-4.0 license. Please ensure to adhere to the terms of this license when using or distributing this dataset." ]

[ "task_categories:conversational", "language:en", "license:cc-by-4.0", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-conversational #language-English #license-cc-by-4.0 #region-us \n" ]

[ "arxiv:2212.10560", "arxiv:2304.01196", "region:us" ]

[ "2212.10560", "2304.01196" ]

[]

[ "# 🇻🇳 Vietnamese Self-Chat Dataset\n\nThis dataset is designed to enhance the model's ability to engage in multi-turn conversations with humans.\n\nTo construct this dataset, we follow a two-step process:\n\n- Step 1: Instruction Generation\nWe employ the methodology outlined in the Self-Instruct paper to craft a diverse set of instructions. This paper serves as a guide for aligning pretrained language models with specific instructions, providing a structured foundation for subsequent dialogue generation.\n\n- Step 2: Synthetic Self-Chat Conversations\nBuilding upon the instructions generated in the first step, we draw inspiration from the Baize paper. The goal is to simulate synthetic multi-turn interactions that the model can learn from.\n\nBy combining these two steps, we aim to create a robust and versatile dataset that empowers the model to navigate and contribute effectively in complex conversational scenarios. This dataset serves as a valuable resource for refining the model's language understanding and response generation capabilities in the context of human-like dialogue." ]

[ "TAGS\n#arxiv-2212.10560 #arxiv-2304.01196 #region-us \n", "# 🇻🇳 Vietnamese Self-Chat Dataset\n\nThis dataset is designed to enhance the model's ability to engage in multi-turn conversations with humans.\n\nTo construct this dataset, we follow a two-step process:\n\n- Step 1: Instruction Generation\nWe employ the methodology outlined in the Self-Instruct paper to craft a diverse set of instructions. This paper serves as a guide for aligning pretrained language models with specific instructions, providing a structured foundation for subsequent dialogue generation.\n\n- Step 2: Synthetic Self-Chat Conversations\nBuilding upon the instructions generated in the first step, we draw inspiration from the Baize paper. The goal is to simulate synthetic multi-turn interactions that the model can learn from.\n\nBy combining these two steps, we aim to create a robust and versatile dataset that empowers the model to navigate and contribute effectively in complex conversational scenarios. This dataset serves as a valuable resource for refining the model's language understanding and response generation capabilities in the context of human-like dialogue." ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of Cartinoe5930/SOLAR-DUS-implement\n\n\n\nDataset automatically created during the evaluation run of model Cartinoe5930/SOLAR-DUS-implement 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 2024-01-16T14:37:28.066845(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/SOLAR-DUS-implement\n\n\n\nDataset automatically created during the evaluation run of model Cartinoe5930/SOLAR-DUS-implement 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 2024-01-16T14:37:28.066845(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 xriminact/TarsChattyBasev0.2\n\n\n\nDataset automatically created during the evaluation run of model xriminact/TarsChattyBasev0.2 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 2024-01-16T14:41:25.883812(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 xriminact/TarsChattyBasev0.2\n\n\n\nDataset automatically created during the evaluation run of model xriminact/TarsChattyBasev0.2 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 2024-01-16T14:41:25.883812(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 mosaicml/mpt-7b-8k\n\n\n\nDataset automatically created during the evaluation run of model mosaicml/mpt-7b-8k 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 2024-01-16T14:43:48.109992(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 mosaicml/mpt-7b-8k\n\n\n\nDataset automatically created during the evaluation run of model mosaicml/mpt-7b-8k 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 2024-01-16T14:43:48.109992(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 AIGeekLabs/radiantloom-mixtral-8x7b-fusion\n\n\n\nDataset automatically created during the evaluation run of model AIGeekLabs/radiantloom-mixtral-8x7b-fusion 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 2024-01-16T18:14:22.936356(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 AIGeekLabs/radiantloom-mixtral-8x7b-fusion\n\n\n\nDataset automatically created during the evaluation run of model AIGeekLabs/radiantloom-mixtral-8x7b-fusion 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 2024-01-16T18:14:22.936356(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 zhengr/NeuralPipe-7B-slerp\n\n\n\nDataset automatically created during the evaluation run of model zhengr/NeuralPipe-7B-slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-16T14:47:04.829049(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/NeuralPipe-7B-slerp\n\n\n\nDataset automatically created during the evaluation run of model zhengr/NeuralPipe-7B-slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-16T14:47:04.829049(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 \"0-10000-ultrafeedback-binarized-preferences-cleaned-ita\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"0-10000-ultrafeedback-binarized-preferences-cleaned-ita\"\n\nMore Information needed" ]

[ "size_categories:1K<n<10K", "language:en", "license:mit", "arxiv:2312.15685", "region:us" ]

[ "2312.15685" ]

[ "en" ]

[]

[ "TAGS\n#size_categories-1K<n<10K #language-English #license-mit #arxiv-2312.15685 #region-us \n" ]

[ "task_categories:text-generation", "size_categories:1M<n<10M", "language:en", "region:us" ]

[]

[ "en" ]

[ "## LitScan EPMC Subset\n\nThis dataset is a subset of afg1/epmc-oa-subset, \nwhich itself comes from the Europe PMC open access subset of about 5.9 million articles.\n\nHere, we take the ~960 parquet files from the full OA subset and join them against a list of PMCIDs for articles found by LitScan,\nwhich should discuss ncRNA for the ~9.6 million IDs searched from RNAcentral. The result is a collection of just over 1 million open access\nfulltext articles ostensibly about ncRNA.\n\nThe primary use case for this is pre-finetuning on domain specific text. This idea of domain adaptation is similar to what \nNVIDIA have done with their ChipNeMo model.\n\nWe are planning to finetune some models on this dataset, probably TinyLlama, since it is quite quick to train. \nThese will be useful for e.g. generating embeddings for RAG, or further downstream finetuning on tasks like summarisation.", "## Limitations\n\nThe epmc-oa-subset parquet files are parsed from JATS, which does not always go entirely to plan. As a result, there are likely to be some \narticles with missing text, or strange tags left in. These should be quite rare, but I can't guarantee they're not in there.\n\nLitScan itself also has some limitations, namely that there is quite a high false positive rate for those RNA IDs that are a bit generic. This\nmeans that while most of the articles in this dataset should be focused on RNA, there will be a significant minority that are about all sorts of\nother things, including but not limited to: concrete, female mice, recurrent neural networks. This is a very tricky problem to solve!" ]

[ "TAGS\n#task_categories-text-generation #size_categories-1M<n<10M #language-English #region-us \n", "## LitScan EPMC Subset\n\nThis dataset is a subset of afg1/epmc-oa-subset, \nwhich itself comes from the Europe PMC open access subset of about 5.9 million articles.\n\nHere, we take the ~960 parquet files from the full OA subset and join them against a list of PMCIDs for articles found by LitScan,\nwhich should discuss ncRNA for the ~9.6 million IDs searched from RNAcentral. The result is a collection of just over 1 million open access\nfulltext articles ostensibly about ncRNA.\n\nThe primary use case for this is pre-finetuning on domain specific text. This idea of domain adaptation is similar to what \nNVIDIA have done with their ChipNeMo model.\n\nWe are planning to finetune some models on this dataset, probably TinyLlama, since it is quite quick to train. \nThese will be useful for e.g. generating embeddings for RAG, or further downstream finetuning on tasks like summarisation.", "## Limitations\n\nThe epmc-oa-subset parquet files are parsed from JATS, which does not always go entirely to plan. As a result, there are likely to be some \narticles with missing text, or strange tags left in. These should be quite rare, but I can't guarantee they're not in there.\n\nLitScan itself also has some limitations, namely that there is quite a high false positive rate for those RNA IDs that are a bit generic. This\nmeans that while most of the articles in this dataset should be focused on RNA, there will be a significant minority that are about all sorts of\nother things, including but not limited to: concrete, female mice, recurrent neural networks. This is a very tricky problem to solve!" ]

[ "size_categories:1K<n<10K", "language:en", "license:mit", "arxiv:2312.15685", "region:us" ]

[ "2312.15685" ]

[ "en" ]

[]

[ "TAGS\n#size_categories-1K<n<10K #language-English #license-mit #arxiv-2312.15685 #region-us \n" ]

[ "task_categories:text-generation", "task_categories:question-answering", "size_categories:1M<n<10M", "language:en", "language:zh", "license:apache-2.0", "cot", "conversational", "region:us" ]

[]

[ "en", "zh" ]

[ "# Dataset Card for \"CoT_reformatted\"\n\nThis dataset is reformatted from: QingyiSi/Alpaca-CoT\n\nAll credit goes there. Thanks to QingyiSi for the work in consolidating many diverse sources for comparison and cross-file analysis.\n\nThere were some issues loading files from that dataset for a testing project. \n\nI extracted the following data files for this subset:\n\n- alpaca_data_cleaned\n- CoT_data\n- firefly \n- instruct\n- alpaca_gpt4_data\n- dolly \n- GPTeacher\n- thoughtsource\n- finance_en\n- instinwild_en" ]

[ "TAGS\n#task_categories-text-generation #task_categories-question-answering #size_categories-1M<n<10M #language-English #language-Chinese #license-apache-2.0 #cot #conversational #region-us \n", "# Dataset Card for \"CoT_reformatted\"\n\nThis dataset is reformatted from: QingyiSi/Alpaca-CoT\n\nAll credit goes there. Thanks to QingyiSi for the work in consolidating many diverse sources for comparison and cross-file analysis.\n\nThere were some issues loading files from that dataset for a testing project. \n\nI extracted the following data files for this subset:\n\n- alpaca_data_cleaned\n- CoT_data\n- firefly \n- instruct\n- alpaca_gpt4_data\n- dolly \n- GPTeacher\n- thoughtsource\n- finance_en\n- instinwild_en" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"cai-conversation-dev1705423021\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"cai-conversation-dev1705423021\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"FineTuneDataset512FS\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"FineTuneDataset512FS\"\n\nMore Information needed" ]

[ "task_categories:conversational", "language:en", "license:cc-by-4.0", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-conversational #language-English #license-cc-by-4.0 #region-us \n" ]

[ "task_categories:conversational", "task_categories:text-generation", "language:ar", "license:cc-by-nc-4.0", "arxiv:2203.02155", "region:us" ]

[ "2203.02155" ]

[ "ar" ]

[ "### بطاقة مجموعة البيانات لـ \"لا روبوت\" ‍️", "#### ملخص\n\n\n\"لا روبوتات\" هي مجموعة بيانات تتكون من 10000 تعليمة وعرض، تم إنشاؤها بواسطة ملصقين محترفين. تمت ترجمتها باستخدام Google Cloud Platform Translation API. يمكن استخدام هذه المجموعة لتدريب نماذج اللغة على تتبع التعليمات بشكل أفضل (تدريب دقيق موجه بالتعليمات - SFT). تم إنشاء مجموعة \"لا روبوتات\" استنادًا إلى مجموعة البيانات الموصوفة في ورقة OpenAI's InstructGPT، وتشمل التصنيفات التالية:", "#### اللغات\n\n\nتتوفر مجموعة البيانات هذه على اللغة العربية فقط. يمكن العثور على النسخة الأصلية باللغة الإنجليزية على هذا الرابط، والنسخة التركية على هذا الرابط.", "#### حقول البيانات\n\n\nالأعمدة كالتالي:\n\n\n* 'prompt': يحدد التعليمة التي يجب أن يتبعها النموذج.\n* 'prompt\\_id': معرف فريد.\n* 'messages': قائمة تحتوي على قواميس، كل قاموس يصف رسالة (key: content) ومن أرسلها (key: role).\n* 'category': فئة المهمة، لم أقم بترجمة هذا.", "#### التقسيمات", "#### الترخيص\n\n\nمجموعة البيانات متاحة تحت رخصة (CC BY-NC 4.0).", "#### معلومات الاقتباس" ]

[ "TAGS\n#task_categories-conversational #task_categories-text-generation #language-Arabic #license-cc-by-nc-4.0 #arxiv-2203.02155 #region-us \n", "### بطاقة مجموعة البيانات لـ \"لا روبوت\" ‍️", "#### ملخص\n\n\n\"لا روبوتات\" هي مجموعة بيانات تتكون من 10000 تعليمة وعرض، تم إنشاؤها بواسطة ملصقين محترفين. تمت ترجمتها باستخدام Google Cloud Platform Translation API. يمكن استخدام هذه المجموعة لتدريب نماذج اللغة على تتبع التعليمات بشكل أفضل (تدريب دقيق موجه بالتعليمات - SFT). تم إنشاء مجموعة \"لا روبوتات\" استنادًا إلى مجموعة البيانات الموصوفة في ورقة OpenAI's InstructGPT، وتشمل التصنيفات التالية:", "#### اللغات\n\n\nتتوفر مجموعة البيانات هذه على اللغة العربية فقط. يمكن العثور على النسخة الأصلية باللغة الإنجليزية على هذا الرابط، والنسخة التركية على هذا الرابط.", "#### حقول البيانات\n\n\nالأعمدة كالتالي:\n\n\n* 'prompt': يحدد التعليمة التي يجب أن يتبعها النموذج.\n* 'prompt\\_id': معرف فريد.\n* 'messages': قائمة تحتوي على قواميس، كل قاموس يصف رسالة (key: content) ومن أرسلها (key: role).\n* 'category': فئة المهمة، لم أقم بترجمة هذا.", "#### التقسيمات", "#### الترخيص\n\n\nمجموعة البيانات متاحة تحت رخصة (CC BY-NC 4.0).", "#### معلومات الاقتباس" ]

[ "region:us" ]

[]

[]

[ "# Results for *Keypoint-based Stereophotoclinometry for Characterizing and Navigating Small Bodies: A Factor Graph Approach* presented at the 2024 AIAA SciTech Forum\n\n<a href=\"URL src=\"https://i.URL title=\"source: URL\" /></a>\n\nSee URL for instructions on loading and manipulating the reconstructions.\n\nIf you utilize our reconstructions or data, please our paper:" ]

[ "TAGS\n#region-us \n", "# Results for *Keypoint-based Stereophotoclinometry for Characterizing and Navigating Small Bodies: A Factor Graph Approach* presented at the 2024 AIAA SciTech Forum\n\n<a href=\"URL src=\"https://i.URL title=\"source: URL\" /></a>\n\nSee URL for instructions on loading and manipulating the reconstructions.\n\nIf you utilize our reconstructions or data, please our paper:" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of decruz07/kellemar-DPO-Orca-Distilled-7B-SLERP\n\n\n\nDataset automatically created during the evaluation run of model decruz07/kellemar-DPO-Orca-Distilled-7B-SLERP on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-16T17:19:43.357839(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 decruz07/kellemar-DPO-Orca-Distilled-7B-SLERP\n\n\n\nDataset automatically created during the evaluation run of model decruz07/kellemar-DPO-Orca-Distilled-7B-SLERP on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-16T17:19:43.357839(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 \"WCEP-filtered\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of KnutJaegersberg/Nanbeige-16B-Base-32K-llama\n\n\n\nDataset automatically created during the evaluation run of model KnutJaegersberg/Nanbeige-16B-Base-32K-llama 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 2024-01-16T17:54:07.755069(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of KnutJaegersberg/Nanbeige-16B-Base-32K-llama\n\n\n\nDataset automatically created during the evaluation run of model KnutJaegersberg/Nanbeige-16B-Base-32K-llama 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 2024-01-16T17:54:07.755069(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:conversational", "task_categories:text-generation", "size_categories:10K<n<100K", "language:pt", "language:en", "license:apache-2.0", "alignment", "instruction", "chat", "region:us" ]

[]

[ "pt", "en" ]

[ "# Instruct-Aira Dataset version 2.0", "## Table of Contents\n\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Repository: URL\n- Point of Contact: AIRES at PUCRS", "### Dataset Summary\n\nThis dataset contains a collection of single-turn conversations between an assistant and a user. Conversations were generated by user interactions with already-tuned models (ChatGPT, LLama 2, Open-Assistant, etc). The dataset is available in Portuguese and English.", "### Supported Tasks and Leaderboards\n\nThis dataset can be utilized for various natural language processing tasks, including but not limited to:\n\n- Language modeling.\n- Question-answering systems.\n- Chatbot development.\n- Evaluation of language models.\n- Alignment research.", "### Languages\n\nEnglish and Portuguese.", "## Dataset Structure", "### Data Instances\n\nThe dataset consists of the following features:\n\n- Conversation ID: Identifier of the conversation.\n- Conversations: A list of dictionaries following a chat format.", "### Data Fields", "### Data Splits\n\nAvailable splits are 'english' and 'portuguese'.", "## Dataset Creation", "### Curation Rationale\n\nThis dataset was developed are part of Nicholas Kluge's doctoral dissertation, \"_Dynamic Normativity: Necessary and Sufficient Conditions for Value Alignment._\" This research was funded by CNPq (Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul), FAPERGS (Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul), and DAAD (Deutscher Akademischer Austauschdienst), as part of a doctoral research project tied to Philosophy departments of PUCRS (Pontifícia Universidade Católica do Rio Grande do Sul) and the University of Bonn.", "### Source Data", "#### Initial Data Collection and Normalization\n\nAll completions were generated by querying already-tuned models (ChatGPT, LLama 2, Open-Assistant, etc.). Prompts were gathered from publicly available datasets.", "#### Who are the source language producers?\n\nAll completions were generated by querying already-tuned models (ChatGPT, LLama 2, Open-Assistant, etc.). Prompts were gathered from publicly available datasets.", "### Annotations", "#### Annotation process\n\nAll completions were generated by querying already-tuned models (ChatGPT, LLama 2, Open-Assistant, etc.). Prompts were gathered from publicly available datasets.", "#### Who are the annotators?\n\nNo annotators were used.", "### Personal and Sensitive Information\n\nNo personal or sensitive information is part of this dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nNo considerations.", "### Discussion of Biases\n\nNo considerations.", "### Other Known Limitations\n\nNo considerations.", "## Additional Information", "### Dataset Curators\n\nNicholas Kluge Corrêa.", "### Licensing Information\n\nThis dataset is licensed under the Apache License, version 2.0.", "### Contributions\n\nIf you would like to contribute, contact me at nicholas@URL!" ]

[ "TAGS\n#task_categories-conversational #task_categories-text-generation #size_categories-10K<n<100K #language-Portuguese #language-English #license-apache-2.0 #alignment #instruction #chat #region-us \n", "# Instruct-Aira Dataset version 2.0", "## Table of Contents\n\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Repository: URL\n- Point of Contact: AIRES at PUCRS", "### Dataset Summary\n\nThis dataset contains a collection of single-turn conversations between an assistant and a user. Conversations were generated by user interactions with already-tuned models (ChatGPT, LLama 2, Open-Assistant, etc). The dataset is available in Portuguese and English.", "### Supported Tasks and Leaderboards\n\nThis dataset can be utilized for various natural language processing tasks, including but not limited to:\n\n- Language modeling.\n- Question-answering systems.\n- Chatbot development.\n- Evaluation of language models.\n- Alignment research.", "### Languages\n\nEnglish and Portuguese.", "## Dataset Structure", "### Data Instances\n\nThe dataset consists of the following features:\n\n- Conversation ID: Identifier of the conversation.\n- Conversations: A list of dictionaries following a chat format.", "### Data Fields", "### Data Splits\n\nAvailable splits are 'english' and 'portuguese'.", "## Dataset Creation", "### Curation Rationale\n\nThis dataset was developed are part of Nicholas Kluge's doctoral dissertation, \"_Dynamic Normativity: Necessary and Sufficient Conditions for Value Alignment._\" This research was funded by CNPq (Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul), FAPERGS (Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul), and DAAD (Deutscher Akademischer Austauschdienst), as part of a doctoral research project tied to Philosophy departments of PUCRS (Pontifícia Universidade Católica do Rio Grande do Sul) and the University of Bonn.", "### Source Data", "#### Initial Data Collection and Normalization\n\nAll completions were generated by querying already-tuned models (ChatGPT, LLama 2, Open-Assistant, etc.). Prompts were gathered from publicly available datasets.", "#### Who are the source language producers?\n\nAll completions were generated by querying already-tuned models (ChatGPT, LLama 2, Open-Assistant, etc.). Prompts were gathered from publicly available datasets.", "### Annotations", "#### Annotation process\n\nAll completions were generated by querying already-tuned models (ChatGPT, LLama 2, Open-Assistant, etc.). Prompts were gathered from publicly available datasets.", "#### Who are the annotators?\n\nNo annotators were used.", "### Personal and Sensitive Information\n\nNo personal or sensitive information is part of this dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nNo considerations.", "### Discussion of Biases\n\nNo considerations.", "### Other Known Limitations\n\nNo considerations.", "## Additional Information", "### Dataset Curators\n\nNicholas Kluge Corrêa.", "### Licensing Information\n\nThis dataset is licensed under the Apache License, version 2.0.", "### Contributions\n\nIf you would like to contribute, contact me at nicholas@URL!" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of Swisslex/Mixtral-8x7b-DPO-v0.2\n\n\n\nDataset automatically created during the evaluation run of model Swisslex/Mixtral-8x7b-DPO-v0.2 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 2024-01-16T18:18:43.502951(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 Swisslex/Mixtral-8x7b-DPO-v0.2\n\n\n\nDataset automatically created during the evaluation run of model Swisslex/Mixtral-8x7b-DPO-v0.2 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 2024-01-16T18:18:43.502951(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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[ "TAGS\n#license-apache-2.0 #region-us \n" ]

[ "region:us" ]

[]

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[ "# Dataset Card for \"covost2_extract_unit\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"covost2_extract_unit\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of AiMavenAi/AiMaven-SmartDawg-7b\n\n\n\nDataset automatically created during the evaluation run of model AiMavenAi/AiMaven-SmartDawg-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 2024-01-16T18:46:13.340145(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 AiMavenAi/AiMaven-SmartDawg-7b\n\n\n\nDataset automatically created during the evaluation run of model AiMavenAi/AiMaven-SmartDawg-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 2024-01-16T18:46:13.340145(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 \"10000-20000-ultrafeedback-binarized-preferences-cleaned-ita\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"10000-20000-ultrafeedback-binarized-preferences-cleaned-ita\"\n\nMore Information needed" ]

[ "region:us" ]

[]

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[ "# Dataset Card for Evaluation run of pinkyponky/Mistral-7B-Instruct-Sft-Tuned-V0.2\n\n\n\nDataset automatically created during the evaluation run of model pinkyponky/Mistral-7B-Instruct-Sft-Tuned-V0.2 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 2024-01-17T02:42:41.237336(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 pinkyponky/Mistral-7B-Instruct-Sft-Tuned-V0.2\n\n\n\nDataset automatically created during the evaluation run of model pinkyponky/Mistral-7B-Instruct-Sft-Tuned-V0.2 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 2024-01-17T02:42:41.237336(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 ajibawa-2023/Code-290k-13B\n\n\n\nDataset automatically created during the evaluation run of model ajibawa-2023/Code-290k-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 2024-01-16T19:33:39.851103(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 ajibawa-2023/Code-290k-13B\n\n\n\nDataset automatically created during the evaluation run of model ajibawa-2023/Code-290k-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 2024-01-16T19:33:39.851103(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", "task_ids:language-modeling", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:100M<n<1B", "source_datasets:original", "language:ru", "license:cc0-1.0", "region:us" ]

[]

[ "ru" ]

[ "# Dataset Card for Wildberries products", "### Dataset Summary\n\nThis dataset was scraped from product pages on the Russian marketplace Wildberries. It includes all information from the product card and metadata from the API, excluding image URLs. The dataset was collected by processing approximately 160 million products out of a potential 230 million, starting from the first product. Data collection had to be stopped due to serious rate limits that prevented further progress. The data is in zstd archives containing jsonl files. Each archive contains data from a specific Wildberries data server identified by a basket server number.", "### Languages\n\nThe dataset is mostly in Russian, but there may be other languages present.", "## Dataset Structure", "### Data Fields\n\nThis dataset includes the following fields:\n\n- 'imt_id': Identifier for the item (integer)\n- 'nm_id': Numeric identifier associated with the item (integer)\n- 'imt_name': Name of the product (string)\n- 'subj_name': Subject name (string)\n- 'subj_root_name': Root subject name (string)\n- 'nm_colors_names': Colors names (string, may be empty)\n- 'vendor_code': Vendor code (string)\n- 'description': Description of the product (string, may be empty)\n- 'brand_name': Name of the brand (string)", "### Data Splits\n\nAll examples are in the train split, there is no validation split.", "## Additional Information", "### License\n\nThis dataset is dedicated to the public domain under the Creative Commons Zero (CC0) license. This means you can:\n\n* Use it for any purpose, including commercial projects.\n* Modify it however you like.\n* Distribute it without asking permission.\n\nNo attribution is required, but it's always appreciated!\n\nCC0 license: URL\n\nTo learn more about CC0, visit the Creative Commons website: URL", "### Dataset Curators\n\n- nyuuzyou" ]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-100M<n<1B #source_datasets-original #language-Russian #license-cc0-1.0 #region-us \n", "# Dataset Card for Wildberries products", "### Dataset Summary\n\nThis dataset was scraped from product pages on the Russian marketplace Wildberries. It includes all information from the product card and metadata from the API, excluding image URLs. The dataset was collected by processing approximately 160 million products out of a potential 230 million, starting from the first product. Data collection had to be stopped due to serious rate limits that prevented further progress. The data is in zstd archives containing jsonl files. Each archive contains data from a specific Wildberries data server identified by a basket server number.", "### Languages\n\nThe dataset is mostly in Russian, but there may be other languages present.", "## Dataset Structure", "### Data Fields\n\nThis dataset includes the following fields:\n\n- 'imt_id': Identifier for the item (integer)\n- 'nm_id': Numeric identifier associated with the item (integer)\n- 'imt_name': Name of the product (string)\n- 'subj_name': Subject name (string)\n- 'subj_root_name': Root subject name (string)\n- 'nm_colors_names': Colors names (string, may be empty)\n- 'vendor_code': Vendor code (string)\n- 'description': Description of the product (string, may be empty)\n- 'brand_name': Name of the brand (string)", "### Data Splits\n\nAll examples are in the train split, there is no validation split.", "## Additional Information", "### License\n\nThis dataset is dedicated to the public domain under the Creative Commons Zero (CC0) license. This means you can:\n\n* Use it for any purpose, including commercial projects.\n* Modify it however you like.\n* Distribute it without asking permission.\n\nNo attribution is required, but it's always appreciated!\n\nCC0 license: URL\n\nTo learn more about CC0, visit the Creative Commons website: URL", "### Dataset Curators\n\n- nyuuzyou" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of PotatoOff/HamSter-0.2\n\n\n\nDataset automatically created during the evaluation run of model PotatoOff/HamSter-0.2 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 2024-01-16T20:12:25.047225(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 PotatoOff/HamSter-0.2\n\n\n\nDataset automatically created during the evaluation run of model PotatoOff/HamSter-0.2 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 2024-01-16T20:12:25.047225(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 flemmingmiguel/DareBeagle-7B\n\n\n\nDataset automatically created during the evaluation run of model flemmingmiguel/DareBeagle-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 2024-01-16T20:16:03.594958(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/DareBeagle-7B\n\n\n\nDataset automatically created during the evaluation run of model flemmingmiguel/DareBeagle-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 2024-01-16T20:16:03.594958(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:audio-classification", "license:cc-by-nc-sa-4.0", "medical", "region:us" ]

[]

[]

[]

[ "TAGS\n#task_categories-audio-classification #license-cc-by-nc-sa-4.0 #medical #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of NeuralNovel/Gecko-7B-v0.1-DPO\n\n\n\nDataset automatically created during the evaluation run of model NeuralNovel/Gecko-7B-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 2024-01-16T21:04:36.728046(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 NeuralNovel/Gecko-7B-v0.1-DPO\n\n\n\nDataset automatically created during the evaluation run of model NeuralNovel/Gecko-7B-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 2024-01-16T21:04:36.728046(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 rombodawg/Leaderboard-killer-MoE_4x7b\n\n\n\nDataset automatically created during the evaluation run of model rombodawg/Leaderboard-killer-MoE_4x7b 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 2024-01-16T21:07:48.403934(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 rombodawg/Leaderboard-killer-MoE_4x7b\n\n\n\nDataset automatically created during the evaluation run of model rombodawg/Leaderboard-killer-MoE_4x7b 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 2024-01-16T21:07:48.403934(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" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of LoSboccacc/orthogonal-2x7B-base\n\n\n\nDataset automatically created during the evaluation run of model LoSboccacc/orthogonal-2x7B-base 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 2024-01-16T21:21:27.618218(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 LoSboccacc/orthogonal-2x7B-base\n\n\n\nDataset automatically created during the evaluation run of model LoSboccacc/orthogonal-2x7B-base 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 2024-01-16T21:21:27.618218(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-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

[]

[]

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

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

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

[]

[]

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

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

[ "region:us" ]

[]

[]

[]

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

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

[]

[]

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

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

[ "task_categories:text-generation", "size_categories:100K<n<1M", "language:en", "license:cc-by-nc-sa-4.0", "legal", "finance", "region:us" ]

[]

[ "en" ]

[ "# Material Contracts (Exhibit 10) from SEC 10-K, 8-K, and 10-Q filings from 1994 to 2024.\n\nWIP. I started the crawling on Jan 14, 2024, and expect to finish in a month or two. I will upload the data in batches." ]

[ "TAGS\n#task_categories-text-generation #size_categories-100K<n<1M #language-English #license-cc-by-nc-sa-4.0 #legal #finance #region-us \n", "# Material Contracts (Exhibit 10) from SEC 10-K, 8-K, and 10-Q filings from 1994 to 2024.\n\nWIP. I started the crawling on Jan 14, 2024, and expect to finish in a month or two. I will upload the data in batches." ]

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "language:uk", "region:us" ]

[]

[ "uk" ]

[ "### Dataset Description\n\nLegal Contracts Dataset for Training NER Model\nThis repository contains a specially curated dataset consisting of legal contracts. It is designed for the purpose of training a Named Entity Recognition (NER) model, with the aim to recognize and classify four types of entities in the text:\n\nContract Type,\nClause Title,\nClause Number,\nDefinition Title\n\n\nThe dataset includes a broad variety of legal contracts, covering diverse domains such as employment, real estate, services, sale, lease, etc.\n\nEntities in the text have been manually labeled by experts in the field, ensuring high-quality training data for the model.\n\nEach document in the dataset has been annotated in the following format:\n\n(Start_Position, End_Position, Entity_Label)\n\nFor example, a clause title may be annotated as follows: (102, 115, 'clause title')\n\nThis will assist the NER model in identifying not only the text of the entity, but also its position within the document.\n\nUsage Guidelines" ]

[ "TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #language-Ukrainian #region-us \n", "### Dataset Description\n\nLegal Contracts Dataset for Training NER Model\nThis repository contains a specially curated dataset consisting of legal contracts. It is designed for the purpose of training a Named Entity Recognition (NER) model, with the aim to recognize and classify four types of entities in the text:\n\nContract Type,\nClause Title,\nClause Number,\nDefinition Title\n\n\nThe dataset includes a broad variety of legal contracts, covering diverse domains such as employment, real estate, services, sale, lease, etc.\n\nEntities in the text have been manually labeled by experts in the field, ensuring high-quality training data for the model.\n\nEach document in the dataset has been annotated in the following format:\n\n(Start_Position, End_Position, Entity_Label)\n\nFor example, a clause title may be annotated as follows: (102, 115, 'clause title')\n\nThis will assist the NER model in identifying not only the text of the entity, but also its position within the document.\n\nUsage Guidelines" ]

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

[]

[]

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

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